title: Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope

abstract: Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods.


title: Artificial Intelligence ―Creating Automated Insights for Customer Relationship Management

abstract: Artificial Intelligence (AI) is finally ready to challenge the human intelligence. Computers defeated human in complicated board games like Chess and Chinese game Go. AI is now ready to write poems and complex codes, predict choices, interact with human in real time, mining trillions of data and providing solutions in less than milliseconds. Today AI is driving almost every business unit and Customer Relationship Management (CRM) is one area that is benefitting the most in leveraging better customer experience (CX). In this research paper, researchers studied various AI enabled tools and finalized five AI enabled tools to study the Consumer Awareness, Effectiveness and Loyalty. They used survey based research methodology and collected primary data through Google form. After the data analysis researchers found that consumers are aware of the AI enabled tools and the tools under study are all effective and consumers are also loyal towards the tools.


title: Access Log Anomaly Detection

abstract: Maintaining network security is very important and tedious in today's world. Since web applications are not built on sound security methodology, they are the major target for the attackers. Analyzing access logs for detecting anomalous activities is a form of defense achieved in this paper. Anomaly detection is important because if the anomalies are not detected apriori, it may lead to hacking of the entire system. This paper is based on analyzing the stored access logs and detecting the anomalous events. Our experiment evaluates both static and dynamic logs. In dynamic implementation, the pattern matching approach is used to detect the anomalies from access logs. In Weka, the supervised neural network approach gives better anomaly prediction than unsupervised neural network approach for static logs. Maximum prediction accuracy is achieved in supervised neural networks by using Naive Bayes Multinomial Text Algorithm. Since the input attributes (logs) are strings, the use of Bayes classifier gives us a better accuracy rate while compared to other classifier algorithms. The proposed approach identifies the suspicious activities and serious anomalies that may be one of the way for the hackers to hack our system. Overall error rate of our supervised method is less than 10% and unsupervised method is approximately 30%.


title: Degradation of groundwater quality of quaternary aquifer at Qena, Egypt

abstract: Summary Qena area, Upper Egypt has a limited water resources represented by the River Nile, irrigation canals and groundwater resources. The Quaternary groundwater aquifer is the main water source in the study area. Urbanization and agricultural activities in the study area constitute the main pollution sources of groundwater. Pollution sources in the study area include mixing from wastewater and agrochemicals from agricultural activities. Results of hydrochemical analysis of thirty groundwater samples collected from the study area revealed that groundwater sources are polluted by nitrates, phosphates, ammonia and E.Coli bacteria. Groundwater quality in the study area varied from locality to another concerning its suitability for drinking purposes according to the World Health Organization and Egyptian standards. Most of the collected groundwater samples are not suitable for drinking purposes. On the other hand, groundwater is suitable for irrigation purposes with limited restriction. The present study recommended management of pollution sources including application of agrochemicals and controlling the seepage of wastewater from rural areas as well as management of wastewater treatment plants.


title: Becoming happier takes both a will and a proper way: An experimental longitudinal intervention to boost well-being. Manuscript under review

abstract: An 8-month-long experimental study examined the immediate and longer term effects of regularly practicing two assigned positive activities (expressing optimism and gratitude) on well-being. More important, this intervention allowed us to explore the impact of two metafactors that are likely to influence the success of any positive activity: whether one self-selects into the study knowing that it is about increasing happiness and whether one invests effort into the activity over time. Our results indicate that initial self-selection makes a difference, but only in the two positive activity conditions, not the control, and that continued effort also makes a difference, but, again, only in the treatment conditions. We conclude that happiness interventions are more than just placebos, but that they are most successful when participants know about, endorse, and commit to the intervention.


title: A Case of Tardive Dyskinesia and Parkinsonism Following Use of Phentermine for Weight Loss (P4.082)

abstract: Objective: To report a case of phentermine-induced tardive dyskinesia and parkinsonism. Background: Tardive dyskinesia is a movement disorder characterized by the delayed onset of involuntary muscle movements usually following use of dopaminergic antagonists. It can be associated with several other classes of medications including both prescribed and illicit stimulants. Phentermine is an amphetamine that is approved for weight loss but only with short-term use. Design/Methods: Case report and review of the literature. Results: We present the case of a 56-year-old man with a history of depression and obesity who presented with the complaint of unilateral tremor and tongue thrusting. He was on no medication. On exam, his mental status was notable only for impaired delayed recall. He had repetitive tongue thrusts, of which he was unaware. He had a 4–6 Hz resting tremor in his right hand. He had mild bradykinesia and rigidity in his right upper extremity. He had normal strength and sensation throughout. He had no axial or appendicular ataxia. His gait was normal and he had no retropulsion on pull testing. A MRI brain was normal. He had no history of dopamine replacement or dopamine antagonist use. His father had Parkinson’s disease but it occurred late in life. His tongue thrusting and rest tremor had arisen following a two-month trial of phentermine which was prescribed for weight loss. Conclusions: This is the second report of a tardive syndrome arising in the setting of phentermine use. Phentermine is an amphetamine and can lead to dopamine depletion. This case illustrates the potential for tardive syndromes associated with stimulant use. While atypical, it is important to recognize the potential occurrence of this side effect. Additionally, it is possible that his parkinsonism represented either an unmasking of idiopathic Parkinson’s disease or a drug-induced phenomenon. We will discuss diagnostic possibilities and treatment of these movement disorders. Disclosure: Dr. Barmore has nothing to disclose. Dr. Hess has nothing to disclose. Dr. McFarland has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Novartis. Dr. Okun has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with National Parkinson Foundation, Medscape, Mededicus.


title: Polyphonic sound event detection using multi label deep neural networks

abstract: In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods. Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work. The model is evaluated with recordings from realistic everyday environments and the obtained overall accuracy is 63.8%. The method is compared against a state-of-the-art method using non-negative matrix factorization as a pre-processing stage and hidden Markov models as a classifier. The proposed method improves the accuracy by 19% percentage points overall.


title: Techniques and Systems for Image and Video Retrieval

abstract: Storage and retrieval of multimedia has become a requirement for many contemporary information systems. These systems need to provide browsing, querying, navigation, and, sometimes, composition capabilities involving various forms of media. In this survey, we review techniques and systems for image and video retrieval. We first look at visual features for image retrieval such as color, texture, shape, and spatial relationships. The indexing techniques are discussed for these features. Nonvisual features include captions, annotations, relational attributes, and structural descriptions. Temporal aspects of video retrieval and video segmentation are discussed next. We review several systems for image and video retrieval including research, commercial, and World Wide Web-based systems. We conclude with an overview of current challenges and future trends for image and video retrieval.


title: Video summarization and scene detection by graph modeling

abstract: We propose a unified approach for video summarization based on the analysis of video structures and video highlights. Two major components in our approach are scene modeling and highlight detection. Scene modeling is achieved by normalized cut algorithm and temporal graph analysis, while highlight detection is accomplished by motion attention modeling. In our proposed approach, a video is represented as a complete undirected graph and the normalized cut algorithm is carried out to globally and optimally partition the graph into video clusters. The resulting clusters form a directed temporal graph and a shortest path algorithm is proposed to efficiently detect video scenes. The attention values are then computed and attached to the scenes, clusters, shots, and subshots in a temporal graph. As a result, the temporal graph can inherently describe the evolution and perceptual importance of a video. In our application, video summaries that emphasize both content balance and perceptual quality can be generated directly from a temporal graph that embeds both the structure and attention information.


Predicting Yelp Star Ratings Based on Text Analysis of User


title: Efficient algorithm for k-barrier coverage based on integer linear programming

abstract: Barrier coverage of wireless sensor networks is an important issue in the detection of intruders who are attempting to cross a region of interest. However, in certain applications, barrier coverage cannot be satisfied after random deployment. In this paper, we study how mobile sensors can be efficiently relocated to achieve k-barrier coverage. In particular, two problems are studied: relocation of sensors with minimum number of mobile sensors and formation of k-barrier coverage with minimum energy cost. These two problems were formulated as 0-1 integer linear programming (ILP). The formulation is computationally intractable because of integrality and complicated constraints. Therefore, we relax the integrality and complicated constraints of the formulation and construct a special model known as RELAX-RSMN with a totally unimodular constraint coefficient matrix to solve the relaxed 0-1 ILP rapidly through linear programming. Theoretical analysis and simulation were performed to verify the effectiveness of our approach.


title: GraphFrames: an integrated API for mixing graph and relational queries

abstract: Graph data is prevalent in many domains, but it has usually required specialized engines to analyze. This design is onerous for users and precludes optimization across complete workflows. We present GraphFrames, an integrated system that lets users combine graph algorithms, pattern matching and relational queries, and optimizes work across them. GraphFrames generalize the ideas in previous graph-on-RDBMS systems, such as GraphX and Vertexica, by letting the system materialize multiple views of the graph (not just the specific triplet views in these systems) and executing both iterative algorithms and pattern matching using joins. To make applications easy to write, GraphFrames provide a concise, declarative API based on the "data frame" concept in R that can be used for both interactive queries and standalone programs. Under this API, GraphFrames use a graph-aware join optimization algorithm across the whole computation that can select from the available views. We implement GraphFrames over Spark SQL, enabling parallel execution on Spark and integration with custom code. We find that GraphFrames make it easy to express end-to-end workflows and match or exceed the performance of standalone tools, while enabling optimizations across workflow steps that cannot occur in current systems. In addition, we show that GraphFrames' view abstraction makes it easy to further speed up interactive queries by registering the appropriate view, and that the combination of graph and relational data allows for other optimizations, such as attribute-aware partitioning.


title: Anterior Teeth and Smile Designing: A Prospective View

abstract: Smile is a person's greatest beauty asset. A defective smile might be considered as a physical handicap as the mouth act as a focal point where a large share of attention is directed toward mouth and teeth, it would be nice to have some sort of tools to assess beauty, which currently does not exist.Standards for beauty are actually a compilation and comparison of everything we have seen or experienced. Smile design is different from tooth design, with tooth design one is concerned with contour, position and color of individual teeth, while integration of these teeth into the face and facial features is paramount in smile design.This article describes some average desirable characteristic features of smile to help and achieve optimum result in esthetic oral rehabilitation.


title: CONSERVATIVE MANAGEMENT OF HORIZONTAL ROOT FRACTURES — CASE SERIES

abstract: Radicular fractures in permanent teeth are uncommon injuries among dental traumas, being only 0.5-7% of the cases. Root fractures occur mainly in the central (68%) and lateral (27%) maxillary incisors; in contrast, only 5% of root fractures are found in mandibular incisors (Caliskan & Pehlivan 1996). This article presents a case series describing successful conservative management of middle third and incisal third root fractures of the maxillary central incisors.


Evaluation of pH telemetry in the clinical study of gastric physiology.


title: An Ultrawideband Diversity Antenna

abstract: A compact diversity antenna operating at an ultrawideband (UWB) frequency range of 3.1-5 GHz is proposed for use in portable devices. The antenna printed on a printed circuit board (PCB) slab consists of two notched triangular radiating elements with two feeding ports. A ground plane is etched on the reverse side of the PCB. The shape of the ground plane is optimized to improve the isolation between the ports as well as impedance matching. The simulated and measured results show that across the operating bandwidth, the antenna can achieve a broad impedance bandwidth with good performance in terms of isolation of -2 dBi, and efficiency of > 70%. The correlation between the radiation patterns shows consistent diversity performance across the UWB bandwidth. A method to derive thetransfer function of the antenna has been proposed, which can be used to obtain the radiated pulses in the time domain. Furthermore, a parametric study is conducted to provide antenna engineers with useful information for designing and optimizing the antenna.


“ The Pope Has a New Baby ! ” Fake News Detection Using Deep Learning


title: QSplat: a multiresolution point rendering system for large meshes

abstract: Advances in 3D scanning technologies have enabled the practical creation of meshes with hundreds of millions of polygons. Traditional algorithms for display, simplification, and progressive transmission of meshes are impractical for data sets of this size. We describe a system for representing and progressively displaying these meshes that combines a multiresolution hierarchy based on bounding spheres with a rendering system based on points. A single data structure is used for view frustum culling, backface culling, level-of-detail selection, and rendering. The representation is compact and can be computed quickly, making it suitable for large data sets. Our implementation, written for use in a large-scale 3D digitization project, launches quickly, maintains a user-settable interactive frame rate regardless of object complexity or camera position, yields reasonable image quality during motion, and refines progressively when idle to a high final image quality. We have demonstrated the system on scanned models containing hundreds of millions of samples.


title: Word Semantic Representations using Bayesian Probabilistic Tensor Factorization

abstract: Many forms of word relatedness have been developed, providing different perspectives on word similarity. We introduce a Bayesian probabilistic tensor factorization model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The resulting word vectors, when combined with the per-perspective linear transformation, approximately recreate while also regularizing and generalizing, each word similarity perspective. Our method can combine manually created semantic resources with neural word embeddings to separate synonyms and antonyms, and is capable of generalizing to words outside the vocabulary of any particular perspective. We evaluated the word embeddings with GRE antonym questions, the result achieves the state-ofthe-art performance.


title: High dimensions and heavy tails for natural evolution strategies

abstract: The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization. NES follows the natural gradient of the expected fitness on the parameters of its search distribution. While general in its formulation, previous research has focused on multivariate Gaussian search distributions. Here we exhibit problem classes for which other search distributions are more appropriate, and then derive corresponding NES-variants. First, for separable distributions we obtain SNES, whose complexity is only O(d) instead of O(d3). We apply SNES to problems of previously unattainable dimensionality, recovering lowest-energy structures on the Lennard-Jones atom clusters, and obtaining state-of-the-art results on neuro-evolution benchmarks. Second, we develop a new, equivalent formulation based on invariances. This allows for generalizing NES to heavy-tailed distributions, even those with undefined variance, which aids in overcoming deceptive local optima.


title: An Unbiased Detector of Curvilinear Structures

abstract: The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise subpixel line position, but also the width of the line for each line point, also with subpixel accuracy.


title: Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language

abstract: Despite intensive work on language – brain relations, and a fairly impressive accumulation of knowledge over the last several decades, there has been little progress in developing large-scale models of the functional anatomy of language that integrate neuropsychological, neuroimaging, and psycholinguistic data. Drawing on relatively recent developments in the cortical organization of vision, and on data from a variety of sources, we propose a new framework for understanding aspects of the functional anatomy of language which moves towards remedying this situation. The framework posits that early cortical stages of speech perception involve auditory fields in the superior temporal gyrus bilaterally (although asymmetrically). This cortical processing system then diverges into two broad processing streams, a ventral stream, which is involved in mapping sound onto meaning, and a dorsal stream, which is involved in mapping sound onto articulatory-based representations. The ventral stream projects ventro-laterally toward inferior posterior temporal cortex (posterior middle temporal gyrus) which serves as an interface between sound-based representations of speech in the superior temporal gyrus (again bilaterally) and widely distributed conceptual representations. The dorsal stream projects dorso-posteriorly involving a region in the posterior Sylvian fissure at the parietal – temporal boundary (area Spt), and ultimately projecting to frontal regions. This network provides a mechanism for the development and maintenance of “parity” between auditory and motor representations of speech. Although the proposed dorsal stream represents a very tight connection between processes involved in speech perception and speech production, it does not appear to be a critical component of the speech perception process under normal (ecologically natural) listening conditions, that is, when speech input is mapped onto a conceptual representation. We also propose some degree of bi-directionality in both the dorsal and ventral pathways. We discuss some recent empirical tests of this framework that utilize a range of


title: Anomaly detection for big log data using a Hadoop ecosystem

abstract: In this paper, we address a novel method to efficiently manage and analyze a large amount of log data. First, we present a new Apache Hive-based data storage and analysis architecture to process a large volume of Hadoop log data, which rapidly occur in multiple nodes. Second, we design and implement three simple but efficient anomaly detection methods. These methods use moving average and 3-sigma techniques to detect anomalies in log data. Finally, we show that all the three methods detect abnormal intervals properly, and the weighted anomaly detection methods are more precise than the basic one. These results indicate that our research is an excellent and simple approach in detecting anomalies of log data on a Hadoop ecosystem.


title: The RDFa Content Editor - From WYSIWYG to WYSIWYM

abstract: Recently practical approaches for managing and supporting the life-cycle of semantic content on the Web of Data made quite some progress. However, the currently least developed aspect of the semantic content life-cycle is the user-friendly manual and semi-automatic creation of rich semantic content. In this paper we present the RDFaCE approach for combining WYSIWYG text authoring with the creation of rich semantic annotations. Our approach is based on providing four different views to the content authors: a classical WYSIWYG view, a WYSIWYM (What You See Is What You Mean) view making the semantic annotations visible, a fact view and the respective HTML/RDFa source code view. The views are synchronized such that changes made in one of the views automatically update the others. They provide different means of semantic content authoring for the different personas involved in the content creation life-cycle. For bootstrapping the semantic annotation process we integrate five different text annotation services. We evaluate their accuracy and empirically show that a combination of them yields superior results.


Disarray Based Signcryption Scheme


CertCoin : A NameCoin Based Decentralized Authentication System 6 . 857 Class Project


The Role of Motivation in Promoting and Sustaining Self-Regulated Learning


title: A geometric viewpoint of manifold learning

abstract: In many data analysis tasks, one is often confronted with very high dimensional data. The manifold assumption, which states that the data is sampled from a submanifold embedded in much higher dimensional Euclidean space, has been widely adopted by many researchers. In the last 15 years, a large number of manifold learning algorithms have been proposed. Many of them rely on the evaluation of the geometrical and topological of the data manifold. In this paper, we present a review of these methods on a novel geometric perspective. We categorize these methods by three main groups: Laplacian-based, Hessian-based, and parallel field-based methods. We show the connection and difference between these three groups on their continuous and discrete counterparts. The discussion is focused on the problem of dimensionality reduction and semi-supervised learning.


title: Loda: Lightweight on-line detector of anomalies

abstract: In supervised learning it has been shown that a collection of weak classifiers can result in a strong classifier with error rates similar to those of more sophisticated methods. In unsupervised learning, namely in anomaly detection such a paradigm has not yet been demonstrated despite the fact that many methods have been devised as counterparts to supervised binary classifiers. This work partially fills the gap by showing that an ensemble of very weak detectors can lead to a strong anomaly detector with a performance equal to or better than state of the art methods. The simplicity of the proposed ensemble system (to be called Loda) is particularly useful in domains where a large number of samples need to be processed in real-time or in domains where the data stream is subject to concept drift and the detector needs to be updated on-line. Besides being fast and accurate, Loda is also able to operate and update itself on data with missing variables. Loda is thus practical in domains with sensor outages. Moreover, Loda can identify features in which the scrutinized sample deviates from the majority. This capability is useful when the goal is to find out what has caused the anomaly. It should be noted that none of these favorable properties increase Loda's low time and space complexity. We compare Loda to several state of the art anomaly detectors in two settings: batch training and on-line training on data streams. The results on 36 datasets from UCI repository illustrate the strengths of the proposed system, but also provide more insight into the more general questions regarding batch-vs-on-line anomaly detection.


title: Firewall Fingerprinting and Denial of Firewalling Attacks

abstract: Firewalls are critical security devices handling all traffic in and out of a network. Firewalls, like other software and hardware network devices, have vulnerabilities, which can be exploited by motivated attackers. However, just like any other networking and computing devices, firewalls often have vulnerabilities that can be exploited by attackers. In this paper, first, we investigate some possible firewall fingerprinting methods and surprisingly found that these methods can achieve quite high accuracy. Second, we study what we call denial of firewalling (DoF) attacks, where attackers use carefully crafted traffic to effectively overload a firewall. To the best of our knowledge, this paper represents the first study of firewall fingerprinting and DoF attacks.


title: SPRK: A low-cost stewart platform for motion study in surgical robotics

abstract: To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm x 20cm x 10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of +/- 1.27 cm in translation along x, y, and z directions and has motion modes for sinusoidal motion and breathing-inspired motion. Modular platform mounts were also designed for pattern cutting and debridement experiments. The platform's positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. All the details, CAD models, and control software for the platform is available at github.com/BerkeleyAutomation/sprk.


title: Relative fat mass (RFM) as a new estimator of whole-body fat percentage ─ A cross-sectional study in American adult individuals

abstract: High whole-body fat percentage is independently associated with increased mortality. We aimed to identify a simple anthropometric linear equation that is more accurate than the body mass index (BMI) to estimate whole-body fat percentage among adult individuals. National Health and Nutrition Examination Survey (NHANES) 1999–2004 data (n = 12,581) were used for model development and NHANES 2005–2006 data (n = 3,456) were used for model validation. From the 365 anthropometric indices generated, the final selected equation was as follows: 64 − (20 × height/waist circumference) + (12 × sex), named as the relative fat mass (RFM); sex = 0 for men and 1 for women. In the validation dataset, compared with BMI, RFM better predicted whole-body fat percentage, measured by dual energy X-ray absorptiometry (DXA), among women and men. RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men. RFM reduced total obesity misclassification among all women and all men and, overall, among Mexican-Americans, European-Americans and African-Americans. In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European or African ethnicity.


title: All-digital MEMS gyro-sensor using TAD-digital-synchronous-detection (TAD-DSD) By TAD-ADPLL

abstract: As sensor digitization for IoT-applications, this paper presents an all-digital MEMS gyro-sensor circuit method, using TAD (Time-A/D converter)-based digital synchronous detection (TAD-DSD) by applying entirely all-digital time-domain processing, which uses no conventional methods such as analogmixers, -filters and -ADCs. The proposed method involves twostep processing: 1) voltage-signal levels of the first-half and lasthalf of one-cycle carrier-waveform are averaged and digitized simultaneously with 0.18μm TAD's moving-average effect using a sampling clock frequency fs twice higher than carrier-wave frequency fcar, then 2) the difference between the first-half and last-half of the digitized carrier-wave voltage levels is calculated to obtain the information of actual carrier-wave amplitude, which indicates angular-rate information from a vibrating MEMS element. In this method, a TAD-sampling clock is generated with a TAD-based all-digital PLL (TAD-ADPLL) to drive the MEMS element. By using a conventional piezoelectric MEMS tuning-fork element, we experimentally confirmed its sensitivity of 0.14(°/s)/LSB and peak noises of ±0.18°/s at BW = 10Hz, achieving a digital-type gyro-sensor for CMOS scaling.


title: Psychophysiology of False Memories in a Deese-Roediger-McDermott Paradigm with Visual Scenes

abstract: Remembering something that has not in fact been experienced is commonly referred to as false memory. The Deese-Roediger-McDermott (DRM) paradigm is a well-elaborated approach to this phenomenon. This study attempts to investigate the peripheral physiology of false memories induced in a visual DRM paradigm. The main research question is whether false recognition is different from true recognition in terms of accompanying physiological responses. ::: ::: Sixty subjects participated in the experiment, which included a study phase with visual scenes each showing a group of interrelated items in social contexts. Subjects were divided into an experimental group undergoing a classical DRM design and a control group without DRM manipulation. The control group was implemented in order to statistically control for possible biases produced by memorability differences between stimulus types. After a short retention interval, a pictorial recognition phase was conducted in the manner of a Concealed Information Test. Simultaneous recordings of electrodermal activity, respiration line length, phasic heart rate, and finger pulse waveform length were used. Results yielded a significant Group by Item Type interaction, showing that true recognition is accompanied by greater electrodermal activity than false recognition. ::: ::: Results are discussed in the light of Sokolov's Orienting Reflex, the Preliminary Process Theory and the Concealed Information Test. Implications and restrictions of the introduced design features are critically discussed. This study demonstrates the applicability of measures of peripheral physiology to the field of false memory research.


title: Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata

abstract: There is a growing interest in developing and then evaluating Music Information Retrieval (MIR) systems that can provide automated access to the mood dimension of music. Mood as a music access feature, however, is not well understood in that the terms used to describe it are not standardized and their application can be highly idiosyncratic. To better understand how we might develop methods for comprehensively developing and formally evaluating useful automated mood access techniques, we explore the relationships that mood has with genre, artist and usage metadata. Statistical analyses of term interactions across three metadata collections (AllMusicGuide.com, epinions.com and Last.fm) reveal important consistencies within the genre-mood and artist-mood relationships. These consistencies lead us to recommend a cluster-based approach that overcomes specific term-related problems by creating a relatively small set of data-derived “mood spaces” that could form the ground-truth for a proposed MIREX “Automated Mood Classification” task.


title: Privacy, Security and Trust Issues Arising from Cloud Computing

abstract: Cloud computing is an emerging paradigm for large scale infrastructures. It has the advantage of reducing cost by sharing computing and storage resources, combined with an on-demand provisioning mechanism relying on a pay-per-use business model. These new features have a direct impact on the budgeting of IT budgeting but also affect traditional security, trust and privacy mechanisms. Many of these mechanisms are no longer adequate, but need to be rethought to fit this new paradigm. In this paper we assess how security, trust and privacy issues occur in the context of cloud computing and discuss ways in which they may be addressed.


title: Dual band Koch antenna for RF energy harvesting

abstract: Nowadays, our living environment is rich in radio-frequency energy suitable for harvesting. This energy can be used for supplying low-power consumption devices. In this paper, we analyze a new type of a Koch-like antenna which was designed for energy harvesting specifically. The designed antenna covers two different frequency bands (GSM 900 and Wi-Fi). Functionality of the antenna is verified by simulations and measurements.


title: The Invisible Continent: Global Strategy in the New Economy

abstract: From the Publisher: ::: The internationally renowned business consultant and bestselling author of The Borderless World offers invaluable advice for the economies of the 21st century ::: Kenichi Ohmae has been called one of the "century's best business thinkers" by the U.S. Journal of Business Strategy. In The Invisible Continent, this top corporate strategist charts a course for the coming economic revolution. Expanding his theory of borderless economies, he superimposes two additional dimensions: cyber-economy and multiples-based economies. The winner in the next century will have to be able to work not only in the local and national economies, but all three new dimensions of the battlegrounds. In this book, Kenichi Ohmae opens up a totally new world in which the economic war between the companies and countries will be fought. His ideas on innovative actions from the Internet to e-commerce, and banking to telecommunications are a must-read for managers, CEOs, entrepreneurs, day traders, and businesspeople everywhere. With many examples from today's cutting-edge companies like Cisco, Dell, and Microsoft, Ohmae shows how the traditional boundaries that defined economies are breaking down. In their place, he sees a "new continent" where what he calls Godzilla corporations, with their high multiples and cyber skills, grow ten times faster than any other corporations before, and the battle between them to control the platforms on which the future of global commerce is built. Like Ohmae's groundbreaking book The Borderless World, The Invisible Continent will soon be seen as a seminal business book of the technology age. ::: About the Author: ::: Kenichi Ohmae, the former chairman of McKinsey & Company in Asia/Pacific, lives in Tokyo, Japan.


title: CPDist: Deep Siamese Networks for Learning Distances Between Structured Preferences

abstract: Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, e.g., the preferences of a user and a the descriptions of objects to be recommended. We present CPDist, a novel neural network to address the problem of learning to measure the distance between structured preference representations. We use the popular CP-net formalism to represent preferences and then leverage deep neural networks to learn a recently proposed metric function that is computationally hard to compute directly. CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations. We find that CPDist is able to learn the distance function with high accuracy and outperform existing approximation algorithms on both the regression and classification task using less computation time. Performance remains good even when CPDist is trained with only a small number of samples compared to the dimension of the solution space, indicating the network generalizes well.


title: Learning Category-Specific Deformable 3D Models for Object Reconstruction

abstract: We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce a complementary network for the task of camera viewpoint prediction. These predictors are very powerful, but still not perfect given the stringent requirements of shape reconstruction. Our main contribution is a new class of deformable 3D models that can be robustly fitted to images based on noisy pose and silhouette estimates computed upstream and that can be learned directly from 2D annotations available in object detection datasets. Our models capture top-down information about the main global modes of shape variation within a class providing a “low-frequency” shape. In order to capture fine instance-specific shape details, we fuse it with a high-frequency component recovered from shading cues. A comprehensive quantitative analysis and ablation study on the PASCAL 3D+ dataset validates the approach as we show fully automatic reconstructions on PASCAL VOC as well as large improvements on the task of viewpoint prediction.


GAIN SCHEDULING CONTROL FOR THE HYDRAULIC ACTUATION OF THE HYQ ROBOT LEG


title: Multi-modal Language Models for Lecture Video Retrieval

abstract: We propose Multi-modal Language Models (MLMs), which adapt latent variable techniques for document analysis to exploring co-occurrence relationships in multi-modal data. In this paper, we focus on the application of MLMs to indexing text from slides and speech in lecture videos, and subsequently employ a multi-modal probabilistic ranking function for lecture video retrieval. The MLM achieves highly competitive results against well established retrieval methods such as the Vector Space Model and Probabilistic Latent Semantic Analysis. When noise is present in the data, retrieval performance with MLMs is shown to improve with the quality of the spoken text extracted from the video.


title: Quasi-hyperbolic momentum and Adam for deep learning

abstract: Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. PyTorch code is immediately available.


title: FlowQA: Grasping Flow in History for Conversational Machine Comprehension

abstract: Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.


title: Overview of CAIL2018: Legal Judgment Prediction Competition

abstract: In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to predict the judgment results according to the given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for the contestants, i.e., predicting relevant law articles, charges and prison terms given the fact descriptions. CAIL2018 has attracted several hundreds participants (601 teams, 1, 144 contestants from 269 organizations). In this paper, we provide a detailed overview of the task definition, related works, outstanding methods and competition results in CAIL2018.


title: Short- Versus Long-Term Duration of Dual-Antiplatelet Therapy After Coronary StentingClinical Perspective

abstract: Background— The optimal duration of dual-antiplatelet therapy and the risk-benefit ratio for long-term dual-antiplatelet therapy after coronary stenting remain poorly defined. We evaluated the impact of up to 6 versus 24 months of dual-antiplatelet therapy in a broad all-comers patient population receiving a balanced proportion of Food and Drug Administration–approved drug-eluting or bare-metal stents. ::: ::: Methods and Results— We randomly assigned 2013 patients to receive bare-metal, zotarolimus-eluting, paclitaxel-eluting, or everolimus-eluting stent implantation. At 30 days, patients in each stent group were randomly allocated to receive up to 6 or 24 months of clopidogrel therapy in addition to aspirin. The primary end point was a composite of death of any cause, myocardial infarction, or cerebrovascular accident. The cumulative risk of the primary outcome at 2 years was 10.1% with 24-month dual-antiplatelet therapy compared with 10.0% with 6-month dual-antiplatelet therapy (hazard ratio, 0.98; 95% confidence interval, 0.74–1.29; P =0.91). The individual risks of death, myocardial infarction, cerebrovascular accident, or stent thrombosis did not differ between the study groups; however, there was a consistently greater risk of hemorrhage in the 24-month clopidogrel group according to all prespecified bleeding definitions, including the recently proposed Bleeding Academic Research Consortium classification. ::: ::: Conclusions— A regimen of 24 months of clopidogrel therapy in patients who had received a balanced mixture of drug-eluting or bare-metal stents was not significantly more effective than a 6-month clopidogrel regimen in reducing the composite of death due to any cause, myocardial infarction, or cerebrovascular accident. ::: ::: Clinical Trial Registration— URL: . Unique identifier: [NCT00611286][1]. ::: ::: # Clinical Perspective {#article-title-26} ::: ::: [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00611286&atom=%2Fcirculationaha%2F125%2F16%2F2015.atom


title: Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing Students

abstract: We take a qualitative approach to understanding deaf and hard of hearing (DHH) students' experiences with real-time captioning as an access technology in mainstream university classrooms. We consider both existing human-based captioning as well as new machine-based solutions that use automatic speech recognition (ASR). We employed a variety of qualitative research methods to gather data about students' captioning experiences including in-class observations, interviews, diary studies, and usability evaluations. We also conducted a co-design workshop with 8 stakeholders after our initial research findings. Our results show that accuracy and reliability of the technology are still the most important issues across captioning solutions. However, we additionally found that current captioning solutions tend to limit students' autonomy in the classroom and present a variety of user experience shortcomings, such as complex setups, poor feedback and limited control over caption presentation. Based on these findings, we propose design requirements and recommend features for real-time captioning in mainstream classrooms.


title: “I want my robot to look for food”: Comparing Kindergartner’s programming comprehension using tangible, graphic, and hybrid user interfaces

abstract: In recent years, educational robotics has become an increasingly popular research area. However, limited studies have focused on differentiated learning outcomes based on type of programming interface. This study aims to explore how successfully young children master foundational programming concepts based on the robotics user interface (tangible, graphical, hybrid) taught in their curriculum. Thirty-five Kindergarten students participated in a 9-week robotics curriculum using the LEGO WeDo robotics construction kit and the Creative Hybrid Environment for Robotic Programming (CHERP) programming language. A mixed methods data collection approach was employed, including qualitative observational data from the classrooms, as well as quantitative mid- and post-test assessments of students’ programming knowledge using CHERP. The findings show little association between user interface and programming comprehension, although there may be an order-affect when introducing user interfaces. Implications for best practices when introducing programming in early childhood settings are discussed.


title: The impact of Nintendo Wii to physical education students' balance compared to the traditional approaches

abstract: The purpose of this study was to determine whether there is a difference between an exergame-based and a traditional balance training program, in undergraduate Physical Education students. Thirty two third-year undergraduate students at the Democritus University of Thrace were randomly divided into two training program groups of 16 students each, a traditional and a Nintendo Wii group. The two training program groups performed a specific balance program for 8 weeks, two times per week, and 24 min per session. The Nintendo Wii group used the interactive games Wii Fit Plus of the Nintendo Wii console, as a training method to improve their balance, while the traditional group used an exercise program with mini trampoline and inflatable discs. Before and after the completion of the eight-week balance program, participants completed a single leg static balance assessment for both limbs on the Biodex stability system. Two-way analyses of variance (ANOVAs), with repeated measures on the last factor, were conducted to determine effect of training program groups (traditional, Nintendo Wii) and measures (pre-test, post-test) on balance test indices (SI, API, and MLI). Where initial differences between groups were verified, one-way analyses of covariance (ANCOVAs) were applied. Analysis of the data illustrated that both groups demonstrated an improvement in SI, API and MLI mean scores for the right and the left limp as well. Conclusively, findings support the effectiveness of using the Nintendo Wii gaming console as an intervention for undergraduate Physical Education students, and specifically, its effects on physical function related to balance competence.


title: Altruism and Indirect Reciprocity: The Interaction of Person and Situation in Prosocial Behavior:

abstract: A persistent puzzle in the social and biological sciences is the existence of prosocial behavior, actions that benefit others, often at a cost to oneself. Recent theoretical models and empirical studies of indirect reciprocity show that actors behave prosocially in order to develop an altruistic reputation and receive future benefits from third parties. Accordingly, individuals should stop investing in reputations via prosocial behavior when a future benefit (via indirect reciprocity) is unlikely. The conclusion that the absence of reputational incentives necessarily leads to egoistic behavior contrasts sharply with models of heterogeneous social preferences. Such models demonstrate the theoretical plausibility of populations composed of egoists and altruists. Results of Study One show that actors classified a priori as egoists respond strategically to reputational incentives, whereas those classified a priori as altruists are less affected by these incentives. Egoists act prosocially when reputational in...


title: The Sequential Normal Scores Transformation

abstract: The sequential analysis of series often requires nonparametric procedures, where the most powerful ones frequently use rank transformations. Re-ranking the data sequence after each new observation can become too intensive computationally. This led to the idea of sequential ranks, where only the most recent observation is ranked. However, difficulties finding, or approximating, the null distribution of the statistics may have contributed to the lack of popularity of these methods. In this paper, we propose transforming the sequential ranks into sequential normal scores which are independent, and asymptotically standard normal random variables. Thus original methods based on the normality assumption may be used. A novel approach permits the inclusion of a priori information in the form of quantiles. It is developed as a strategy to increase the sensitivity of the scoring statistic. The result is a powerful convenient method to analyze non-normal data sequences. Also, four variations of sequential normal scores are presented using examples from the literature. Researchers and practitioners might find this approach useful to develop nonparametric procedures to address new problems extending the use of parametric procedures when distributional assumptions are not met. These methods are especially useful with large data streams where efficient computational methods are required.


title: Connectivity Learning in Multi-Branch Networks

abstract: While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for significant improvements in performance. To combat the complexity of design choices in multi-branch architectures, prior work has adopted simple strategies, such as a fixed branching factor, the same input being fed to all parallel branches, and an additive combination of the outputs produced by all branches at aggregation points. In this work we remove these predefined choices and propose an algorithm to learn the connections between branches in the network. Instead of being chosen a priori by the human designer, the multi-branch connectivity is learned simultaneously with the weights of the network by optimizing a single loss function defined with respect to the end task. We demonstrate our approach on the problem of multi-class image classification using three different datasets where it yields consistently higher accuracy compared to the state-of-the-art"ResNeXt"multi-branch network given the same learning capacity.


title: Detection of pathological heart sounds.

abstract: Heart sound analysis has been a major topic of research over the past few decades. However, the necessity for a large and reliable database has been a major concern in these studies. ::: ::: ::: OBJECTIVE ::: Noting that the current heart sound classification methods do not work properly for noisy signals, the PhysioNet/CinC Challenge 2016 aims to develop the heart sound classification algorithms by providing a global open database for challengers. This paper addresses the problem of heart sound classification methods within noisy real-world phonocardiogram recordings by implementing an innovative approach. ::: ::: ::: SIGNIFICANCE ::: After locating the fundamental heart sounds and the systolic and diastolic components, a novel method named cycle quality assessment is applied to each recording. The presented method detects those cycles which are less affected by noise and better segmented by the use of two criteria here proposed in this paper. The selected cycles are the inputs of a further feature extraction process. ::: ::: ::: APPROACH ::: Due to the variability of the heart sound signal induced by various cardiac arrhythmias, four sets of features from the time, time-frequency and perceptual domains are extracted. Before starting the main classification process, the obtained 90-dimensional feature vector is mapped to a new feature space to pre-detect normal recordings by applying a Fisher's discriminant analysis. The main classification procedure is then done based on three feed-forward neural networks and a voting system among classifiers. ::: ::: ::: MAIN RESULTS ::: The presented method is evaluated using the training and hidden test sets of the PhysioNet/CinC Challenge 2016. Also, the results are compared with the top five ranked submissions. The results indicate that the proposed method is effective in classifying heart sounds as normal versus abnormal recordings.


title: Visible-Light-Activated Bactericidal Functions of Carbon "Quantum" Dots.

abstract: Carbon dots, generally defined as small carbon nanoparticles with various surface passivation schemes, have emerged as a new class of quantum-dot-like nanomaterials, with their optical properties and photocatalytic functions resembling those typically found in conventional nanoscale semiconductors. In this work, carbon dots were evaluated for their photoinduced bactericidal functions, with the results suggesting that the dots were highly effective in bacteria-killing with visible-light illumination. In fact, the inhibition effect could be observed even simply under ambient room lighting conditions. Mechanistic implications of the results are discussed and so are opportunities in the further development of carbon dots into a new class of effective visible/natural light-responsible bactericidal agents for a variety of bacteria control applications.


title: Privacy-Aware Tag Recommendation for Image Sharing

abstract: Image tags are very important for indexing, sharing, searching, and surfacing images with private content that needs protection. As the tags are at the sole discretion of users, they tend to be noisy and incomplete. In this paper, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images' original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image obtained from a large collection. Experimental results show that privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction. Crowd-sourcing predicted tags exhibit the quality of the recommended tags.


title: Workspace mapping and force control for small haptic device based robot teleoperation

abstract: When a large robot manipulator is remotely controlled by means of a small haptic device, there are two issues which should be addressed: mapping of robot arm workspace and haptic device workspace, and accurate and safe control of the movement of the robot arm. This paper presents a haptic device workspace spanning control method for haptic-based teleoperation. The spanning control method includes haptic-space scaling control, drift control and edge motion control. A force control algorithm is also presented to control the robot arm's motion in complex 3D environments. Experimental results demonstrate that the mapping method and the force control algorithm can remotely control a robot arm to rapidly reach target positions, minimize the oscillation of the haptic device handle, achieve accurate positioning and provide the operator with efficient touch sensing.


title: Topic- and Time-Oriented Visual Text Analysis

abstract: In this article, we first present the benefit of combing text analysis (topic models in particular) with interactive visualization. We then highlight examples from prior work on topic and time-oriented visual text analysis. Lastly, we present four challenges that warrant additional future research in the field of visual text analysis.


title: Constructing City Ontology from Expert for Smart City Management

abstract: City Ontology plays an important role in smart city management for data integration, reasoning decision support etc. With these managerial domain knowledge scattered among a large number of experts, researchers face a huge challenge constructing a complete ontology for city management. This paper presents a simple yet efficient method for non-computer science experts to construct an ontology. We use a middle part that acts as a transition layer called activity model which is later merged into the city managerial ontology. We prove the effectiveness of this method by constructing a managerial ontology for two departments in Karamay's Smart City Program.


title: Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

abstract: One-hot CNN (convolutional neural network) has been shown to be effective for text categorization (Johnson&Zhang, 2015). We view it as a special case of a general framework which jointly trains a linear model with a non-linear feature generator consisting of `text region embedding + pooling'. Under this framework, we explore a more sophisticated region embedding method using Long Short-Term Memory (LSTM). LSTM can embed text regions of variable (and possibly large) sizes, whereas the region size needs to be fixed in a CNN. We seek effective and efficient use of LSTM for this purpose in the supervised and semi-supervised settings. The best results were obtained by combining region embeddings in the form of LSTM and convolution layers trained on unlabeled data. The results indicate that on this task, embeddings of text regions, which can convey complex concepts, are more useful than embeddings of single words in isolation. We report performances exceeding the previous best results on four benchmark datasets.


title: Real-time human motion analysis by image skeletonization

abstract: In this paper a process is described for analysing the motion of a human target in a video stream. Moving targets are detected and their boundaries extracted. From these, a "star" skeleton is produced. Two motion cues are determined from this skeletonization: body posture, and cyclic motion of skeleton segments. These cues are used to determine human activities such as walking or running, and even potentially, the target's gait. Unlike other methods, this does not require an a priori human model, or a large number of "pixels on target". Furthermore, it is computationally inexpensive, and thus ideal for real-world video applications such as outdoor video surveillance.


title: Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking

abstract: One fundamental problem in object retrieval with the bag-of-visual words (BoW) model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the BoW model, most of them are either too strict or too loose so that they are only effective in limited cases. We propose a new spatially-constrained similarity measure (SCSM) to handle object rotation, scaling, view point change and appearance deformation. The similarity measure can be efficiently calculated by a voting-based method using inverted files. Object retrieval and localization are then simultaneously achieved without post-processing. Furthermore, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial search results. Extensive performance evaluations on six public datasets show that SCSM significantly outperforms other spatial models, while k-NN re-ranking outperforms most state-of-the-art approaches using query expansion.


title: Canny edge detection enhancement by scale multiplication

abstract: The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.


title: Design and Development of a 50kW Z-Source Inverter for Fuel Cell Vehicles

abstract: A detailed design process of the Z-source inverter is presented in this paper. A dc rail clamp circuit is used to reduce the overshoot of the device during turn off. The thermal and 3-D design process is gone through, and the loss calculation of the inverter is discussed, which is different from traditional PWM inverters. A 50 kW inverter for fuel cell vehicle is developed to demonstrate the validity of the design process. Experimental results confirmed the design process and demonstrated the high efficiency characteristic of the Z-source inverter.


title: A survey on time series data mining

abstract: In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.


title: Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

abstract: Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.


title: Incremental knowledge base construction using DeepDive

abstract: Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.


title: Semi-supervised learning for big social data analysis

abstract: Abstract In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different areas, with a growing impact on everyday life, such as in education, health, commerce and tourism, leading to an exponential growth in the size of the social Web. However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task. Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being well suited for human use, still barely accessible to machines. In this work, we explore the potential of a novel semi-supervised learning model based on the combined use of random projection scaling as part of a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter is developed by merging a graph representation of commonsense with a linguistic resource for the lexical representation of affect. Comparative simulation results show a significant improvement in tasks such as emotion recognition and polarity detection, and pave the way for development of future semi-supervised learning approaches to big social data analytics.


title: Model-based monitoring for early warning flood detection

abstract: Predictive environmental sensor networks provide complex engineering and systems challenges. These systems must withstand the event of interest, remain functional over long time periods when no events occur, cover large geographical regions of interest to the event, and support the variety of sensor types needed to detect the phenomenon. Prediction of the phenomenon on the network complicates the system further, requiring additional computation on themicrocontrollers and utilizing prediction models that are not typically designed for sensor networks. This paper describes a system architecture and deployment to meet the design requirements and to allow model-driven control, thereby optimizing the prediction capability of the system. We explore the application of river flood prediction using this architecture, describing our work on a centralized form of the prediction model, network implementation, component testing and infrastructure development in Honduras, deployment on a river in Massachusetts, and results of the field experiments. Our system uses only a small number of nodes to cover basins of 1000-10000 square km2 using an unique heterogeneous communication structure to provide real-time sensed data, incorporating self-monitoring for failure, and adapting measurement schedules to capture events of interest.


title: Wearable haptic augmentation system using skin vibration sensor

abstract: The aim of study is to complement and enhance the haptic sensation of human fingers that have different tactile perceptions owing to the age and proficiency. In this paper, we developed a wearable haptic augmentation system to modulate the haptic sensations in real-time using polyvinylidene fluoride (PVDF) skin vibration sensor and vibro-tactile feedback. We observed that the users could get more exhilarating experiences when touching the surrounding objects, and recognition of haptic properties became easier. The experimental result shows that the haptic sensation obtained by our proposed condition is approximately 5% higher than the no haptic feedback condition.


title: A rotating contactless power transfer system for space applications

abstract: In this paper, a rotating contactless power transfer system achieving 200W for space application is presented. Both transformer and electronics designs are presented. A proof-of-concept prototype, making use of components with space grade equivalent has been build and tested.


title: A 1-V, self adjusting, 5-MHz CMOS RC-oscillator

abstract: A 5-MHz, self adjusting CMOS RC-oscillator for portable biomedical applications is presented. The oscillator is capable of operating. with a 1-V power supply and it features low current consumption (20 /spl mu/A @ 1 V) and low sensitivity to supply voltage and temperature variations. The total accuracy of the oscillator is within 5 % with component tolerances of 1 % in external elements R and C. The design was fabricated in a 0.35 /spl mu/m n-well standard digital CMOS process with threshold voltages of 0.5 V and -0.65 V. The design and the operation of the RC-oscillator with measurement results of fabricated chips are presented.


title: About the Influence of Illumination Models on Image Comprehension in Direct Volume Rendering

abstract: In this paper, we present a user study in which we have investigated the influence of seven state-of-the-art volumetric illumination models on the spatial perception of volume rendered images. Within the study, we have compared gradient-based shading with half angle slicing, directional occlusion shading, multidirectional occlusion shading, shadow volume propagation, spherical harmonic lighting as well as dynamic ambient occlusion. To evaluate these models, users had to solve three tasks relying on correct depth as well as size perception. Our motivation for these three tasks was to find relations between the used illumination model, user accuracy and the elapsed time. In an additional task, users had to subjectively judge the output of the tested models. After first reviewing the models and their features, we will introduce the individual tasks and discuss their results. We discovered statistically significant differences in the testing performance of the techniques. Based on these findings, we have analyzed the models and extracted those features which are possibly relevant for the improved spatial comprehension in a relational task. We believe that a combination of these distinctive features could pave the way for a novel illumination model, which would be optimized based on our findings.


title: RRect: A Novel Server-centric Data Center Network with High Availability

abstract: In this paper, we propose a novel server-centric network for data centers, called RRect. Compared to existing server-centric networks, RRect has a linear diameter to the network order and abundant parallel paths with near-equal lengths, so that traffic in RRect enjoys a short and predictable communication latency. We present an efficient routing algorithm to find paths between any pair of servers in RRect. A complete addressing scheme and recursive RRect construction procedure are also provided in this paper. Meanwhile, to meet today's stringent high availability requirement, unlike existing server-centric network structures, RRect can be configured into redundancy and failover scheme, in which the backup server can fully take the place of the corresponding malfunctioning server without losing topological advantages, such as multiple near-equal parallel paths. Our comprehensive simulations show that RRect gives a better average path lengths and a more balanced path distribution among all pairs of servers. Meanwhile, RRect can maintain the same performance on many critical metrics as BCube, including short diameter and excellent aggregate throughput. All these features make RRect a very empirical structure for enterprise dater center network products.


title: Backpropagation for energy-efficient neuromorphic computing

abstract: Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural networks have recently achieved unprecedented energy efficiency. To bring these two advances together, we must first resolve the incompatibility between backpropagation, which uses continuous-output neurons and synaptic weights, and neuromorphic designs, which employ spiking neurons and discrete synapses. Our approach is to treat spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation. The trained network naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging. To demonstrate, we trained a sparsely connected network that runs on the TrueNorth chip using the MNIST dataset. With a high performance network (ensemble of 64), we achieve 99.42% accuracy at 108 μJ per image, and with a high efficiency network (ensemble of 1) we achieve 92.7% accuracy at 0.268 μJ per image.


title: Automatic Web Content Extraction by Combination of Learning and Grouping

abstract: Web pages consist of not only actual content, but also other elements such as branding banners, navigational elements, advertisements, copyright etc. This noisy content is typically not related to the main subjects of the webpages. Identifying the part of actual content, or clipping web pages, has many applications, such as high quality web printing, e-reading on mobile devices and data mining. Although there are many existing methods attempting to address this task, most of them can either work only on certain types of Web pages, e.g. article pages, or has to develop different models for different websites. We formulate the actual content identifying problem as a DOM tree node selection problem. We develop multiple features by utilizing the DOM tree node properties to train a machine learning model. Then candidate nodes are selected based on the learning model. Based on the observation that the actual content is usually located in a spatially continuous block, we develop a grouping technology to further filter out noisy data and pick missing data for the candidate nodes. We conduct extensive experiments on a real dataset and demonstrate our solution has high quality outputs and outperforms several baseline methods.


title: A Fully Integrated Dual-Mode Highly Linear 2.4 GHz CMOS Power Amplifier for 4G WiMax Applications

abstract: In recent years, there has been tremendous interest in trying to implement the power amplifier in CMOS, due to its cost and integration benefits. Most of the high power (watt-level) CMOS PAs reported to date have not exhibited sufficient linearity required for next generation wireless standards. In this paper, we report a single-chip linear CMOS PA with sufficient power and linearity for emerging OFDM-based 4G WiMAX applications. This 90 nm 2.4 GHz CMOS linear power amplifier uses a two-stage transformer-based power combiner and produces a saturated output power of 30.1 dBm with 33% PAE and 28 dB small-signal gain. A novel bypass network is introduced to ensure stability without sacrificing gain. The choice of optimal biasing and capacitive compensation produces very flat AM-AM and AM-PM response up to high power. The PA has been tested with OFDM modulated signal and produces EVM better than -25 dB at 22.7 dBm average power. Graceful power back-off is demonstrated through turning off one of the stages, allowing low-power operation with enhanced efficiency.


title: Pathogenic CD4 + T cells in patients with asthma

abstract: Asthma encompasses a variety of clinical phenotypes that involve distinct T cell-driven inflammatory processes. Improved understanding of human T-cell biology and the influence of innate cytokines on T-cell responses at the epithelial barrier has led to new asthma paradigms. This review captures recent knowledge on pathogenic CD4+ T cells in asthmatic patients by drawing on observations in mouse models and human disease. In patients with allergic asthma, TH2 cells promote IgE-mediated sensitization, airway hyperreactivity, and eosinophilia. Here we discuss recent discoveries in the myriad molecular pathways that govern the induction of TH2 differentiation and the critical role of GATA-3 in this process. We elaborate on how cross-talk between epithelial cells, dendritic cells, and innate lymphoid cells translates to T-cell outcomes, with an emphasis on the actions of thymic stromal lymphopoietin, IL-25, and IL-33 at the epithelial barrier. New concepts on how T-cell skewing and epitope specificity are shaped by multiple environmental cues integrated by dendritic cell "hubs" are discussed. We also describe advances in understanding the origins of atypical TH2 cells in asthmatic patients, the role of TH1 cells and other non-TH2 types in asthmatic patients, and the features of T-cell pathogenicity at the single-cell level. Progress in technologies that enable highly multiplexed profiling of markers within a single cell promise to overcome barriers to T-cell discovery in human asthmatic patients that could transform our understanding of disease. These developments, along with novel T cell-based therapies, position us to expand the assortment of molecular targets that could facilitate personalized treatments.


title: Maximal oxygen uptake, anaerobic threshold and running economy in women and men with similar performances level in marathons

abstract: Sex differences in running economy (gross oxygen cost of running, CR), maximal oxygen uptake (VO2max), anaerobic threshold (Than), percentage utilization of aerobic power (% VO2max), and Than during running were investigated. There were six men and six women aged 20–30 years with a performance time of 2 h 40 min over the marathon distance. The VO2max, Than, and CR were measured during controlled running on a treadmill at 1° and 3° gradient. From each subject's recorded time of running in the marathon, the average speed (vM) was calculated and maintained during the treadmill running for 11 min. The VO2 max was inversely related to body mass (mb), there were no sex differences, and the mean values of the reduced exponent were 0.65 for women and 0.81 for men. These results indicate that for running the unit ml·kg−0.75·min−1 is convenient when comparing individuals with different mb. The VO2max was about 10% (23 ml·kg−0.75·min−1) higher in the men than in the women. The women had on the average 10–12 ml·kg−0.75·min−1 lower VO2 than the men when running at comparable velocities. Disregarding sex, the mean value of CR was 0.211 (SEM 0.005) ml·kg−1·m−1 (resting included), and was independent of treadmill speed. No sex differences in Than expressed as % VO2max or percentage maximal heart rate were found, but Than expressed as VO2 in ml·kg−0.75·min−1 was significantly higher in the men compared to the women. The percentage utilization of femax and concentration of blood lactate at vM was higher for the female runners. The women ran 2 days more each week than the men over the first 4 months during the half year preceding the marathon race. It was concluded that the higher VO2max and Than in the men was compensated for by more running, superior CR, and a higher exercise intensity during the race in the performance-matched female marathon runners.


A comparison of trading algorithms based on machine learning classifiers : application on the S & P 500


Effects of Tramadol on Fertility Hormones (Follicle Stimulating Hormone, Leutinizing Hormone, Prolactin, Testosterone, Estrogen and β-HCG) in Laboratory Rabbits


Image Captioning with Pragmatics


title: The CRISPR/Cas9 system produces specific and homozygous targeted gene editing in rice in one generation

abstract: The CRISPR/Cas9 system has been demonstrated to efficiently induce targeted gene editing in a variety of organisms including plants. Recent work showed that CRISPR/Cas9-induced gene mutations in Arabidopsis were mostly somatic mutations in the early generation, although some mutations could be stably inherited in later generations. However, it remains unclear whether this system will work similarly in crops such as rice. In this study, we tested in two rice subspecies 11 target genes for their amenability to CRISPR/Cas9-induced editing and determined the patterns, specificity and heritability of the gene modifications. Analysis of the genotypes and frequency of edited genes in the first generation of transformed plants (T0) showed that the CRISPR/Cas9 system was highly efficient in rice, with target genes edited in nearly half of the transformed embryogenic cells before their first cell division. Homozygotes of edited target genes were readily found in T0 plants. The gene mutations were passed to the next generation (T1) following classic Mendelian law, without any detectable new mutation or reversion. Even with extensive searches including whole genome resequencing, we could not find any evidence of large-scale off-targeting in rice for any of the many targets tested in this study. By specifically sequencing the putative off-target sites of a large number of T0 plants, low-frequency mutations were found in only one off-target site where the sequence had 1-bp difference from the intended target. Overall, the data in this study point to the CRISPR/Cas9 system being a powerful tool in crop genome engineering.


title: Sentence Ordering and Coherence Modeling using Recurrent Neural Networks

abstract: Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure. We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem. Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles. Furthermore, our work shows that useful text representations can be obtained by learning to order sentences. Visualizing the learned sentence representations shows that the model captures high-level logical structure in paragraphs. Our representations perform comparably to state-of-the-art pre-training methods on sentence similarity and paraphrase detection tasks.


title: Multimodal People Tracking and Trajectory Prediction based on Learned Generalized Motion Patterns

abstract: A sensor-based model of a service robot's environment is a prerequisite for interaction. Such a model should contain the positions of the robot's interaction partners. Additionally to the actual positions of the partners it is important for the service robot to predict their possible future positions. This knowledge could for example be used to realize efficient path planning for delivery tasks. In this paper we propose an extensible framework for systems that combine different sensor modalities in a general tracking system. Exemplarily, a tracking system is implemented that fuses tracking algorithms in laser range scans as well as in camera images by a particle filter. We point out the real-time capabilities of our tracking algorithms implementation. Practical experiments show that multimodality increases the system's robustness to incorrect measurements of single sensors. Furthermore, the observed trajectories are generalized to trajectory patterns by a novel method which uses Self Organizing Maps. Those patterns are used to predict trajectories of the currently observed persons. It is also demonstrated that a self organizing map is suitable for learning and generalizing trajectories. Convenient predictions of future trajectories are presented which are deduced from these generalizations.


title: Multiscale Scattering for Audio Classification

abstract: Mel-frequency cepstral coefficients (MFCCs) are efficient audio descriptors providing spectral energy measurements over short time windows of length 23 ms. These measurements, however, lose non-stationary spectral information such as transients or time-varying structures. It is shown that this information can be recovered as spectral co-occurrence coefficients. Scattering operators compute these coefficients with a cascade of wavelet filter banks and modulus rectifiers. The signal can be reconstructed from scattering coefficients by inverting these wavelet modulus operators. An application to genre classification shows that second-order cooccurrence coefficients improve results obtained by MFCC and Delta-MFCC descriptors. 1


title: A millimeter-wave perpendicular coax-to-microstrip transition

abstract: A novel transition from coaxial cable to microstrip is presented in which the coax connector is perpendicular to the substrate of the printed circuit. Such a right-angle transition has practical advantages over more common end-launch geometries in some situations. The design is compact, easy to fabricate, and provides repeatable performance of better than 14 dB return loss and 0.4 dB insertion loss from DC to 40 GHz.


title: Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information

abstract: Data-driven approaches have gained increasing interests in the fault detection of wind turbines (WTs) due to the difficulty in system modeling and the availability of sensor data. However, the nonlinearity of WTs, uncertainty of disturbances and measurement noise, and temporal dependence in time-series data still pose grand challenges to effective fault detection. To this end, this paper proposes a new fault detector based on a recently developed unsupervised learning method, denoising autoencoder (DAE), which offers the learning of robust nonlinear representations from data against noise and input fluctuation. A DAE is used to build a robust multivariate reconstruction model on raw time-series data from multiple sensors, and then, the reconstruction error of the DAE trained with normal data is analyzed for fault detection. In addition, we apply the sliding-window technique to consider temporal information inherent in time-series data by including the current and past information within a small time window. A key advantage of the proposed approach is the ability to capture the nonlinear correlations among multiple sensor variables and the temporal dependence of each sensor variable simultaneously, which significantly enhanced the fault detection performance. Simulated data from a generic WT benchmark and field supervisory control and data acquisition data from a real wind farm are used to evaluate the proposed approach. The results of two case studies demonstrate the effectiveness and advantages of our proposed approach.


title: Modeling WiFi Active Power/Energy Consumption in Smartphones

abstract: We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.


title: For personal use only--not for distribution. Expert Performance Its Structure and Acquisition

abstract: Counter to the common belief that expert performance reflects innate abilities and capacities, recent research in different domains of expertise has shown that expert performance is predominantly mediated by acquired complex skills and physiological adaptations. For elite performers, supervised practice starts at very young ages and is maintained at high daily levels for more than a decade. The effects of extended deliberate practice are more far-reaching than is commonly believed. Performers can acquire skills that circumvent basic limits on working memory capacity and sequential processing. Deliberate practice can also lead to anatomical changes resulting from adaptations to intense physical activity. The study of expert performance has important implications for our understanding of the structure and limits of human adaptation and optimal learning.


title: Reducing Partial Equivalence to Partial Correctness

abstract: Two programs P and Q are partially equivalent if, when both terminate on the same input, they end up with equivalent outputs. Establishing partial equivalence is useful in, e.g., Compiler verification, when P is the source program and Q is the target program, or in compiler optimisation, when P is the initial program and Q is the optimised program. A program R is partially correct if, when it terminates, it ends up in a "good" state. We show that, somewhat surprisingly, the problem of establishing partial equivalence can be reduced to the problem of showing partial correctness in an aggregated language, where programs R consist of pairs of programs <P, Qa#x232A. Our method is crucially based on the recently-introduced matching logic, which allows to faithfully define the operational semantics of any language. We show that we can construct the aggregated language mechanically, from the semantics of the initial languages. Furthermore, matching logic gives us for free a proof system for partial correctness for the resulting language. This proof system can then be used to prove partial equivalence.


title: The Online Romance Scam: A Serious Cybercrime

abstract: The Online Romance Scam is a relatively new form of fraud that became apparent in about 2008. In this crime, criminals pretend to initiate a relationship through online dating sites then defraud their victims of large sums of money. This paper presents some descriptive statistics about knowledge and victimization of the online dating romance scam in Great Britain. Our study found that despite its newness, an estimated 230,000 British citizens may have fallen victim to this crime. We conclude that there needs to be some rethinking about providing avenues for victims to report the crime or at least making them more comfortable when doing so.


title: A tool to analyze the performance of multithreaded programs on NUMA architectures

abstract: Almost all of today's microprocessors contain memory controllers and directly attach to memory. Modern multiprocessor systems support non-uniform memory access (NUMA): it is faster for a microprocessor to access memory that is directly attached than it is to access memory attached to another processor. Without careful distribution of computation and data, a multithreaded program running on such a system may have high average memory access latency. To use multiprocessor systems efficiently, programmers need performance tools to guide the design of NUMA-aware codes. To address this need, we enhanced the HPCToolkit performance tools to support measurement and analysis of performance problems on multiprocessor systems with multiple NUMA domains. With these extensions, HPCToolkit helps pinpoint, quantify, and analyze NUMA bottlenecks in executions of multithreaded programs. It computes derived metrics to assess the severity of bottlenecks, analyzes memory accesses, and provides a wealth of information to guide NUMA optimization, including information about how to distribute data to reduce access latency and minimize contention. This paper describes the design and implementation of our extensions to HPCToolkit. We demonstrate their utility by describing case studies in which we use these capabilities to diagnose NUMA bottlenecks in four multithreaded applications.


Support Vector Machines For Synthetic Aperture Radar Automatic Target Recognition


title: Supporting Students’ Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor

abstract: Self-assessment and study choice are two important metacognitive processes involved in Self-Regulated Learning. Yet not much empirical work has been conducted in ITSs to investigate how we can best support these two processes and improve students’ learning outcomes. The present work redesigned an Open Learner Model (OLM) with three features aimed at supporting self-assessment (self-assessment prompts, delaying the update of the skill bars and progress information on the problem type level). We also added a problem selection feature. A 2x2 experiment with 62 7th graders using variations of an ITS for linear equation solving found that students who had access to the OLM performed significantly better on the post-test. To the best of our knowledge, the study is the first experimental study that shows an OLM enhances students’ learning outcomes with an ITS. It also helps establish that self-assessment has key influence on student learning of problem solving tasks.


title: Robust real-time visual odometry for dense RGB-D mapping

abstract: This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features.