title: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

abstract: We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks.


title: Coccydynia: an overview of the anatomy, etiology, and treatment of coccyx pain.

abstract: Background: Despite its small size, the coccyx has several important functions. Along with being the insertion site for multiple muscles, ligaments, and tendons, it also serves as one leg of the tripod—along with the ischial tuberosities—that provides weight-bearing support to a person in the seated position. The incidence of coccydynia (pain in the region of the coccyx) has not been reported, but factors associated with increased risk of developing coccydynia include obesity and female gender. Methods: This article provides an overview of the anatomy, physiology, and treatment of coccydynia. Results: Conservative treatment is successful in 90% of cases, and many cases resolve without medical treatment. Treatments for refractory cases include pelvic floor rehabilitation, manual manipulation and massage, transcutaneous electrical nerve stimulation, psychotherapy, steroid injections, nerve block, spinal cord stimulation, and surgical procedures. Conclusion: A multidisciplinary approach employing physical therapy, ergonomic adaptations, medications, injections, and, possibly, psychotherapy leads to the greatest chance of success in patients with refractory coccyx pain. Although new surgical techniques are emerging, more research is needed before their efficacy can be established.


Valorization of Lignocellulosic Wastes – Evaluation of Its Toxicity When Used in Adsorption Systems


title: Metabolic signals and innate immune activation in obesity and exercise.

abstract: The combination of a sedentary lifestyle and excess energy intake has led to an increased prevalence of obesity which constitutes a major risk factor for several co-morbidities including type 2 diabetes and cardiovascular diseases. Intensive research during the last two decades has revealed that a characteristic feature of obesity linking it to insulin resistance is the presence of chronic low-grade inflammation being indicative of activation of the innate immune system. Recent evidence suggests that activation of the innate immune system in the course of obesity is mediated by metabolic signals, such as free fatty acids (FFAs), being elevated in many obese subjects, through activation of pattern recognition receptors thereby leading to stimulation of critical inflammatory signaling cascades, like IκBα kinase/nuclear factor-κB (IKK/NF- κB), endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) and NOD-like receptor P3 (NLRP3) inflammasome pathway, that interfere with insulin signaling. Exercise is one of the main prescribed interventions in obesity management improving insulin sensitivity and reducing obesity- induced chronic inflammation. This review summarizes current knowledge of the cellular recognition mechanisms for FFAs, the inflammatory signaling pathways triggered by excess FFAs in obesity and the counteractive effects of both acute and chronic exercise on obesity-induced activation of inflammatory signaling pathways. A deeper understanding of the effects of exercise on inflammatory signaling pathways in obesity is useful to optimize preventive and therapeutic strategies to combat the increasing incidence of obesity and its comorbidities.


title: Effective Fashion Retrieval Based on Semantic Compositional Networks

abstract: Typical approaches for fashion retrieval rank clothing images according to the similarity to a user-provided query image. Similarity is usually assessed by encoding images in terms of visual elements such as color, shape and texture. In this work, we proceed differently and consider that the semantics of an outfit is mainly comprised of environmental and cultural concepts such as occasion, style and season. Thus, instead of retrieving outfits using strict visual elements, we find semantically similar outfits that fall into similar clothing styles and are adequate for the same occasions and seasons. We propose a compositional approach for fashion retrieval by arguing that the semantics of an outfit can be recognised by their constituents (i.e., clothing items and accessories). Specifically, we present a semantic compositional network (Comp-Net) in which clothing items are detected from the image and the probability of each item is used to compose a vector representation for the outfit. Comp-Net employs a normalization layer so that weights are updated by taking into consideration the previously known co-occurrence patterns between clothing items. Further, Comp-Net minimizes a cost-sensitive loss function as errors have different costs depending on the clothing item that is misclassified. This results in a space in which semantically related outfits are placed next to each other, enabling to find semantically similar outfits that may not be visually similar. We designed an evaluation setup that takes into account the association between different styles, occasions and seasons, and show that our compositional approach significantly outperforms a variety of recently proposed baselines.


title: InFO (Wafer Level Integrated Fan-Out) Technology

abstract: A powerful integrated fan-out (InFO) wafer level system integration (WLSI) technology has been developed to integrate application processor chip with memory package for smart mobile devices. This novel InFO technology is the first high performance Fan-Out Wafer Level Package (FO_WLP) with multi-layer high density interconnects proposed to the industry. In this paper we present the detailed comparison of InFO packages on package (InFO_PoP) with several other previously proposed 3D package solutions. Result shows that InFO_PoP has more optimized overall results on system performance, leakage power and area (form factor) than others, to meet the ever-increasing system requirements of mobile computing. InFO technology has been successfully qualified on package level with robust component and board level reliability. It is also qualified at interconnect level with high electromigration resistance. With its high flexibility and strong capability of multi-chips integration for both homogeneous and heterogeneous sub-systems, InFO technology not only provides a system scaling solution but also complements the chip scaling and helps to sustain the Moore's Law for the smart mobile as well as internet of things (IoT) applications.


title: Factors affecting degree of foreign accent in an L2: a review

abstract: Abstract This article had two aims: to provide a thorough review of the existing literature examining overall degree of foreign accent in a second language (L2), and to present a new foreign accent experiment. The literature review suggested that a wide variety of variables influence degree of foreign accent. These variables include age of L2 learning, length of residence in an L2-speaking country, gender, formal instruction, motivation, language learning aptitude and amount of native language (L1) use. Age of L2 learning appears to be the most important predictor of degree of foreign accent. However, the relative importance of the other variables is uncertain. This is because many variables relating to subject characteristics tend to be confounded, and because of lack of adequate experimental control in some studies. The experiment presented here examined the influence of Italian-English bilinguals' age of L2 learning, length of residence in an L2-speaking environment (Canada), gender, amount of continued L1 (Italian) use and self-estimated L1 ability on degree of L2 foreign accent. As expected from the literature review, both age of L2 learning and amount of continued L1 use were found to affect degree of foreign accent. Gender, length of residence in an L2-speaking country and self-estimated L1 ability, on the other hand, were not found to have a significant, independent effect on overall L2 pronunciation accuracy.


title: 5G high mobility wireless communications: Challenges and solutions

abstract: The fifth generation (5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections, to achieve better spectrum- and energy-efficiency, as well as quality of service (QoS) in terms of delay, reliability and security. Furthermore, the 5G network shall also incorporate high mobility requirements as an integral part, providing satisfactory service to users travelling at a speed up to 500 km/h. This paper provides a survey of potential high mobility wireless communication (HMWC) techniques for 5G network. After discussing the typical requirements and challenges of HMWC, key techniques to cope with the challenges are reviewed, including transmission techniques under the fast time-varying channels, network architecture with mobility support, and mobility management. Finally, future research directions on 5G high mobility communications are given.


title: Concurrent multiband low-noise amplifiers-theory, design, and applications

abstract: The concept of concurrent multiband low-noise-amplifiers (LNAs) is introduced. A systematic way to design concurrent multiband integrated LNAs in general is developed. Applications of concurrent multiband LNAs in concurrent multiband receivers together with receiver architecture are discussed. Experimental results of a dual-band LNA implemented in a 0.35-/spl mu/m CMOS technology as a demonstration of the concept and theory is presented.


title: Detection of driver's drowsiness by means of HRV analysis

abstract: It is estimated that 10–30% of road fatalities are related to drowsy driving or driver fatigue. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous Nervous System (ANS) activity, which can be measured non-invasively from the Heart Rate Variability (HRV) signal obtained from surface ECG, presents alterations during stress, ex-trem fatigue and drowsiness episodes. Our hypothesis is that these alterations manifest on HRV. In this work we de-velope an on-line detector of driver's drowsiness based on HRV analysis. Two databases have been analyzed: one of driving simulation in which subjects were sleep deprived, and the other of real situation with no sleep deprivation. An external observer annotated each minute of the recordings as drowsy or awake, and constitutes our reference. The proposed detector classified drowsy minutes with a sensitivity of 0.85 and a predictive positive value of 0.93, using 25 features.


title: The Race to 5G Era; LTE and Wi-Fi

abstract: We are on the brink of a new era for the wireless telecommunications, an era that will change the way that business is done. The fifth generation (5G) systems will be the first realization in this new digital era where the various networks will be interconnected forming a unified system. With support for higher capacity as well as low-delay and machine-type communication services, the 5G networks will significantly improve performance over the current fourth generation (4G) systems and will also offer seamless connectivity to numerous devices by integrating different technologies, intelligence, and flexibility. In addition to ongoing 5G standardization activities and technologies under consideration in the Third Generation Partnership Project (3GPP), the Institute of Electrical and Electronic Engineers (IEEE)-based technologies operating on unlicensed bands, will also be an integral part of a 5G eco-system. Along with the 3GPP-based cellular technology, the IEEE standards and technologies are also evolving to keep pace with the user demands and new 5G services. In this paper, we provide an overview of the evolution of the cellular and Wi-Fi standards over the last decade with a particular focus on the Medium Access Control and Physical layers, and highlight the ongoing activities in both camps driven by the 5G requirements and use-cases.


title: Coming: a Tool for Mining Change Pattern Instances from Git Commits

abstract: Software repositories such as Git have become a relevant source of information for software engineer researchers. For instance, the detection of commits that fulfill a given criterion (e.g., bugfixing commits) is one of the most frequent tasks done to understand the software evolution. However, to our knowledge, there is no open-source tool that, given a Git repository, returns all the instances of a given code change pattern. In this paper we present Coming, a tool that takes as input a Git repository and mines instances of code change patterns present on each commit. For that, Coming computes fine-grained code changes between two consecutive revisions, analyzes those changes to determine if they correspond to an instance of a change pattern (specified by the user using XML), and finally, after analyzing all the commits, it presents a) the frequency of code changes and b) the instances found in each commit. We evaluate Coming on a set of 28 pairs of revisions from Defects4J, finding instances of change patterns that involve If conditions on 26 of them.


title: A High Performance Scan Flip-Flop Design for Serial and Mixed Mode Scan Test

abstract: Over the years, serial scan design has become the de-facto design for testability technique. The ease of testing and high test coverage has made it gain widespread industrial acceptance. However, there are penalties associated with the serial scan design. These penalties include performance degradation, test data volume, test application time, and test power dissipation. The performance overhead of scan design is due to the scan multiplexers added to the inputs of every flip-flop. In today’s very high-speed designs with minimum possible combinational depth, the performance degradation caused by the scan multiplexer has become magnified. Hence, to maintain circuit performance, the timing overhead of scan design must be addressed. In this paper, we propose a new scan flip-flop design that eliminates the performance overhead of serial scan. The proposed design removes the scan multiplexer from the functional path. The proposed design can help improve the functional frequency of performance critical designs. Furthermore, the proposed design can be used as a common scan flip-flop in the “mixed scan” test wherein it can be used as a serial scan cell as well as a random access scan (RAS) cell. The mixed scan test architecture has been implemented using the proposed scan flip-flop. The experimental results show a promising reduction in interconnect wire length, test time, and test data volume, compared to the state-of-the-art RAS and multiple serial scan implementations.


title: Work and personal e-mail use by university employees: PIM practices across domain boundaries

abstract: In this article, we present findings from a survey of nearly 600 university employees' e-mail use. The study provides a detailed comparison of use patterns between work and personal e-mail accounts. Our results suggest that users engage in more “keeping” behaviors with work e-mail than with personal e-mail—respondents reported more frequent use of keeping actions and larger inbox sizes for their work accounts. However, we found correlations between individual respondents' e-mail behaviors in the two contexts, indicating that personal preferences can play a role. We also report results pointing to e-mail as an important boundary management artifact. We show evidence that the use of multiple e-mail accounts may be a work-personal boundary placement strategy, but also observe that a fair amount of boundary permeation occurs through e-mail. To our knowledge, this study is one of the first to compare e-mail use in both work and personal contexts across the same sample. Our findings extend prior research on personal information management regarding e-mail use, and help inform the role of e-mail in managing work-personal boundaries. The results have implications for the design of e-mail systems, organizational e-mail policies, user training, and understanding the impacts of technology on daily life.


title: 5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning

abstract: The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.


title: Advanced smart biomaterials and constructs for hard tissue engineering and regeneration

abstract: Hard tissue repair and regeneration cost hundreds of billions of dollars annually worldwide, and the need has substantially increased as the population has aged. Hard tissues include bone and tooth structures that contain calcium phosphate minerals. Smart biomaterial-based tissue engineering and regenerative medicine methods have the exciting potential to meet this urgent need. Smart biomaterials and constructs refer to biomaterials and constructs that possess instructive/inductive or triggering/stimulating effects on cells and tissues by engineering the material’s responsiveness to internal or external stimuli or have intelligently tailored properties and functions that can promote tissue repair and regeneration. The smart material-based approaches include smart scaffolds and stem cell constructs for bone tissue engineering; smart drug delivery systems to enhance bone regeneration; smart dental resins that respond to pH to protect tooth structures; smart pH-sensitive dental materials to selectively inhibit acid-producing bacteria; smart polymers to modulate biofilm species away from a pathogenic composition and shift towards a healthy composition; and smart materials to suppress biofilms and avoid drug resistance. These smart biomaterials can not only deliver and guide stem cells to improve tissue regeneration and deliver drugs and bioactive agents with spatially and temporarily controlled releases but can also modulate/suppress biofilms and combat infections in wound sites. The new generation of smart biomaterials provides exciting potential and is a promising opportunity to substantially enhance hard tissue engineering and regenerative medicine efficacy.Bone repair: a new generation of smart biomaterialsSmart biomaterials that are able to instruct bone repair can overcome some of the shortcomings of bone grafting and meet the growing need for hard tissue regeneration in ageing populations. Hockin Xu at the University of Maryland in Baltimore, United States, and colleagues review recent advances in the development of smart biomaterials for repairing and regenerating damaged bones and teeth. They highlight scaffolds that closely mimic natural bone tissue and immunomodulatory biomaterials that can prevent infection and promote cell survival for guiding and enhancing bone regeneration. Furthermore, scaffolds with shape-memory capability and materials that provide tailored spatio-temporal delivery of drugs or bioactive agents in response to internal or external stimuli, hold great promise not only for bone tissue engineering but also for dentistry.


title: A Review and Meta-Analysis of Age-Based Stereotype Threat: Negative Stereotypes, Not Facts, Do the Damage

abstract: Stereotype threat effects arise when an individual feels at risk of confirming a negative stereotype about their group and consequently underperforms on stereotype relevant tasks (Steele, 2010). Among older people, underperformance across cognitive and physical tasks is hypothesized to result from age-based stereotype threat (ABST) because of negative age-stereotypes regarding older adults’ competence. The present review and meta-analyses examine 22 published and 10 unpublished articles, including 82 effect sizes (N 3882) investigating ABST on older people’s (Mage 69.5) performance. The analysis revealed a significant small-to-medium effect of ABST (d .28) and important moderators of the effect size. Specifically, older adults are more vulnerable to ABST when (a) stereotype-based rather than fact-based manipulations are used (d .52); (b) when performance is tested using cognitive measures (d .36); and (c) occurs reliably when the dependent variable is measured proximally to the manipulation. The review raises important theoretical and methodological issues, and areas for future research.


title: Computationally Efficient Face Spoofing Detection with Motion Magnification

abstract: For a robust face biometric system, a reliable anti-spoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance, especially HOOF descriptor yielding a near perfect half total error rate of 0%and 1.25% respectively.


title: Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach

abstract: Microscopic urban traffic simulators embed the most detailed traveler behavior and network supply models. These simulators represent individual vehicles and can therefore account for vehicle-specific technologies. They can be coupled with instantaneous fuel consumption models to yield detailed network-wide fuel consumption estimates. Nonetheless, there is currently a lack of computationally efficient optimization techniques that enable the use of these complex integrated models to design sustainable transportation strategies. ::: ::: This paper proposes a methodology that combines a stochastic microscopic traffic simulation model with an instantaneous vehicular fuel consumption model. The combined models are embedded within a simulation-based optimization algorithm and used to address a signal control problem that accounts for both travel times and fuel consumption. The proposed technique couples detailed, stochastic, and computationally inefficient models, yet is an efficient optimization technique. Efficiency is achieved by combining simulated observations with analytical approximations of both travel time and fuel consumption. ::: ::: This methodology is applied to a network in the Swiss city of Lausanne. Within a tight computational budget, the proposed method identifies signal plans with improved travel time and fuel consumption metrics. It outperforms traditional methodologies, which use only simulated information or only analytical information. The case study illustrates the added value of combining simulated and analytical information when performance metrics with high variance, such as fuel consumption, are used. This method enables the use of disaggregate instantaneous vehicle-specific information to inform and improve traffic operations at the network-scale.


title: Adaptive Modified Projective Synchronization and Parameter Estimation for Chaotic Systems with Uncertain Parameters

abstract: A modified projective synchronization between a new chaotic system and a Genesio-Tesi chaotic system is investigated,and the unknown parameters of the new chaotic system are also esti- mated.The nonlinear controller and the parameter adaptive updating law are successfully designed according to the active control principle.Moreover,a strict proof of the asymptotic stability of the origin for the error system is derived by the Lyapunov stability theory.The effectiveness and the ro- bustness under a bounded noises disturbance of the proposed nonlinear controller and the parameters adaptive updating law are validated through numerical simulations.


title: Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

abstract: In an experiment on Airbnb, we find that applications from guests with distinctively African American names are 16 percent less likely to be accepted relative to identical guests with distinctively white names. Discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties. It is most pronounced among hosts who have never had an African American guest, suggesting only a subset of hosts discriminate. While rental markets have achieved significant reductions in discrimination in recent decades, our results suggest that Airbnb's current design choices facilitate discrimination and raise the possibility of erasing some of these civil rights gains.


title: Updated Taxonomy for the Network and Service Management Research Field

abstract: Network and service management is an established research field within the general area of computer networks. A few years ago, an initial taxonomy, organizing a comprehensive list of terms and topics, was established through interviews with experts from both industry and academia. This taxonomy has since been used to better partition standardization efforts, identify classes of managed objects and improve the assignment of reviewers to papers submitted in the field. Because the field of network and service management is rapidly evolving, a biyearly update of the taxonomy was proposed. In this paper, a large-scale questionnaire is presented which was answered by experts in the field, evaluating the relevance of each individual topic for the next five years. Missing topics, which are likely to become relevant over the next few years, are identified as well. Furthermore, an analysis is performed of the records of papers submitted to major conferences in the area. Based on the obtained results, an updated version of the taxonomy is proposed.


MAIME: A Maintenance Manager for ETL Processes


title: An improved methodology to map Snow Cover by means of Landsat and MODIS imagery

abstract: In this article we propose a methodology to determine snow cover by means of Landsat-7 ETM+ and Landsat-5 TM images, as well as an improvement in daily Snow Cover TERRA- MODIS product (MOD10A1), between 2002 and 2005. Both methodologies are based on a NDSI threshold > 0.4. In the Landsat case, and although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. Moreover, an important improvement in snow cover mapping in shadow cast areas by means of a hybrid classification has been obtained. Using these results as ground truth we have verified MODIS Snow Cover product using coincident dates. In the MODIS product, we have noted important commission errors in water bodies, forest covers and orographic shades because of the NDVI-NDSI filter applied to this product. In order to improve MODIS snow cover determination using MODIS images, we propose a hybrid methodology based on experience with Landsat images, which provide greater spatial resolution.


title: A closeness centrality analysis algorithm for workflow-supported social networks

abstract: The purpose of this paper is to build a theoretical approach for numerically analyzing closeness centrality measures among workflow-actors of workflow-supported social network models to be formed through BPM(workflow)-driven organizational operations. The essential part of the proposed approach is a closeness centrality analysis equation to calculate each performer's closeness centrality measure on a workflow-supported social network model. In this paper, we try to develop an algorithm that is able to efficiently compute the closeness centrality analysis equation suggested from the conventional social network analysis literature, and eventually the developed algorithm will be applied to analyzing the degree of work-intimacy among those workflow-actors who are allocated to perform the corresponding workflow model.


title: Small signal stability analysis of a hybrid AC/DC microgrid with static and dynamic loads

abstract: Majority of the stability studies conducted on AC and DC microgrids have investigated stability with only static loads in the microgrid. Dynamic loads significantly influence the stability of mirogrids due to their voltage and frequency dependent nature. Therefore, small variation in voltage and frequency can make significant impact on the stability of the microgrid with the presence of dynamic loads. Thus, it is imperative to consider the dynamic loads in the microgrid for stability studies. Moreover, stability issues become more severe if the microgrid is operated in the standalone mode. This paper characterises the small-signal stability of a hybrid AC/DC microgrid with static and dynamic loads using state-space and dynamic simulation models developed in MATLAB/Simulink. This study investigates the critical parameters of the dynamic load which significantly affect the stability of the microgrid. Subsequently, eigenvalue analysis and time-domain simulations have been carried out to investigate the small-signal stability of the hybrid AC/DC microgrid. Eigen-sensitivity analysis has revealed that induction machine rotor resistance has the highest participation on the critical modes of the hybrid AC/DC microgrid. Additionally, the controller gains of the voltage source converters may require re-tuning due to the presence of dynamic loads.


title: Ensemble classifiers with improved overfitting

abstract: Overfitting has been always considered as a challenging problem in designing and training of ensemble classifiers. Obviously, the use of complex multiple classifiers may increase the success of ensemble classifier in feature space division with intertwined data and also may decrease the training error to minimum value. However, this success does not exist on the test data. Ensemble classifiers are more prone to overfitting than single classifiers because ensemble classifiers have been formed of several base classifiers and overfitting occurrence in each base classifier can transfer the problem to the final decision of the ensemble. In this paper, after quantitative and qualitative analysis of overfitting, a solution for improving overfitting is proposed by using heuristic algorithms. In this way, Multi-Objective Inclined Planes Optimization (MOIPO) and Multi-Objective Particle Swarm Optimization (MOPSO) are used and their results are compared with each other. Simulation results show that the simultaneous minimization of ensemble size and error rate in the training phase, can lead to a significant reduction in the amount of overfitting. In fact, with this approach in the training phase, the ensemble classifier is required to minimize the error with the most simple and minimum number of base classifiers and therefore overfitting is prevented. However, previous researches related to overfitting have ignored the ensemble size as an objective function.


title: CAVE-SOM: Immersive visual data mining using 3D Self-Organizing Maps

abstract: Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.


title: How to get pavement distress detection ready for deep learning? A systematic approach

abstract: Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.


title: A qualitative analysis of software security patterns

abstract: Software security, which has attracted the interest of the industrial and research community during the last years, aims at preventing security problems by building software without the so-called security holes. One way to achieve this goal is to apply specific patterns in software architecture. In the same way that the well-known design patterns for building well-structured software have been defined, a new kind of patterns called security patterns have emerged. These patterns enable us to incorporate a level of security already at the design phase of a software system. There exists no strict set of rules that can be followed in order to develop secure software. However, a number of guidelines have already appeared in the literature. Furthermore, the key problems in building secure software and major threat categories for a software system have been identified. An attempt to evaluate known security patterns based on how well they follow each principle, how well they encounter with possible problems in building secure software and for which of the threat categories they do take care of, is performed in this paper. Thirteen security patterns were evaluated based on these three sets of criteria. The ability of some of these patterns to enhance the security of the design of a software system is also examined by an illustrative example of fortifying a published design.


title: Reinforcement learning of motor skills in high dimensions: A path integral approach

abstract: Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far due to the computational difficulties that reinforcement learning encounters in high dimensional continuous state-action spaces. In this paper, we derive a novel approach to RL for parameterized control policies based on the framework of stochastic optimal control with path integrals. While solidly grounded in optimal control theory and estimation theory, the update equations for learning are surprisingly simple and have no danger of numerical instabilities as neither matrix inversions nor gradient learning rates are required. Empirical evaluations demonstrate significant performance improvements over gradient-based policy learning and scalability to high-dimensional control problems. Finally, a learning experiment on a robot dog illustrates the functionality of our algorithm in a real-world scenario. We believe that our new algorithm, Policy Improvement with Path Integrals (PI2), offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL in robotics.


title: A Framework for Understanding and Classifying Ontology Applications

abstract: In1 this paper, we draw attention to common goals and supporting technologies of several relatively distinct communities to facilitate closer cooperation and faster progress. The common thread is the need for sharing the meaning of terms in a given domain, which is a central role of ontologies. The different communities include ontology research groups, software developers and standards organizations. Using a broad definition of ‘ontology’, we show that much of the work being done by those communities may be viewed as practical applications of ontologies. To achieve this, we present a framework for understanding and classifying ontology applications. We identify three main categories of ontology applications: 1) neutral authoring, 2) common access to information, and 3) indexing for search. In each category, we identify specific ontology application scenarios. For each, we indicate their intended purpose, the role of the ontology, the supporting technologies and who the principal actors are and what they do. We illuminate the similarities and differences between scenarios. The copyright of this paper belongs to the papers authors. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5) Stockholm, Sweden, August 2, 1999 (V.R. Benjamins, B. Chandrasekaran, A. Gomez-Perez, N. Guarino, M. Uschold, eds.) http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-18/ 1The order of authors was determined by a coin flip.


title: Sub-100 ps monocycle pulses for 5G UWB communications

abstract: Pulse-based ultra-wideband (UWB) transmission has been identified as one of the emerging opportunities for high data rate communications in the microwave and millimeter-wave frequency ranges. In this frame, monocycle pulse emerged as one of the most interesting pulse formats candidate in support of next-generation (5G) UWB communications. However, the silicon implementation of narrow monocycle pulses has been proven for UWB systems for duration time above 100 ps. This paper explores the opportunity offered by the well-established monocycle pulse generator for the implementation of monocycle pulses with duration time below 100 ps, by pushing the boundaries of the circuit topology and silicon technology through the latest nano-scale CMOS technology nodes. The results show that the circuit is capable of providing monocycle pulses with amplitude of 1.18 V and duration time of 70 ps, which represent the proof of principle in support of the feasibility of fully integrated transmitters in silicon technology for emerging 5G UWB communications.


Logic on MARS: Ontologies for Generalised Property Graphs


title: NLP as an essential ingredient of effective OSINT frameworks

abstract: The immense amount of information that can be collected from open sources have become increasingly important to the academic and business communities over the past two decades. Also national and global security is becoming more and more reliant on rapidly making sense of and managing the relevant intelligence data. The enormous size and complexity of the gathered data require advanced and unique data storage, management, analysis, and visualization technologies. The technologies and applications currently adopted typically involve elements like text mining, natural language processing, and stochastic-based algorithms. Natural language processing, in particular, has been a prominent research topic for many years now. We have conceptualized an analysis framework with a strong focus on various techniques of natural language processing to aggregate, manipulate, and analyze intelligence information.


title: Analysis of Software Defined Network firewall (SDF)

abstract: Software Defined Networks (SDN) technology can alternate the network, unlock critical intelligence, and help deliver the new services and analytics needed to run on-demand applications for today's businesses and consumers. So, SDN has been much request for adoption in mostly data centre.


title: Safeguarding SCADA Systems with Anomaly Detection

abstract: This paper will show how the accuracy and security of SCADA systems can be improved by using anomaly detection to identify bad values caused by attacks and faults. The performance of invariant induction and n- gram anomaly-detectors will be compared and this paper will also outline plans for taking this work further by integrating the output from several anomaly- detecting techniques using Bayesian networks. Although the methods outlined in this paper are illustrated using the data from an electricity network, this research springs from a more general attempt to improve the security and dependability of SCADA systems using anomaly detection.


title: Chordal Digraphs

abstract: Chordal graphs, also called triangulated graphs, are important in algorithmic graph theory. In this paper we generalise the definition of chordal graphs to the class of directed graphs. Several structural properties of chordal graphs that are crucial for algorithmic applications carry over to the directed setting, including notions like simplicial vertices, perfect elimination orderings, and characterisation by forbidden subgraphs resembling chordless cycles. Moreover, just as chordal graphs are related to treewidth, the chordal digraphs will be related to Kelly-width. Chordal digraphs coincide with the perfect elimination digraphs arising in the study of Gaussian elimination on sparse linear systems [Haskins and Rose, 1973].


title: d-Separation: From Theorems to Algorithms

abstract: An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.


title: Giraph Unchained: Barrierless Asynchronous Parallel Execution in Pregel-like Graph Processing Systems

abstract: The bulk synchronous parallel (BSP) model used by synchronous graph processing systems allows algorithms to be easily implemented and reasoned about. However, BSP can suffer from poor performance due to stale messages and frequent global synchronization barriers. Asynchronous computation models have been proposed to alleviate these overheads but existing asynchronous systems that implement such models have limited scalability or retain frequent global barriers, and do not always support graph mutations or algorithms with multiple computation phases. We propose barrierless asynchronous parallel (BAP), a new computation model that reduces both message staleness and global synchronization. This enables BAP to overcome the limitations of existing asynchronous models while retaining support for graph mutations and algorithms with multiple computation phases. We present GiraphUC, which implements our BAP model in the open source distributed graph processing system Giraph, and evaluate our system at scale with large real-world graphs on 64 EC2 machines. We show that GiraphUC provides across-the-board performance improvements of up to 5× faster over synchronous systems and up to an order of magnitude faster than asynchronous systems. Our results demonstrate that the BAP model provides efficient and transparent asynchronous execution of algorithms that are programmed synchronously.


title: End-to-end scene text recognition

abstract: This paper focuses on the problem of word detection and recognition in natural images. The problem is significantly more challenging than reading text in scanned documents, and has only recently gained attention from the computer vision community. Sub-components of the problem, such as text detection and cropped image word recognition, have been studied in isolation [7, 4, 20]. However, what is unclear is how these recent approaches contribute to solving the end-to-end problem of word recognition. We fill this gap by constructing and evaluating two systems. The first, representing the de facto state-of-the-art, is a two stage pipeline consisting of text detection followed by a leading OCR engine. The second is a system rooted in generic object recognition, an extension of our previous work in [20]. We show that the latter approach achieves superior performance. While scene text recognition has generally been treated with highly domain-specific methods, our results demonstrate the suitability of applying generic computer vision methods. Adopting this approach opens the door for real world scene text recognition to benefit from the rapid advances that have been taking place in object recognition.


Filling the Blanks (hint: plural noun) for Mad Libs Humor.


title: Efficient elasticity for character skinning with contact and collisions

abstract: We present a new algorithm for near-interactive simulation of skeleton driven, high resolution elasticity models. Our methodology is used for soft tissue deformation in character animation. The algorithm is based on a novel discretization of corotational elasticity over a hexahedral lattice. Within this framework we enforce positive definiteness of the stiffness matrix to allow efficient quasistatics and dynamics. In addition, we present a multigrid method that converges with very high efficiency. Our design targets performance through parallelism using a fully vectorized and branch-free SVD algorithm as well as a stable one-point quadrature scheme. Since body collisions, self collisions and soft-constraints are necessary for real-world examples, we present a simple framework for enforcing them. The whole approach is demonstrated in an end-to-end production-level character skinning system.


title: Fine-Grained Acceleration of Binary Neural Networks Using Intel® Xeon® Processor with Integrated FPGA

abstract: Binary weighted networks (BWN) for imageclassification reduce computation for convolutional neuralnetworks (CNN) from multiply-adds to accumulates with littleto no accuracy loss. Hardware architectures such as FPGA cantake full advantage of BWN computations because of theirability to express weights represented as 0 and 1 efficientlythrough customizable logic. In this paper, we present animplementation on Intel®'s Xeon® processor with integratedFPGA to accelerate binary weighted networks. We interfaceIntel's Accelerator Abstraction Layer (AAL) with Caffe toprovide a robust framework used for accelerating CNN. Utilizing the low latency Quick Path Interconnect (QPI) between the Broadwell Xeon® processor and Arria10 FPGA, we can perform fine-grained offloads for specific portions ofthe network. Due to convolution layers making up most of thecomputation in our experiments, we offload the feature andweight data to customized binary hardware in the FPGA forfaster execution. An initial proof of concept design shows thatby using both the Xeon processor and FPGA together we canimprove the throughput by 2x on some layers and by 1.3xoverall while utilizing only a small percentage of FPGA core logic.


title: Erasing bad memories: Agent-side summarization for long-term mapping

abstract: Precisely estimating the pose of an agent in a global reference frame is a crucial goal that unlocks a multitude of robotic applications, including autonomous navigation and collaboration. In order to achieve this, current state-of-the-art localization approaches collect data provided by one or more agents and create a single, consistent localization map, maintained over time. However, with the introduction of lengthier sorties and the growing size of the environments, data transfers between the backend server where the global map is stored and the agents are becoming prohibitively large. While some existing methods partially address this issue by building compact summary maps, the data transfer from the agents to the backend can still easily become unmanageable. In this paper, we propose a method that is designed to reduce the amount of data that needs to be transferred from the agent to the backend, functioning in large-scale, multi-session mapping scenarios. Our approach is based upon a landmark selection method that exploits information coming from multiple, possibly weak and correlated, landmark utility predictors; fused using learned feature coefficients. Such a selection yields a drastic reduction in data transfer while maintaining localization performance and the ability to efficiently summarize environments over time. We evaluate our approach on a data set that was autonomously collected in a dynamic indoor environment over a period of several months.


NETWORKING PROTOCOLS AND STANDARDS FOR INTERNET OF THINGS


title: Two Methods for Turning and Positioning and the Effect on Pressure Ulcer Development: A Comparison Cohort Study.

abstract: PURPOSE ::: We evaluated 2 methods for patient positioning on the development of pressure ulcers; specifically, standard of care (SOC) using pillows versus a patient positioning system (PPS). The study also compared turning effectiveness as well as nursing resources related to patient positioning and nursing injuries. ::: ::: ::: DESIGN ::: A nonrandomized comparison design was used for the study. ::: ::: ::: SUBJECTS AND SETTING ::: Sixty patients from a trauma/neurointensive care unit were included in the study. Patients were randomly assigned to 1 of 2 teams per standard bed placement practices at the institution. Patients were identified for enrollment in the study if they were immobile and mechanically ventilated with anticipation of 3 days or more on mechanical ventilation. Patients were excluded if they had a preexisting pressure ulcer. ::: ::: ::: METHODS ::: Patients were evaluated daily for the presence of pressure ulcers. Data were collected on the number of personnel required to turn patients. Once completed, the angle of the turn was measured. The occupational health database was reviewed to determine nurse injuries. ::: ::: ::: RESULTS ::: The final sample size was 59 (SOC = 29; PPS = 30); there were no statistical differences between groups for age (P = .10), body mass index (P = .65), gender (P = .43), Braden Scale score (P = .46), or mobility score (P = .10). There was a statistically significant difference in the number of hospital-acquired pressure ulcers between turning methods (6 in the SOC group vs 1 in the PPS group; P = .042). The number of nurses needed for the SOC method was significantly higher than the PPS (P ≤ 0.001). The average turn angle achieved using the PPS was 31.03°, while the average turn angle achieved using SOC was 22.39°. The difference in turn angle from initial turn to 1 hour after turning in the SOC group was statistically significant (P < .0001). No nurse injuries were reported for either group during the study. ::: ::: ::: CONCLUSIONS ::: Findings suggest that assistive devices such as a PPS can be effective in achieving proper positioning of patients to prevent development of pressure ulcers.


title: Recognizing cited facts and principles in legal judgements

abstract: In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, $$\kappa =0.65$$ź=0.65 and $$\kappa =0.95$$ź=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall $$\kappa$$ź of 0.72 with the human-annotated gold standard.


title: Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

abstract: Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism to incentivize workers to answer only the questions that they are sure of and skip the rest. We show that surprisingly, under a mild and natural "no-free-lunch" requirement, this mechanism is the one and only incentive-compatible payment mechanism possible. We also show that among all possible incentive-compatible mechanisms (that may or may not satisfy no-free-lunch), our mechanism makes the smallest possible payment to spammers. Interestingly, this unique mechanism takes a "multiplicative" form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over several hundred workers, we observe a significant reduction in the error rates under our unique mechanism for the same or lower monetary expenditure.


title: Symbolic kinematic and dynamic modelling toolbox for Multi-DOF robotic manipulators

abstract: The objective of this article is to present a method to model the kinematics and dynamics of robot manipulators. A complete description of the procedure to model and control a Multi-DOF 3D robot manipulator is detailed and simulated using designed toolbox in MATLAB. Examples of path planning, symbolic dynamic derivation and control strategy designs are presented. Advanced path planning by constrained optimization has been developed and verified by this toolbox. One of the advanced position controllers for robot manipulators is the Sliding Mode Control with desired gravity and friction compensation which has been applied and verified in the toolbox. Force contacts and collisions are modeled in nonlinear near real situations. This helps designing more robust force controlled systems.


title: Robust error detection: A hybrid approach combining unsupervised error detection and linguistic knowledge

abstract: This article presents a robust probabilistic method for the detection of context-sensitive spelling errors. The algorithm identifies lessfrequent grammatical constructions and attempts to transform them into more-frequent constructions while retaining similar syntactic structure. If the transformations result in lowfrequency constructions, the text is likely to contain an error. A first unsupervised approach uses only information derived from a part-ofspeech tagged corpus. This experiment shows a good error detection capacity but also a high rate of false alarms, in many cases due to phrase and clause boundaries. In a second approach, we combine the first method with robust phrase and clause recognition to avoid many of the false alarms in the first experiment. A comparative evaluation of the experiments shows that the introduction of linguistic knowledge dramatically increases the precision of the error detection method.


title: The software model checker BLAST Applications to software engineering

abstract: Blast is an automatic verification tool for checking temporal safety properties of C programs. Given a C program and a temporal safety property, Blast either statically proves that the program satisfies the safety property, or provides an execution path that exhibits a violation of the property (or, since the problem is undecidable, does not terminate). Blast constructs, explores, and refines abstractions of the program state space based on lazy predicate abstraction and interpolation-based predicate discovery. This paper gives an introduction to Blast and demonstrates, through two case studies, how it can be applied to program verification and test-case generation. In the first case study, we use Blast to statically prove memory safety for C programs. We use CCured, a type-based memory-safety analyzer, to annotate a program with run-time assertions that check for safe memory operations. Then, we use Blast to remove as many of the run-time checks as possible (by proving that these checks never fail), and to generate execution scenarios that violate the assertions for the remaining run-time checks. In our second case study, we use Blast to automatically generate test suites that guarantee full coverage with respect to a given predicate. Given a C program and a target predicate p, Blast determines the program locations q for which there exists a program execution that reaches q with p true, and automatically generates a set of test vectors that cause such executions. Our experiments show that Blast can provide automated, precise, and scalable analysis for C programs.


title: Effects of eHealth physical activity encouragement in adolescents with complex congenital heart disease: The PReVaiL randomized clinical trial.

abstract: OBJECTIVE ::: To assess benefit and harms of adding an eHealth intervention to health education and individual counseling in adolescents with congenital heart disease. ::: ::: ::: DESIGN ::: Randomized clinical trial. ::: ::: ::: SETTING ::: Denmark. ::: ::: ::: PATIENTS ::: A total of 158 adolescents aged 13-16years with no physical activity restrictions after repaired complex congenital heart disease. ::: ::: ::: INTERVENTIONS ::: PReVaiL consisted of individually tailored eHealth encouragement physical activity for 52weeks. All patients received 45min of group-based health education and 15min of individual counseling involving patients' parents. ::: ::: ::: OUTCOMES ::: The primary outcome was maximal oxygen uptake (VO2 peak) at 52weeks after randomization. The secondary outcome was physical activity. Exploratory outcomes were generic and disease-specific questionnaires. ::: ::: ::: RESULTS ::: In the intervention group, 58 patients (72%) completed the final test, but of those, only 46 (57%) fulfilled the compliance criteria of using the eHealth application for at least 2 consecutive weeks. In the control group, 61 patients (79%) completed both exercise tests. Adjusted for baseline values, the difference between the intervention group and the control group in mean VO2 peak at 1year was -0.65ml·kg(-1)·min(-1) (95% CI -2.66 to 1.36). Between-group differences at 1year in physical activity, generic health-related quality of life, and disease-specific quality of life were not statistically significant. ::: ::: ::: CONCLUSIONS ::: Adding a tailored eHealth intervention to health education and individual counseling did not affect outcomes among adolescents with congenital heart disease. Our results do not support the use of this eHealth intervention in adolescents with complex congenital heart disease. ::: ::: ::: TRIAL REGISTRATION ::: Clinical trials.gov identifier: NCT01189981.


title: Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

abstract: Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the stateaction space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.1


title: Supervised Fitting of Geometric Primitives to 3D Point Clouds

abstract: Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it enables many downstream applications in 3D data processing. For a long time, RANSAC-based methods have been the gold standard for such primitive fitting problems, but they require careful per-input parameter tuning and thus do not scale well for large datasets with diverse shapes. In this work, we introduce Supervised Primitive Fitting Network (SPFN), an end-to-end neural network that can robustly detect a varying number of primitives at different scales without any user control. The network is supervised using ground truth primitive surfaces and primitive membership for the input points. Instead of directly predicting the primitives, our architecture first predicts per-point properties and then uses a differential model estimation module to compute the primitive type and parameters. We evaluate our approach on a novel benchmark of ANSI 3D mechanical component models and demonstrate a significant improvement over both the state-of-the-art RANSAC-based methods and the direct neural prediction.


title: Deep Neural Network Hyper-Parameter Setting for Classification of Obstructive Sleep Apnea Episodes

abstract: The wide availability of sensing devices in the medical domain causes the creation of large and very large data sets. Hence, tasks as the classification in such data sets becomes more and more difficult. Deep Neural Networks (DNNs) are very effective in classification, yet finding the best values for their hyper-parameters is a difficult and time-consuming task. This paper introduces an approach to decrease execution times to automatically find good hyper-parameter values for DNN through Evolutionary Algorithms when classification task is faced. This decrease is obtained through the combination of two mechanisms. The former is constituted by a distributed version for a Differential Evolution algorithm. The latter is based on a procedure aimed at reducing the size of the training set and relying on a decomposition into cubes of the space of the data set attributes. Experiments are carried out on a medical data set about Obstructive Sleep Anpnea. They show that sub-optimal DNN hyper-parameter values are obtained in a much lower time with respect to the case where this reduction is not effected, and that this does not come to the detriment of the accuracy in the classification over the test set items.


title: Investigation of relaxations of the rabbit anococcygeus muscle by nerve stimulation and ATP using the ATP antagonist ANAPP3.

abstract: When tone was raised by histamine (10(-6) M), field stimulation (0.2-8 Hz) induced relaxation of the rabbit anococcygeus muscle in the continuous presence of guanethidine (10(-5) M) and atropine (10(-6) M). Similar relaxations could be induced by ATP and adenosine, which were approximately equipotent, but the non-hydrolyzable analogue beta-gamma-methylene ATP was less potent and produced relaxations which were slower. Although PGE2 was a potent relaxant in this muscle, release of endogenous prostaglandins does not appear to mediate the response to ATP since indomethacin (2 x 10(-5) M) pretreatment did not reduce responses to ATP. The specific ATP receptor antagonist, ANAPP3 (10(-4) or 10(-3) M) did not reduce responses to nerve stimulation and only slightly reduced those of exogenous ATP. The results indicate that responses to ATP could be mediated partly by the products of its hydrolysis and do not support the proposal that ATP is the inhibitory transmitter in this muscle.


title: The Role of Emotion-Driven Impulse Control Difficulties in the Relation Between Social Anxiety and Aggression

abstract: Objectives ::: To enhance our understanding of the factors that may account for increased aggression in socially anxious individuals, this study examined associations among emotion-driven impulse control difficulties, social anxiety, and dimensions of aggression (i.e., hostility, anger, physical aggression, verbal aggression). ::: ::: Method ::: Individuals (N = 107; 73.8% male; Mage = 40.8 years) receiving residential substance abuse treatment participated in this cross-sectional study. ::: ::: Results ::: Social anxiety symptoms were significantly positively correlated with emotion-driven impulse control difficulties, anger, and hostility, but not verbal or physical aggression. Separate models for each aggression facet were examined to test the direct and indirect paths. Bootstrapped mediation analyses indicated a significant indirect path from social anxiety symptoms to each facet of aggression through emotion-driven impulse control difficulties (ps < .05). ::: ::: Conclusion ::: Results highlight the potential utility of targeting emotion-driven impulse control difficulties to decrease aggression among socially anxious individuals.


title: BUSINESS-TO-BUSINESS E-BUSINESS MODELS: CLASSIFICATION AND TEXTILE INDUSTRY IMPLICATIONS

abstract: Since the introduction of the Internet and e-commerce in the mid-1990s, there has been a lot of hype surrounding e-business, the impact that it will have on the way that companies do business, and how it will change the global economy as a whole. Since the crash of the dotcom companies in 2001, there has been much less hype surrounding the use of the Internet for business. There seems to have been a realization that e-business may not be the answer to all of a company’s problems, but can be a great asset in the struggle to increase efficiencies in daily business dealings, and that the Web is primarily a new way of relating to customers and suppliers. This paper categorizes and discusses the different types of business-to-business electronic business models currently being used by businesses and discussed in the academic literature, and shows how these business models are being implemented within the textile industry. This paper is divided into three parts. Part I gives an overview and some important definitions associated with businessto-business e-business, and discusses some characteristics that are unique to doing business on the Internet. Risks and benefits associated with doing business online are also discussed. Part II analyzes the different types of e-business models seen in the academic literature. Based on the analysis of the literature, a taxonomy of e-business models was developed. This new classification system organized e-business models into the following categories: sourcing models, ownership models, service-based models, customer relationship management models, supply chain models, interaction Models and revenue models. Part III reviews how these e-business models are currently being used within the textile industry. A preliminary analysis of 79 textile manufacturing companies was conducted to identify the applications of e-business.


title: Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

abstract: We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data points into clusters such that the variables are jointly conditionally independent given the cluster. This method can be used to identify finite confounders.


title: DESIGN OF FRAGMENT-TYPE ISOLATION STRUCTURES FOR MIMO ANTENNAS

abstract: Fragment structure should flnd its application in acquiring high isolation between multiple- input multiple-output (MIMO) antennas. By gridding a design space into fragment cells, a fragment- type isolation structure can be constructed by metalizing some of the fragment cells. For MIMO isolation design, cells to be metalized can be selected by optimization searching scheme with objectives such as isolation, return losses, and even radiation patterns of MIMO antennas. Due to the ∞exibility of fragment-type isolation structure, fragment-type structure has potentials to yield isolation higher than canonical isolation structures. In this paper, multi-objective evolutionary algorithm based on decomposition combined with genetic operators (MOEA/D-GO) is applied to design fragment-type isolation structures for MIMO patch antennas and MIMO PIFAs. It is demonstrated that isolation can be improved to difierent extents by using fragment-type isolation design. Some technique aspects related to the fragment-type isolation design, such as efiects of fragment cell size, design space, density of metal cells, and e-ciency consideration, are further discussed.


title: NoSta-D Named Entity Annotation for German: Guidelines and Dataset

abstract: We describe the annotation of a new dataset for German Named Entity Recognition (NER). The need for this dataset is motivated by licensing issues and consistency issues of existing datasets. We describe our approach to creating annotation guidelines based on linguistic and semantic considerations, and how we iteratively refined and tested them in the early stages of annotation in order to arrive at the largest publicly available dataset for German NER, consisting of over 31,000 manually annotated sentences (over 591,000 tokens) from German Wikipedia and German online news. We provide a number of statistics on the dataset, which indicate its high quality, and discuss legal aspects of distributing the data as a compilation of citations. The data is released under the permissive CC-BY license, and will be fully available for download in September 2014 after it has been used for the GermEval 2014 shared task on NER. We further provide the full annotation guidelines and links to the annotation tool used for the creation of this resource.


title: A low cost single-phase to three-phase power converter for low-power motor drive applications

abstract: This paper presents an economic single-phase to three-phase converter which provides variable output voltage and soft starting capability by using four high frequency switches (IGBT, MOSFET), four diodes, and a triac. This converter can run a three-phase Induction motor which is much more efficient compared to a single phase motor. In order to have a balanced output voltage in all modes of operation (start-up, speed control, and steady state) two closed loop controllers has been utilized: one for dc link voltage and the other one for inverter output. The proposed scheme with variable output voltage and fixed frequency provides a limited-range speed control of the induction motor. As a result, the new single-phase to three-phase converter brings the controllable output voltage as in a six-switches standard three-phase inverter. The front-end rectifier has the capability of active input current shaping. Analysis and simulation results are presented in the result section to demonstrate these new features.


title: Attribute-based access control for ICN naming scheme

abstract: Information Centric Networking (ICN) is a new network architecture that aims to overcome the weakness of existing IP-based networking architecture. Instead of establishing a connection between the communicating hosts, ICN focuses on the content, i.e. data, transmitted in network. Content copies in ICN can be cached at different locations. The content is out of the owner's control once it's published. Thus, enforcing access control policies on distributed content copies is crucial in ICN. Attribute-Based Encryption (ABE) is a feasible approach to enforce such control mechanisms. However, applying ABE in ICN has two challenges: from management perspective, managing attributes is complicated in distributed manners; from privacy perspective, unlike in traditional networks, the enforced content access policies are public to all the ICN users. Thus, it is desirable that unauthorized content viewers are not able to retrieve the access policy. To this end, a privacy-preserving access control scheme for ICN and associated attribute management solution are presented in this paper. This proposed approach is compatible with existing flat name based ICN architectures.


title: SociaLite: Datalog extensions for efficient social network analysis

abstract: With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. We evaluated SociaLite by running eight graph algorithms (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) on two real-life social graphs, Live-Journal and Last.fm. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50% for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, giving up only 16% for the largest benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to make social network queries easily and efficiently.


title: Face Transfer with Generative Adversarial Network

abstract: Face transfer animates the facial performances of the character in the target video by a source actor. Traditional methods are typically based on face modeling. We propose an end-to-end face transfer method based on Generative Adversarial Network. Specifically, we leverage CycleGAN to generate the face image of the target character with the corresponding head pose and facial expression of the source. In order to improve the quality of generated videos, we adopt PatchGAN and explore the effect of different receptive field sizes on generated images.


The Architecture of PIER: an Internet-Scale Query Processor


title: Does sex education before college protect students from sexual assault in college?

abstract: Purpose ::: College-bound young people experience sexual assault, both before and after they enter college. This study examines historical risk factors (experiences and exposures that occurred prior to college) for penetrative sexual assault (PSA) victimization since entering college. ::: ::: ::: Methods ::: A cross-sectional study, including an online population-based quantitiative survey with undergraduate students was conducted in spring 2016. Bivariate analyses and multivariable regressions examined risk and protective factors associated with ever experiencing PSA since entering college. Concurrently-collected in-depth ethnographic interviews with 151 students were reviewed for information related to factors identified in the survey. ::: ::: ::: Results ::: In bivariate analyses, multiple historical factors were significantly associated with PSA in college including adverse childhood experiences and having experienced unwanted sexual contact before college (for women) and initiation of alcohol, marijuana, and sexual behaviors before age 18. Significant independent risk factors for college PSA included female gender, experiencing unwanted sexual contact before college, first oral sex before age 18, and “hooking up” (e.g., causual sex or sex outside a committed partnership) in high school. Receipt of school-based sex education promoting refusal skills before age 18 was an independent protective factor; abstinence-only instruction was not. In the ethnographic interviews, students reported variable experiences with sex education before college; many reported it was awkward and poorly delivered. ::: ::: ::: Conclusions ::: Multiple experiences and exposures prior to college influenced the risk of penetrative sexual assault in college. Pre-college comprehensive sexuality education, including skills-based training in refusing unwanted sex, may be an effective strategy for preventing sexual assault in college. Sexual assault prevention needs to begin earlier; successful prevention before college should complement prevention efforts once students enter college.


title: A Probabilistic Enterprise Architecture Model Evolution

abstract: Enterprise Architecture (EA) is a widely accepted means to ease the alignment of IS projects with enterprise-wide objectives. One central artifact of EA are EA models, which provide a holistic view on the organization and support EA's stakeholder to create added value. As EA collects its data from different sources, the data can be contradictory. This work contributes to existing research by proposing a novel approach to deal with contradictory data without solving the thereby caused conflicts. In order to achieve this objective, we refine the Predictive, Probabilistic Architecture Modeling Framework (P²AMF) introduced by Johnson et al., which already incorporates a way to represent uncertainty regarding the existence of modelled entities. To make our technique usable, we generalize P²AMF from its UML/OCL notation to a graph presentation in order to apply it to EA models notated in arbitrary notations like ArchiMate. Furthermore, we add alternative scenarios in different versions along a time series to meet the requirements of a distributed EA evolution. To show the applicability of our approach, we developed a proof of concept prototype by implementing the proposed calculations and guidelines on a Neo4j graph database. Last, we argue that our approach meets the stated requirements of a distributed EA evolution.


title: PCAS: Pruning Channels with Attention Statistics.

abstract: To implement deep neural networks on small embedded devices, conventional techniques use channel pruning looking considering manual compression rate per layer to reduce parameters. Besides it is difficult to consider the relationships between layers and it takes a lot of time for deeper models. For addressing these issues, we propose a new channel pruning technique based on attention that can evaluate the importance of channels. We improved the method with the criterion to allow the automatic channel selection using a single compression rate for the entire model. Experimental results showed that a parameter reduction of 90.8% and FLOPs reduction of 79.4% was achieved with an accuracy degradation of around 1% for the compressed ResNet-50 model on the CIFAR-10 benchmark.


title: Including Uncertainty when Learning from Human Corrections

abstract: It is difficult for humans to efficiently teach robots how to correctly perform a task. One intuitive solution is for the robot to iteratively learn the human's preferences from corrections, where the human improves the robot's current behavior at each iteration. When learning from corrections, we argue that while the robot should estimate the most likely human preferences, it should also know what it does not know, and integrate this uncertainty as it makes decisions. We advance the state-of-the-art by introducing a Kalman filter for learning from corrections: this approach obtains the uncertainty of the estimated human preferences. Next, we demonstrate how the estimate uncertainty can be leveraged for active learning and risk-sensitive deployment. Our results indicate that obtaining and leveraging uncertainty leads to faster learning from human corrections.


title: Effects of cyclic hydraulic pressure on osteocytes

abstract: Bone is able to adapt its composition and structure in order to suit its mechanical environment. Osteocytes, bone cells embedded in the calcified matrix, are believed to be the mechanosensors and responsible for orchestrating the bone remodeling process. Recent in vitro studies have shown that osteocytes are able to sense and respond to substrate strain and fluid shear. However the capacity of osteocytes to sense cyclic hydraulic pressure (CHP) associated with physiological mechanical loading is not well understood. In this study, we subjected osteocyte-like MLO-Y4 cells to controlled CHP of 68 kPa at 0.5 Hz, and investigated the effects of CHP on intracellular calcium concentration, cytoskeleton organization, mRNA expression of genes related to bone remodeling, and osteocyte apoptosis. We found that osteocytes were able to sense CHP and respond by increased intracellular calcium concentration, altered microtubule organization, a time-dependent increase in COX-2 mRNA level and RANKL/OPG mRNA ratio, and decreased apoptosis. These findings support the hypothesis that loading induced cyclic hydraulic pressure in bone serves as a mechanical stimulus to osteocytes and may play a role in regulating bone remodeling in vivo.


title: A 0.2–3.6-GHz 10-dBm B1dB 29-dBm IIP3 Tunable Filter for Transmit Leakage Suppression in SAW-Less 3G/4G FDD Receivers

abstract: A tunable $N$ -path filter with more than one decade tuning range is demonstrated to address transmit (TX) leakage in a surface-acoustic-wave-less diversity path receiver for frequency-division duplexing cellular systems. The proposed filter simultaneously creates a reject band and passband anywhere from 0.2 to 3.6 GHz to suppress TX leakage while minimizing insertion loss of the receive signal and is implemented in 45-nm CMOS silicon-on-insulator. Measurements show the 3-dB bandwidth of the passband is greater than 80 MHz with an independently tunable reject band, providing the ultimate rejection is from 33 to 41 dB while the passband insertion loss is between 2.6 dB and 4.3 dB over the tuning range. The proposed filter offers 29-dBm out-of-band (OOB) third-order input-intercept power (IIP3) and 22-dBm in-band (IB) IIP3 with a 10-dBm blocker 1-dB compression point (B1dB). To the authors’ knowledge, this is the highest B1dB, IB IIP3, and OOB IIP3 for a CMOS tunable filter.


title: Tackling mental health by integrating unobtrusive multimodal sensing

abstract: Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. In this study, we investigate how users' online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states. First, we extract a suite of multimodal time-series signals using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit emotions and emotion transitions. Next, we use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. Finally, we validate the effectiveness of our approach using two groups of recruited subjects.


title: Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection

abstract: We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a short detection delay of 3.4 s it is suitable for the application in an implantable closed-loop device.


title: Autism diagnostic interview: a standardized investigator-based instrument.

abstract: The development of a new standardized investigator-based interview for use in the differential diagnosis of pervasive developmental disorders is described, together with a diagnostic algorithm (using ICD-10 criteria) based on its use. Good interrater reliability for algorithm items was shown between four raters, two in Canada and two in the UK, who rated 32 videotaped interviews. The items also significantly discriminated between 16 autistic and 16 nonautistic mentally handicapped subjects. The algorithm based on ICD-10 identified all 16 autistic individuals and none of the 16 nonautistic subjects.


title: Intelligent critic system for architectural design

abstract: The paper describes an intelligent computer aided architectural design system (ICAAD) called ICADS. ICADS encapsulates different types of design knowledge into independent "critic" modules. Each "critic" module possesses expertise in evaluating an architect's work in different areas of architectural design and can offer expert advice when needed. This research focuses on the representation of spatial information encoded in architectural floor plans and the representation of expert design knowledge. Described in the paper is our research in designing and developing two particular "critic" modules. The first module, FPDX, checks a residential apartment floor plan, verifies that the plan meets a set of government regulations, and offers suggestions for floor plan changes if regulations are not met. The second module, IDX, analyzes room and furniture layout according to a set of interior design guidelines and offers ideas on how furniture should be moved if the placement does not follow good design principles.


title: Image Enhancement using Bio-inspired Algorithms on mammogram for cancer detection

abstract: Image preprocessing and image enhancement plays a critical role in medical image processing. Considering the case study of breast cancer detection, it was found that there are various schemes of optimization techniques which is either training based or leads to recursive iterations leading to computationally complex process. Hence, the proposed system implements a unique and novel optimization technique called as Image Enhancement using Bio-inspired Algorithms. Different from existing bio-inspired algorithm, the proposed system doesn't use any training sequences, or depends on single fitness function or performs recursive operation for exploring elite population. The algorithm performs automatic segmentation process followed by three level of enhancement operation for achieving local to global best optimization without using any forms of recursive functions. The outcomes are visually defined and well resolution to prove success factor.


title: An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider

abstract: The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider's profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.


title: A Reputation and Knowledge Based Trust Service Platform for Trustworthy Social Internet of Things

abstract: The Internet of Things has attracted a plenty of research in this decade and imposed fascinating services where large numbers of heterogeneous-features entities socially collaborate together to solve complex scenarios. However, these entities need to trust each other prior to exchanging data or offering services. In this paper, we briefly present our ongoing project called Trust Service Platform, which offers trust assessment of any two entities in the Social Internet of Things to applications and services. We propose a trust model that incorporates both reputation properties as Recommendation and Reputation trust metrics; and knowledge-based property as Knowledge trust metric. For the trust service platform deployment, we propose a reputation system and a functional architecture with Trust Agent, Trust Broker and Trust Analysis and Management modules along with mechanisms and algorithms to deal with the three trust metrics. We also present a utility theory-based mechanism for trust calculation. To clarify our trust service platform, we describe the trust models and mechanisms in accordance with a trust car-sharing service. We believe this study offers the better understanding of the trust as a service in the platform and will impose many trust-related research challenges as the future work.


title: SIFT Hardware Implementation for Real-Time Image Feature Extraction

abstract: This paper introduces a high-speed all-hardware scale-invariant feature transform (SIFT) architecture with parallel and pipeline technology for real-time extraction of image features. The task-level parallel and pipeline structure are exploited between the hardware blocks, and the data-level parallel and pipeline architecture are exploited inside each block. Two identical random access memories are adopted with ping-pong operation to execute the key point detection module and the descriptor generation module in task-level parallelism. With speeding up the key point detection module of SIFT, the descriptor generation module has become the bottleneck of the system's performance; therefore, this paper proposes an optimized descriptor generation algorithm. A novel window-dividing method is proposed with square subregions arranged in 16 directions, and the descriptors are generated by reordering the histogram instead of window rotation. Therefore, the main orientation detection block and descriptor generation block run in parallel instead of interactively. With the optimized algorithm cooperating with pipeline structure inside each block, we not only improve the parallelism of the algorithm, but also avoid floating data calculation to save hardware consumption. Thus, the descriptor generation module leads the speed almost 15 times faster than a recent solution. The proposed system was implemented on field programmable gate array and the overall time to extract SIFT features for an image having 512×512 pixels is only 6.55 ms (sufficient for real-time applications), and the number of feature points can reach up to 2900.


title: Hypertension in Saudi Arabia

abstract: OBJECTIVES ::: To determine the prevalence of hypertension among Saudis of both gender, between the ages of 30-70 years in rural as well as urban communities. This work is part of a major national study on Coronary Artery Disease in Saudis Study (CADISS). ::: METHODS ::: This is a community-based study conducted by examining subjects in the age group of 30-70 years of selected households during a 5-year period between 1995 and 2000 in Saudi Arabia. Data were obtained from history using a validated questionnaire, and examination including measurement of blood pressure. The data were analyzed to provide prevalence of hypertension. Logistic regression was used to develop a risk assessment model for prevalence of hypertension. ::: RESULTS ::: The total number of subjects included in the study was 17,230. The prevalence of hypertension was 26.1% in crude terms. For males, the prevalence of hypertension was 28.6%, while for females; the prevalence was significantly lower at 23.9% (p<0.001). The urban population showed significantly higher prevalence of hypertension of 27.9%, compared to rural population's prevalence of 22.4% (p<0.001). The prevalence of CAD among hypertensive patients was 8.2%, and 4.5% among normotensive subjects (p<0.001). Increasing weight showed significant increase in prevalence of hypertension in a linear relationship. ::: CONCLUSIONS ::: Hypertension is increasing in prevalence in KSA affecting more than one fourth of the adult Saudi population. We recommend aggressive management of hypertension as well as screening of adults for hypertension early to prevent its damaging consequences if left untreated. Public health awareness of simple measures, such as low salt diet, exercise, and avoiding obesity, to maintain normal arterial blood pressure need to be implemented by health care providers.


Neural Networks in Data Mining


title: Simulation and Performance Evaluation of Vehicle-to-Vehicle (V2V) Propagation Model in Urban Environment

abstract: Vehicle-to-Vehicle (V2V) communications offer support to several applications that can make the modern day travel safer, more efficient and enjoyable. However, reliable and efficient V2V application development requires better understanding and knowledge of the surrounding environment in which the vehicles operate them. The urban environment consists of open, downtown, suburban, wider and narrower spaces which vary significantly in their radio channel characteristics. Therefore studies on V2V propagation channels that take in to account random and time-varying of different environments are highly sought-after. In this paper a detailed overview of V2V propagation modeling techniques is provided. Using a case study for the city of Doha, Qatar we explained the metropolitan-area wide characterization of received signal strength. Finally, a comprehensive simulation-based evaluation is presented to measure the impact of geometry-based propagation model on V2V performance in an urban environment using the publicly available simulator. The output from the propagation model simulations is used to analyze V2V communication in terms of several communications and networking performance metrics such received signal strength, packet delivery ratio, and reliable communication range for the given data rate settings.


title: Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane

abstract: This paper considers the problem of embedding trees into the hyperbolic plane. We show that any tree can be realized as the Delaunay graph of its embedded vertices. Particularly, a weighted tree can be embedded such that the weight on each edge is realized as the hyperbolic distance between its embedded vertices. Thus the embedding preserves the metric information of the tree along with its topology. The distance distortion between non adjacent vertices can be made arbitrarily small --- less than a (1+e) factor for any given e. Existing results on low distortion of embedding discrete metrics into trees carry over to hyperbolic metric through this result. The Delaunay character implies useful properties such as guaranteed greedy routing and realization as minimum spanning trees.


Cancer Stem Cells: Cellular Plasticity, Niche, and its Clinical Relevance