title: Deep Video-Based Performance Cloning

abstract: We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space-time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses \emph{paired} training data, self-generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances. Supplementary video: this https URL.


title: Assisting IoT Projects and Developers in Designing Interoperable Semantic Web of Things Applications

abstract: Internet of Things (IoT) is becoming more and more popular. Increasingly, European projects (CityPulse, IoT.est, IoT-i and IERC), standard development organizations (ETSI M2M, oneM2M and W3C) and developers are involved in integrating Semantic Web technologies to Internet of Things. All of them design IoT application uses cases which are not necessarily interoperable with each other. The main innovative research challenge is providing a unified system to build interoperable semantic-based IoT applications. In this paper, to overcome this challenge, we design the Semantic Web of Things (SWoT) generator to assist IoT projects and developers in: (1) building interoperable Semantic Web of Things (SWoT) applications by providing interoperable semantic-based IoT application templates, (2) easily inferring high-level abstractions from sensor measurements thanks to the rules provided by the template, (3) designing domain-specific or inter-domain IoT applications thanks to the interoperable domain knowledge provided by the template, and (4) encouraging to reuse as much as possible the background knowledge already designed. We demonstrate the usefulness of our contribution though three use cases: (1) cloud-based IoT developers, (2) mobile application developers, and (3) assisting IoT projects. A proof-of concept for providing Semantic Web of Things application templates is available at http://www.sensormeasurement.appspot.com/?p=m3api.


title: Print or digital? Reading behavior and preferences in Japan

abstract: In today's digital age, daily reading may be becoming digital reading. To understand this possible shift from reading print media to reading digital media, we investigated reading behavior for 11 media and reading preferences between print and digital in different circumstances. In August 2012, an online survey was used to inquire about the reading behavior and preference of 1,755 participants, ranging in age from 18 to 69 years. The participants contained equal numbers of men and women from five age brackets. Our main finding was that approximately 70% of total reading time was spent on digital media and that preferences favored print media. Cluster analysis of reading time by media was used to categorize respondents into eight clusters, and a second cluster analysis on stated preference digital or print yielded six clusters. The correspondence analysis between reading behavior clusters and preference clusters revealed that there is a mismatch between reading behavior and stated preference for either print or digital media.


title: 1 Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk

abstract: Bitcoin has enjoyed wider adoption than any previous crypto- currency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk investors face from Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 40 Bitcoin exchanges established over the past three years, and find that 18 have since closed, with customer account balances often wiped out. Fraudsters are sometimes to blame, but not always. Using a proportional hazards model, we find that an exchange’s transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. We also present a logistic regression showing that popular exchanges are more likely to suffer a security breach.


title: Short bowel syndrome and intestinal failure: consensus definitions and overview.

abstract: Short bowel syndrome (SBS)-associated intestinal failure is a highly disabling condition that impairs quality of life and social integration. Although the condition is not uniformly fatal, it might lead to serious, life-threatening complications. The basic goals of medical treatment are to maintain fluid, electrolyte, and nutrient balances and to make appropriate modifications in disease management to avoid side effects. Various definitions have been proposed for SBS and intestinal failure within the medical literature, but many focus on different aspects of the conditions, leading to confusion. In the past, identifying the cause of intestinal failure was of little consequence, because all patients were managed on total parenteral nutrition at home. However, with the recent development of medical therapies such as recombinant growth hormone, octreotide, and glucagon-like peptide-2 analogues and with improvements in small bowel transplantation, many patients can be made nutritionally autonomous. To evaluate the relative efficacy of these therapies, there is now a need to develop consensus definitions so that patients can be properly categorized before therapy. To this end, a group of experts on the subject was convened to develop the following new definitions: "Intestinal failure results from obstruction, dysmotility, surgical resection, congenital defect, or disease-associated loss of absorption and is characterized by the inability to maintain protein-energy, fluid, electrolyte, or micronutrient balance." "Short-bowel syndrome results from surgical resection, congenital defect, or disease-associated loss of absorption and is characterized by the inability to maintain protein-energy, fluid, electrolyte, or micronutrient balances when on a conventionally accepted, normal diet."


title: Targeting Accurate Object Extraction From an Image: A Comprehensive Study of Natural Image Matting

abstract: With the development of digital multimedia technologies, image matting has gained increasing interests from both academic and industrial communities. The purpose of image matting is to precisely extract the foreground objects with arbitrary shapes from an image or a video frame for further editing. It is generally known that image matting is inherently an ill-posed problem because we need to output three images out of only one input image. In this paper, we provide a comprehensive survey of the existing image matting algorithms and evaluate their performance. In addition to the blue screen matting, we systematically divide all existing natural image matting methods into four categories: 1) color sampling-based; 2) propagation-based; 3) combination of sampling-based and propagation-based; and 4) learning-based approaches. Sampling-based methods assume that the foreground and background colors of an unknown pixel can be explicitly estimated by examining nearby pixels. Propagation-based methods are instead based on the assumption that foreground and background colors are locally smooth. Learning-based methods treat the matting process as a supervised or semisupervised learning problem. Via the learning process, users can construct a linear or nonlinear model between the alpha mattes and the image colors using a training set to estimate the alpha matte of an unknown pixel without any assumption about the characteristics of the testing image. With three benchmark data sets, the various matting algorithms are evaluated and compared using several metrics to demonstrate the strengths and weaknesses of each method both quantitatively and qualitatively. Finally, we conclude this paper by outlining the research trends and suggesting a number of promising directions for future development.


title: Assessing the Effectiveness of Neurofeedback Training in the Context of Clinical and Social Neuroscience

abstract: Social neuroscience benefits from the experimental manipulation of neuronal activity. One possible manipulation, neurofeedback, is an operant conditioning-based technique in which individuals sense, interact with, and manage their own physiological and mental states. Neurofeedback has been applied to a wide variety of psychiatric illnesses, as well as to treat sub-clinical symptoms, and even to enhance performance in healthy populations. Despite growing interest, there persists a level of distrust and/or bias in the medical and research communities in the USA toward neurofeedback and other functional interventions. As a result, neurofeedback has been largely ignored, or disregarded within social neuroscience. We propose a systematic, empirically-based approach for assessing the effectiveness, and utility of neurofeedback. To that end, we use the term perturbative physiologic plasticity to suggest that biological systems function as an integrated whole that can be perturbed and guided, either directly or indirectly, into different physiological states. When the intention is to normalize the system, e.g., via neurofeedback, we describe it as self-directed neuroplasticity, whose outcome is persistent functional, structural, and behavioral changes. We argue that changes in physiological, neuropsychological, behavioral, interpersonal, and societal functioning following neurofeedback can serve as objective indices and as the metrics necessary for assessing levels of efficacy. In this chapter, we examine the effects of neurofeedback on functional connectivity in a few clinical disorders as case studies for this approach. We believe this broader perspective will open new avenues of investigation, especially within social neuroscience, to further elucidate the mechanisms and effectiveness of these types of interventions, and their relevance to basic research.


title: Multistatic Ground-Penetrating Radar Experiments

abstract: A multistatic ground-penetrating radar (GPR) system has been developed and used to measure the response of a number of targets to produce data for the investigation of multistatic inversion algorithms. The system consists of a linear array of resistive-vee antennas, microwave switches, a vector network analyzer, and a 3-D positioner, all under computer control. The array has two transmitters and four receivers which provide eight bistatic spacings from 12 to 96 cm in 12-cm increments. Buried targets are scanned with and without surface clutter, which is a layer of rocks whose spacing is empirically chosen to maximize the clutter effect. The measured responses are calibrated so that the direct coupling in the system is removed, and the signal reference point is located at the antenna drive point. Images are formed using a frequency-domain beamforming algorithm that compensates for the phase response of the antennas. Images of targets in air validate the system calibration and the imaging algorithm. Bistatic and multistatic images for the buried targets are very good, and they show the effectiveness of the system and processing.


title: Web3D-based automatic furniture layout system using recursive case-based reasoning and floor field

abstract: Furniture layout in a virtual 3D scene is an important and challenging task, as it is time-consuming and requires experience. To address this issue, we propose automatic furniture layout algorithms to help users to rapidly generate reasonable layout results. Specifically, our algorithms divide a scene layout into four layout modes, namely, coupled mode, enclosed mode, matrix mode, and circular mode. Then each model is solved independently. The coupled mode is solved using recursive techniques and case-based reasoning. The enclosed mode is solved using floor field. The distance and angle among the furniture are determined by ergonomics guidelines. Finally, the layout results of the scene can be obtained by combining the solutions from these layout modes, and an evaluation method for the layout results based on user feedback is proposed. For a room with non-rectangular floor, our algorithms can also handle this case using shape standardization techniques. Based on our algorithms, an online 3D furniture layout system is developed. Many experiments are conducted on the system with the real interior design cases, and we compared our algorithms with other popular algorithms. The experimental results show that our algorithms are efficient and can meet the real response requirements of online furniture layout.


title: Enriching Taxonomies With Functional Domain Knowledge

abstract: The rising need to harvest domain specific knowledge in several applications is largely limited by the ability to dynamically grow structured knowledge representations, due to the increasing emergence of new concepts and their semantic relationships with existing ones. Such enrichment of existing hierarchical knowledge sources with new information to better model the "changing world" presents two-fold challenges: (1) Detection of previously unknown entities or concepts, and (2) Insertion of the new concepts into the knowledge structure, respecting the semantic integrity of the created relationships. To this end we propose a novel framework, ETF, to enrich large-scale, generic taxonomies with new concepts from resources such as news and research publications. Our approach learns a high-dimensional embedding for the existing concepts of the taxonomy, as well as for the new concepts. During the insertion of a new concept, this embedding is used to identify semantically similar neighborhoods within the existing taxonomy. The potential parent-child relationships linking the new concepts to the existing ones are then predicted using a set of semantic and graph features. Extensive evaluation of ETF on large, real-world taxonomies of Wikipedia and WordNet showcase more than 5% F1-score improvements compared to state-of-the-art baselines. We further demonstrate that ETF can accurately categorize newly emerging concepts and question-answer pairs across different domains.


title: An Accurate GPS-Based Localization in Wireless Sensor Networks: A GM-WLS Method

abstract: For wireless sensor networks, localization is crucial due to the dynamic nature of deployment. In absolute localization, a few nodes (called beacon nodes or anchors) need to know their absolute positions, and all the other nodes are absolutely localized in the coordinate system of the beacon nodes. Most of GPS-based localizations belong to absolute localization, and localization systems enable nodes to fix their positions in a global coordinate system using a relatively small number of beacon nodes that know their position through external measurement (e.g., GPS). Considering inevitable errors from unreliable GPS observations, the localization result will not be accurate. In this paper, we propose a Weighted Least Square (WLS) method for GPS-based localization to get more accurate position of sensor nodes that are not equipped with GPSreceivers. Following the description of the WLS algorithm as well as the localization system based on it, simulation analysis and real world experiments demonstrate the effectiveness of the proposed approach.


title: Music therapy for depression.

abstract: BACKGROUND ::: Depression is a highly prevalent disorder associated with reduced social functioning, impaired quality of life, and increased mortality. Music therapy has been used in the treatment of a variety of mental disorders, but its impact on those with depression is unclear. ::: ::: ::: OBJECTIVES ::: To examine the efficacy of music therapy with standard care compared to standard care alone among people with depression and to compare the effects of music therapy for people with depression against other psychological or pharmacological therapies. ::: ::: ::: SEARCH STRATEGY ::: CCDANCTR-Studies and CCDANCTR-References were searched on 7/11/2007, MEDLINE, PsycINFO, EMBASE, PsycLit, PSYindex, and other relevant sites were searched in November 2006. Reference lists of retrieved articles were hand searched, as well as specialist music and arts therapies journals. ::: ::: ::: SELECTION CRITERIA ::: All randomised controlled trials comparing music therapy with standard care or other interventions for depression. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Data on participants, interventions and outcomes were extracted and entered onto a database independently by two review authors. The methodological quality of each study was also assessed independently by two review authors. The primary outcome was reduction in symptoms of depression, based on a continuous scale. ::: ::: ::: MAIN RESULTS ::: Five studies met the inclusion criteria of the review. Marked variations in the interventions offered and the populations studied meant that meta-analysis was not appropriate. Four of the five studies individually reported greater reduction in symptoms of depression among those randomised to music therapy than to those in standard care conditions. The fifth study, in which music therapy was used as an active control treatment, reported no significant change in mental state for music therapy compared with standard care. Dropout rates from music therapy conditions appeared to be low in all studies. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Findings from individual randomised trials suggest that music therapy is accepted by people with depression and is associated with improvements in mood. However, the small number and low methodological quality of studies mean that it is not possible to be confident about its effectiveness. High quality trials evaluating the effects of music therapy on depression are required.


title: The use of meta-heuristics for airport gate assignment

abstract: Improper assignment of gates may result in flight delays, inefficient use of the resource, customer's dissatisfaction. A typical metropolitan airport handles hundreds of flights a day. Solving the gate assignment problem (GAP) to optimality is often impractical. Meta-heuristics have recently been proposed to generate good solutions within a reasonable timeframe. In this work, we attempt to assess the performance of three meta-heuristics, namely, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and a hybrid approach based on SA and TS. Flight data from Incheon International Airport are collected to carry out the computational comparison. Although the literature has documented these algorithms, this work may be a first attempt to evaluate their performance using a set of realistic flight data.


Approach for Information System Maturity Assessment


title: Dante II: Technical Description, Results, and Lessons Learned

abstract: Dante II is a unique walking robot that provides important insight into high-mobility robotic locomotion and remote robotic exploration. Dante II’s uniqueness stems from its combined legged and rappelling mobility system, its scanning-laser rangefinder, and its multilevel control scheme. In 1994 Dante II was deployed and successfully tested in a remote Alaskan volcano, as a demonstration of the fieldworthiness of these technologies. For more than five days the robot explored alone in the volcano crater using a combination of supervised autonomous control and teleoperated control. Human operators were located 120 km distant during the mission. This article first describes in detail the robot, support systems, control techniques, and user interfaces. We then describe results from the battery of field tests leading up to and including the volcanic mission. Finally, we put forth important lessons which comprise the legacy of this project. We show that framewalkers are appropriate for rappelling in severe terr...


title: Gender Differences in Implicit Self-Esteem Following a Romantic Partner’s Success or Failure

abstract: This research examined the influence of a romantic partner’s success or failure on one’s own implicit and explicit self-esteem. In Experiment 1, men had lower implicit self-esteem when their partner did well at a “social intelligence” task than when their partner did poorly. Women’s implicit self-esteem was unaffected by partner performance. Experiments 2 and 3 showed that Dutch men’s implicit self-esteem was negatively affected by their romantic partner’s success. In Experiment 4, we replicated Experiments 1–3 in both the academic and social domains, and in Experiment 5, we demonstrated that men’s implicit self-esteem is negatively influenced by thinking about a romantic partner’s success both when the success is relative and when it is not. In sum, men’s implicit self-esteem is lower when a partner succeeds than when a partner fails, whereas women’s implicit self-esteem is not. These gender differences have important implications for understanding social comparison in romantic relationships.


title: Liquid-phase gallium-indium alloy electronics with microcontact printing.

abstract: Liquid-phase electronic circuits are patterned on an elastomer substrate with a microcontact printer. The printer head dips into a pool of a liquid-phase gallium-indium alloy, e.g., eutectic gallium-indium (EGaIn) or gallium-indium-tin (Galinstan), and deposits a single drop on a silicone elastomer substrate. After patterned deposition, the liquid-phase circuit is sealed with an additional layer of silicone elastomer. We also demonstrate patterned deposition of the liquid-phase GaIn alloy with a molded polydimethylsiloxane stamp that is manually inked and pressed into an elastomer substrate. As with other liquid-phase electronics produced through needle injection or masked deposition, the circuit is elastically deformable and can be stretched to several times its natural length without losing electronic functionality. In contrast to existing fabrication techniques, microcontact printing and stamp lithography can be used to produce circuits with any planar geometric feature, including electrodes with large planar area, intersecting and closed-loop wires, and combs with multiple terminal electrodes. In air, the surface of the coalesced droplets oxidize to form a thin oxide skin that preserves the shape of the circuit during sealing. This first demonstration of soft-lithography fabrication with liquid-phase GaIn alloy expands the space of allowable circuit geometries and eliminates the need for mold or mask fabrication.


title: Risky business: the neuroeconomics of decision making under uncertainty

abstract: Many decisions involve uncertainty, or imperfect knowledge about how choices lead to outcomes. Colloquial notions of uncertainty, particularly when describing a decision as 'risky', often carry connotations of potential danger as well. Gambling on a long shot, whether a horse at the racetrack or a foreign oil company in a hedge fund, can have negative consequences, but the impact of uncertainty on decision making extends beyond gambling. Indeed, uncertainty in some form pervades nearly all our choices in daily life. Stepping into traffic to hail a cab, braving an ice storm to be the first at work, or dating the boss's son or daughter also offer potentially great windfalls, at the expense of surety. We continually face trade-offs between options that promise safety and others that offer an uncertain potential for jackpot or bust. When mechanisms for dealing with uncertain outcomes fail, as in mental disorders such as problem gambling or addiction, the results can be disastrous. Thus, understanding decision making—indeed, understanding behavior itself—requires knowing how the brain responds to and uses information about uncertainty.


title: Histogram-based image hashing scheme robust against geometric deformations

abstract: In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase.


Boolean Algebras with Operators


DIOTIMA TANTRA : A CROSS-CULTURAL CRITIQUE OF THE SOCRATIC SOLUTION TO THE ENIGMA OF BEAUTY ANWAY MUKHOPADHYAY


title: A performance comparison of SQL and NoSQL databases

abstract: With the current emphasis on “Big Data”, NoSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. In this paper we aim to independently investigate the performance of some NoSQL and SQL databases in the light of key-value stores. We compare read, write, delete, and instantiate operations on key-value stores implemented by NoSQL and SQL databases. Besides, we also investigate an additional operation: iterating through all keys. An abstract key-value pair framework supporting these basic operations is designed and implemented using all the databases tested. Experimental results measure the timing of these operations and we summarize our findings of how the databases stack up against each other. Our results show that not all NoSQL databases perform better than SQL databases. Some are much worse. And for each database, the performance varies with each operation. Some are slow to instantiate, but fast to read, write, and delete. Others are fast to instantiate but slow on the other operations. And there is little correlation between performance and the data model each database uses.


title: Banking Market Structure , Financial Dependence and Growth : International Evidence from Industry Data

abstract: This paper explores the empirical relevance of banking market structure on growth. There is substantial evidence of a positive relationship between the level of development of the banking sector of an economy and its long-run output growth. Little is known, however, about the role played by the market structure of the banking sector on the dynamics of capital accumulation. This paper provides evidence that bank concentration promotes the growth of those industrial sectors that are more in need of external finance by facilitating credit access to younger firms. However, we also find evidence of a general depressing effect on growth associated with a concentrated banking industry, which impacts all sectors and all firms indiscriminately.


title: MEGAN analysis of metagenomic data

abstract: Metagenomics is the study of the genomic content of a sample of organisms obtained from a common habitat using targeted or random sequencing. Goals include understanding the extent and role of microbial diversity. The taxonomical content of such a sample is usually estimated by comparison against sequence databases of known sequences. Most published studies use the analysis of paired-end reads, complete sequences of environmental fosmid and BAC clones, or environmental assemblies. Emerging sequencing-by-synthesis technologies with very high throughput are paving the way to low-cost random “shotgun” approaches. This paper introduces MEGAN, a new computer program that allows laptop analysis of large metagenomic data sets. In a preprocessing step, the set of DNA sequences is compared against databases of known sequences using BLAST or another comparison tool. MEGAN is then used to compute and explore the taxonomical content of the data set, employing the NCBI taxonomy to summarize and order the results. A simple lowest common ancestor algorithm assigns reads to taxa such that the taxonomical level of the assigned taxon reflects the level of conservation of the sequence. The software allows large data sets to be dissected without the need for assembly or the targeting of specific phylogenetic markers. It provides graphical and statistical output for comparing different data sets. The approach is applied to several data sets, including the Sargasso Sea data set, a recently published metagenomic data set sampled from a mammoth bone, and several complete microbial genomes. Also, simulations that evaluate the performance of the approach for different read lengths are presented.


title: Robotic Surgery A Current Perspective

abstract: OBJECTIVE ::: To review the history, development, and current applications of robotics in surgery. ::: ::: ::: BACKGROUND ::: Surgical robotics is a new technology that holds significant promise. Robotic surgery is often heralded as the new revolution, and it is one of the most talked about subjects in surgery today. Up to this point in time, however, the drive to develop and obtain robotic devices has been largely driven by the market. There is no doubt that they will become an important tool in the surgical armamentarium, but the extent of their use is still evolving. ::: ::: ::: METHODS ::: A review of the literature was undertaken using Medline. Articles describing the history and development of surgical robots were identified as were articles reporting data on applications. ::: ::: ::: RESULTS ::: Several centers are currently using surgical robots and publishing data. Most of these early studies report that robotic surgery is feasible. There is, however, a paucity of data regarding costs and benefits of robotics versus conventional techniques. ::: ::: ::: CONCLUSIONS ::: Robotic surgery is still in its infancy and its niche has not yet been well defined. Its current practical uses are mostly confined to smaller surgical procedures.


title: Protection coordination in distribution systems with and without distributed energy resources- a review

abstract: Emission of greenhouse gases and depletion of fossil fuel reserves are two key drivers, which are forcing the mankind to generate the future energy demand from the renewable energy resources. These resources are generally distributed in nature and are directly integrated at distribution levels. Increasing penetration of the distributed energy resources in distribution power networks creates additional operational and control issues. These are mostly regulatory, economical load dispatching, power quality and protection issues.


title: Greedy strikes back: improved facility location algorithms

abstract: A fundamental facility location problem is to choose the location of facilities, such as industrial plants and warehouses, to minimize the cost of satisfying the demand for some commodity. There are associated costs for locating the facilities, as well as transportation costs for distributing the commodities. We assume that the transportation costs form a metric. This problem is commonly referred to as theuncapacitated facility locationproblem. Application to bank account location and clustering, as well as many related pieces of work, are discussed by Cornuejols, Nemhauser, and Wolsey. Recently, the first constant factor approximation algorithm for this problem was obtained by Shmoys, Tardos, and Aardal. We show that a simple greedy heuristic combined with the algorithm by Shmoys, Tardos, and Aardal, can be used to obtain an approximation guarantee of 2.408. We discuss a few variants of the problem, demonstrating better approximation factors for restricted versions of the problem. We also show that the problem is max SNP-hard. However, the inapproximability constants derived from the max SNP hardness are very close to one. By relating this problem to Set Cover, we prove a lower bound of 1.463 on the best possible approximation ratio, assumingNP?DTIMEnO(loglogn)].


title: Image Captioning

abstract: This paper discusses and demonstrates the outcomes from our experimentation on Image Captioning. Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects/concepts in the image and the creation of a succinct sentential narration. Experiments on several labeled datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. As a toy application, we apply image captioning to create video captions, and we advance a few hypotheses on the challenges we encountered.


title: Deep Reinforcement Learning for General Video Game AI

abstract: The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. ::: In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.


title: Approximate Frequency Counts over Data Streams

abstract: Research in data stream algorithms has blossomed since late 90s. The talk will trace the history of the Approximate Frequency Counts paper, how it was conceptualized and how it influenced data stream research. The talk will also touch upon a recent development: analysis of personal data streams for improving our quality of lives.


title: Deferred Lightweight Indexing for Log-Structured Key-Value Stores

abstract: The recent shift towards write-intensive workload on bigdata (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable [1], Apache HBase [2] andCassandra [3]. While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI'ssystem design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBasewithout internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance.


title: Statistical Sandhi Splitter and its Effect on NLP Applications

abstract: This paper revisits the work of (Kuncham et al., 2015) which developed a statistical sandhi splitter (SSS) for agglutinative languages that was tested for Telugu and Malayalam languages. Handling compound words is a major challenge for Natural Language Processing (NLP) applications for agglutinative languages. Hence, in this paper we concentrate on testing the effect of SSS on the NLP applications like Machine Translation, Dialogue System and Anaphora Resolution and show that the accuracy of these applications is consistently improved by using SSS. We shall also discuss in detail the performance of SSS on these applications.


title: Identification and Validation of Selected Universal Stress Protein Domain Containing Drought-Responsive Genes in Pigeonpea (Cajanus cajan L.)

abstract: Pigeonpea is a resilient crop, which is relatively more drought tolerant than many other legume crops. To understand the molecular mechanisms of this unique feature of pigeonpea, 51 genes were selected using the Hidden Markov Models those codes for proteins having close similarity to universal stress protein domain. Validation of these genes was conducted on three pigeonpea genotypes (ICPL 151, ICPL 8755 and ICPL 227) having different levels of drought tolerance. Gene expression analysis using qRT-PCR revealed 6, 8 and 18 genes to be ≥2 fold differentially expressed in ICPL 151, ICPL 8755 and ICPL 227, respectively. A total of 10 differentially expressed genes showed ≥2 fold up-regulation in the more drought tolerant genotype. Of these, four genes each encoded proteins for plant U-box and universal stress protein A- (uspA) like, while one gene encoded for cation/H(+) antiporter protein and one uncharacterized protein. Genes C.cajan_29830 and C.cajan_33874 belonging to uspA, were found significantly expressed in all the three genotypes with ≥2 fold expression variations. Expression profiling of these two genes on the four other legume crops revealed their specific role in pigeonpea. Therefore, these genes seem to be promising candidates for conferring drought tolerance specifically to pigeonpea.


Learning Latent Space Models with Angular Constraints.


title: Birthweight and coronary heart disease in a cohort born 1857-1900 in Melbourne, Australia.

abstract: BACKGROUND ::: The widely observed association between birth size and risk of later coronary heart disease (CHD) has not been examined in an impoverished pre-20th century birth cohort. ::: ::: ::: METHODS ::: Birth weights and maternal characteristics, for births between 1857 and 1900 in a charity hospital, were recorded from preserved ledgers. Names were linked to death certificates to determine age and cause of death. Death with CHD was coded using specific criteria, and survival analysis methods were used to relate risk of CHD to birth weight, allowing for competing causes of death and adjusting for potentially confounding maternal factors. ::: ::: ::: RESULTS ::: Death certificates were traced for 8,584 (53%) of 16,272 registered live-births. Survival analyses were confined to 2,938 subjects (1,572 male, 1,366 female) who survived beyond age 40, since none of the 486 CHD cases was recorded earlier. CHD risk increased with time, but there was no evidence that it was related to birth weight, in men or women. ::: ::: ::: CONCLUSIONS ::: We did not replicate findings in more recent cohorts. This may represent a true lack of association in a historical cohort who we believe remained impoverished through their early life. However, we acknowledge the strong possibility of misclassification of cause of death by the person filling in the death certificate and/or our coding criteria, and temporal change in diagnostic criteria for CHD. We cannot exclude the possibility that low birth weight babies 'programmed' in utero for later CHD were more likely to die in infancy, in this cohort with a high infant mortality rate.


title: Feeling before knowing why: The role of the orbitofrontal cortex in intuitive judgments—an MEG study

abstract: In theory, intuitive decisions are made immediately, without conscious, reasoned thought. They are experienced as decisions based on hunches that cannot be explicitly described but, nevertheless, guide subsequent action. Investigating the underlying neural mechanisms, previous research has found the orbitofrontal cortex (OFC) to be crucial to intuitive processes, but its specific role has remained unclear. On the basis of a two-stage conceptualization of intuition suggested by Bowers, Regehr, Balthazard, and Parker Cognitive Psychology, 22, 72-110 (1990), we attempt to clarify the OFC’s role in intuitive processing. We propose that it functions as an early integrator of incomplete stimulus input guiding subsequent processing by means of a coarse representation of the gist of the information. On the subjective level, this representation would be perceived as a (gut) feeling biasing the decision. Our aim in the present study was to test this neural model and rule out alternative explanations of OFC activation in intuitive judgments. We used magnetoencephalography (MEG) to record participants' electromagnetic brain responses during a visual coherence judgment task. As in earlier studies, the OFC was found to be activated when participants perceived coherence. Using MEG, it could be shown that this increase in activation began earlier in the OFC than in temporal object recognition areas. Moreover, the present study demonstrated that OFC activation was independent of physical stimulus characteristics, task requirements, and participants’ explicit recognition of the stimuli presented. These results speak to the OFC’s fundamental role in the early steps of intuitive judgments and suggest the proposed neural model as a promising starting point for future investigations.


title: EPSSR: Energy preserving system for smart rooms

abstract: Wastage of electricity is one of the main problems which we are facing now a day. In our home, school, colleges or industry we see that lights are kept on even if there is nobody in the room or area. This happens due to negligence or because we forgot to turn the lights off or when we are in hurry. In this paper, an Energy Preserving System for Smart Rooms (EPSSR) is proposed to save energy in smart rooms. Using the ESP8266 chip which is a Wi-Fi chip with full TCP/IP stack and MCU capability we developed a lighting controls to reduce electrical usage. Based on the technology of the Internet of Things (IoT), a lot of solutions may be done to control smart rooms light without the need of accessing the electrical sockets or plug. Our research idea focuses on measuring the number of persons entering any room like seminar hall, conference room and classroom using pair of Infrared sensors and the chip. When a person enters the room, counter will be incremented with lightening the room and the light continue lightening while persons counter greater than zero. When a person leaves the room, the counter is decreased by one. If the persons counter reaches zero, the lights inside the room will be turned off using a relay interface. This paper provides a real energy preserving model that could be used in daily life.


title: SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks

abstract: 3D shape models are naturally parameterized using vertices and faces, i.e., composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized representation of the object. Lifting convolution operators from the traditional 2D to 3D results in high computational overhead with little additional benefit as most of the geometry information is contained on the surface boundary. Here we study the problem of directly generating the 3D shape surface of rigid and non-rigid shapes using deep convolutional neural networks. We develop a procedure to create consistent ‘geometry images’ representing the shape surface of a category of 3D objects. We then use this consistent representation for category-specific shape surface generation from a parametric representation or an image by developing novel extensions of deep residual networks for the task of geometry image generation. Our experiments indicate that our network learns a meaningful representation of shape surfaces allowing it to interpolate between shape orientations and poses, invent new shape surfaces and reconstruct 3D shape surfaces from previously unseen images. Our code is available at https://github.com/sinhayan/surfnet.


title: A genetic algorithm to obtain the optimal recurrent neural network

abstract: Abstract Selecting the optimal topology of a neural network for a particular application is a difficult task. In the case of recurrent neural networks, most methods only induce topologies in which their neurons are fully connected. In this paper, we present a genetic algorithm capable of obtaining not only the optimal topology of a recurrent neural network but also the least number of connections necessary. Finally, this genetic algorithm is applied to a problem of grammatical inference using neural networks, with very good results.


title: Fast and scalable polynomial kernels via explicit feature maps

abstract: Approximation of non-linear kernels using random feature mapping has been successfully employed in large-scale data analysis applications, accelerating the training of kernel machines. While previous random feature mappings run in O(ndD) time for $n$ training samples in d-dimensional space and D random feature maps, we propose a novel randomized tensor product technique, called Tensor Sketching, for approximating any polynomial kernel in O(n(d+D \log{D})) time. Also, we introduce both absolute and relative error bounds for our approximation to guarantee the reliability of our estimation algorithm. Empirically, Tensor Sketching achieves higher accuracy and often runs orders of magnitude faster than the state-of-the-art approach for large-scale real-world datasets.


title: Facial animation parameters extraction and expression detection using HMM

abstract: Abstract The video analysis system described in this paper aims at facial expression recognition consistent with the MPEG4 standardized parameters for facial animation, FAP. For this reason, two levels of analysis are necessary: low-level analysis to extract the MPEG4 compliant parameters and high-level analysis to estimate the expression of the sequence using these low-level parameters. The low-level analysis is based on an improved active contour algorithm that uses high level information based on principal component analysis to locate the most significant contours of the face (eyebrows and mouth), and on motion estimation to track them. The high-level analysis takes as input the FAP produced by the low-level analysis tool and, by means of a Hidden Markov Model classifier, detects the expression of the sequence.


title: Media Images of Men: Trends and Consequences of Body Conceptualization

abstract: In the current investigation, the authors examined how men are presented in popular media and the effects of such presentations on male adolescents’ self-evaluations. In a content analysis of male models in advertisements of Sports Illustrated, Study 1 showed that media ideals increasingly emphasize aesthetic versus performance attributes of men. In Study 2, male adolescents (N 107) were randomly assigned to view either images of male ideals emphasizing aesthetic attributes, images of male ideals emphasizing performance attributes, or neutral images. Results showed that viewing media ideals that emphasize aesthetic attributes contributes to negative selfevaluations whereas viewing media ideals that emphasize performance attributes contributes to positive self-evaluations. These findings suggest that body conceptualization, and not simply body type (i.e., muscularity), plays a role in how men feel about themselves and their bodies.


title: Collaborative filtering: Techniques and applications

abstract: During the last decade a huge amount of data have been shown and introduced in the Internet. Recommender systems are thus predicting the rating that a user would give to an item. Collaborative filtering (CF) techniques are the most popular and widely used by recommender systems technique, which utilize similar neighbors to generate recommendations. This paper provides the concepts, methods, applications and evaluations of the CF based on the literature review. The paper also highlights the discussion of the types of the recommender systems as general and types of CF such as; memory based, model based and hybrid model. In addition, this paper discusses how to choose an appropriate type of CF. The evaluation methods of the CF systems are also provided throughout the paper.


title: Designing AI systems that obey our laws and values

abstract: More and more machines -- from driver-less cars to weapons to search engines -- are using AI as part of their operational system. Because these systems are semi-autonomous and learning on their own from experience -- they are likely to stray from their programmers' instructions. These cars may learn to speed; AI-equipped drones hit wrong targets; and search engines discriminate.We suggest that to prevent such problems, society needs a whole new kind of AI programs, supervisory ones, that will monitor, audit, or correct the operational AI programs. In some cases they will ensure that laws are observed. In others, that the machines follow the values of their users, a special kind of AI program, the ethics bot, that will discern these values by observing the users of the machines.


title: Rapid, complete and large-scale generation of post-mitotic neurons from the human LUHMES cell line

abstract: J. Neurochem. (2011) 119, 957–971. ::: ::: ::: ::: Abstract ::: ::: We characterized phenotype and function of a fetal human mesencephalic cell line (LUHMES, Lund human mesencephalic) as neuronal model system. Neurodevelopmental profiling of the proliferation stage (d0, day 0) of these conditionally-immortalized cells revealed neuronal features, expressed simultaneously with some early neuroblast and stem cell markers. An optimized 2-step differentiation procedure, triggered by shut-down of the myc transgene, resulted in uniformly post-mitotic neurons within 5 days (d5). This was associated with down-regulation of some precursor markers and further up-regulation of neuronal genes. Neurite network formation involved the outgrowth of 1–2, often > 500 μm long projections. They showed dynamic growth cone behavior, as evidenced by time-lapse imaging of stably GFP-over-expressing cells. Voltage-dependent sodium channels and spontaneous electrical activity of LUHMES continuously increased from d0 to d11, while levels of synaptic markers reached their maximum on d5. The developmental expression patterns of most genes and of the dopamine uptake- and release-machinery appeared to be intrinsically predetermined, as the differentiation proceeded similarly when external factors such as dibutyryl-cAMP and glial cell derived neurotrophic factor were omitted. Only tyrosine hydroxylase required the continuous presence of cAMP. In conclusion, LUHMES are a robust neuronal model with adaptable phenotype and high value for neurodevelopmental studies, disease modeling and neuropharmacology.


title: A Long Short-Term Memory Convolutional Neural Network for First-Person Vision Activity Recognition

abstract: Temporal information is the main source of discriminating characteristics for the recognition of proprioceptive activities in first-person vision (FPV). In this paper, we propose a motion representation that uses stacked spectrograms. These spectrograms are generated over temporal windows from mean grid-optical-flow vectors and the displacement vectors of the intensity centroid. The stacked representation enables us to use 2D convolutions to learn and extract global motion features. Moreover, we employ a long short-term memory (LSTM) network to encode the temporal dependency among consecutive samples recursively. Experimental results show that the proposed approach achieves state-of-the-art performance in the largest public dataset for FPV activity recognition.


title: Full Spectrum Sharing in Cognitive Radio Networks Toward 5G: A Survey

abstract: With the development of wireless communication technology, the need for bandwidth is increasing continuously, and the growing need makes wireless spectrum resources more and more scarce. Cognitive radio (CR) has been identified as a promising solution for the spectrum scarcity, and its core idea is the dynamic spectrum access. It can dynamically utilize the idle spectrum without affecting the rights of primary users, so that multiple services or users can share a part of the spectrum, thus achieving the goal of avoiding the high cost of spectrum resetting and improving the utilization of spectrum resources. In order to meet the critical requirements of the fifth generation (5G) mobile network, especially the Wider-Coverage , Massive-Capacity , Massive-Connectivity , and Low-Latency four application scenarios, the spectrum range used in 5G will be further expanded into the full spectrum era, possibly from 1 GHz to 100 GHz. In this paper, we conduct a comprehensive survey of CR technology and focus on the current significant research progress in the full spectrum sharing towards the four scenarios. In addition, the key enabling technologies that may be closely related to the study of 5G in the near future are presented in terms of full-duplex spectrum sensing, spectrum-database based spectrum sensing, auction based spectrum allocation, carrier aggregation based spectrum access. Subsequently, other issues that play a positive role for the development research and practical application of CR, such as common control channel, energy harvesting, non-orthogonal multiple access, and CR based aeronautical communication are discussed. The comprehensive overview provided by this survey is expected to help researchers develop CR technology in the field of 5G further.


title: Anatomy and Aesthetics of the Labia Minora: The Ideal Vulva?

abstract: Female genital cosmetic surgery is becoming more and more widespread both in the field of plastic and gynaecological surgery. The increased demand for vulvar surgery is spurred by the belief that the vulva is abnormal in appearance. What is normal in terms of labial anatomy? Labia minora enlargement or hypertrophy remains a clinical diagnosis which is poorly defined as it could be considered a variation of the normal anatomy. Enlarged labia minora can cause functional, aesthetic and psychosocial problems. In reality, given the wide variety of vulvar morphology among people, it is a very subjective issue to define the “normal” vulva. The spread of nudity in the general media plays a major role in creating an artificial image and standards with regard to the ideal form. Physicians should be aware that the patient’s self-perception of the normal or ideal vulva is highly influenced by the arguably distorted image related to our socio-psychological environment, as presented to us by the general media and internet. As physicians, we have to educate our patients on the variation of vulvar anatomy and the potential risks of these surgeries.


title: A 25 Gb/s full-rate CDR circuit based on quadrature phase generation in data path

abstract: This paper presents a 25 Gb/s clock and data recovery circuit using a full-rate clock and quadrature data phases. An adaptive slicer is used in the system front-end to equalize the distorted data to minimize duty-cycle distortion due to inter-symbol interference. The proposed structure uses an open-loop phase averaging block in the data path to generate the required quadrature phases for phase detection using a mixer. A receiver chip that uses an external clock and is based on the proposed technique is designed and laid out in a 90-nm CMOS process. The chip occupies 0.846 mm2 and based on post-layout simulation results, it consumes 107 mW from a 1.2 V supply.


Interactive Neural Translation Assistance for Human Translators


title: Facial expression recognition using wavelet transform and local binary pattern

abstract: This paper aims to experimental evaluation of different methodologies to recognize human face based on different facial expression. The face and facial images were captured locally, as the experiment is aimed to be done in India domain. The features were extracted based on two techniques, viz, Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The range of extracted feature is 150,300,600,1200 and 2400. Further, mean and standard deviation are computed for feature vector generation. Support Vector Machine (SVM) is used for classification/recognition. The experiment was carried out on different range of features as 160×15 samples. The result varies from 72% to 100% for various ranges of features. The performance of the proposed system is found to be satisfactory as compared to the existing system.


title: Modelling of the human shoulder girdle as a 6-4 parallel mechanism with a moving scapulothoracic joint

abstract: Abstract Human shoulder movements involve motions at four different articulations, one of which is the contact between the scapula bone and the ribcage. The shoulder biomechanical models become less reliable when the scapulothoracic (ST) contact, which is not a joint in the anatomical sense, is not considered. On the other hand, constraints posed by the ST contact reduce the number of degrees of freedom (DOF) and introduce the interdependencies between the joint coordinates which in turn complicates the motion planning. However, a minimal parameterization that incorporates the constraints, notably simplifies costly computational procedure related to the model predictions. In this paper, the complex kinematics of the human shoulder is analyzed considering the point-contact model between scapula bone and thorax. Later, replacing the contact constraint with an equivalent kinematic chain and adding parallel kinematic links, the human shoulder girdle is modelled as a 6-4 parallel mechanism. A novel minimal set of independent parameters equal to the number of degrees of freedom is then devised in terms of the parallel mechanism's link lengths and the shoulder joint angles. The proposed parallel mechanism can also emulate the moving ST contact point during the shoulder motions. Finally, the shoulder motion planning method in terms of the time-dependent minimal coordinates is presented.


title: EEG Recorded from the Ear: Characterizing the Ear-EEG Method

abstract: A method for measuring electroencephalograms (EEG) from the outer ear, so-called ear-EEG, has recently been proposed. The method could potentially enable robust recording of EEG in natural environments. The objective of this study was to substantiate the ear-EEG method by using a larger population of subjects and several paradigms. For rigour, we considered simultaneous scalp and ear-EEG recordings with common reference. More precisely, 32 conventional scalp electrodes and 12 ear electrodes allowed a thorough comparison between conventional and ear electrodes, testing several different placements of references. The paradigms probed of auditory onset response, mismatch negativity, auditory steady state response and alpha power attenuation. By comparing event related potential (ERP) waveforms from the mismatch response paradigm, the signal measured from the ear electrodes was found to reflect the same cortical activity as that from nearby scalp electrodes. It was also found that referencing the ear-EEG electrodes to another within-ear electrode affects the time-domain recorded waveform (relative to scalp recordings), but not the timing of individual components. It was furthermore found that auditory steady state responses and alpha-band modulation were measured reliably with the ear-EEG modality. Finally, our findings showed that the auditory mismatch response was difficult to monitor with the ear-EEG. We conclude that ear-EEG yields similar performance as conventional EEG for spectrogram-based analysis, similar timing of ERP components, and equal signal strength for sources close to the ear. Ear-EEG can reliably measure activity from regions of the cortex which are located close to the ears, especially in paradigms employing frequency-domain analyses.


title: High-Efficiency Line-Start Synchronous Reluctance Motor for Fan and Pump Applications

abstract: In the last years the line-start synchronous motors have gained a growing interest thanks to the remarkable energy efficiency, since the operation at synchronous speed ensures the absence of losses in the squirrel cage at steady state. Typical application fields are fans, pumps, compressors and textile industry. Many papers propose line-start PM motors, due to their high efficiency and power density: rare earths magnets feat to this role thanks to the good coercive force, but they are also characterized by very high prices. Moreover, the price volatility of these alloys represents a drawback for mass production. On the other hand, line-start synchronous reluctance machines are very cost-effective and easy to manufacture, and they can compete with the induction motor in terms of reliability and cost. In this paper the design of a high efficiency 3 kW, 2 poles, frame size IEC 100 motor is proposed. A new design procedure is introduced and an optimization algorithm is applied. Two different geometries have been developed and compared, leading to a final design. The capability of the machine both during the starting transient and steady state operation has been analyzed.


title: Cross-Agent Action Recognition

abstract: An action is something which is done by an agent. Most action recognition researchers merely focus on the actions to be recognized, and ignore the differences of agents. Philosophers and behaviorists discover that actions are common among many species, but are performed in different ways and with different levels of sophistication. In this paper, in order to bridge action recognition tasks between different agents, we introduce a new problem, cross-agent action recognition, i.e., recognizing action for one particular agent (target) while training from other agents (source). We model this problem under three different scenarios: single source and single target , multiple sources and single target , and multiple sources and multiple targets . To this end, corresponding methods based on transfer learning are proposed to address these problems. We further design three different strategies to model the situation when a partial labeled data is provided for the target. Experimental results show that the performances of the transfer method are generally better than those of the comparative method without transfer learning, especially when we have multiple sources. Particularly, the transfer method outperforms the others significantly when the source is a human adult. In addition, cross-agent method significantly improves the results when partially labeled data is provided for the target. These demonstrate that for action recognition, knowledge can be transferred across different agents. A straightforward application of this finding is to use human action (training data is abundant) data to enhance animal action recognition.


INDIVIDUAL AND SOCIAL FACTORS RELATED TO STUDENTS ’ ACADEMIC ACHIEVEMENT AND MOTIVATION FOR LEARNING


Daily and annual physiological and behavioural rhythms in fish: Implications for the domestication of bluefin tuna


title: Deep Learning of Scene-Specific Classifier for Pedestrian Detection

abstract: The performance of a detector depends much on its training dataset and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In order to bridge this appearance gap, we propose a deep model to automatically learn scene-specific features and visual patterns in static video surveillance without any manual labels from the target scene. It jointly learns a scene-specific classifier and the distribution of the target samples. Both tasks share multi-scale feature representations with both discriminative and representative power. We also propose a cluster layer in the deep model that utilizes the scene-specific visual patterns for pedestrian detection. Our specifically designed objective function not only incorporates the confidence scores of target training samples but also automatically weights the importance of source training samples by fitting the marginal distributions of target samples. It significantly improves the detection rates at 1 FPPI by 10% compared with the state-of-the-art domain adaptation methods on MIT Traffic Dataset and CUHK Square Dataset.


title: VisualBackProp: visualizing CNNs for autonomous driving

abstract: This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data and reveals that it compares favorably to the LRP approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.


title: Effect of human disturbance on the structure and regeneration of forests in the Nevado de Toluca National Park, Mexico

abstract: Sample plots were established in the principal forest types in the the Nevado de Toluca National Park, Mexico including those dominated by Pinus hartwegii, Abies religiosa, Quercus laurina and Alnus jorullensis. The vertical structure was defined by three strata in the coniferous forests and two strata in the broadleaved forests. Timber harvesting in Abies religiosa and Quercus laurina forests and fires generated by humans in Pinus hartwegii forests impeded the recruitment of saplings. Mature trees were also heavily impacted by logging in Pinus hartwegii forests. On the contrary, Alnus jorullensis forests were increasing due to the disturbance of Pinus and Quercus forests, as well abandoned crop lands within the park. A combination of logging, uncontrolled fire, and grazing appears to be compromising the recruitment of important tree species in this national park. These factors, together with human settlements, have also increased the proportion of early successional species. Changes in forest structure from human disturbance indicate a need to control these activities if conservation goals are not to be compromised.


title: Driver/vehicle state estimation and detection

abstract: The authors present a cyber-physical systems related study on the estimation and prediction of driver states in autonomous vehicles. The first part of this study extends on a previously developed general architecture for estimation and prediction of hybrid-state systems. The extended system utilizes the hybrid characteristics of decision-behavior coupling of many systems such as the driver and the vehicle; uses Kalman Filter estimates of observable parameters to track the instantaneous discrete state, and predicts the most likely outcome. Prediction of the likely driver state outcome depends on the higher level discrete model and the observed behavior of the continuous subsystem. Two approaches to estimate the discrete driver state from filtered continuous observations are presented: rule based estimation, and Hidden Markov Model (HMM) based estimation. Extensions to a prediction application is described through the use of Hierarchical Hidden Markov Models (HHMMs). The proposed method is suitable for scenarios that involve unknown decisions of other individuals, such as lane changes or intersection precedence/access. An HMM implementation for multiple tasks of a single vehicle at an intersection is presented along with preliminary results.


title: Histological features of nodular gastritis and its endoscopic classification

abstract: OBJECTIVES ::: To clarify the histological features and endoscopic classifications of nodular gastritis (NG). ::: ::: ::: METHODS ::: Overall 40 996 patients who had undergone an upper gastrointestinal endoscopy were enrolled. NG is defined as a uniform and diffuse protrusion from the antrum to angulus, which has two types at endoscopy: nodular (N) and granular (G). Three biopsy specimens were taken from the antrum, angulus and corpus. The histological features were evaluated using the updated Sydney System (USS). The topography of gastritis (antrum-predominant, pangastritis or corpus-predominant) and the prevalence of lymphoid follicles were also investigated. ::: ::: ::: RESULTS ::: Overall 89 patients (0.22%) were diagnosed with NG, which tended to decrease in prevalence over age and predominantly affected women. All the patients were Helicobacter pylori-positive. Among these, 65 patients underwent biopsy. Activity and inflammation were mostly moderate or severe, while intestinal metaplasia and atrophy were mostly absent at all three sites. Pangastritis was the most frequent (72%) type of gastritis. Lymphoid follicles were found in 69% at the antrum, 65% at the angulus and 51% at the corpus. There were no significant differences between N and G types in clinical features, USS scores, topography of gastritis, and prevalence of lymphoid follicles. ::: ::: ::: CONCLUSIONS ::: Atrophy and intestinal metaplasia are rare but activity and chronic inflammation are severe at the antrum, angulus and corpus in NG. Pangastritis is the commonest type of gastritis. Lymphoid follicles affect everything up to the upper corpus, contrary to endoscopic protrusion found only at angulus. There was no correlation with pathological features between N and G types.


Echo hiding


title: DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction

abstract: This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.


title: AMRZone: A Runtime AMR Data Sharing Framework for Scientific Applications

abstract: Frameworks that facilitate runtime data sharingacross multiple applications are of great importance for scientificdata analytics. Although existing frameworks work well overuniform mesh data, they can not effectively handle adaptive meshrefinement (AMR) data. Among the challenges to construct anAMR-capable framework include: (1) designing an architecturethat facilitates online AMR data management, (2) achievinga load-balanced AMR data distribution for the data stagingspace at runtime, and (3) building an effective online indexto support the unique spatial data retrieval requirements forAMR data. Towards addressing these challenges to supportruntime AMR data sharing across scientific applications, wepresent the AMRZone framework. Experiments over real-worldAMR datasets demonstrate AMRZone's effectiveness at achievinga balanced workload distribution, reading/writing large-scaledatasets with thousands of parallel processes, and satisfyingqueries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to16384 cores, in the best case, our framework achieves a 46% performance improvement.


title: SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles

abstract: This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.


title: Segmentation Based Urdu Nastalique OCR

abstract: Urdu Language is written in Nastalique writing style, which is highly cursive, context sensitive and is difficult to process as only the last character in its ligature resides on the baseline. This paper focuses on the development of OCR using Hidden Markov Model and rule based post-processor. The recognizer gets the main body (without diacritics) as input and recognizes the corresponding ligature. Accuracy of the system is 92.73% for printed and then scanned document images at 36 font size.


title: Work-Function-Tuned TiN Metal Gate FDSOI Transistors for Subthreshold Operation

abstract: The effective work function of a reactively sputtered TiN metal gate is shown to be tunable from 4.30 to 4.65 eV. The effective work function decreases with nitrogen flow during reactive sputter deposition. Nitrogen annealing increases the effective work function and reduces Dit. Thinner TiN improves the variation in effective work function and reduces gate dielectric charge. Doping of the polysilicon above the TiN metal gate with B or P has negligible effect on the effective work function. The work-function-tuned TiN is integrated into ultralow-power fully depleted silicon-on-insulator CMOS transistors optimized for subthreshold operation at 0.3 V. The following performance metrics are achieved: 64-80-mV/dec subthreshold swing, PMOS/NMOS on-current ratio near 1, 71% reduction in Cgd, and 55% reduction in Vt variation when compared with conventional transistors, although significant short-channel effects are observed.


title: Tour the world: Building a web-scale landmark recognition engine

abstract: Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) ~20 million GPS-tagged photos and (2) online tour guide Web pages. Candidate images for each landmark are then obtained from photo sharing Websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency.


title: Efficient algorithms for Bézier curves

abstract: For evaluating Bezier curves, the de Casteljau algorithm is commonly used. Even though it is simple and stable, this algorithm is not efficient. Using the relationships between Bezier curves and two generalized Ball curves, this paper proposes three more efficient algorithms, of which the best has a linear time complexity.


title: The Responsive Bystander: How Social Group Membership and Group Size can Encourage as well as Inhibit Bystander Intervention.

abstract: Four experiments explored the interaction of group size, social categorization, and bystander behavior. In Study 1, increasing group size inhibited intervention in a street violence scenario when bystanders were strangers but encouraged intervention when bystanders were friends. Study 2 replicated and extended these findings to social category members. When gender identity was salient, group size encouraged intervention when bystanders and victim shared social category membership. In addition, group size interacted with context-specific norms that both inhibit and encourage helping. Study 3 used physical co-presence and gender identities to examine these social category effects. Increasing group size of women produced greater helping of a female victim, but increasing group size of men did not. Additionally, increasing numbers of out-group bystanders resulted in less intervention from women but more intervention from men. Study 4 replicated these findings with a measure of real-life helping behavior. Taken together, the findings indicate that the bystander effect is not a generic consequence of increasing group size. When bystanders share group-level psychological relationships, group size can encourage as well as inhibit helping.


title: Distributed Robust Power Minimization for the Downlink of Multi-Cloud Radio Access Networks

abstract: Abstract-Conventional cloud radio access networks assume single cloud processing and treat inter-cloud interference as background noise. This paper considers the downlink of a multi-cloud radio access network (CRAN) where each cloud is connected to several base-stations (BS) through limited-capacity wireline backhaul links. The set of BSs connected to each cloud, called cluster, serves a set of pre-known mobile users (MUs). The performance of the system becomes therefore a function of both inter-cloud and intra-cloud interference, as well as the compression schemes of the limited capacity backhaul links. The paper assumes independent compression scheme and imperfect channel state information (CSI) where the CSI errors belong to an ellipsoidal bounded region. The problem of interest becomes the one of minimizing the network total transmit power subject to BS power and quality of service constraints, as well as backhaul capacity and CSI error constraints. The paper suggests solving the problem using the alternating direction method of multipliers (ADMM). One of the highlight of the paper is that the proposed ADMM-based algorithm can be implemented in a distributed fashion across the multi-cloud network by allowing a limited amount of information exchange between the coupled clouds. Simulation results show that the proposed distributed algorithm provides a similar performance to the centralized algorithm in a reasonable number of iterations.


title: Iterative Methods For Total Variation Denoising

abstract: Total variation (TV) methods are very effective for recovering “blocky,” possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TV penalized least squares functional is presented and compared with existing minimization schemes. A variant of the cell-centered finite difference multigrid method of Ewing and Shen is implemented for solving the (large, sparse) linear subproblems. Numerical results are presented for one- and two-dimensional examples; in particular, the algorithm is applied to actual data obtained from confocal microscopy.


title: A MODEL OF SUCCESS FACTORS FOR E-GOVERNMENT ADOPTION - THE CASE OF POLAND

abstract: Ƞ This research focuses on the possible success factors of e-government in Poland. The purpose of this research was to identify success factors for adopting e-government in this country. The paper continues as follows. Firstly, the paper clarifies the concept of e-government and reviews success factors of e-government proposed by various authors in literature. Secondly, the success factors for adopting e-government in Poland are identified and the model of success factors is proposed. This model embraces the four categories of success factors. They are: economic, socio-cultural, technological and organizational. All the factors are considered in three stages: (1) ICTs access - reflecting technical and economic accessibilities of ICTs, (2) ICTs competences - reflecting competences and awareness related to the use of ICTs, (3) ICTs use - reflecting actual usage of ICTs. The paper concludes with a discussion on research findings, implications, and avenues for further research. This work contributes to extant research by showing a holistic approach to the success factors for adopting e-government, especially in transition economies.


title: Posttraumatic Stress and Posttraumatic Stress Disorder after Termination of Pregnancy and Reproductive Loss: A Systematic Review

abstract: OBJECTIVE ::: The aims of this systematic review were to integrate the research on posttraumatic stress (PTS) and posttraumatic stress disorder (PTSD) after termination of pregnancy (TOP), miscarriage, perinatal death, stillbirth, neonatal death, and failed in vitro fertilisation (IVF). ::: ::: ::: METHODS ::: Electronic databases (AMED, British Nursing Index, CINAHL, MEDLINE, SPORTDiscus, PsycINFO, PubMEd, ScienceDirect) were searched for articles using PRISMA guidelines. ::: ::: ::: RESULTS ::: Data from 48 studies were included. Quality of the research was generally good. PTS/PTSD has been investigated in TOP and miscarriage more than perinatal loss, stillbirth, and neonatal death. In all reproductive losses and TOPs, the prevalence of PTS was greater than PTSD, both decreased over time, and longer gestational age is associated with higher levels of PTS/PTSD. Women have generally reported more PTS or PTSD than men. Sociodemographic characteristics (e.g., younger age, lower education, and history of previous traumas or mental health problems) and psychsocial factors influence PTS and PTSD after TOP and reproductive loss. ::: ::: ::: CONCLUSIONS ::: This systematic review is the first to investigate PTS/PTSD after reproductive loss. Patients with advanced pregnancies, a history of previous traumas, mental health problems, and adverse psychosocial profiles should be considered as high risk for developing PTS or PTSD following reproductive loss.


title: Monitoring of soil parameters for effective irrigation using Wireless Sensor Networks

abstract: WSN (Wireless Sensor Network) has attracted the attention of researchers due to its wide applicability to various fields such as disaster management, health and environment monitoring, agriculture, ecology, industrial automation and in military field applications like battlefield surveillance etc. In agricultural ecosystem -situ - continuous smart monitoring of soil parameters like humidity, pH, irrigation control systems etc. can be measured using WSN with high end precision. In this manuscript, we propose to develop a WSN based Dynamic and Automated Irrigation System design and instrumentation. This process maintains soil type and software for real time sensing and control of agricultural irrigation system. The efficiency of irrigation systems can be enhanced by automated remote sensing and continuous analysis of soil parameters. Thus the data acquired is useful to the agricultural sector, namely pest management, irrigation management and soil management. Ultimately, this reduces the fresh water consumption and irrigation costs by maximizing the crop yield.


title: MoSh: motion and shape capture from sparse markers

abstract: Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. We argue that important information about body surface motion is present in standard marker sets but is lost in extracting a skeleton. We demonstrate a new approach called MoSh (Motion and Shape capture), that automatically extracts this detail from mocap data. MoSh estimates body shape and pose together using sparse marker data by exploiting a parametric model of the human body. In contrast to previous work, MoSh solves for the marker locations relative to the body and estimates accurate body shape directly from the markers without the use of 3D scans; this effectively turns a mocap system into an approximate body scanner. MoSh is able to capture soft tissue motions directly from markers by allowing body shape to vary over time. We evaluate the effect of different marker sets on pose and shape accuracy and propose a new sparse marker set for capturing soft-tissue motion. We illustrate MoSh by recovering body shape, pose, and soft-tissue motion from archival mocap data and using this to produce animations with subtlety and realism. We also show soft-tissue motion retargeting to new characters and show how to magnify the 3D deformations of soft tissue to create animations with appealing exaggerations.


title: Barbed Sutures in Aesthetic Plastic Surgery: Evolution of Thought and Process

abstract: The evolution of barbed suture technologies and their application in the field of plastic surgery is now in its third decade. Much has been learned along the way. Initial excitement was often followed by disappointment as we learned more about the limited longevity of minimally invasive procedures and complications arising from various suture designs of the past. Some of the early designs, developed primarily for use in aesthetic facial procedures, included free-floating, bidirectionally barbed, nonabsorbable sutures; unidirectional barbed, nonabsorbable sutures; anchored, bidirectional, nonabsorbable double-threaded sutures; and a technology combining a nonabsorbable knotted thread and absorbable cones. More recently, a new, absorbable, unidirectional barbed suture design has become available. However, it should be noted that very limited data are available for any of the modified suture designs used in this field, and much of what has been published is based on the experience of a single user. The author has used the bidirectionally barbed Quill Knotless Tissue-Closure Device (Angiotech Pharmaceuticals, Inc, Vancouver, British Columbia, Canada), the most common barbed suture in both facial and other aesthetic plastic surgery procedures, with considerable success in various open aesthetic facial procedures, including suspension of the brow and midface, platysmaplasty, and lateral neck suspension. It is the author's experience that completion of 1 to 2 cases with this technology is sufficient to achieve competency in the closure techniques discussed in this article and that time savings can be realized using this device in various breast and body contouring procedures, including mastopexy, reduction mammoplasty, abdominoplasty, bodylift, and brachioplasty.


title: Piercing the dark matter: bioinformatics of long-range sequencing and mapping

abstract: Several new genomics technologies have become available that offer long-read sequencing or long-range mapping with higher throughput and higher resolution analysis than ever before. These long-range technologies are rapidly advancing the field with improved reference genomes, more comprehensive variant identification and more complete views of transcriptomes and epigenomes. However, they also require new bioinformatics approaches to take full advantage of their unique characteristics while overcoming their complex errors and modalities. Here, we discuss several of the most important applications of the new technologies, focusing on both the currently available bioinformatics tools and opportunities for future research. Various genomics-related fields are increasingly taking advantage of long-read sequencing and long-range mapping technologies, but making sense of the data requires new analysis strategies. This Review discusses bioinformatics tools that have been devised to handle the numerous characteristic features of these long-range data types, with applications in genome assembly, genetic variant detection, haplotype phasing, transcriptomics and epigenomics.


title: High speed cascode flyback converter using multilayered coreless printed circuit board (PCB) step-down power transformer

abstract: In this paper, design and analysis of the high speed isolated cascode flyback converter using multilayered coreless PCB step down power transformer is presented. The converter is tested for the input voltage variation of 60–120V with a nominal DC input voltage of 90V. The designed converter was simulated and tested successfully up to the output power level of 30W within the switching frequency range of 2.6–3.7MHz. The cascode flyback converter is compared with the single switch flyback converter in terms of operating frequency, gate drive power consumption, conduction losses and stresses on MOSFETs. The maximum energy efficiency of the cascode converter is approximately 81% with a significant improvement of about 3–4% compared to single switch flyback converter. The gate drive power consumption which is more dominant compared to conduction losses of the cascode converter using GaN MOSFET is found to be negligible compared to single switch flyback converter.


title: An introduction to some imperfections of CCD sensors

abstract: CCD sensors do not deliver a perfect image of the light they receive. Beyond the well known linear image smearing due to diffusion of charges during their drift towards the pixel wells, non-linear effects are at play in these sensors. We now have ample evidence for both a flux- dependent and static image distortions, especially but not only, on deep-depleted CCDs. For large surveys relying on CCD sensors, these effects should now be taken into account when reducing data. We present here a summary of current results on sensor characterization and mitigation methods.


title: Edge-Based Defocus Blur Estimation With Adaptive Scale Selection

abstract: Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges. An initial blur estimate is computed at the detected scale-consistent edge points and a novel connected edge filter is proposed to smooth the sparse blur map based on pixel connectivity within detected edge contours. Finally, a fast guided filter is used to propagate the sparse blur map through the whole image. Experimental results show that the proposed approach presents a very good compromise between estimation error and running time when compared with the state-of-the-art methods. We also explore our blur estimation method in the context of image deblurring, and show that metrics typically used to evaluate blur estimation may not correlate as expected with the visual quality of the deblurred image.


title: Resource Management for Cellular Network Using Socially Intelligent Multi Agent System

abstract: Channel borrowing schemes are used to increase the utility of the system in terms of cell-level quality of service parameters and reduce congestion in cellular system. Multi Agent based interaction helps in doing so in coordinated ways. This paper investigates the effectiveness of Multi Agent based social reasoning strategies, which not only maximize the utility but also balance utility of individual cells and thus work towards enhancing the social welfare of the network.


title: Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle

abstract: The use of mobile robots in search-and-rescue and disaster-response missions has increased significantly in recent years. However, they are still remotely controlled by expert professionals on an actuator set-point level, and they would benefit, therefore, from any bit of autonomy added. This would allow them to execute high-level commands, such as "execute this trajectory" or "map this area." In this paper, we describe a vision-based quadrotor micro aerial vehicle that can autonomously execute a given trajectory and provide a live, dense three-dimensional 3D map of an area. This map is presented to the operator while the quadrotor is mapping, so that there are no unnecessary delays in the mission. Our system does not rely on any external positioning system e.g., GPS or motion capture systems as sensing, computation, and control are performed fully onboard a smartphone processor. Since we use standard, off-the-shelf components from the hobbyist and smartphone markets, the total cost of our system is very low. Due to its low weight below 450 g, it is also passively safe and can be deployed close to humans. We describe both the hardware and the software architecture of our system. We detail our visual odometry pipeline, the state estimation and control, and our live dense 3D mapping, with an overview of how all the modules work and how they have been integrated into the final system. We report the results of our experiments both indoors and outdoors. Our quadrotor was demonstrated over 100 times at multiple trade fairs, at public events, and to rescue professionals. We discuss the practical challenges and lessons learned. Code, datasets, and videos are publicly available to the robotics community.


title: 3D building model reconstruction from point clouds and ground plans

abstract: Airborne laser altimetry has become a very popular technique for the acquisition of digital elevation models. The high point density that can be achieved with this technique enables applications of laser data for many other purposes. This paper deals with the construction of 3D models of the urban environment. A three-dimensional version o f the well-known Hough transform is used for the e xtraction o f planar faces from the irregularly distributed point clouds. To support the 3D reconstruction usage is made of available ground plans of the buildings. Two different strategies are explored to reconstruct building models from the detected planar faces and segmented ground plans. Whereas the first strategy tries to detect intersection lines and height jump edges, the second one assumes that all detected planar faces should model some part of the building. Experiments show that the second strategy is able to reconstruct more buildings and more details of this buildings, but that it sometimes leads to additional parts of the model that do not exist. When restricted to buildings with rectangular segments of the ground plan, the second strategy was able to reconstruct 83 buildings out of a dataset with 94 buildings.


title: Risk–benefit assessment of closed intra-abdominal drains after pancreatic surgery: a systematic review and meta-analysis assessing the current state of evidence

abstract: BACKGROUND ::: This systematic review aims to analyse the risk-benefit association of (1) prophylactic drains and/or (2) the time of their removal after pancreatic resection. ::: ::: ::: MATERIALS AND METHODS ::: A systematic literature search (Medline, Embase, Biosis, and The Cochrane Library) was performed to identify all types of controlled trials comparing the role of drainage or the time of their removal on postoperative complications following pancreatic surgery. Meta-analysis was performed using a random-effects model. ::: ::: ::: RESULTS ::: Four studies, two randomised trials and two prospective cohort studies, were included in the systematic review and meta-analysis. Removal of drains at day 5 or later did not show an influence on mortality, morbidity, re-intervention or hospital stay compared to no insertion of drains. Early (day 3-4) compared to late (≥ day 5) drain removal significantly reduced pancreatic fistulas (odds ratio (OR) 0.13; 95% confidence interval (CI) 0.03-0.32; P = 0.0002), intra-abdominal collections (OR 0.08; 95% CI 0.01-0.67; P = 0.02) and abscesses (OR 0.26; 95% CI 0.07-1.00; P = 0.05). Moreover, hospital stay was significantly reduced after early drain removal (mean difference -2.60 days; 95% CI -4.74 to -0.46; P = 0.02) ::: ::: ::: CONCLUSION ::: Further randomised controlled trials are warranted to clarify whether drains are of any use. In case of drain insertion, early removal seems to be superior to late removal.


title: Pardicle: Parallel Approximate Density-Based Clustering

abstract: Dbscan is a widely used isodensity-based clustering algorithm for particle data well-known for its ability to isolate arbitrarily-shaped clusters and to filter noise data. The algorithm is super-linear (O(nlogn)) and computationally expensive for large datasets. Given the need for speed, we propose a fast heuristic algorithm for Dbscan using density based sampling, which performs equally well in quality compared to exact algorithms, but is more than an order of magnitude faster. Our experiments on astrophysics and synthetic massive datasets (8.5 billion numbers) shows that our approximate algorithm is up to 56x faster than exact algorithms with almost identical quality (Omega-Index ≥ 0.99). We develop a new parallel Dbscan algorithm, which uses dynamic partitioning to improve load balancing and locality. We demonstrate near-linear speedup on shared memory (15x using 16 cores, single node Intel® Xeon® processor) and distributed memory (3917x using 4096 cores, multinode) computers, with 2x additional performance improvement using Intel® Xeon Phi™ coprocessors. Additionally, existing exact algorithms can achieve up to 3.4 times speedup using dynamic partitioning.