title: Dynamic Inference of Likely Metamorphic Properties to Support Differential Testing

abstract: Metamorphic testing is an advanced technique to test programs without a true test oracle such as machine learning applications. Because these programs have no general oracle to identify their correctness, traditional testing techniques such as unit testing may not be helpful for developers to detect potential bugs. This paper presents a novel system, K abu , which can dynamically infer properties of methods' states in programs that describe the characteristics of a method before and after transforming its input. These Metamorphic Properties (MPs) are pivotal to detecting potential bugs in programs without test oracles, but most previous work relies solely on human effort to identify them and only considers MPs between input parameters and output result (return value) of a program or method. This paper also proposes a testing concept, Metamorphic Differential Testing (MDT). By detecting different sets of MPs between different versions for the same method, K abu reports potential bugs for human review. We have performed a preliminary evaluation of K abu by comparing the MPs detected by humans with the MPs detected by K abu . Our preliminary results are promising: K abu can find more MPs than human developers, and MDT is effective at detecting function changes in methods.


title: Unpacking "privacy" for a networked world

abstract: Although privacy is broadly recognized as a dominant concern for the development of novel interactive technologies, our ability to reason analytically about privacy in real settings is limited. A lack of conceptual interpretive frameworks makes it difficult to unpack interrelated privacy issues in settings where information technology is also present. Building on theory developed by social psychologist Irwin Altman, we outline a model of privacy as a dynamic, dialectic process. We discuss three tensions that govern interpersonal privacy management in everyday life, and use these to explore select technology case studies drawn from the research literature. These suggest new ways for thinking about privacy in socio-technical environments as a practical matter.


title: An effective automated system for grading severity of retinal arteriovenous nicking in colour retinal images

abstract: Retinal arteriovenous (AV) nicking is a precursor for hypertension, stroke and other cardiovascular diseases. In this paper, an effective method is proposed for the analysis of retinal venular widths to automatically classify the severity level of AV nicking. We use combination of intensity and edge information of the vein to compute its widths. The widths at various sections of the vein near the crossover point are then utilized to train a random forest classifier to classify the severity of AV nicking. We analyzed 47 color retinal images obtained from two population based studies for quantitative evaluation of the proposed method. We compare the detection accuracy of our method with a recently published four class AV nicking classification method. Our proposed method shows 64.51% classification accuracy in-contrast to the reported classification accuracy of 49.46% by the state of the art method.


title: CAPES: unsupervised storage performance tuning using neural network-based deep reinforcement learning

abstract: Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to find the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system's state, and trains a DNN which uses Q-learning to suggest changes to the system's current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system's daily operation. Evaluation of a prototype on a Lustre file system demonstrates an increase in I/O throughput up to 45% at saturation point.


IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGES


Deep Semantic Classification for 3 D LiDAR Data Ayush


title: Simplifying Graph Convolutional Networks

abstract: Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.


title: Contextual Word Representations: A Contextual Introduction

abstract: This introduction aims to tell the story of how we put words into computers. It is part of the story of the field of natural language processing (NLP), a branch of artificial intelligence. It targets a wide audience with a basic understanding of computer programming, but avoids a detailed mathematical treatment, and it does not present any algorithms. It also does not focus on any particular application of NLP such as translation, question answering, or information extraction. The ideas presented here were developed by many researchers over many decades, so the citations are not exhaustive but rather direct the reader to a handful of papers that are, in the author's view, seminal. After reading this document, you should have a general understanding of word vectors (also known as word embeddings): why they exist, what problems they solve, where they come from, how they have changed over time, and what some of the open questions about them are. Readers already familiar with word vectors are advised to skip to Section 5 for the discussion of the most recent advance, contextual word vectors.


title: Data clouds: summarizing keyword search results over structured data

abstract: Keyword searches are attractive because they facilitate users searching structured databases. On the other hand, tag clouds are popular for navigation and visualization purposes over unstructured data because they can highlight the most significant concepts and hidden relationships in the underlying content dynamically. In this paper, we propose coupling the flexibility of keyword searches over structured data with the summarization and navigation capabilities of tag clouds to help users access a database. We propose using clouds over structured data (data clouds) to summarize the results of keyword searches over structured data and to guide users to refine their searches. The cloud presents the most significant words associated with the search results. Our keyword search model allows searching for entities than can span multiple tables in the database rather than just tuples, as existing keyword searches over databases do. We present several methods to compute the scores both for the entities and for the terms in the search results. We describe algorithms for keyword searches with data clouds and we present our system, CourseCloud, that offers a unified search and browse interface to a course database. We present experimental results showing (a) the appropriateness of the methods used for scoring terms, (b) the performance of the proposed algorithms, and (c) the effectiveness of CourseCloud compared to typical search and browse interfaces to a course database.


title: TwitterMonitor: trend detection over the twitter stream

abstract: We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend. We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.


title: Flexible signature descriptions for adaptivemotion trajectory representation , perception and recognition

abstract: Motion trajectory is a meaningful and informative clue in characterizing the motions of human, robots or moving objects. Hence, it is important to explore effective motion trajectory modeling. However, with the existing methods, a motion trajectory is used in its raw data form and effective trajectory description is lacking. In this paper, we propose a novel 3D motion trajectory signature descriptor and develop three signature descriptions for motion characterization. The flexible descriptions give the signature high functional adaptability to meet various application requirements in trajectory representation, perception and recognition. The full signature, optimized signature and cluster signature are firstly defined for trajectory representation. Then we explore the motion perception from a single signature, inter-signature matching and the generalization of a cluster signature. Furthermore, three solutions for signature recognition are investigated corresponding to different signature descriptions. The conducted experiments verified the signature's capabilities and flexibility. The signature's application to robot learning is also discussed.


title: MicroStache: A Lightweight Execution Context for In-Process Safe Region Isolation

abstract: In this work we present, MicroStache, a specialized hardware mechanism and new process abstraction for accelerating safe region security solutions. In the safe region paradigm, an application is split into safe and unsafe parts. Unfortunately, frequent mixing of safe and unsafe operations stresses memory isolation mechanisms. MicroStache addresses this challenge by adding an orthogonal execution domain into the process abstraction, consisting of a memory segment and minimal instruction set. Unlike alternative hardware, MicroStache implements a simple microarchitectural memory segmentation scheme while integrating it with paging, and also extends the safe region abstraction to isolate data in the processor cache, allowing it to protect against cache side channel attacks. A prototype is presented that demonstrates how to automatically leverage MicroStache to enforce security polices, SafeStack and CPI, with 5% and 1.2% overhead beyond randomized isolation. Despite specialization, MicroStache enhances a growing and critical programming paradigm with minimal hardware complexity.


title: Test-driven learning in early programming courses

abstract: Coercing new programmers to adopt disciplined development practices such as thorough unit testing is a challenging endeavor. Test-driven development (TDD) has been proposed as a solution to improve both software design and testing. Test-driven learning (TDL) has been proposed as a pedagogical approach for teaching TDD without imposing significant additional instruction time. This research evaluates the effects of students using a test-first (TDD) versus test-last approach in early programming courses, and considers the use of TDL on a limited basis in CS1 and CS2. Software testing, programmer productivity, programmer performance, and programmer opinions are compared between test-first and test-last programming groups. Results from this research indicate that a test-first approach can increase student testing and programmer performance, but that early programmers are very reluctant to adopt a test-first approach, even after having positive experiences using TDD. Further, this research demonstrates that TDL can be applied in CS1/2, but suggests that a more pervasive implementation of TDL may be necessary to motivate and establish disciplined testing practice among early programmers.


title: 2011 Special Issue: A just-in-time adaptive classification system based on the intersection of confidence intervals rule

abstract: Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.


title: Discriminative Learning of Deep Convolutional Feature Point Descriptors

abstract: Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and testing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available.


title: MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification

abstract: Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations between MRI and PET modalities. The quality of the estimated PET scans is measured using quantitative metrics such as MAE, PSNR and SSIM. The efficacy of the proposed method is evaluated in the context of Alzheimer's disease classification. The accuracy using only MRI is 70.18% while joint classification using synthesized PET and MRI is 74.43% with a p-value of $0.06$. The significant improvement in diagnosis demonstrates the utility of the synthesized PET scans for multi-modal analysis.


title: Trajectory planning for car-like vehicles: A modular approach

abstract: We consider the problem of trajectory planning with geometric constraints for a car-like vehicle. The vehicle is described by its dynamic model, considering such effects as lateral slipping and aerodynamic drag. We propose a modular solution, where three different problems are identified and solved by specific modules. The execution of the three modules can be orchestrated in different ways in order to produce efficient solutions to a variety of trajectory planning problems (e.g., obstacle avoidance, or overtake). As a specific example, we show how to generate the optimal lap on a specified racing track. The numeric examples provided in the paper are good witnesses of the effectiveness of our strategy.


title: Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

abstract: The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team"binghsu&MLRush&BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.


title: Which Came First, IT or Productivity? The Virtuous Cycle of Investment and Use in Enterprise Systems

abstract: While it is now well established that IT intensive firms are more productive, a critical question remains: Does IT cause productivity or are productive firms simply willing to spend more on IT? We address this question by examining the productivity and performance effects of enterprise systems investments in a uniquely detailed and comprehensive data set of 623 large, public U.S. firms. The data represent all U.S. customers of a large vendor during 1998–2005 and include the vendor’s three main enterprise system suites: Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM). A particular benefit of our data is that they distinguish the purchase of enterprise systems from their installation and use. Since enterprise systems often take years to implement, firm performance at the time of purchase often differs markedly from performance after the systems “go live.” Specifically, in our ERP data, we find that purchase events are uncorrelated with performance while go-live events are positively correlated. This indicates that the use of ERP systems actually causes performance gains rather than strong performance driving the purchase of ERP. In contrast, for SCM and CRM, we find that performance is correlated with both purchase and golive events. Because SCM and CRM are installed after ERP, these results imply that firms that experience performance gains from ERP go on to purchase SCM and CRM. Our results are robust against several alternative explanations and specifications and suggest that a causal relationship between ERP and performance triggers additional IT adoption in firms that derive value from their initial investment. These results provide an explanation of simultaneity in IT value research that fits with rational economic decision-making: Firms that successfully implement IT, react by investing in more IT. Our work suggests replacing “either-or” views of causality with a positive feedback loop conceptualization in which successful IT investments initiate a “virtuous cycle” of investment and gain. Our work also reveals other important estimation issues that can help researchers identify relationships between IT and business value.


title: Theoretical foundations of active learning

abstract: I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of convergence achievable by active learning, under various noise models and under general conditions on the hypothesis class. I also study the theoretical advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms with asymptotically superior label complexity. Finally, I study generalizations of active learning to more general forms of interactive statistical learning.


IMPEDIMENTS IN TRANSITIONING TO AGILE Impediments in Transitioning to Agile Time-boxing Testing


title: The Linear Monge-Kantorovitch Linear Colour Mapping for Example-Based Colour Transfer. Keywords: Colour Transfer, Colour Grading, Monge-

abstract: A common task in image editing is to change the colours of a picture to match the desired colour grade of another picture. Finding the correct colour mapping is tricky because it involves numerous interrelated operations, like balancing the colours, mixing the colour channels or adjusting the contrast. Recently, a number of automated tools have been proposed to find an adequate one-to-one colour mapping. The focus in this paper is on finding the best linear colour transformation. Linear transformations have been proposed in the literature but independently. The aim of this paper is thus to establish a common mathematical background to all these methods. Also, this paper proposes a novel transformation, which is derived from the Monge-Kantorovitch theory of mass transportation. The proposed solution is optimal in the sense that it minimises the amount of changes in the picture colours. It favourably compares theoretically and experimentally with other techniques for various images and under various colour spaces. (9 pages)


title: What do people study when they study Twitter? Classifying Twitter related academic papers

abstract: Purpose – Since its introduction in 2006, messages posted to the microblogging system Twitter have provided a rich dataset for researchers, leading to the publication of over a thousand academic papers. This paper aims to identify this published work and to classify it in order to understand Twitter based research.Design/methodology/approach – Firstly the papers on Twitter were identified. Secondly, following a review of the literature, a classification of the dimensions of microblogging research was established. Thirdly, papers were qualitatively classified using open coded content analysis, based on the paper's title and abstract, in order to analyze method, subject, and approach.Findings – The majority of published work relating to Twitter concentrates on aspects of the messages sent and details of the users. A variety of methodological approaches is used across a range of identified domains.Research limitations/implications – This work reviewed the abstracts of all papers available via database search...


title: Foldable and self-deployable pocket sized quadrotor

abstract: Aerial robots provide valuable support in several high-risk scenarios thanks to their capability to quickly fly to locations dangerous or even inaccessible to humans. In order to fully benefit from these features, aerial robots should be easy to transport and rapid to deploy. With this aim, this paper focuses on the development of a novel pocket sized quadrotor with foldable arms. The quadrotor can be packaged for transportation by folding its arms around the main frame. Before flight, the quadrotor's arms self-deploy in 0.3 seconds thanks to the torque generated by the propellers. The paper describes the design strategies used for developing lightweight, stiff and self-deployable foldable arms for miniature quadrotors. The arms are manufactured according to an origami technique with a foldable multi-layer material. A prototype of the quadrotor is presented as a proof of concept and performance of the system is assessed.


title: Deep Learning for Identifying Metastatic Breast Cancer

abstract: The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won both competitions in the grand challenge, obtaining an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and a score of 0.7051 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. Combining our deep learning system's predictions with the human pathologist's diagnoses increased the pathologist's AUC to 0.995, representing an approximately 85 percent reduction in human error rate. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses.


title: Using Search Queries to Understand Health Information Needs in Africa

abstract: The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding health information needs of people. Without understanding people’s everyday concerns, health organizations and policymakers are less able to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data to uncover and gain insight into health information needs of individuals in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common topics, revealing a widespread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and gender) and country. Using finergrained data, we also uncover discrepancies in the quality of content returned by search engines to users by topic and highlight differences in user behavior and satisfaction. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and off-line.


title: Managing Emotional Requirements in a Context-Aware Mobile Application for Tourists

abstract: The objective of this study was to unveil the importance of emotions and feelings in developing mobile-based tourism applications. We gathered and analyzed emotional requirements to develop a mobile context-aware application for tourists. Emotional requirements are non-functional requirements affecting users’ emotional experiences around using applications, which are important for sustainable application usage. Many tourism applications exist, but were designed without considering emotional requirements or related UX factors and emotions. We developed a proof-of-concept prototype service-based context-aware tourism application (SCATA), and users participated in the design and evaluation processes. Emotional requirements are key to sustainable usage, especially regarding security. This paper details the application design and evaluation processes, emotional requirements analysis in each design phase, and the emotional effects of content accessibility in the application’s offline mode in unknown environments. The results show that trust, security, adjustability, and reliability are important factors to users, especially in unknown environments.


title: Using LLC Resonant Converter for Designing Wide-Range Voltage Source

abstract: LLC resonant converter with significant resonant inductance is proposed for designing an adjustable wide-range regulated voltage source. Large resonant inductance increases output voltage adjustment range and conversion efficiency, particularly at light loads. Soft switching is achieved for all power devices under all operating conditions by choosing the dead time and maximum switching frequency properly and operating in the converter's inductive region. Using a power-factor-correction (PFC) converter reduces the resonant converter's output voltage dependence to the variations of the main ac input voltage. Thus, the compensation of the load variations and the wide-range adjustment of the regulated output voltage can be achieved by using small switching frequency variations. The proposed method has been used to implement an adjustable wide-range voltage source (35-165 V dc), as an ion implanter arc power supply. An almost flat efficiency curve with maximum value of 94% has been achieved for the converter. Its output current can change from no load to 3 A dc.


title: Capturing and animating the morphogenesis of polygonal tree models

abstract: Given a static tree model we present a method to compute developmental stages that approximate the tree's natural growth. The tree model is analyzed and a graph-based description its skeleton is determined. Based on structural similarity, branches are added where pruning has been applied or branches have died off over time. Botanic growth models and allometric rules enable us to produce convincing animations from a young tree that converge to the given model. Furthermore, the user can explore all intermediate stages. By selectively applying the process to parts of the tree even complex models can be edited easily. This form of reverse engineering enables users to create rich natural scenes from a small number of static tree models.


title: Developing patient-reported outcome measures for pain clinical trials: IMMPACT recommendations

abstract: a University of Washington, Seattle, WA 98195, USA b University of Rochester School of Medicine and Dentistry, Rochester, NY, USA c United States Food and Drug Administration, Rockville, MD, USA d Northwestern University, Chicago, IL, USA e Johnson and Johnson, Raritan, NY, USA f AstraZeneca, Wilmington, DE, USA g University of California San Diego, La Jolla, CA, USA h Merck and Company, Blue Bell, PA, USA i University of Texas, M.D. Anderson Cancer Center, USA j American Chronic Pain Association, Rocklin, CA, USA k Allergan, Inc, Irvine, CA, USA l National Institute of Dental and Craniofacial Research, Bethesda, MD, USA m University of Pennsylvania, Philadelphia, PA, USA n Johns Hopkins University, Baltimore, MD, USA o University Health Network and University of Toronto, Toronto, Canada p Novartis Pharmaceuticals, East Hanover, NJ, USA q VA Connecticut Healthcare System, West Haven, CT, USA r Yale University, New Haven, CT, USA s Celgene Corporation, Warren, NJ, USA t Pfizer Global Research and Development, Ann Arbor, MI, USA u Dalhousie University, Halifax, Nova Scotia, Canada v London Regional Cancer Centre, London, Ont., Canada


title: Compensation of Amplitude Imbalance and Imperfect Quadrature in Resolver Signals for PMSM Drives

abstract: This paper proposes a compensation algorithm to reduce position errors due to nonideal resolver signals in vector control drive system of permanent magnet synchronous motor (PMSM). Resolvers are commonly used to obtain an absolute rotor position information in severe environments. However, resolver generates periodic position errors because of amplitude imbalance and imperfect quadrature between resolver output signals. As a result, the d- and q-axis currents of the synchronous reference frame have ripples of twice the stator fundamental frequency in PMSM drives. In this paper, the compensation algorithm is proposed to reduce the position errors caused by the amplitude imbalance and imperfect quadrature, respectively. Moreover, this is implemented in the vector control drive system of PMSM. The proposed algorithm does not require any additional hardware and needs a few computation times to implement it. Experimental results verify the effectiveness of the proposed algorithm.


title: Peephole Optimization Technique for analysis and review of Compiler Design and Construction

abstract: In term of Compiler optimization, classical compilers implemented with an effective optimization technique called Peephole optimization. In the early stage of the implementation of this technique basically applied using string pattern matching performed on the regular expression and which are known as classical peephole optimizers. In the classical optimizers, the string pattern matching approach is considered as a processing of the input of the syntax of assembly code but its meaning gets lost. This paper explores prior research and current research issues in term of 'Optimizing' compilers with the implementation of Peephole optimization using different pattern matching approach that focuses on regular expression. In several research discussed below targets the implementation of pattern matching approach based on rule application strategy to achieve optimization of assembly code syntax for specific machine or retargetable (machine independent) in context of structured programming, procedure oriented programming and Object Oriented Programming.


title: "It feels so good it almost hurts": Young adults' experiences of orgasm and sexual pleasure

abstract: Orgasm is a “goal” of much sexual activity, and a source of potentially intense pleasure and fulfillment, yet can be fraught with difficulty or distress. Relatively little social science research has explored people's experiences around, and their meanings related to, orgasm, and indeed other sexual pleasures, especially with young adults. This study aimed to provide a rich exploration of the meanings associated with orgasm and sexual pleasure during sex with a partner, to understand the social patterning of orgasm experience. A qualitative survey was used to collect data from 119 sexually experienced British young adults (81% women, mean age 20, 92% heterosexual). A descriptive form of thematic analysis that prioritizes participants' meanings and experiences was used to identify and explore patterns in the data. Five main themes are reported here: (a) orgasm: the purpose and end of sex; (b) “it's more about my partner's orgasm”; (c) orgasm: the ultimate pleasure?; (d) orgasm is not a simple physiological...


title: Personalized Age Progression with Bi-level Aging Dictionary Learning

abstract: Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.


title: Slip classification for dynamic tactile array sensors

abstract: The manipulation of objects held in a robotic hand or gripper is accompanied by events such as making and breaking contact and slippage, between the fingertips and the grasped object and between the grasped object and external surfaces. Humans can distinguish among such events, in part, because they excite the various mechanoreceptors in the hands differently. As part of an effort to provide robots with a similar capability, we propose two features that can be extracted from dynamic tactile array data and used to discriminate between hand/object and object/world slips. Both features rely on examining how slippage affects an array of dynamic tactile sensors compared with the way it affects individual elements of the array. In comparison with approaches that require extensive training with particular combinations of objects and skin, the features work for a wide range of frequencies and grasp conditions. The performance and generalizability of the features are verified with testing on three different kinds of sensors and for a range of object textures, grasp forces and slip conditions. Both features demonstrate greater than 85% accuracy in identifying the location of slip.


Semantic Concept Mining Based on Hierarchical Event Detection for Soccer Video Indexing


title: Evaluation in Music Information Retrieval

abstract: The field of Music Information Retrieval has always acknowledged the need for rigorous scientific evaluations, and several efforts have set out to develop and provide the infrastructure, technology and methodologies needed to carry out these evaluations. The community has enormously gained from these evaluation forums, but we have reached a point where we are stuck with evaluation frameworks that do not allow us to improve as much and as well as we want. The community recently acknowledged this problem and showed interest in addressing it, though it is not clear what to do to improve the situation. We argue that a good place to start is again the Text IR field. Based on a formalization of the evaluation process, this paper presents a survey of past evaluation work in the context of Text IR, from the point of view of validity, reliability and efficiency of the experiments. We show the problems that our community currently has in terms of evaluation, point to several lines of research to improve it and make various proposals in that line.


title: XGSN: An Open-source Semantic Sensing Middleware for the Web of Things

abstract: We present XGSN, an open-source system that relies on semantic representations of sensor metadata and observations, to guide the process of annotating and publishing sensor data on the Web. XGSN is able to handle the data acquisition process of a wide number of devices and protocols, and is designed as a highly extensible platform, leveraging on the existing capabilities of the Global Sensor Networks (GSN) middleware. Going beyond traditional sensor management systems, XGSN is capable of enriching virtual sensor descriptions with semantically annotated content using standard vocabularies. In the proposed approach, sensor data and observations are annotated using an ontology network based on the SSN ontology, providing a standardized queryable representation that makes it easier to share, discover, integrate and interpret the data. XGSN manages the annotation process for the incoming sensor observations, producing RDF streams that are sent to the cloud-enabled Linked Sensor Middleware, which can internally store the data or perform continuous query processing. The distributed nature of XGSN allows deploying different remote instances that can interchange observation data, so that virtual sensors can be aggregated and consume data from other remote virtual sensors. In this paper we show how this approach has been implemented in XGSN, and incorporated to the wider OpenIoT platform, providing a highly flexible and scalable system for managing the life-cycle of sensor data, from acquisition to publishing, in the context of the semantic Web of Things.


title: Streptococcus anginosus (Streptococcus milleri Group) Pyomyositis in a 50-Year-Old Man with Acquired Immunodeficiency Syndrome: Case Report and Review of Literature

abstract: We describe the first reported case of bacterial pyomyositis of the right thigh caused by Streptococcus anginosus (S. milleri group) in an HIV-infected patient. The clinical presentation was complicated by multiple ring-enhancing lesions detected on magnetic resonance imaging of the brain. Evaluation for central nervous system toxoplasmosis, syphilis, and cryptococcal infection was negative. Aggressive antibiotic therapy directed against S. anginosus and surgical debridement were limb salvaging. Clinicians should consider S. anginosus as a causative pathogen in HIV-associated pyomyositis, particularly in complex presentations. Prompt surgical drainage may minimize complications due to S. anginosus, a pathogen associated with significant sequelae due to its invasive nature.


title: Target simulator concept for chirp modulated 77 GHz automotive radar sensors

abstract: Automotive radar sensors are key components for driver assistant systems. In order to handle complex traffic scenarios an advanced separability is required with respect to object angle, distance and velocity. In this contribution a highly integrated automotive radar sensor enabling chirp sequence modulation will be presented and discussed. Furthermore, the development of a target simulator which is essential for the characterization of such radar sensors will be introduced including measurements demonstrating the performance of our system.


title: Building a bionic nervous system

abstract: It's an electrifying time to be in neuroscience. Using implanted devices that send pulses of electricity through the nervous system, physicians are learning how to influence the neural systems that control people's bodies and minds. These devices give neurologists new ways to treat patients with a wide range of disorders, including epilepsy, chronic pain, depression, and Parkinson's disease. · So far, these stimulators have been oneway devices that deliver a steady sequence of pulses to the nervous system but can't react to changes in the patient's body. Now, at last, medical device companies are coming out with dynamic neural stimulators that have a bit of "brain" themselves. These smart systems can detect changes in a physiological signal and then respond by delivering a therapy or adjusting the patient's treatment in real time.


title: $\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space

abstract: It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient descent optimize the neural networks in the vector space of weights, which is, however, not positively scale-invariant. This mismatch may lead to problems during the optimization process. Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }? In this paper, we provide our positive answer to this question. First, we conduct a formal study on the positive scaling operators which forms a transformation group, denoted as $\mathcal{G}$. We show that the value of a path (i.e. the product of the weights along the path) in the neural network is invariant to positive scaling and prove that the value vector of all the paths is sufficient to represent the neural networks under mild conditions. Second, we show that one can identify some basis paths out of all the paths and prove that the linear span of their value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower dimension under the positive scaling group. Finally, we design stochastic gradient descent algorithm in $\mathcal{G}$-space (abbreviated as $\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural networks with little extra cost by leveraging back-propagation. Our experiments show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD algorithm in optimizing ReLU networks on benchmark datasets.


title: Aggression in Huntington's disease: a systematic review of rates of aggression and treatment methods.

abstract: Aggression is commonly reported in individuals with Huntington's disease (HD). While correlating factors for aggression are often speculated about, features that are associated with, and contribute to, aggression in this population have not been clearly determined. This systematic review investigates rates of aggression and treatment options for aggression in HD. A number of key findings were revealed. Studies reporting on rates of aggression revealed that its prevalence is high, falling between 22 and 66 percent in the majority of studies. Aggression may be more common in males with HD, and is also found in higher rates in individuals who experience frequent falls, have obsessive-compulsive symptoms and suicidal ideation. There is little research investigating antecedents for aggression in HD. A wide variety of psychotropic medications have been reported in the literature to treat individuals with HD and aggressive behaviour. However, due to methodological limitations, no treatment recommendations can be made, based on the current literature. Two non-medication therapies have been investigated, behaviour support and sensory modulation intervention. However, again, due to methodological limitations with these studies, further research is needed before they can be recommended as frontline interventions. This review highlights the need for further methodologically rigorous studies investigating the treatment of aggression in HD. Language: en


title: Anti-Aβ Therapeutics in Alzheimer's Disease: The Need for a Paradigm Shift

abstract: Most current Alzheimer's disease (AD) therapies in advanced phases of development target amyloid β-peptide (Aβ) production, aggregation, or accumulation. Translational models suggest that anti-Aβ therapies may be highly effective if tested as agents to prevent or delay development of the disease or as therapies for asymptomatic patients with very early signs of AD pathology. However, anti-Aβ therapeutics are currently being tested in symptomatic patients where they are likely to be much less effective or ineffective. The lack of alignment between human clinical studies and preclinical studies, together with predictions about optimal trial design based on our understanding of the initiating role of Aβ aggregates in AD, has created a treatment versus prevention dilemma. In this perspective, we discuss why it is imperative to resolve this dilemma and suggest ways for moving forward in the hopes of enhancing the development of truly effective AD therapeutics.


title: DFTL: a flash translation layer employing demand-based selective caching of page-level address mappings

abstract: Recent technological advances in the development of flash-memory based devices have consolidated their leadership position as the preferred storage media in the embedded systems market and opened new vistas for deployment in enterprise-scale storage systems. Unlike hard disks, flash devices are free from any mechanical moving parts, have no seek or rotational delays and consume lower power. However, the internal idiosyncrasies of flash technology make its performance highly dependent on workload characteristics. The poor performance of random writes has been a cause of major concern, which needs to be addressed to better utilize the potential of flash in enterprise-scale environments. We examine one of the important causes of this poor performance: the design of the Flash Translation Layer (FTL), which performs the virtual-to-physical address translations and hides the erase-before-write characteristics of flash. We propose a complete paradigm shift in the design of the core FTL engine from the existing techniques with our Demand-based Flash Translation Layer (DFTL), which selectively caches page-level address mappings. We develop a flash simulation framework called FlashSim. Our experimental evaluation with realistic enterprise-scale workloads endorses the utility of DFTL in enterprise-scale storage systems by demonstrating: (i) improved performance, (ii) reduced garbage collection overhead and (iii) better overload behavior compared to state-of-the-art FTL schemes. For example, a predominantly random-write dominant I/O trace from an OLTP application running at a large financial institution shows a 78% improvement in average response time (due to a 3-fold reduction in operations of the garbage collector), compared to a state-of-the-art FTL scheme. Even for the well-known read-dominant TPC-H benchmark, for which DFTL introduces additional overheads, we improve system response time by 56%.


title: 3D Convolutional Neural Network Based on Face Anti-spoofing

abstract: Face anti-spoofing is very significant to the security of face recognition. Many existing literatures focus on the study of photo attack. For the video attack, however, the related research efforts are still insufficient. In this paper, instead of extracting features from a single image, features are learned from video frames. To realize face anti-spoofing, the spatiotemporal features of continuous video frames are extracted using 3D convolution neural network (CNN) from the short video frame level. Experimental results show that the two sets of face anti-spoofing public databases, Replay-Attack and CASIA, have achieved the HTER (Half Total Error Rate) of 0.04% and 10.65%, respectively, which is better than the state-of-the-art.


title: ATM:Adversarial-neural Topic Model

abstract: Abstract Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, they are not designed to generate word-level semantic representations. To address the limitations, we propose a neural topic modeling approach based on the Generative Adversarial Nets (GANs), called Adversarial-neural Topic Model (ATM) in this paper. To our best knowledge, this work is the first attempt to use adversarial training for topic modeling. The proposed ATM models topics with dirichlet prior and employs a generator network to capture the semantic patterns among latent topics. Meanwhile, the generator could also produce word-level semantic representations. Besides, to illustrate the feasibility of porting ATM to tasks other than topic modeling, we apply ATM for open domain event extraction. To validate the effectiveness of the proposed ATM, two topic modeling benchmark corpora and an event dataset are employed in the experiments. Our experimental results on benchmark corpora show that ATM generates more coherence topics (considering five topic coherence measures), outperforming a number of competitive baselines. Moreover, the experiments on event dataset also validate that the proposed approach is able to extract meaningful events from news articles.


title: Scale alignment of 3D point clouds with different scales

abstract: In this paper, we propose two methods for estimating the scales of point clouds to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation.


title: Edge-preserving photometric stereo via depth fusion

abstract: We present a sensor fusion scheme that combines active stereo with photometric stereo. Aiming at capturing full-frame depth for dynamic scenes at a minimum of three lighting conditions, we formulate an iterative optimization scheme that (1) adaptively adjusts the contribution from photometric stereo so that discontinuity can be preserved; (2) detects shadow areas by checking the visibility of the estimated point with respect to the light source, instead of using image-based heuristics; and (3) behaves well for ill-conditioned pixels that are under shadow, which are inevitable in almost any scene. Furthermore, we decompose our non-linear cost function into subproblems that can be optimized efficiently using linear techniques. Experiments show significantly improved results over the previous state-of-the-art in sensor fusion.


title: Virtual teams: a review of current literature and directions for future research

abstract: Information technology is providing the infrastructure necessary to support the development of new organizational forms. Virtual teams represent one such organizational form, one that could revolutionize the workplace and provide organizations with unprecedented levels of flexibility and responsiveness. As the technological infrastructure necessary to support virtual teams is now readily available, further research on the range of issues surrounding virtual teams is required if we are to learn how to manage them effectively. While the findings of team research in the traditional environment may provide useful pointers, the idiosyncratic structural and contextual issues surrounding virtual teams call for specific research attention.This article provides a review of previously published work and reports on the findings from early virtual team research in an effort to take stock of the current state of the art. The review is organized around the input - process - output model and categorizes the literature into issues pertaining to inputs, socio-emotional processes, task processes, and outputs. Building on this review we critically evaluate virtual team research and develop research questions that can guide future inquiry in this fertile are of inquiry.


title: The Impact of Windstorm Damage in the Assessment of the Carbon Balance in Even-Aged Fagus sylvatica L. Stands

abstract: Due to the fact that forest ecosystems can potentially mitigate the impact of climate change, the carbon balance of managed forests has caught the attention of a large scientific community. Some authors conclude that extending rotation lengths would actually favour the climate change mitigation effect since more carbon would be stored in the biomass on the average. However, when the occurrence of catastrophic disturbances such as windstorms is not considered, the advantage of extending the rotation length might be overestimated for some species. In this study, we addressed this issue by coupling a growth model, a windstorm damage model and a carbon assessment tool. The evolution of an even-aged European beech (Fagus sylvatica L.) stand was simulated under three different rotation lengths. Simulations including stochastic windstorm events were run and compared with deterministic simulations with no catastrophic disturbance. Our results indicate that when disturbances caused by storms were not taken into account, the carbon balance was actually overestimated in some cases and that this overestimation increased with the rotation length. In our case study, omitting windstorm damage resulted in an overestimation as large as 8% for the longer rotation length. Nevertheless, when windstorm damage was taken into account in the simulation, the longer rotation length still stored more carbon on the average than shorter rotation lengths. However, the marginal gain in carbon storage induced by the increase of the rotation length was reduced.


title: A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes

abstract: Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is proposed. This method generates the parameters of the hidden nodes in such a way that nonlinear fragments of the activation functions are located in the input space regions with data and can be used to construct the surface approximating a nonlinear target function. The weights and biases are dependent on the input data range and activation function type. The proposed methods allows us to control the generalization degree of the model. These all lead to improvement in approximation performance of the network. Several experiments show very promising results.


The societal opportunities and challenges of genome editing


title: Wideband dual-polarized omnidirectional antenna with very high isolation across 1.65–2.7 GHz

abstract: Wideband dual polarized omni-directional antenna is proposed. The design includes concentric ring and monopole antennas, placed over a square ground plane. Both of the antennas retain 10dB return loss across the 1.65 - 2.7 GHz range. Of importance is that port isolation is 70dB across the frequency band of interest in spite of collocation of antennas. The overall antenna dimensions are 4cm × 18cm × 18cm. Radiation pattern and high isolation make it useful for simultaneous transmit (TX) / receive (RX).


title: On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services

abstract: AbstractThe financial services industry has been experiencing the recent emergence of new technology innovations and process disruptions. The industry overall, and many fintech start-ups are lookin...


title: A Wideband Millimeter-Wave SIW-to-RWG Transition for Thin Single Layer Substrates with Thick Metal Cladding

abstract: In this paper, a vertical transition from substrate integrated waveguide (SIW) to rectangular waveguide (RWG) in a single layer thin substrate PCB with thick metal cladding is proposed. A dual resonance cavity in SIW and a stepped impedance transformer in the RWG allow matching of more than 25 % of relative bandwidth at 94 GHz center frequency. 3D-electromagnetic simulations are used for optimization and compared to measured results to confirm the results and the functionality of the transition. Within the measured frequency range of 82 to 107 GHz, the single transitions measurement results show less than 0.7 dB insertion loss and more than 12 dB return loss.


title: A Study of Distinctiveness in Web Results of Two Search Engines

abstract: Google and Bing have emerged as the diarchy that arbitrates what documents are seen by Web searchers, particularly those desiring English language documents. We seek to study how distinctive are the top results presented to the users by the two search engines. A recent eye-tracking has shown that the web searchers decide whether to look at a document primarily based on the snippet and secondarily on the title of the document on the web search result page, and rarely based on the URL of the document. Given that the snippet and title generated by different search engines for the same document are often syntactically different, we first develop tools appropriate for conducting this study. Our empirical evaluation using these tools shows a surprising agreement in the results produced by the two engines for a wide variety of queries used in our study. Thus, this study raises the open question whether it is feasible to design a search engine that would produce results distinct from those produced by Google and Bing that the users will find helpful.


title: CXTFIT/Excel-A modular adaptable code for parameter estimation, sensitivity analysis and uncertainty analysis for laboratory or field tracer experiments

abstract: We implemented the widely used CXTFIT code in Excel to provide flexibility and added sensitivity and uncertainty analysis functions to improve transport parameter estimation and to facilitate model discrimination for multi-tracer experiments on structured soils. Analytical solutions for one-dimensional equilibrium and nonequilibrium convection dispersion equations were coded as VBA functions so that they could be used as ordinary math functions in Excel for forward predictions. Macros with user-friendly interfaces were developed for optimization, sensitivity analysis, uncertainty analysis, error propagation, response surface calculation, and Monte Carlo analysis. As a result, any parameter with transformations (e.g., dimensionless, log-transformed, species-dependent reactions, etc.) could be estimated with uncertainty and sensitivity quantification for multiple tracer data at multiple locations and times. Prior information and observation errors could be incorporated into the weighted nonlinear least squares method with a penalty function. Users are able to change selected parameter values and view the results via embedded graphics, resulting in a flexible tool applicable to modeling transport processes and to teaching students about parameter estimation. The code was verified by comparing to a number of benchmarks with CXTFIT 2.0. It was applied to improve parameter estimation for four typical tracer experiment data sets in the literature using multi-model evaluation and comparison. Additional examples were included to illustrate the flexibilities and advantages of CXTFIT/Excel. The VBA macros were designed for general purpose and could be used for any parameter estimation/model calibration when the forward solution is implemented in Excel. A step-by-step tutorial, example Excel files and the code are provided as supplemental material.


title: Pareto-based multi-objective differential evolution

abstract: Evolutionary multiobjective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multiobjective problem. The purpose is to describe a newly developed evolutionary approach-Pareto-based multiobjective differential evolution (MODE). The concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multiobjective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.


title: Low Power VLSI Circuit Design with Efficient HDL Coding

abstract: In this paper, four-bit unsigned up counter with an asynchronous clear and a clock enable is designed in Xilinx ISE 14.2 and implemented on high performance Virtex-6 FPGA, XC6VLX240T device, -1 speed grade, FFG1156 package and ML605 board. User constraints file (ucf) and net list constraints design (ncd) file are taken into consideration with XPower 14.2 for power consumption analysis. We take two codes. Our first code maps the clock enable signal to LUTs then the power consumption is 3.423 Watt. Our second code maps the clock enable signal to control ports then the power consumption is 3.625 Watt. By changing mapping style, we reduce 6% power reduction and also reduce number of LUT and D flip-flop used in implementation leads to area efficient design. By efficiently mapping, we reduce power consumption in multiple of power reduction with single statements. The experimental result shows the power analysis of both HDL mapping code.


title: Concepts of digital topology

abstract: Abstract In an earlier paper written for a different readership [Computers and Graphics 13(2) (1989) 159–166] the first author defined a digital fundamental group—an analog, for binary digital pictures, of the fundamental group. In general the definition of the digital fundamental group involves continuous deformation. But an alternative, discrete, definition of the digital fundamental group was proposed for the strongly normal digital picture spaces defined in the same paper. The above-mentioned paper also defined a “continuous analog” C( P ) for each binary digital picture P on such a DPS (DPS=digital picture space). C( P ) is a polyhedron constructed by “filling in the gaps” between black points (1's) of the binary digital picture P in a specific way. Other kinds of continuous analog had previously been used by the first two authors. In seeking the simplest and most efficient algorithms for performing image processing operations, researchers have considered many different combinations of grids and adjacency relations. Almost all of those combinations are isomorphic to special cases of the concept of a strongly normal DPS. The main contribution of the present paper is a proof that the digital fundamental groups of binary digital pictures on a strongly normal DPS are naturally isomorphic to the fundamental groups of the digital pictures' continuous analogs. We use this result to establish that on a strongly normal DPS the discrete and continuous definitions of the digital fundamental group are equivalent, up to a natural group isomorphism. We also show that many topological results which hold in the Euclidean plane or Euclidean 3-space have analogs that hold in every strongly normal DPS. Our results suggest that a strongly normal DPS is a suitable domain for studying topology-related image processing operations such as thinning, border tracking and contour filling. The definitions of digital fundamental group, strongly normal DPS and continuous analog are included in this paper so as to make it self-contained.


title: NoScope : Optimizing Neural Network Queries over Video at Scale

abstract: Recent advances in computer vision---in the form of deep neural networks---have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-of-the-art object detector in real time (i.e., 30+ frames per second) to a single video requires a $4000 GPU. In response, we present NoScope, a system for querying videos that can reduce the cost of neural network video analysis by up to three orders of magnitude via inference-optimized model search. Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive. NoScope cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames. We show that the optimal cascade architecture differs across videos and objects, so NoScope uses an efficient cost-based optimizer to search across models and cascades. With this approach, NoScope achieves two to three order of magnitude speed-ups (265-15,500x real-time) on binary classification tasks over fixed-angle webcam and surveillance video while maintaining accuracy within 1--5% of state-of-the-art neural networks.


title: Delayed development of sensorineural hearing loss after neonatal hyperbilirubinemia: a case report with brain magnetic resonance imaging.

abstract: Sensorineural hearing loss has long been known to be a clinical consequence of kernicterus. Brainstem auditory evoked potentials (BAEPs) that occur in hyperbilirubinemic infants, can be reversed in the neonatal period by exchange transfusion. The case was reported in an infant with neonatal hyperbilirubinemia from hemolysis due to glucose-6-phosphate dehydrogenase (G6PD) deficiency and napthalene exposure. BAEPs showed that the baby had normal hearing at 30 decibels at 13 days of age, after exchange transfusions, but had developed profound bilateral sensorineural hearing loss by 7 months of age. The brain magnetic resonance imaging (MRI) findings at 7 months are also presented.


title: Simple Customization of Recursive Neural Networks for Semantic Relation Classification

abstract: In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.


title: A Self-Training Learning Document Binarization Framework

abstract: Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a self-training learning framework for document image binarization. Based on reported binarization methods, the proposed framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories. Finally, the uncertain pixels are classified using the learned pixel classifier. Extensive experiments have been conducted over the dataset that is used in the recent Document Image Binarization Contest(DIBCO) 2009. Experimental results show that our proposed framework significantly improves the performance of reported document image binarization methods.


title: Real-time video transmission over different underwater wireless optical channels using a directly modulated 520 nm laser diode

abstract: We experimentally demonstrate high-quality real-time video streaming over an underwater wireless optical communication (UWOC) link up to 5 m distance using phase-shift keying (PSK) modulation and quadrature amplitude modulation (QAM) schemes. The communication system uses software defined platforms connected to a commercial TO-9 packaged pigtailed 520 nm directly modulated laser diode (LD) with 1.2 GHz bandwidth as the optical transmitter and an avalanche photodiode (APD) module as the receiver. To simulate various underwater channels, we perform laboratory experiments on clear, coastal, harbor I, and harbor II ocean water types. The measured bit error rates of the received video streams are 1.0 × 10-9 for QPSK, 4-QAM, and 8-QAM and 9.9 × 10-9 for 8-PSK. We further evaluate the quality of the received live video images using structural similarity and achieve values of about 0.9 for the first three water types, and about 0.7 for harbor II. To the best of our knowledge, these results present the highest quality video streaming ever achieved in UWOC systems that resemble communication channels in real ocean water environments.


title: Assessing Celebrity Endorsement Effects in China: A Consumer-Celebrity Relational Approach

abstract: Celebrity endorsement is a salient executional strategy in China, where national celebrities often endorse more than 20 brands. This paper adopts a relational perspective to examine this research issue. The relational perspective is driven by three core Chinese cultural values: collectivism, risk aversion, and power distance. The authors propose a model that postulates how celebrity-worship leads to value-transfer that, in turn, affects brand purchase intent. Findings from a survey involving 1,030 respondents from a national panel of consumers, showed that consumer celebrity worship is a significant antecedent to endorser effects; over-endorsement by a celebrity is an important moderator; and the model is robust across both sports and entertainment celebrities.


title: Exploiting Causal Independence in Bayesian Network Inference

abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.


title: Simulation of a current source inverter drive

abstract: A novel simulation of a current source inverter is developed which retains the effects of commutation but minimizes the number of analog computer components. Simulation traces are compared with tested results on an actual system.


title: Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

abstract: We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.


title: 3D Surface Reconstruction Using a Generalized Distance Function

abstract: We define a generalized distance function on an unoriented 3D point set and describe how it may be used to reconstruct a surface approximating these points. This distance function is shown to be a Mahalanobis distance in a higher-dimensional embedding space of the points, and the resulting reconstruction algorithm a natural extension of the classical Radial Basis Function (RBF) approach. Experimental results show the superiority of our reconstruction algorithm to RBF and other methods in a variety of practical scenarios.


title: Iterative Viterbi A* Algorithm for K-Best Sequential Decoding

abstract: Sequential modeling has been widely used in a variety of important applications including named entity recognition and shallow parsing. However, as more and more real time large-scale tagging applications arise, decoding speed has become a bottleneck for existing sequential tagging algorithms. In this paper we propose 1-best A*, 1-best iterative A*, k-best A* and k-best iterative Viterbi A* algorithms for sequential decoding. We show the efficiency of these proposed algorithms for five NLP tagging tasks. In particular, we show that iterative Viterbi A* decoding can be several times or orders of magnitude faster than the state-of-the-art algorithm for tagging tasks with a large number of labels. This algorithm makes real-time large-scale tagging applications with thousands of labels feasible.


title: Queer Film and Media Pedagogy

abstract: Gay, lesbian, bisexual, transgendered, and queer (GLBTQ) scholars today face an unprecedented travesty of their long-standing struggles for liberation and equality both in academe and throughout the public sphere. Notwithstanding increased positive GLBTQ representation in films and on television, anti-GLBTQ violence and disempowering legislative initiatives evidence a pervasive chipping away at hard-won pro-GLBTQ battles by neoconservative and neoliberal forces, which have insinuated themselves through the back door. The acuteness of these troubling conditions prompts urgent questions among GLBTQ film and media scholars, not least because their primary areas of study lie at the center of battles for control of public knowledge production about the general subject. These battles are now producing some of the most egregious consolidations of media industrial power in history: recalling Ben H. Bagdikian, the communications industry has become a “media monopoly” that works to sustain multinational rivalry over the world’s dwindling natural resources and increasingly disenfranchised labor force, dumb down the populace, politically isolate demographic regions, and suppress speech and free dissent, especially regarding strategies of resistance, wherever they may flourish.1 The following roundtable on queer film and media pedagogy is intended to


title: The flanker effect does not reflect the processing of “task-irrelevant” stimuli: Evidence from inattentional blindness

abstract: It is often the case that stimuli (or aspects of a stimulus) are referred to as being “task-irrelevant.” Here, we recount where this label originated and argue that the use of this label is at odds with the concept of “relevance” that has arisen in the contingent-capture literature. This is not merely a matter of labels, but a matter of inference: When people describe a flanker stimulus as being “task-irrelevant,” they may be (and sometimes are) tempted to infer that the conditions that were studied in the flanker task generalize to other tasks and other types of stimuli. Here, we show that this generalization is not warranted. The flanker effect exists not because attention has failed at selecting only the target from the display, but rather, the effect arises precisely because attention succeeded at selecting target-like (i.e., attentionally relevant) stimuli from the display. As a result, the flanker effect should not be used to infer how stimuli that are entirely unrelated to a participant’s main task would be processed. We propose the use of a new terminology to replace this potentially misleading label.


title: Rule mining with AMIE+ Fouille de règles avec AMIE+

abstract: Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from these KBs, such as “If two persons are married, then they (usually) live in the same city”. While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE [16] has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. We also explain how the precision of the rule predictions can be increased to 70%. Extensive experiments show how our new approach, AMIE+, extends to areas of mining that were previously beyond reach. The software and the data are available at http://www.mpi-inf.mpg.de/departments/ databases-and-information-systems/research/yago-naga/amie/.


title: Exploring Non-touchscreen Gestures for Smartwatches

abstract: Although smartwatches are gaining popularity among mainstream consumers, the input space is limited due to their small form factor. The goal of this work is to explore how to design non-touchscreen gestures to extend the input space of smartwatches. We conducted an elicitation study eliciting gestures for 31 smartwatch tasks. From this study, we demonstrate that a consensus exists among the participants on the mapping of gesture to command and use this consensus to specify a user-defined gesture set. Using gestures collected during our study, we define a taxonomy describing the mapping and physical characteristics of the gestures. Lastly, we provide insights to inform the design of non-touchscreen gestures for smartwatch interaction.


IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT, 2010. A study of Edge Detection Techniques for Segmentation Computing Approaches


title: Underwater 3D capture using a low-cost commercial depth camera

abstract: This paper presents underwater 3D capture using a commercial depth camera. Previous underwater capture systems use ordinary cameras, and it is well-known that a calibration procedure is needed to handle refraction. The same is true for a depth camera being used underwater. We describe a calibration method that corrects the depth maps of refraction effects. Another challenge is that depth cameras use infrared light (IR) which is heavily attenuated in water. We demonstrate scanning is possible with commercial depth cameras for ranges up to 20 cm in water. The motivation for using a depth camera under water is the same as in air — it provides dense depth data and higher quality 3D reconstruction than multi-view stereo. Underwater 3D capture is being increasingly used in marine biology and oceanology; our approach offers exciting prospects for such applications. To the best of our knowledge, ours is the first approach that successfully demonstrates underwater 3D capture using low cost depth cameras like Intel RealSense. We describe a complete system, including protective housing for the depth camera which is suitable for handheld use by a diver. Our main contribution is an easy-to-use calibration method, which we evaluate on exemplar data as well as 3D reconstructions in a lab aquarium. We also present initial results of ocean deployment.


Discrete-time signal processing (2nd ed.)


title: Blind separation of speech mixtures via time-frequency masking

abstract: Binary time-frequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary time-frequency masks is possible provided the time-frequency representations of the sources do not overlap: a condition we call W-disjoint orthogonality. We introduce here the concept of approximate W-disjoint orthogonality and present experimental results demonstrating the level of approximate W-disjoint orthogonality of speech in mixtures of various orders. The results demonstrate that there exist ideal binary time-frequency masks that can separate several speech signals from one mixture. While determining these masks blindly from just one mixture is an open problem, we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided. Motivated by the maximum likelihood mixing parameter estimators, we define a power weighted two-dimensional (2-D) histogram constructed from the ratio of the time-frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters. The histogram is used to create time-frequency masks that partition one of the mixtures into the original sources. Experimental results on speech mixtures verify the technique. Example demixing results can be found online at http://alum.mit.edu/www/rickard/bss.html.


title: A computer-based multi-media tutor for beginning piano students

abstract: Abstract The Piano Tutor provides computer‐based instruction to beginning piano students. Intended as a supplement to traditional instruction, the Piano Tutor helps students by correcting mistakes before they become ingrained through practice and by teaching new material as soon as the student is ready. The Piano Tutor combines an expert system with state‐of‐the‐art music recognition software and multimedia output devices to provide a stimulating learning environment that tailors instruction to the student's needs.


PERSPECTIVES Drug Addiction, Dysregulation of Reward,


title: Risk-taking bias in human decision-making is encoded via a right–left brain push–pull system

abstract: A person’s decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push–pull neural system governing counterintuitive and highly variable decision-making in humans.


Content-Based Genre Classification and Sample Recognition Using Topic Models


title: Uncomplicated acute diverticulitis of the cecum and ascending colon: sonographic findings in 18 patients.

abstract: To determine the sonographic features of uncomplicated acute diverticulitis of the cecum and ascending colon, the sonographic findings in 534 patients who presented with right lower quadrant pain were reviewed. Of these, 18 patients had uncomplicated acute diverticulitis of the cecum and ascending colon. The diagnosis was confirmed by surgery (one patient), clinical course (17 patients), CT (eight patients), or contrast enema (11 patients). On sonography, a round or oval focus of varying echogenicity, which protruded from a segmentally thickened colonic wall and was surrounded by a hyperechoic area, was seen in all 18 patients. These were hypoechoic foci (12 patients), hypoechoic foci with internal strong echoes (three patients), and echogenic shadowing foci with surrounding hypoechoic bands (three patients). Extraluminal gas (one patient) and thickening of lateroconal fascia (six patients) were seen also. Findings of enlarged appendix, frank abscess, and ascites were absent. All patients, including the one who had laparotomy, were successfully treated medically for diverticulitis. Of 515 patients without diverticulitis, in only one patient with acute appendicitis did sonography show a hypoechoic protruding focus. Our experience indicates that the major sonographic finding in patients with uncomplicated acute diverticulitis of the right colon is a hypoechoic round or oval focus protruding from a segmentally thickened colonic wall.


title: A 'cool' way of improving the reliability of HPC machines

abstract: Soaring energy consumption, accompanied by declining reliability, together loom as the biggest hurdles for the next generation of supercomputers. Recent reports have expressed concern that reliability at exascale level could degrade to the point where failures become a norm rather than an exception. HPC researchers are focusing on improving existing fault tolerance protocols to address these concerns. Research on improving hardware reliability, i.e., machine component reliability, has also been making progress independently. In this paper, we try to bridge this gap and explore the potential of combining both software and hardware aspects towards improving reliability of HPC machines. Fault rates are known to double for every 10°C rise in core temperature. We leverage this notion to experimentally demonstrate the potential of restraining core temperatures and load balancing to achieve two-fold benefits: improving reliability of parallel machines and reducing total execution time required by applications. Our experimental results show that we can improve the reliability of a machine by a factor of 2.3 and reduce the execution time by 12%. In addition, our scheme can also reduce machine energy consumption by as much as 25%. For a 350K socket machine, regular checkpoint/restart fails to make progress (less than 1% efficiency), whereas our validated model predicts an efficiency of 20% by improving the machine reliability by a factor of up to 2.29.


The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering


title: A Humanlike Predictor of Facial Attractiveness

abstract: This work presents a method for estimating human facial attractiveness, based on supervised learning techniques. Numerous facial features that describe facial geometry, color and texture, combined with an average human attractiveness score for each facial image, are used to train various predictors. Facial attractiveness ratings produced by the final predictor are found to be highly correlated with human ratings, markedly improving previous machine learning achievements. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments which are remarkably similar to those of humans. These experiments shed new light on existing theories of facial attractiveness such as the averageness, smoothness and symmetry hypotheses. It is intriguing to find that a machine trained explicitly to capture an operational performance criteria such as attractiveness rating, implicitly captures basic human psychophysical biases characterizing the perception of facial attractiveness in general.


title: Statistics and Social Network of YouTube Videos

abstract: YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.


title: Dynamic hysteresis band control of the buck converter with fast transient response

abstract: A dynamic hysteresis control of the buck converter for achieving high slew-rate response to disturbances is proposed. The hysteresis band is derived from the output capacitor current that predicts the output voltage magnitude after a hypothesized switching action. Four switching criteria are formulated to dictate the state of the main switch. The output voltage can revert to the steady state in two switching actions after a large-signal disturbance. The technique is verified with the experimental results of a 50 W buck converter.


title: Development of humanoid robot HRP-3P

abstract: A development of humanoid robot HRP-3P, which is a humanoid robot HRP-3 prototype, is presented in this paper. HRP-3 is under development as the succession humanoid to HRP-2, which we developed in phase two of HRP (humanoid robotics project). One of futures of HRP-3P is that its main mechanical and structural components are designed for protection against dust and water. Another is that node controllers are developed for realization of distributed control system. Real-time communication on Ethernet is also newly developed for communication between main controller and node controllers. In this paper, mechanical features and electrical features are presented with specifications of HRP-3P


title: Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

abstract: Recent breakthroughs in the development of multi-layer convolutional neural networks have led to stateof-the-art improvements in the accuracy of non-trivial recognition tasks such as large-category image classification and automatic speech recognition [1]. These many-layered neural networks are large, complex, and require substantial computing resources to train and evaluate [2]. Unfortunately, these demands come at an inopportune moment due to the recent slowing of gains in commodity processor performance. Hardware specialization in the form of GPGPUs, FPGAs, and ASICs offers a promising path towards major leaps in processing capability while achieving high energy efficiency. To harness specialization, an effort is underway at Microsoft to accelerate Deep Convolutional Neural Networks (CNN) using servers augmented with FPGAs—similar to the hardware that is being integrated into some of Microsoft’s datacenters [3]. Initial efforts to implement a single-node CNN accelerator on a mid-range FPGA show significant promise, resulting in respectable performance relative to prior FPGA designs and high-end GPGPUs, at a fraction of the power. In the future, combining multiple FPGAs over a low-latency communication fabric offers further opportunity to train and evaluate models of unprecedented size and quality. Background State-of-the-art deep convolutional neural networks are typically organized into alternating convolutional and max-pooling neural network layers followed by a number of dense, fully-connected layers—as illustrated in the well-known topology by Krizhevsky et al. in Figure 1 [1]. Each 3D volume represents an input to a layer, and is transformed into a new 3D volume feeding the subsequent layer. In the example below, there are five convolutional layers, three max-pooling layers, and three fully-connected layers. Figure 1. Example of Deep Convolutional Neural Network for Image Classification. Image source: [1]. 1 General Purpose Computing on Graphics Processing Units, Field Programmable Gate Arrays, ApplicationSpecific Integrated Circuits.


title: Sensing WiFi packets in the air: practicality and implications in urban mobility monitoring

abstract: Mobile sensing systems employ various sensors in smartphones to extract human-related information. As the demand for sensing systems increases, a more effective mechanism is required to sense information about human life. In this paper, we present a systematic study on the feasibility and gaining properties of a crowdsensing system that primarily concerns sensing WiFi packets in the air. We propose that this method is effective for estimating urban mobility by using only a small number of participants. During a seven-week deployment, we collected smartphone sensor data, including approximately four million WiFi packets from more than 130,000 unique devices in a city. Our analysis of this dataset examines core issues in urban mobility monitoring, including feasibility, spatio-temporal coverage, scalability, and threats to privacy. Collectively, our findings provide valuable insights to guide the development of new mobile sensing systems for urban life monitoring.


title: What Yelp Fake Review Filter Might Be Doing ?

abstract: Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.