title: Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network

abstract: Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets. We are particularly concerned with heavy rain, where rain streaks of various sizes and directions can overlap each other and the veiling effect reduces contrast severely. To achieve our goal, we introduce a scale-aware multi-stage convolutional neural network. Our main idea here is that different sizes of rain-streaks visually degrade the scene in different ways. Large nearby streaks obstruct larger regions and are likely to reflect specular highlights more prominently than smaller distant streaks. These different effects of different streaks have their own characteristics in their image features, and thus need to be treated differently. To realize this, we create parallel sub-networks that are trained and made aware of these different scales of rain streaks. To our knowledge, this idea of parallel sub-networks that treats the same class of objects according to their unique sub-classes is novel, particularly in the context of rain removal. To verify our idea, we conducted experiments on both synthetic and real images, and found that our method is effective and outperforms the state-of-the-art methods.


title: Gate drive design considerations for high voltage cascode GaN HEMT

abstract: This paper investigates gate drive design for high voltage gallium nitride (GaN) high electron-mobility transistors (HEMT) in a cascade structure. High dv/dt and di/dt switching characteristics of GaN device and its influences on high-side gate drive are analyzed on an 8.4kW bidirectional multi-channel buck/boost battery charger operating in critical conduction mode (CRM). Driving candidates for high-side gate drive are reviewed, and digital isolator based driving architecture is proposed with discussion of PCB layout and package parasitics. Experimental results are conducted in each step for concepts validation.


A Recurrent Neural Network for Musical Structure Processing and Expectation


title: Root cause analysis to support infection control in healthcare premises.

abstract: Infection control teams (ICTs) seek to prevent healthcare-associated infections (HCAIs). They undertake surveillance and prevention, promote safety and quality of care, and evaluate and manage risk. Root cause analysis (RCA) can support this work but is not widely used by ICTs. This paper describes how ICTs can use RCA to enhance their day-to-day work. Many different tools and methods exist for RCA. Its primary aim is to identify the factors that have led to HCAI, but RCA can also be used for near-misses. A team effort and multidisciplinary work are usually required. Published accounts and personal experience in the field indicate that an ICT that correctly uses RCA implements more effective prevention measures, improves practice and collaborative working, enhances teamwork, and reduces the risk of HCAI. RCA should be promoted among ICTs because it adds value to their work and helps to develop a hospital culture that anticipates and pre-empts problems.


title: Edge-Facilitated Wireless Distributed Computing

abstract: We propose a framework for edge-facilitated wireless distributed computing, in which several mobile users connected to an access point collaborate for a distributed computing task. We characterize the minimum communication load, both in uplink (from users to the access point) and downlink (from access point to the users), required for distributed computing. In particular, we develop a communication scheme and a dataset placement strategy that induces a particular overlap of computations at the users, which can then be exploited for coding at both users and the access point to significantly reduce the communication load. We demonstrate that the reduction in communication load (compared to uncoded solutions) can scale linearly with the size of the network (i.e., the number of users), hence our proposed scheme can result in a "scalable" design for edge- facilitated wireless distributed computing (i.e., accommodating any number of users without incurring extra communication load). Furthermore, we establish the optimality of the proposed scheme by developing a tight information theoretic outer- bound, and demonstrate that the proposed scheme achieves the minimum uplink and downlink communication load simultaneously. We also generalize the results to a decentralized setting, in which a random and a priori unknown subset of users may participate in distributed computing at each time, and characterize the minimum communication load for uniformly random dataset placement at users.


This information is current as Nanotubes in HIV-1 Spread in Macrophages Potential Role of the Formation of Tunneling


title: Introducing machine learning for power system operation support

abstract: We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production system, including conventional power plants, less predictable renewable energies (such as wind or solar power), and the possibility of buying/selling electricity on the international market with more and more actors involved at a Europeanscale. This problem is becoming ever more challenging in an aging network infrastructure. One of the primary goals of dispatchers is to protect equipment (e.g. avoid that transmission lines overheat) with few degrees of freedom: we are considering in this paper solely modifications in network topology, i.e. re-configuring the way in which lines, transformers, productions and loads are connected in sub-stations. Using years of historical data collected by the French Transmission Service Operator (TSO)"R\'eseau de Transport d'Electricit\'e"(RTE), we develop novel machine learning techniques (drawing on"deep learning") to mimic human decisions to devise"remedial actions"to prevent any line to violate power flow limits (so-called"thermal limits"). The proposed technique is hybrid. It does not rely purely on machine learning: every action will be tested with actual simulators before being proposed to the dispatchers or implemented on the grid.


title: Relative Entropy Policy Search

abstract: Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients (Bagnell and Schneider 2003), many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems.


title: EXSYST: Search-based GUI testing

abstract: Test generation tools commonly aim to cover structural artefacts of software, such as either the source code or the user interface. However, focusing only on source code can lead to unrealistic or irrelevant test cases, while only exploring a user interface often misses much of the underlying program behavior. Our EXSYST prototype takes a new approach by exploring user interfaces while aiming to maximize code coverage, thus combining the best of both worlds. Experiments show that such an approach can achieve high code coverage matching and exceeding the code coverage of traditional unit-based test generators; yet, by construction every test case is realistic and relevant, and every detected failure can be shown to be caused by a real sequence of input events.


title: A grounded theory of psychological resilience in Olympic champions

abstract: Abstract Objective Although it is well-established that the ability to manage stress is a prerequisite of sporting excellence, the construct of psychological resilience has yet to be systematically examined in athletic performers. The study reported here sought to explore and explain the relationship between psychological resilience and optimal sport performance. Design and method Twelve Olympic champions (8 men and 4 women) from a range of sports were interviewed regarding their experiences of withstanding pressure during their sporting careers. A grounded theory approach was employed throughout the data collection and analysis, and interview transcripts were analyzed using open, axial and selective coding. Methodological rigor was established by incorporating various verification strategies into the research process, and the resultant grounded theory was also judged using the quality criteria of fit, work, relevance, and modifiability. Results and conclusions Results indicate that numerous psychological factors (relating to a positive personality, motivation, confidence, focus, and perceived social support) protect the world’s best athletes from the potential negative effect of stressors by influencing their challenge appraisal and meta-cognitions. These processes promote facilitative responses that precede optimal sport performance. The emergent theory provides sport psychologists, coaches and national sport organizations with an understanding of the role of resilience in athletes’ lives and the attainment of optimal sport performance.


title: DressUp!: outfit synthesis through automatic optimization

abstract: We present an automatic optimization approach to outfit synthesis. Given the hair color, eye color, and skin color of the input body, plus a wardrobe of clothing items, our outfit synthesis system suggests a set of outfits subject to a particular dress code. We introduce a probabilistic framework for modeling and applying dress codes that exploits a Bayesian network trained on example images of real-world outfits. Suitable outfits are then obtained by optimizing a cost function that guides the selection of clothing items to maximize the color compatibility and dress code suitability. We demonstrate our approach on the four most common dress codes: Casual, Sportswear, Business-Casual, and Business. A perceptual study validated on multiple resultant outfits demonstrates the efficacy of our framework.


A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification


title: Cross domain similarity mining: research issues and potential applications including supporting research by analogy

abstract: This paper defines the cross domain similarity mining (CDSM) problem, and motivates CDSM with several potential applications. CDSM has big potential in (1) supporting understanding transfer and (2) supporting research by analogy, since similarity is vital to understanding/meaning and to identifying analogy, and since analogy is a fundamental approach frequently used in hypothesis generation and in research. CDSM also has big potential in (3) advancing learning transfer since cross domain similarities can shed light on how to best adapt classifiers/clusterings across given domains and how to avoid negative transfer. CDSM can also be useful for (4) solving the schema/ontology matching problem. Moreover, this paper gives a list of potential research questions for CDSM, and compares CDSM with related studies. One purpose of this paper is to introduce the CDSM problem to the wide KDD community in order to quickly realize the full potential of CDSM.


title: Analysis of a software product line architecture: an experience report Abstract

abstract: This paper describes experiences with the architectural specification and tool-assisted architectural analysis of a mission-critical, high-performance software product line. The approach used defines a "good" product line architecture in terms of those quality attributes required by the particular product line under development. Architectures are analyzed against several criteria by both manual and tool-supported methods. The approach described in this paper provides a structured analysis of an existing product line architecture using (1) architecture recovery and specification, (2) architecture evaluation, and (3) model checking of behavior to determine the level of robustness and fault tolerance at the architectural level that are required for all systems in the product line. Results of an application to a software product line of spaceborne telescopes are used to explain the approach and describe lessons learned.


title: Step-climbing wheelchair with lever propelled rotary legs

abstract: There are a few types of step-climbing wheelchairs in the world, but most of them are large and heavy because they are power-assisted. Therefore, they require large space to maneuver, which is not always feasible with existing house architectures. This study proposes a novel step-climbing wheelchair based on lever propulsion control using human upper limbs. The developed step-climbing wheelchair device consists of manual wheels with casters for moving around and a rotary-legs mechanism that is capable of climbing steps. The wheelchair also has a passive mechanism for posture transition to shift the center of gravity of the person between the desired positions for planar locomotion and step-climbing. The proposed design consists of passive parts, and this leads the wheelchair being compact and lightweight. In this paper, we present the design of this step-climbing wheelchair and some preliminary experiments to test its usability.


title: A Comprehensive Review on Smart Meter Communication Systems in Smart Grid for Indian Scenario

abstract: In existing power grid, the currently used power meters, are either electronic energy meters or electro-mechanical meters and are limited to record power/energy up to KWh unit only. One of the drawbacks of these power grids is that out of the total power production, 26% of energy gets lost because of various reasons such as power theft, blackouts, etc. To fulfill the continuous drastic demand of energy production and consumption, a new generation power grid is required, which should include communication and information technology (ICT) along with distributed power generation (like solar power and wind power). The concept of a new generation smart power grid, so-called Smart Grid, has been gaining continuous rapid attention worldwide. Smart Grid is considered as a system that can supply bi-directional flow of electricity and information, with better power grid reliability, security, and efficiency of the electrical system from generation to transmission to distribution. To meet the requirement of Smart Grid we require the most fundamental components like Smart Meter, in the intelligent energy networks (IENs). Smart Energy meters are used to control and monitor home appliances and other devices by providing two-way communications among them. This paper reviews the basics of Smart grid and development and deployment of communication technologies used among various Smart Grid components/networks and provides an insight into the current research on Smart meters with some guidelines and future directions for IENs.


title: Solving the all-interval series problem: SAT vs CP

abstract: Although Boolean satisfiability (abbreviated as SAT) is a sub-field of constraint programming (CP), the former states and solves problems as a black-box approach, whereas the latter aims at being tunable and programmable. Although many researches bridging SAT and CP have been provided, surprisingly, only few researchers have compared the SAT and CP approaches on a particular problem. This paper studies how to solve the all-interval series problem through both approaches. We will show that by using a state-of-the-art SAT solver and an appropriate SAT encoding the SAT approach obtains a significantly higher performance over the CP approach. Furthermore, we also provide the state-of-the-art result for several all-interval series instances.


title: A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs

abstract: Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.


title: NFC for Pervasive Healthcare Monitoring

abstract: We undertake a step-by-step approach in the design of two Near Field Communication (NFC) products for pervasive healthcare monitoring. Our first product is an NFC based battery charger circuit to charge a thermometer equipped with wireless communication. Our system design has a simple linear charger, with overvoltage and under voltage protection implemented as an android App. The NFC power source provides 13 -- 15 mW of continuous power and is able to charge a 45mAH battery in about 10 hours from deep discharge to full charge state. Since the weight of the charger is about 3 grams and the size is about 2 cms in diameter, this product is useful for wearable sensor devices and provides a convenient way of recharging the batteries without the need for any connectors in the device. This allows devices to be hermetically sealed, besides enabling smaller form factors. The second product is an NFC based battery-less medical grade thermometer. To obtain the temperature of a single patient, a maximum of 10 seconds is sufficient to read the sensor value starting from placement of a smartphone over the product.


title: Real-time 3D rotation smoothing for video stabilization

abstract: We propose two real-time motion smoothing algorithms for video stabilization using a pure 3D rotation motion model with known camera projection parameters. Both proposed algorithms aim at smoothing 3D rotation matrix sequences in a causal way. The first algorithm smooths the 3D rotation sequences in a way similar to 1st-order IIR filtering. The second algorithm uses sequential probabilistic estimation under a constant angular velocity model. These two algorithms are generalized from classical 2D motion smoothing algorithms. We exploit the manifold structure of the rotation matrices so that the proposed algorithms directly smooth the 3D rotation sequences on the manifold. In addition, we introduce a simple projection step in order to guarantee that no black borders intrude into the stabilized video frames. Experimental results show that the proposed algorithms are able to effectively stabilize video sequences and outperform their 2D counterparts with less jitter and distortion.


title: Ten Commandments of Formal Methods

abstract: Producing correct, reliable software in systems of ever increasing complexity is a problem with no immediate end in sight. The software industry suffers from a plague of bugs on a near-biblical scale. One promising technique in alleviating this problem is the application of formal methods that provide a rigorous mathematical basis to software development. When correctly applied, formal methods produce systems of the highest integrity and thus are especially recommended for security- and safety-critical systems. Unfortunately, although projects based on formal methods are proliferating, the use of these methods is still more the exception than the rule, which results from many misconceptions regarding their costs, difficulties, and payoffs. Surveys of formal methods applied to large problems in industry help dispel these misconceptions and show that formal methods projects can be completed on schedule and within budget. Moreover, these surveys show that formal methods projects produce correct software (and hardware) that is well structured, maintainable, and satisfies customer requirements. Through observations of many recently completed and in-progress projects we have come up with ten guidelines that, if adhered to, greatly increase a project's chances for success. >


title: ARTFul: adaptive review technology for flipped learning

abstract: Internet technology is revolutionizing education. Teachers are developing massive open online courses (MOOCs) and using innovative practices such as flipped learning in which students watch lectures at home and engage in hands-on, problem solving activities in class. This work seeks to explore the design space afforded by these novel educational paradigms and to develop technology for improving student learning. Our design, based on the technique of adaptive content review, monitors student attention during educational presentations and determines which lecture topic students might benefit the most from reviewing. An evaluation of our technology within the context of an online art history lesson demonstrated that adaptively reviewing lesson content improved student recall abilities 29% over a baseline system and was able to match recall gains achieved by a full lesson review in less time. Our findings offer guidelines for a novel design space in dynamic educational technology that might support both teachers and online tutoring systems.


title: Comparison between fixed and solar oriented PV modules' energy production using a simplified PSIM model

abstract: This paper presents a simplified Powersim (PSIM) model for simulating and estimating the power losses due to the change in the relative position of the sun to a fixed south oriented solar panel. The proposed equation used for describing the difference between the sun-tracking system and the fixed system is validated by testing a solar panel guidance system based on data provided by Stellarium software. The results obtained when using a fixed, a single-axis and a dual-axis tracking system are compared for different energy test reports. For example, in summer time (Cluj-Napoca), when using Stellarium indications, if only the azimuth is respected, the energy production can be increased by almost 31%, and by about 35% if the elevation is respected as well. This is compared to a fixed south oriented solar panel tilted at 45 degrees.


A TUTORIAL ON SUBSPACE CLUSTERING


title: Switching Frequency Stabilization Techniques for Adaptive On-Time Controlled Buck Converter With Adaptive Voltage Positioning Mechanism

abstract: Unpredictable continuous conduction mode switching frequency variation that occurs commonly in ripple-based control can lead to serious electronic–magnetic interface noise problems and thus increase the effort required for designing noise filters. Sensorless load current correction and dynamic tolerance window techniques are proposed in this paper to reduce the frequency variations caused by conduction losses without employing complicated circuits such as phase-locked loop or current sensors. The proposed dc–dc buck converter with input voltage ranging from 2.7 to 3.6 V and an output voltage between 1.0 and 1.2 V was fabricated using a standard 0.18-μm CMOS process, and the converter achieved excellent adaptive voltage positioning function. Experimental results show that the switching frequency variation improved approximately 60% over traditional techniques in constant on-time control when the load current was changed from 150 to 1000 mA. The measurement results also show that frequency variation across the entire input/output and loading ranges was lowered to only ±2.6%. The maximum power efficiency was 88.2% at 150 mA with an input voltage of 2.7 V.


title: Top-Down Visual Saliency Guided by Captions

abstract: Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain. Top-down neural saliency methods can find important regions given a high-level semantic task such as object classification, but cannot use a natural language sentence as the top-down input for the task. In this paper, we propose Caption-Guided Visual Saliency to expose the region-to-word mapping in modern encoder-decoder networks and demonstrate that it is learned implicitly from caption training data, without any pixel-level annotations. Our approach can produce spatial or spatiotemporal heatmaps for both predicted captions, and for arbitrary query sentences. It recovers saliency without the overhead of introducing explicit attention layers, and can be used to analyze a variety of existing model architectures and improve their design. Evaluation on large-scale video and image datasets demonstrates that our approach achieves comparable captioning performance with existing methods while providing more accurate saliency heatmaps. Our code is available at visionlearninggroup.github.io/caption-guided-saliency/.


title: On Having No Head: Cognition throughout Biological Systems

abstract: The central nervous system (CNS) underlies memory, perception, decision-making, and behavior in numerous organisms. However, neural networks have no monopoly on the signaling functions that implement these remarkable algorithms. It is often forgotten that neurons optimized cellular signaling modes that existed long before the CNS appeared during evolution, and were used by somatic cellular networks to orchestrate physiology, embryonic development, and behavior. Many of the key dynamics that enable information processing can, in fact, be implemented by different biological hardware. This is widely exploited by organisms throughout the tree of life. Here, we review data on memory, learning, and other aspects of cognition in a range of models, including single celled organisms, plants, and tissues in animal bodies. We discuss current knowledge of the molecular mechanisms at work in these systems, and suggest several hypotheses for future investigation. The study of cognitive processes implemented in aneural contexts is a fascinating, highly interdisciplinary topic that has many implications for evolution, cell biology, regenerative medicine, computer science, and synthetic bioengineering.


title: Seeking the source: software source code as a social and technical artifact

abstract: In distributed software development, two sorts of dependencies can arise. The structure of the software system itself can create dependencies between software elements, while the structure of the development process can create dependencies between software developers. Each of these both shapes and reflects the development process. Our research concerns the extent to which, by looking uniformly at artifacts and activities, we can uncover the structures of software projects, and the ways in which development processes are inscribed into software artifacts. We show how a range of organizational processes and arrangements can be uncovered in software repositories, with implications for collaborative work in large distributed groups such as open source communities.


title: Factors associated with response to treatment of pulmonary exacerbations in cystic fibrosis patients

abstract: Abstract Background Pulmonary exacerbations are associated with significant lung function decline from baseline in cystic fibrosis (CF) and it is not well understood why some patients do not respond to antibiotic therapy. The objective of this study was to identify factors associated with lung function response to antibiotic treatment of pulmonary exacerbations. Methods As a secondary analysis of a randomized, controlled trial of intravenous antibiotic treatment for pulmonary exacerbations in CF patients, we investigated whether baseline factors and changes in sputum bacterial density, serum or sputum inflammatory markers were associated with recovery of lung function and risk of subsequent exacerbation. Results In 36 of the 70 exacerbations (51%), patients' lung function returned to >100% of their baseline at day 14 of antibiotic treatment; 34 exacerbations were classified as non-responders. Baseline characteristics were not significantly different between responders and non-responders. Less of a drop in FEV 1 from baseline to exacerbation (OR 1.09, 95% CI 1.0, 1.18, p=0.04) as well as a greater decrease in sputum neutrophil elastase (OR 2.94, 95% CI 1.07, 8.06, p=0.04) were associated with response to antibiotic treatment at day 14. In addition, higher CRP (HR 1.35 (95% CI: 1.01, 1.78), p=0.04) and sputum neutrophil elastase (HR 1.71 (95% CI: 1.02, 2.88), p=0.04) at day 14 of antibiotic therapy were associated with an increased risk of subsequent exacerbation. Conclusions Inadequate reduction of inflammation during an exacerbation is associated with failure to recover lung function and increased risk of subsequent re-exacerbation in CF patients.


title: Combining Misuse Cases with Attack Trees and Security Activity Models

abstract: Misuse cases and attack trees have been suggested for security requirements elicitation and threat modeling in software projects. Their use is believed to increase security awareness throughout the software development life cycle. Experiments have identified strengths and weaknesses of both model types. In this paper we present how misuse cases and attack trees can be linked to get a high-level view of the threats towards a system through misuse case diagrams and a more detailed view on each threat through attack trees. Further, we introduce links to security activity descriptions in the form of UML activity graphs. These can be used to describe mitigating security activities for each identified threat. The linking of different models makes most sense when security modeling is supported by tools, and we present the concept of a security repository that is being built to store models and relations such as those presented in this paper.


title: Cooperative Security at the Physical Layer: A Summary of Recent Advances

abstract: Wireless communications systems are particularly vulnerable to security attacks because of the inherent openness of the transmission medium. In this article, we focus on guaranteeing confidentiality against eavesdropping attacks where an unauthorized entity aims to intercept an ongoing wireless communication, and we provide a comprehensive summary of recent advances in the area of physical-layer security that guarantees confidentiality by using cooperative techniques unique to the wireless medium. These cooperative techniques consist of carefully designed coding and signaling schemes that are able to harness the properties of the physical layer and to ensure some level of information-theoretic security.


title: Make it our time: In class multitaskers have lower academic performance

abstract: Young adults, especially college students, are consistently engaging in multiple tasks simultaneously. They are texting, reading, and using social media while studying and attending class. While there are a variety of contexts and relationships likely influenced by this, the present research project examines the influence of media multitasking in the context of students in technology-saturated classrooms and how this is impacting learning and academic performance. A survey of college students examined the impact of technology-based multitasking behaviors both within and outside classrooms. Data demonstrate that those who multitask frequently in-class have lower current college GPAs. This relationship remained significant even after controlling for perceived multitasking efficacy and time spent studying outside of class. Texting emerged as a dominant activity both while attending class and while doing homework. Females seem to use technologies more for maintaining mediated interpersonal interactions and social connections. Males seem to use technology more for online information seeking and for consuming online videos. Those who reported multitasking while doing homework spent more time spent studying outside of class, thereby contributing to inefficient study habits. Implications for technology use, best practices and policies in academic settings are discussed.


title: Stigma, obesity, and the health of the nation’s children

abstract: Preventing childhood obesity has become a top priority in efforts to improve our nation’s public health. Although much research is needed to address this health crisis, it is important to approach childhood obesity with an understanding of the social stigma that obese youths face, which is pervasive and can have serious consequences for emotional and physical health. This report reviews existing research on weight stigma in children and adolescents, with attention to the nature and extent of weight bias toward obese youths and to the primary sources of stigma in their lives, including peers, educators, and parents. The authors also examine the literature on psychosocial and physical health consequences of childhood obesity to illustrate the role that weight stigma may play in mediating negative health outcomes. The authors then review stigma-reduction efforts that have been tested to improve attitudes toward obese children, and they highlight complex questions about the role of weight bias in childhood obesity prevention. With these literatures assembled, areas of research are outlined to guide efforts on weight stigma in youths, with an emphasis on the importance of studying the effect of weight stigma on physical health outcomes and identifying effective interventions to improve attitudes.


title: Approximation Algorithms for Submodular Set Cover with Applications

abstract: The main problem considered is submodular set cover, the problem of minimizing a linear function under a nondecreasing submodular constraint, which generalizes both wellknown set cover and minimum matroid base problems. The problem is NP-hard, and two natural greedy heuristics are introduced along with analysis of their performance. As applications of these heuristics we consider various special cases of submodular set cover, including partial cover variants of set cover and vertex cover, and node-deletion problems for hereditary and matroidal properties. An approximation bound derived for each of them is either matching or generalizing the best existing bounds. key words: submodular function, set cover, approximation algorithms, greedy heuristics


title: Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios

abstract: An approach for the detection of straight and curved curbs (border of relevant traffic isles, sidewalks, etc) is presented, in the context of urban driving assistance systems. A rectangular elevation map is built from 3D dense stereo data. Edge detection is applied to the elevation map in order to highlight height variations. We propose a method to reduce significantly the 3D noise from dense stereo, using a multiframe persistence me persistence map: temporal filtering is performed for edge points, based on the ego car motion, and only persistent points are validated. The Hough accumulator for lines is built with the persistent edge points. A scheme for extracting straight curbs (as curb segments) and curved curbs (as chains of curb segments) is proposed. Each curb segment is refined using a RANSAC approach to fit optimally the 3D data of the curb. The algorithm was evaluated in an urban scenario. It works in real-time and provides robust detection of curbs.


title: Image pattern recognition in big data: taxonomy and open challenges: survey

abstract: Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research.


Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking.


title: Modeling Camera Effects to Improve Deep Vision for Real and Synthetic Data

abstract: Recent work has focused on generating synthetic imagery and augmenting real imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling the variation in the sensor domain. Unfortunately, varying sensor effects can degrade performance and generalizability of results for visual tasks trained on human annotated datasets. This paper proposes an efficient, automated physically-based augmentation pipeline to vary sensor effects -- specifically, chromatic aberration, blur, exposure, noise, and color cast -- across both real and synthetic imagery. In particular, this paper illustrates that augmenting training datasets with the proposed pipeline improves the robustness and generalizability of object detection on a variety of benchmark vehicle datasets.


title: Music Style Transfer Issues: A Position Paper.

abstract: Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, "music style" is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of "music style", current studies under the category of "music style transfer", are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we see a vital necessity to re-define music style transfer more precisely and scientifically based on the uniqueness of music representation, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. Otherwise, an accumulated upcoming literature (all named after music style transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer music before the age of deep learning. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.


title: Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation

abstract: We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach (Zhou et al. 2016), which used prediction from the coarse stage to shrink the input region provided to the fine stage. Although this strategy achieves high accuracy, we note that the coarse-scaled and fine-scaled networks were trained and tested individually, which limited the use of multi-stage visual cues for segmentation. This paper presents a Saliency Transformation Network, which contains a trainable saliency transformation module. This module computes spatial weights from the coarse-scaled segmentation score map, and applies them to the fine-scaled input image. In training, the coarse-scaled and fine-scaled segmentation networks are optimized in a joint manner, so that both networks become more powerful when they are evaluated individually. In testing, this strategy makes full use of the segmentation results at the coarse stage, so that we can deliver complementary information to the fine stage rather than merely providing a bounding box. We perform experiments on the NIH pancreas segmentation dataset with 82 CT volumes. Following the same testing process which involves a coarse-to-fine iteration, our approach outperforms the state-of-the-art approach (trained in a stage-wise manner) by an average of over 2%. In addition, our approach enjoys better convergence properties, making it more reliable in practice.


title: Evolutionary Dynamics of Information Diffusion over Social Networks

abstract: Social networks have become ubiquitous in our daily life, as such they have attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of “Big Data.” Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, Erdos-Renyi random network and the Barabasi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.


title: Analysis of NB-IoT Deployment in LTE Guard-Band

abstract: In 3GPP Rel-13, a narrowband wide-area cellular system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, can be deployed in three operation modes - in-band within LTE carrier, in the guard-band of LTE carrier, and stand-alone. Guard-band deployment allows the operator to support NB-IoT without requiring new spectrum and with minimal impact to LTE. This paper provides an overview and presents a study of NB-IoT deployment in LTE guard band. An analysis of coexistence of NB-IoT with LTE is presented and demonstrates that while there is mutual interference between NB-IoT and LTE on the uplink, the impact on either system is small. A performance analysis of NB-IoT is also presented. The link budget analysis demonstrates how the coverage target can be achieved for the uplink and downlink data channels. Illustrative uplink and downlink link-level simulation results are provided. The analysis in the paper shows that guard-band operation of NB-IoT is feasible and can provide support for massive number of low-throughput IoT devices.


Abstractive Sentence Summarization with Attentive Recurrent Neural Networks


title: Prenatal US diagnosis of congenital imperforate hymen

abstract: A 32-year-old woman, gravida 1, para 0, was referred for third-trimester sonography at 34 weeks’ gestation to evaluate fetal growth. Sonography revealed a female fetus with an echogenic, midline, nonvascular pelvic mass (Fig. 1, arrow) and no associated genitourinary abnormality. Differential diagnoses included an ovarian mass, distended rectum, hydrocolpos, vaginal atresia and urogenital sinus. Postnatal US revealed an echogenic, fluid-containing midline pelvic mass (Fig. 2, black arrow) in the setting of an imperforate hymen. The cervix is marked (double white arrows). The


title: Graphene Frequency Multipliers

abstract: In this letter, the ambipolar transport properties of graphene flakes have been used to fabricate full-wave signal rectifiers and frequency-doubling devices. By correctly biasing an ambipolar graphene field-effect transistor in common-source configuration, a sinusoidal voltage applied to the transistor gate is rectified at the drain electrode. Using this concept, frequency multiplication of a 10-kHz input signal has been experimentally demonstrated. The spectral purity of the 20-kHz output signal is excellent, with more than 90% of the radio-frequency power in the 20-kHz frequency. This high efficiency, combined with the high electron mobility of graphene, makes graphene-based frequency multipliers a very promising option for signal generation at ultrahigh frequencies.


title: Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

abstract: BACKGROUND ::: Most errors in a clinical chemistry laboratory are due to preanalytical errors. Preanalytical variability of biospecimens can have significant effects on downstream analyses, and controlling such variables is therefore fundamental for the future use of biospecimens in personalized medicine for diagnostic or prognostic purposes. ::: ::: ::: CONTENT ::: The focus of this review is to examine the preanalytical variables that affect human biospecimen integrity in biobanking, with a special focus on blood, saliva, and urine. Cost efficiency is discussed in relation to these issues. ::: ::: ::: SUMMARY ::: The quality of a study will depend on the integrity of the biospecimens. Preanalytical preparations should be planned with consideration of the effect on downstream analyses. Currently such preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables.


title: Bayesian models of object perception

abstract: The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.


title: Neural and genetic determinants of creativity

abstract: Abstract Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r = 0.267, p = 0.0078) with an accuracy of 64.6%. In addition, the creativity–related functional connectivity network we identified in the first dataset was still significantly correlated with the creativity score in the new dataset (p 10 − 3 ). In summary, our research demonstrates that strong top-down control versus weak bottom-up processes underlie creativity, which is modulated by competition between the glutamate and GABA neurotransmitter systems. Our work provides the first insights into both the neural and the genetic bases of creativity.


title: Where Does EEG Come From and What Does It Mean?

abstract: Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.


title: Conflicted Advice and Second Opinions: Benefits, but Unintended Consequences

abstract: Second opinions have been advocated as an antidote to bias in advice when primary advisors have conflicts of interest. In four experiments, we demonstrate how primary advisors alter their advice due to knowledge of the presence of a second advisor. We show that advisors give more biased advice and adopt a profit-maximizing frame when they are aware of the mere availability of a second opinion. The bias increases when primary advisors are aware that the second opinion is of low quality, and decreases when they know the second opinion is of high quality and easy to access. Both economic concerns (e.g., losing future business) and noneconomic concerns (e.g., concern that a second advisor will expose the poor quality advice) decrease bias in primary advisors’ advice. Based on these findings, we discuss circumstances in which second opinions are likely to be beneficial or detrimental to advice-recipients.


title: A Hang-and-Play Intravenous Infusion Monitoring System

abstract: We have developed an intravenous (IV) infusion monitoring system in nursing-houses/hospitals. The system assists nurses to manage the IV infusion process by integrating hardware devices, composing of a drip-bag weight scale and a RFID/NFC tag reader, and an IV fusion monitoring software. A 6502-based microcontroller is used as a platform of connecting an s-type load cell sensor and a RFID/NFC reader. The load cell transforms tension, pulled by a drip-bag, to weak electrical signal. The electrical signal is amplified and is fed into a 16-bit A/D converter. Finally, the tension is converted to a two-byte digital weight data, ranging from 0 to 65535. The two-byte weight data and the five-byte RFID/NFC ID data are packed as a data packet, which is transmitted via UDP protocol to a data collector module of the IV infusion monitoring system. The packet is unpacked and is stored, according to the ID information in the packet, into the patient IV infusion database. A patient IV infusion indication system graphically displays all patients' IV infusion statuses on a tablet screen, such as nurses can easily read patient IV infusion information. An IV infusion notification system sends notifications of normal and abnormal IV infusion amount and rate. The most significant feature of the IV infusion monitoring system is that a nurse just places a drip-bag and initializes IV infusion process without handling any software interface. The system reads drip-bag weight dynamics and RFID/NFC tag data. The system can detect the error of a misplaced drip bag by comparing the RFID/NFC tag data of a hanged drip bag with the patient ID. In one word, the system assists that nurses manage all patients' IV infusion with a realtime IV infusion indication and a complete notification. The system can also be modified to be used in monitoring drug usage of elders in homes/nursing-houses for helping nurses manage elders' drug.


title: PSMPA: Patient Self-Controllable and Multi-Level Privacy-Preserving Cooperative Authentication in Distributedm-Healthcare Cloud Computing System

abstract: Distributed m-healthcare cloud computing system significantly facilitates efficient patient treatment for medical consultation by sharing personal health information among healthcare providers. However, it brings about the challenge of keeping both the data confidentiality and patients’ identity privacy simultaneously. Many existing access control and anonymous authentication schemes cannot be straightforwardly exploited. To solve the problem, in this paper, a novel authorized accessible privacy model (AAPM) is established. Patients can authorize physicians by setting an access tree supporting flexible threshold predicates. Then, based on it, by devising a new technique of attribute-based designated verifier signature, a patient self-controllable multi-level privacy-preserving cooperative authentication scheme (PSMPA) realizing three levels of security and privacy requirement in distributed m-healthcare cloud computing system is proposed. The directly authorized physicians, the indirectly authorized physicians and the unauthorized persons in medical consultation can respectively decipher the personal health information and/or verify patients’ identities by satisfying the access tree with their own attribute sets. Finally, the formal security proof and simulation results illustrate our scheme can resist various kinds of attacks and far outperforms the previous ones in terms of computational, communication and storage overhead.


title: A supervised method using convolutional neural networks for retinal vessel delineation

abstract: Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application. Several methods have been proposed in the past decades. Here we will present a new supervised method for retinal vessel segmentation. The method is designed to explore the complex relationship between retinal images and their corresponding vessel label maps. Specifically, in order to build a model describing the direct transformation from retinal image to vessel map, we introduce a deep convolutional neural network (abbreviation as CNN), which has strong enough induction ability. For the purpose of constructing the whole vessel probability map, we also design a synthesis method. Our method shows better performance on DRIVE dataset than state-of-the-art of reported approaches in the light of sensitivity (abbreviation as Se), specificity (abbreviation as Sp) and accuracy (abbreviation as Acc). Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases. Meanwhile, the method may offer a novel, general computing framework for segmentation in other fields.


title: A Low-Power 13.56 MHz RF Front-End Circuit for Implantable Biomedical Devices

abstract: A low-power fully-integrated CMOS RF front-end circuit for a passive 13.56 MHz biomedical implant is presented. A 13.56 MHz binary phase shift keying (BPSK) signal is received by an internal coil. This front-end circuit is composed of a full-wave bridge rectifier, a linear regulator, a BPSK demodulator, and a clock/data recovery (CDR). A full-wave bridge rectifier converts the carrier waveform with the BPSK signal to an unregulated DC voltage. A linear regulator stabilizes the unregulated DC voltage to 1.8 V that serves as the DC source for the implant. A BPSK demodulator detects the incoming BPSK signal from the internal coil and translates the demodulated data to the CDR which can successfully recover the clock and data for the system controller. This chip with a core area of 0.45 mm2 has been fabricated in a TSMC 0.18 μm 1P6M CMOS technology. The total power consumed is only 632 μW.


title: Modelling Slotted-ALOHA Simulator Using Computational Spreadsheet Based e-Forms

abstract: This paper demonstrates models that were designed and implemented to simulate slotted ALOHA multiple access computer network protocol. The models are spreadsheet-based simulating e-forms that were designed for students use in college level data communication and networking courses. Specifically, three models for simulating this protocol are fully implemented using spreadsheets. The features of these models are simplicity and quickness of implementation compared with other implementation techniques. These models assisted instructors to develop educational objects that in turn will help students for better understanding and exploring of the scientific concepts related to computer protocols by the aid of visual and interactive spreadsheet-based e-forms. Moreover, advanced spreadsheet techniques such as imagery integration, hyperlinks, conditional structures, conditional formats, and charts insetting, to simulate scientific notions that are taught to undergraduate students were exploited in these models. The models designing technique is characterized by simplicity, flexibility, and affordability. The technique can be applied and used in many disciplines of education, business, science, and technology. Generally, the developed computational e-forms can be used by instructors to illustrate topics in attractive fashions. In addition, students and learners can use the developed educational objects without instructor supervision in self-education or e-learning environments.


Similar DNA Restriction Endonuclease Profiles in Strains of Legionella pneumophila from Different Serogroups


A predictive machine learning approach for microstructure optimization and materials design


Towards a Decentralized Magnetic Indoor Positioning System


Business Model of Botnets


title: A generic framework for privacy preserving deep learning

abstract: We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.


title: RSNAVI: an RFID-based context-aware indoor navigation system for the blind

abstract: Most of the existing indoor navigation systems for the blind do not take into account their specific needs related to orientation and movement in an unfamiliar buildings. This paper presents an indoor blind navigation system called RSNAVI that has the following strengths: First, the system uses 4D modelling of buildings - 3D building and objects geometry and status of all sensors in time. To improve the navigation process we use semantic-rich interior model to describe not only the position and shape of all objects but also their characteristics. Second, the algorithm for route planning uses multi-parametric optimization to obtain the optimal route for the blind users. The system allows automatic re-routing when the blind user deviated from the route or if it detects a change of status of the sensors, for example blocking access to the room due to fire alarm. Finally, only algebraic expressions to calculate optimal route waypoints and to detour obstacles are used.


title: Model Checking of Probabilistic and Nondeterministic Systems

abstract: The temporal logics pCTL and pCTL* have been proposed as tools for the formal specification and verification of probabilistic systems: as they can express quantitative bounds on the probability of system evolutions, they can be used to specify system properties such as reliability and performance. In this paper, we present model-checking algorithms for extensions of pCTL and pCTL* to systems in which the probabilistic behavior coexists with nondeterminism, and show that these algorithms have polynomial-time complexity in the size of the system. This provides a practical tool for reasoning on the reliability and performance of parallel systems.


title: Broadband (100MHz -1GHz), High Power Active Circulator Architecture

abstract: We report very wideband 100 MHz to 1 GHz active circulator with high power operation up to 30 dBm for the first time. In order to achieve broadband high power circulation and isolation, a new architecture is developed using low-loss RF choke concept and it is implemented on alumina substrate with mounted GaN HEMT devices along with other SMTs. The performed test shows minimum 15 to 20 dB isolation up to 30 dBm and 15 dB directivity up to 26 dBm across the band with 2.5~5 dB insertion loss for 15 dB of minimum directivity.


INFORMATION HIDING USING AUDIO STEGANOGRAPHY – A


title: Knowledge Work in Teams

abstract: Covers such topics as cross-functional teamwork, working in public and learning-in-action, organizing knowledge work systems and varieties of knowledge work experience.


title: Printed and Handwritten Character &Number Recognition of Devanagari Script using SVM and KNN

abstract: Recognition of Devanagari scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character. In this paper we have compared SVM and KNN on handwritten as well as on printed character and numerical database for this we have created four different database.


title: PI Controller based MPPT for a PV System

abstract: To meet the future energy requirement and to give quality and pollution free supply to growing and environment conscious population, the present world is giving special attention to natural, clean and renewable energy sources. Due to growing demand for electrical energy and environmental issues such as pollution and global warming effect, solar energy is considered among one of the best options for generating clean energy. In this paper a PI controller is used as an intelligent method for Maximum Power Point Tracking (MPPT) for a PV system. PI controller has been used for achieving the Maximum Power Point of a PV system. A boost converter is used to boost the voltage. Keywords - PI controller, Maximum power point tracking, MPPT algorithm, Photovoltaic system


title: MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

abstract: Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.


title: Virtually Shorted Patch Antenna for Circular Polarization

abstract: This letter presents a new circularly polarized patch antenna with loaded shorting elements to achieve symmetric broadside radiation and size reduction. The loaded elements are parasitic to the driven patch, but are shorted to the ground plane. Conventionally, shorting pins or walls are a common technique for reducing the size of the antenna. However, deterioration in broadside radiation with high cross-polar level occurs. The proposed shorting elements provide a solution for size reduction of the radiating patch while maintaining good broadside radiation pattern. Since the radiating patch is effectively shorted to the ground at the operating frequency, it is designated as a virtually shorted patch antenna (VSPA).


title: Reconstructing 3D Human Pose from 2D Image Landmarks

abstract: Reconstructing an arbitrary configuration of 3D points from their projection in an image is an ill-posed problem. When the points hold semantic meaning, such as anatomical landmarks on a body, human observers can often infer a plausible 3D configuration, drawing on extensive visual memory. We present an activity-independent method to recover the 3D configuration of a human figure from 2D locations of anatomical landmarks in a single image, leveraging a large motion capture corpus as a proxy for visual memory. Our method solves for anthropometrically regular body pose and explicitly estimates the camera via a matching pursuit algorithm operating on the image projections. Anthropometric regularity (i.e., that limbs obey known proportions) is a highly informative prior, but directly applying such constraints is intractable. Instead, we enforce a necessary condition on the sum of squared limb-lengths that can be solved for in closed form to discourage implausible configurations in 3D. We evaluate performance on a wide variety of human poses captured from different viewpoints and show generalization to novel 3D configurations and robustness to missing data.


title: Suicide and friendships among American adolescents.

abstract: OBJECTIVES ::: We investigated the relationship between friendships and suicidality among male and female adolescents. ::: ::: ::: METHODS ::: We analyzed friendship data on 13,465 adolescents from the National Longitudinal Survey of Adolescent Health to explore the relationship between friendship and suicidal ideation and suicide attempts. We controlled for known factors associated with suicidality. ::: ::: ::: RESULTS ::: Having had a friend who committed suicide increased the likelihood of suicidal ideation and attempts for both boys and girls. Socially isolated females were more likely to have suicidal thoughts, as were females whose friends were not friends with each other. Among adolescents thinking about suicide, suicide attempts appear largely stochastic, with few consistent risk factors between boys and girls. ::: ::: ::: CONCLUSIONS ::: The friendship environment affects suicidality for both boys and girls. Female adolescents' suicidal thoughts are significantly increased by social isolation and friendship patterns in which friends were not friends with each other.


Semistructured data


title: Brain Mechanisms in Early Language Acquisition

abstract: The last decade has produced an explosion in neuroscience research examining young children's early processing of language. Noninvasive, safe functional brain measurements have now been proven feasible for use with children starting at birth. The phonetic level of language is especially accessible to experimental studies that document the innate state and the effect of learning on the brain. The neural signatures of learning at the phonetic level can be documented at a remarkably early point in development. Continuity in linguistic development from infants' earliest brain responses to phonetic stimuli is reflected in their language and prereading abilities in the second, third, and fifth year of life, a finding with theoretical and clinical impact. There is evidence that early mastery of the phonetic units of language requires learning in a social context. Neuroscience on early language learning is beginning to reveal the multiple brain systems that underlie the human language faculty.


title: Learning to Train a Binary Neural Network

abstract: Convolutional neural networks have achieved astonishing results in different application areas. Various methods which allow us to use these models on mobile and embedded devices have been proposed. Especially binary neural networks seem to be a promising approach for these devices with low computational power. However, understanding binary neural networks and training accurate models for practical applications remains a challenge. In our work, we focus on increasing our understanding of the training process and making it accessible to everyone. We publish our code and models based on BMXNet for everyone to use. Within this framework, we systematically evaluated different network architectures and hyperparameters to provide useful insights on how to train a binary neural network. Further, we present how we improved accuracy by increasing the number of connections in the network.


title: Predictive Embeddings for Hate Speech Detection on Twitter

abstract: We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.


title: Elimination of an Electrolytic Capacitor in AC/DC Light-Emitting Diode (LED) Driver With High Input Power Factor and Constant Output Current

abstract: While LEDs enjoy relatively long lifetime up to 10 years, the lifetime of traditional LED drivers using electrolytic capacitor as storage element is limited to typically less than 5 years. In this paper, an ac/dc LED driver without electrolytic capacitor is studied. Compared with other methods to eliminate electrolytic capacitor, the proposed driver has the advantages of almost unity input power factor and constant output current for LEDs. The operation principle, detailed design procedure of the main circuit, and control strategy are presented. The feasibility of the proposed converter has been successfully verified by experiments.


Discriminative Convolutional Sum-Product Networks on GPU


title: First demonstration of airborne SAR tomography using multibaseline L-band data

abstract: In synthetic aperture radar (SAR) interferometry, the phase differences between two different sensor positions are used to estimate the terrain topography. Although it is possible in this way to find a three-dimensional (3D) surface representation, the distribution of the different scatterers in the height direction at a fixed range and azimuth position remains unknown. Contrary to this, tomographic techniques enable a real geometric resolution capability in the height direction and introduce new possibilities for many applications and inversion problems. Even misinterpretations in SAR images caused by layover and foreshortening effects can be solved by the tomographic processing. In this paper, the successful experimental realization of polarimetric airborne SAR tomography is demonstrated for the first time. The authors present the concept of aperture synthesis for tomographic imaging for the case of a multibaseline imaging geometry and discuss the constraints arising from the limited number of flight tracks. They propose a method for reduction of the height ambiguities associated to the irregular and undersampled spatial distribution of the imaging positions. Finally, they address the experimental requirements for polarimetric airborne SAR tomography and show experimental results using a multibaseline data set acquired in L-band by DLR's experimental SAR (E-SAR) of a test-site near Oberpfaffenhofen, Germany.


title: Geospatial Structure of a Planetary-Scale Social Network

abstract: Little is known about geographic properties of large-scale social networks. In this paper, we examine the geospatial attributes of a planetary-scale social network of 240 million people and 1.3 billion edges. We study the interplay among topological, geographical, and algorithmically generated paths connecting pairs of nodes in a social network. Starting in the realm of cyberspace, we find that topologically shortest paths of average length of 6.6 exist between pairs of nodes in the network and that the average degree of separation among nodes is robust to removal of hub nodes. Moving to the realm of locations and distances in geographic space, we find that topologically shortest paths in the social graph grow with increasing geographic distance between path’s endpoint nodes. We discover that shortest topological paths are geographically inefficient, but that geography provides an important cue for local algorithmic policies for navigating between source and target nodes. Local algorithmic strategies for navigating the larger network structure in the absence of global navigation procedures have varying success. At the early stages of the navigation, navigating to a hub node helps, while in the middle stage, geography provides the most important clue. While local algorithms for navigating have trouble reaching the target node, they are successful in reaching nodes that are geographically close to the target. Taken together, our results demonstrate a complex interplay between topological and geographical properties of social networks and explain the success of local strategies for navigating such networks.


title: A survey of combinatorial optimization problems in multicast routing

abstract: In multicasting routing, the main objective is to send data from one or more sources to multiple destinations, while at the same time minimizing the usage of resources. Examples of resources which can be minimized include bandwidth, time and connection costs. In this paper, we survey applications of combinatorial optimization to multicast routing. We discuss the most important problems considered in this area, as well as their models. Algorithms for each of the main problems are also presented.


title: Demographics, weather and online reviews: a study of restaurant recommendations

abstract: Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.


title: Feature leaning with deep Convolutional Neural Networks for screening patients with paroxysmal atrial fibrillation

abstract: In this paper, a novel electrocardiogram (ECG) signal classification and patient screening method is developed. The focus is on identifying patients with paroxysmal atrial fibrillation (PAF) which is a life threatening cardiac arrhythmia. The proposed approach uses the raw ECG signal as the input and automatically learns the representative features for PAF to be used by a classification mechanism. The features are learned directly from the time domain ECG signals by using a Convolutional Neural Network (CNN) with one fully connected layer. The learned features can replace the hand-crafted features and our experimental results indicate the effectiveness of the learned features in patient screening. The experimental results indicate that combining the learned features with other classifiers will improve the performance of the patient screening system as compared to an End-to-End convolutional neural network classifier. The major characteristics of the proposed approach are to simplify the process of feature extraction for different cardiac arrhythmias and to remove the need for using a human expert to specify the appropriate features. The effectiveness of the proposed ECG classification method is demonstrated through performing extensive simulation studies.


title: Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space

abstract: In this paper, we propose a novel on-line visual tracking framework based on Siamese matching network and meta-learner network which runs at real-time speed. Conventional deep convolutional feature based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters for solving complex optimization tasks to adapt to the new appearance of a target object. To remove this process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the target object by adding the target-aware feature space. The parameters for the target-specific feature space are provided instantly from a single forward-pass of the meta-learner network. By eliminating the necessity of continuously solving the complex optimization tasks in the course of tracking, experimental results demonstrate that our algorithm performs at a real-time speed of $62$ fps while maintaining a competitive performance among other state-of-the-art tracking algorithms.


title: Ventral posterior visual area of the macaque: visual topography and areal boundaries.

abstract: Using both physiological and anatomical techniques, we have studied the topographic organization of extrastriate visual cortex on the ventral surface of the occipital lobe in macaque monkeys. Our results show that a topographically organized representation of the superior contralateral quadrant of the visual field lies immediately anterior to the ventral half of V2. This area is organized in a mirror symmetric fashion to ventral V2: it shares a horizontal meridian representation with V2 and a representation of the superior vertical meridian forms its anterior border. A well-defined strip of callosal inputs runs along the vertical meridian representation, thereby providing a reliable anatomical marker for areal boundaries in ventral extrastriate cortex. We refer to this area as the ventral posterior area (VP) because it is, in all these respects, notably similar to VP in the owl monkey. Ventral V2 has strong reciprocal connections with VP, and the topography of the V2 projection agrees closely with the topography revealed in our physiological mapping experiments. The visual field representation in VP is strikingly anisotropic, with linear magnification factor being much larger along contours of constant polar angle than along contours of constant eccentricity.


title: An Efficient Learning Procedure for Deep Boltzmann Machines

abstract: We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.


ACTIVE APPEARANCE MODEL AND PCA BASED FACE RECOGNITION SYSTEM


Chapter 15 Time Series Data Clustering


title: Factorization Meets Memory Network: Learning to Predict Activity Popularity

abstract: We address the problem, i.e., early prediction of activity popularity in event-based social networks, aiming at estimating the final popularity of new activities to be published online, which promotes applications such as online advertising recommendation. A key to success for this problem is how to learn effective representations for the three common and important factors, namely, activity organizer (who), location (where), and textual introduction (what), and further model their interactions jointly. Most of existing relevant studies for popularity prediction usually suffer from performing laborious feature engineering and their models separate feature representation and model learning into two different stages, which is sub-optimal from the perspective of optimization. In this paper, we introduce an end-to-end neural network model which combines the merits of Memory netwOrk and factOrization moDels (MOOD), and optimizes them in a unified learning framework. The model first builds a memory network module by proposing organizer and location attentions to measure their related word importance for activity introduction representation. Afterwards, a factorization module is employed to model the interaction of the obtained introduction representation with organizer and location identity representations to generate popularity prediction. Experiments on real datasets demonstrate MOOD indeed outperforms several strong alternatives, and further validate the rational design of MOOD by ablation test.


title: Development of Enhanced Weed Detection System with Adaptive Thresholding and Support Vector Machine

abstract: This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds. In the early stages of the plants' development, the color of both the plants and the weeds are similar, making it difficult to differentiate between the two. The process becomes even harder if the weeds and plants overlap. The proposed system addresses this problem by creating a sophisticated mean for weed identification. The major components of this system are composed of three processes: image segmentation, feature extraction and decision-making. In the image segmentation process, the input images are processed into lower units where the relevant features are extracted. In the decision-making process, the system makes use of the Support Vector Machine to analyze and segregate the weeds from the plants. Afterward, the findings are used to dictate which plants receive herbicides and which do not. The main priority for the image segmentation process is on the overlapping images where weeds need to be isolated from plants so that they can be used for cultivation purpose. The evaluation of the approach is done using an open dataset of images consisting of carrot plants. The system is able to achieve 88.99% accuracy for weed classification using this dataset. This methodology will help to reduce the use of herbicides while improving the performance and costs of precision agriculture.


title: WHAT DRIVES SOCIAL COMMERCE: THE ROLE OF PARASOCIAL INTERACTION

abstract: Social commerce (SC) has become a new business mode for e-commerce along with the increased popularity of social networking sites, such as Facebook, Twitter, and Pinterest. Although SC has enormous business potential for practitioners and broadens research areas for scholars, existing studies on SC were limited. Little is known about the underlying processes and factors of consumers’ social sharing and social shopping behaviour in social commerce community (SCC). To fill the research gap, this study investigates the role of parasocial interaction (PSI) on consumers’ intention to engage in SC activities. An empirical research was conducted in Mogujie (www.mogujie.com), one of the most popular image-sharing SCCs in China, with a sample of 248 users. The results indicated that users’ PSI with other users significantly affects their social commerce intention (SCI). The task attraction and social attraction of SCCs positively affect PSI. However, the influence of physical attraction on PSI is insignificant. Moreover, social attraction is determined by the social-relevant features of SCCs (i.e., similarity, expertise, and likeability) and the task attraction is determined by task-relevant features of SCCs (i.e., information fit to task). The mood-relevant features of SCCs (i.e., visual appeal) positively affect physical attraction. This study has important implications for both information systems researchers and practitioners, especially for designers of SCCs.


title: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake

abstract: In this work, we conducted a case study of a popular Chinese microblogging site, Sina-Weibo, to investigate how Chinese netizens used microblogging in response to a major disaster: the 2010 Yushu Earthquake. We combined multiple analysis methods in this case study, including content analysis of microblog messages, trend analysis of different topics, and an analysis of the information spreading process. This study helped us understand the roles played by microblogging systems in response to major disasters and enabled us to gain insight into how to harness the power of microblogging to facilitate disaster response. In addition, this work supplements existing works with an exploration of a non-Western socio-cultural system: how Chinese Internet users used microblogging in disaster response.


title: Real-time Body Tracking Using a Gaussian Process Latent Variable Model

abstract: In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is achieved by first obtaining a low dimensional representation of the training motion data, using a nonlinear dimensionality reduction technique called back-constrained GPLVM. A prior dynamics model is then learnt from this low dimensional representation by partitioning the motion sequences into elementary movements using an unsupervised EM clustering algorithm. The temporal dependencies between these elementary movements are efficiently captured by a Variable Length Markov Model. The learnt dynamics model is used to bias the propagation of candidate pose feature vectors in the low dimensional space. By combining this with an efficient volumetric reconstruction algorithm, our framework can quickly evaluate each candidate pose against image evidence captured from multiple views. We present results that show our system can accurately track complex structured activities such as ballet dancing in real-time.


title: Gradient vector flow: a new external force for snakes

abstract: Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF) is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.


title: Towards a Serendipitous Research Paper Recommender System Using Bisociative Information Networks (BisoNets)

abstract: In recent times the rate at which information is being processed and shared through the internet has tremendously increased. Internet users are in need of systems and tools that will help them manage this information overload. Search engines and recommendation systems have been recently adopted to help solve this problem. The aim of this research is to model a spontaneous research paper recommender system that recommends serendipitous research papers from two large normally mismatched information spaces or domains using BisoNets. Set and graph theory methods were employed to model the problem, whereas text mining methodologies were used to develop nodes and links of the BisoNets. Nodes were constructed from keywords, while links between nodes were established through weighting that was determined from the co-occurrence of corresponding keywords in the same title and domain. Preliminary results from the word clouds indicates that there is no obvious relationship between the two domains. The strongest links in the established information networks can be exploited to display associations that can be discovered between the two matrices. Research paper recommender systems exploit these latent relationships to recommend serendipitous articles when Bisociative Knowledge Discovery techniques and methodologies are utilized appropriately.


title: The Effect of Bending Compliance on Adhesion Pressure of Hybrid Electrostatic/Gecko-Like Adhesives

abstract: One of the constraints in the design of dry switchable adhesives is the compliance trade-off: compliant structures conform better to surfaces but are limited in strength due to high stored strain energy. In this work we study the effects of bending compliance on the shear adhesion pressures of hybrid electrostatic/gecko-like adhesives of various areas. We reaffirm that normal electrostatic preload increases contact area and show that it is more effective on compliant adhesives. We also show that the gain in contact area can compensate for low shear stiffness and adhesives with high bending compliance outperform stiffer adhesives on substrates with large scale roughness.


title: Side gate HiGT with low dv/dt noise and low loss

abstract: This paper presents a novel side gate HiGT (High-conductivity IGBT) that incorporates historical changes of gate structures for planar and trench gate IGBTs. Side gate HiGT has a side-wall gate, and the opposite side of channel region for side-wall gate is covered by a thick oxide layer to reduce Miller capacitance (Cres). In addition, side gate HiGT has no floating p-layer, which causes the excess Vge overshoot. The proposed side gate HiGT has 75% smaller Cres than the conventional trench gate IGBT. The excess Vge overshoot during turn-on is effectively suppressed, and Eon + Err can be reduced by 34% at the same diode's recovery dv/dt. Furthermore, side gate HiGT has sufficiently rugged RBSOA and SCSOA.


title: Voxelized shadow volumes

abstract: Efficient shadowing algorithms have been sought for decades, but most shadow research focuses on quickly identifying shadows on surfaces. This paper introduces a novel algorithm to efficiently sample light visibility at points inside a volume. These voxelized shadow volumes (VSVs) extend shadow maps to allow efficient, simultaneous queries of visibility along view rays, or can alternately be seen as a discretized shadow volume. We voxelize the scene into a binary, epipolar-space grid where we apply a fast parallel scan to identify shadowed voxels. Using a view-dependent grid, our GPU implementation looks up 128 visibility samples along any eye ray with a single texture fetch. We demonstrate our algorithm in the context of interactive shadows in homogeneous, single-scattering participating media.


title: Gravity and low-frequency geodynamics

abstract: Preface to the Series Physics and Evolution of the Earth's Interior. Preface (R. Teisseyre). 1. The Earth's Gravity Field. Introduction (R. Teisseyre). Properties of the gravitational potential (C. Denis). The Green-Gauss identities (R. Teisseyre). The Earth's gravity field and the Stokes formula (R. Teisseyre). Decomposition of the gravitational potential into spherical harmonics (C. Denis). The Earth's inertia tensor (C. Denis, R. Teisseyre). The geoid (J.B. Zielinski). Gravimetric reductions, isostasy and gravity anomalies (J.B. Zielinski). Molodensky boundary-value problem (E. Majewski). 2. Space Geodesy Methods (J.B. Zielinski). Dynamic of satellite motion. Gravity field parameters from changes or orbits. Direct sounding of the geopotential. Geodetic positioning by satellites. New space methods. Very Long Base Interferometry (VLBI). 3. The Hydrostatic Figure of the Earth (C. Denis). Introduction. The figure of the Earth deduced from satellite data. The symmetry of equilibrium figures. The Clairaut-Laplace internal field theory. Foundation of the theory of figures. On the hydrostatic flattening of the Earth and some geophysical implications. 4. The Earth's Rotation (H. Legros, M. Amalvict). Historical evolution of the problem - rotation and observations. Principles and fundamental concepts. Deformation of the global Earth. Expression for the inner relations - constitutive equations. Systems of equations. Tesseral movements, free and forced. Study of precession and nutations. Axial rotation - rotation and geophysical problems. Numerical Love numbers. Conclusion. 5. Observational Determination of the Earth's Rotations (B. Kolaczek). Historical evolution of observational determination of the Earth's rotation. Methods of observations. Time scales. References coordinate systems. Results of analysis of the Earth's rotation parameters determined observationally. 6. Free Oscillations of the Earth (Z. Czechowski). Introduction. Free oscillations of a homogeneous liquid sphere. SNREI model. Excitation of free oscillations. Effect of general aspherical perturbation on the free oscillations. Generalized spherical harmonics approach. Oscillations of the Earth's fluid core. Inversion of the free oscillation data. 7. Earth Tides (T. Chojnicki). 8. Gravity and Reference Models (J.B. Zielinski, R. Teisseyre). Reference ellipsoid. Combined solutions. Geophysical interpretation. 9. Fundamental Systems of Astronomical and Geodetical Constants (B. Kolaczek). Introduction. The IAU system of astronomical constants. Geodetic reference systems. MERIT standards. Index.