title: Lexical statistical machine translation for language migration

abstract: Prior research has shown that source code also exhibits naturalness, i.e. it is written by humans and is likely to be repetitive. The researchers also showed that the n-gram language model is useful in predicting the next token in a source file given a large corpus of existing source code. In this paper, we investigate how well statistical machine translation (SMT) models for natural languages could help in migrating source code from one programming language to another. We treat source code as a sequence of lexical tokens and apply a phrase-based SMT model on the lexemes of those tokens. Our empirical evaluation on migrating two Java projects into C# showed that lexical, phrase-based SMT could achieve high lexical translation accuracy (BLEU from 81.3-82.6%). Users would have to manually edit only 11.9-15.8% of the total number of tokens in the resulting code to correct it. However, a high percentage of total translation methods (49.5-58.6%) is syntactically incorrect. Therefore, our result calls for a more program-oriented SMT model that is capable of better integrating the syntactic and semantic information of a program to support language migration.


title: 3D Skeleton-Based Body Pose Recovery

abstract: This paper presents an approach to recover body motions from multiple views using a 3D skeletal model. It takes, as input, foreground silhouette sequences from multiple viewpoints, and computes, for each frame, the skeleton pose which best fit the body pose. Skeletal models encode mostly motion information and allows therefore to separate motion estimation from shape estimation for which solutions exist; And focusing on motion parameters significantly reduces the dependancy on specific body shapes, yielding thus more flexible solutions for body motion capture. However, a problem generally faced with skeletal models is to find adequate measurements with which to fit the model. In this paper, we propose to use the medial axis of the body shape to this purpose. Such medial axis can be estimated from the visual hull, a shape approximation which is easily obtained from the silhouette information. Experiments show that this approach is robust to several perturbations in the model or in the input data, and also allows fast body motions or, equivalently, important motions between consecutive frames.


title: Extreme scale with full SQL language support in microsoft SQL Azure

abstract: Cloud SQL Server is an Internet scale relational database service which is currently used by Microsoft delivered services and also offered directly as a fully relational database service known as "SQL Azure". One of the principle design objectives in Cloud SQL Server was to provide true SQL support with full ACID transactions within controlled scale "consistency domains" and provide a relaxed degree of consistency across consistency domains that would be viable to clusters of 1,000's of nodes. In this paper, we describe the implementation of Cloud SQL Server with an emphasis on this core design principle.


title: Coding static natural images using spiking event times: do neurons Cooperate?

abstract: To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of visual processing for static images. We will first present the retinal model which was introduced by Van Rullen and Thorpe and which represents the multiscale contrast values of the image using an orthonormal wavelet transform. These analog values activate a set of spiking neurons which each fire once to produce an asynchronous wave of spikes. According to this model, the image may be progressively reconstructed from this spike wave thanks to regularities in the statistics of the coefficients determined with natural images. Here, we study mathematically how the quality of information transmission carried by this temporal representation varies over time. In particular, we study how these regularities can be used to optimize information transmission by using a form of temporal cooperation of neurons to code analog values. The original model used wavelet transforms that are close to orthogonal. However, the selectivity of realistic neurons overlap, and we propose an extension of the previous model by adding a spatial cooperation between filters. This model extends the previous scheme for arbitrary-and possibly nonorthogonal-representations of features in the images. In particular, we compared the performance of increasingly over-complete representations in the retina. Results show that this algorithm provides an efficient spike coding strategy for low-level visual processing which may adapt to the complexity of the visual input.


title: Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding

abstract: © 1963-2018 ACL. This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).


title: Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information

abstract: A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies.


title: Application of Long-Distance Wireless Communication Technologies in Automatic Water Metering System

abstract: Through comprehending the structure of automatic water metering systems based on wireless communication technologies, as well as understanding of characteristics and service range for the common long-distance wireless communication methods, furthermore, combined with our experiences gained in practical projects, this paper gives a brief description for the choice of wireless communication methods in automatic water metering system.


title: A Novel Hybrid Text Summarization System for Punjabi Text

abstract: Text summarization is the task of shortening text documents but retaining their overall meaning and information. A good summary should highlight the main concepts of any text document. Many statistical-based, location-based and linguistic-based techniques are available for text summarization. This paper has described a novel hybrid technique for automatic summarization of Punjabi text. Punjabi is an official language of Punjab State in India. There are very few linguistic resources available for Punjabi. The proposed summarization system is hybrid of conceptual-, statistical-, location- and linguistic-based features for Punjabi text. In this system, four new location-based features and two new statistical features (entropy measure and Z score) are used and results are very much encouraging. Support vector machine-based classifier is also used to classify Punjabi sentences into summary and non-summary sentences and to handle imbalanced data. Synthetic minority over-sampling technique is applied for over-sampling minority class data. Results of proposed system are compared with different baseline systems, and it is found that F score, Precision, Recall and ROUGE-2 score of our system are reasonably well as compared to other baseline systems. Moreover, summary quality of proposed system is comparable to the gold summary.


A symmetric image encryption scheme based on 3 D chaotic cat maps . Chaos , Solitons & Fractals


title: The Collaborative Image of The City: Mapping the Inequality of Urban Perception

abstract: A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities.


title: Design Space Exploration of FinFETs with Double Fin Heights for Standard Cell Library

abstract: This paper proposes a method to explore the design space of FinFETs with double fin heights. Our study shows that if one fin height is sufficiently larger than the other and the greatest common divisor of their equivalent transistor widths is small, the fin height pair will incur less width quantization effect and lead to better area efficiency. We design a standard cell library based on this technology using a tailored FreePDK15. With respect to a standard cell library designed with FreePDK15, about 86% of the cells designed with FinFETs of double fin heights have a smaller delay and 54% of the cells take a smaller area. We also demonstrate the advantages of FinFETs with double fin heights through chip designs using our cell library.


title: Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis

abstract: In this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment analysis aims to automatically classify its sentiment/opinion (such as thumbs up or thumbs down) based on the textural information. Despite the success of feature engineering in many previous studies, the hand-coded features do not well capture the semantics of texts. In this research, we argue that learning sentiment-specific semantic representations of documents is crucial for document-level sentiment analysis. We decompose the document semantics into four cascaded constitutes: (1) word representation, (2) sentence structure, (3) sentence composition and (4) document composition. Specifically, we learn sentiment-specific word representations, which simultaneously encode the contexts of words and the sentiment supervisions of texts into the continuous representation space. According to the principle of compositionality, we learn sentiment-specific sentence structures and sentence-level composition functions to produce the representation of each sentence based on the representations of the words it contains. The semantic representations of documents are obtained through document composition, which leverages the sentiment-sensitive discourse relations and sentence representations.


title: Characterizations of parallelism in applications and their use in scheduling

abstract: As multiprocessors with large numbers of processors become more prevalent, we face the task of developing scheduling algorithms for the multiprogrammed use of such machines. The scheduling decisions must take into account the number of processors available, the overall system load, and the ability of each application awaiting activation to make use of a given number of processors. The parallelism within an application can be characterized at a number of different levels of detail. At the highest level, it might be characterized by a single parameter (such as the proportion of the application that is sequential, or the average number of processors the application would use if an unlimited number of processors were available). At the lowest level, representing all the parallelism in the application requires the full data dependency graph (which is more information than is practically manageable). In this paper, we examine the quality of processor allocation decisions under multiprogramming that can be made with several different high-level characterizations of application parallelism. We demonstrate that decisions based on parallelism characterizations with two to four parameters are superior to those based on single-parameter characterizations (such as fraction sequential or average parallelism). The results are based predominantly on simulation, with some guidance from a simple analytic model.


title: Load Balancing Mechanisms in the Software Defined Networks: A Systematic and Comprehensive Review of the Literature

abstract: With the expansion of the network and increasing their users, as well as emerging new technologies, such as cloud computing and big data, managing traditional networks is difficult. Therefore, it is necessary to change the traditional network architecture. Lately, to address this issue, a notion named software-defined network (SDN) has been proposed, which makes network management more conformable. Due to limited network resources and to meet the requirements of quality of service, one of the points that must be considered is load balancing issue that serves to distribute data traffic among multiple resources in order to maximize the efficiency and reliability of network resources. Load balancing is established based on the local information of the network in the conventional network. Hence, it is not very precise. However, SDN controllers have a global view of the network and can produce more optimized load balances. Although load balancing mechanisms are important in the SDN, to the best of our knowledge, there exists no precise and systematic review or survey on investigating these issues. Hence, this paper reviews the load balancing mechanisms which have been used in the SDN systematically based on two categories, deterministic and non-deterministic. Also, this paper represents benefits and some weakness regarded of the selected load balancing algorithms and investigates the metrics of their algorithms. In addition, the important challenges of these algorithms have been reviewed, so better load balancing techniques can be applied by the researchers in the future.


title: Robust Prototype-Based Learning on Data Streams

abstract: In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data or ensemble learning a set of weighted base classifiers, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a proposed P-Tree, which are obtained based on the error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drifts in data streams, PCA and statistical analysis based heuristic approaches have been introduced. To further learn the associations among distributed data streams, the extended P-Tree structure and KNN-style strategy are introduced. We demonstrate that our new data stream classification approach has several attractive benefits: (a) SyncStream is capable of dynamically modeling the evolving concepts from even a small set of prototypes. (b) Owing to synchronization-based constrained clustering and P-Tree, SyncStream supports efficient and effective data representation and maintenance. (c) SyncStream is also tolerant of inappropriate or noisy examples via error-driven representativeness learning. (d) SyncStream allows learning relationship among distributed data streams at the instance level. The experimental results indicate its efficiency and effectiveness.


title: Synergy: A HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC

abstract: Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains. There has been significant progress in accelerating both their training and inference using high-performance GPUs, FPGAs, and custom ASICs for datacenter-scale environments. The recent proliferation of mobile and Internet of Things (IoT) devices have necessitated real-time, energy-efficient deep neural network inference on embedded-class, resource-constrained platforms. In this context, we present Synergy, an automated, hardware-software co-designed, pipelined, high-throughput CNN inference framework on embedded heterogeneous system-on-chip (SoC) architectures (Xilinx Zynq). Synergy leverages, through multi-threading, all the available on-chip resources, which includes the dual-core ARM processor along with the FPGA and the NEON Single-Instruction Multiple-Data (SIMD) engines as accelerators. Moreover, Synergy provides a unified abstraction of the heterogeneous accelerators (FPGA and NEON) and can adapt to different network configurations at runtime without changing the underlying hardware accelerator architecture by balancing workload across accelerators through work-stealing. Synergy achieves 7.3X speedup, averaged across seven CNN models, over a well-optimized software-only solution. Synergy demonstrates substantially better throughput and energy-efficiency compared to the contemporary CNN implementations on the same SoC architecture.


title: Reinforcement Learning with Long Short-Term Memory

abstract: This paper presents reinforcement learning with a Long Short-Term Memory recurrent neural network: RL-LSTM. Model-free RL-LSTM using Advantage (λ) learning and directed exploration can solve non-Markovian tasks with long-term dependencies between relevant events. This is demonstrated in a T-maze task, as well as in a difficult variation of the pole balancing task.


title: A compact dual-band (L1/L2) GPS antenna design

abstract: A small slot-loaded patch antenna design developed for receiving both L1 and L2 bands GPS signals is discussed. The dual band coverage is achieved by using a patch mode at L2 band and a slot mode at L1 band. High dielectric material and meandered slot line are employed to reduce the antenna size down to 25.4 mm in diameter. The RHCP is achieved by combining two orthogonal modes via a small 0°–90° hybrid chip. Both patch and slot modes share a single proximity probe conveniently located on the side of the antenna (Fig.1). This paper discusses about the design procedure as well as simulated antenna performance.


title: OmniArt: Multi-task Deep Learning for Artistic Data Analysis

abstract: Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.


title: Learning tone and attribution for financial text mining

abstract: Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.


title: Lymphedema and lipedema - an overview of conservative treatment.

abstract: Lymphedema and lipedema are chronic progressive disorders for which no causal therapy exists so far. Many general practitioners will rarely see these disorders with the consequence that diagnosis is often delayed. The pathophysiological basis is edematization of the tissues. Lymphedema involves an impairment of lymph drainage with resultant fluid build-up. Lipedema arises from an orthostatic predisposition to edema in pathologically increased subcutaneous tissue. Treatment includes complex physical decongestion by manual lymph drainage and absolutely uncompromising compression therapy whether it is by bandage in the intensive phase to reduce edema or with a flat knit compression stocking to maintain volume.


title: A Comparative Study of Multilayer Neural Network and C4.5 Decision Tree Models for Predicting the Risk of Breast Cancer

abstract: Background: Diagnosing breast cancer at an early stage can have a great impact on cancer mortality. One of the fundamental problems in cancer treatment is the lack of a proper method for early detection, which may lead to diagnostic errors. Using data analysis techniques can significantly help in early diagnosis of the disease. The purpose of this study was to evaluate and compare the efficacy of two data mining techniques, i.e., multilayer neural network and C4.5, in early diagnosis of breast cancer. Methods: A data set from Motamed Cancer Institute's breast cancer research clinic, Tehran, containing 2860 records related to breast cancer risk factors were used. Of the records, 1141 (40%) were related to malignant changes and breast cancer and 1719 (60%) to benign tumors. The data set was analyzed using perceptron neural network and decision tree algorithms, and was split into two a training data set (70%) and a testing data set (30%) using Rapid Miner 5.2. Results: For neural networks, accuracy was 80.52%, precision 88.91%, and sensitivity 90.88%; and for decision tree, accuracy was 80.98%, precision 80.97%, and sensitivity 89.32%. Results indicated that both algorithms have acceptable capabilities for analyzing breast cancer data. Conclusion: Although both models provided good results, neural network showed more reliable diagnosis for positive cases. Data set type and analysis method affect results. On the other hand, information about more powerful risk factors of breast cancer, such as genetic mutations, can provide models with high coverage.


title: A C-band microwave rectifier based on harmonic termination and with input filter removed

abstract: This paper presents a C-band rectifier employing a third harmonic termination which is realized by a λ g /12 short-ended microstrip transmission line. The proposed harmonic termination balances the capacitive impedance of the diode at the fundamental frequency and blocks harmonic current of the diode. The input low-pass filter or matching circuit is removed and the bandwidth of rectifier is enhanced. Theoretical analysis and experiments are carried out. The results show that the proposed topology can realize high efficiency with a wide input frequency range. The fabricated rectifier has a compact dimension of 14mm by 22mm. A maximum RF-DC conversion efficiency of 75.6% at 13 dBm input power is observed. The measured efficiency remains above 70% with the operating frequency form 5.2 GHz to 6 GHz.


title: Reinforcement and Imitation Learning for Diverse Visuomotor Skills

abstract: We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in https://youtu.be/EDl8SQUNjj0


title: Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

abstract: Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.


title: A Stagewise Refinement Model for Detecting Salient Objects in Images

abstract: Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such as pooling and convolution lead to a significant decrease in the initial image resolution, which results in loss of spatial details and finer structures. To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection. First, our deep feedward net is used to generate a coarse prediction map with much detailed structures lost. Then, refinement nets are integrated with local context information to refine the preceding saliency maps generated in the master branch in a stagewise manner. Further, a pyramid pooling module is applied for different-region-based global context aggregation. Empirical evaluations over six benchmark datasets show that our proposed method compares favorably against the state-of-the-art approaches.


title: Kernel Pooling for Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full form and later using compact representations, have yielded impressive performance gains on a wide range of visual tasks, including fine-grained visual categorization, visual question answering, face recognition, and description of texture and style. The key to their success lies in the spatially invariant modeling of pairwise (2nd order) feature interactions. In this work, we propose a general pooling framework that captures higher order interactions of features in the form of kernels. We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner. Combined with CNNs, the composition of the kernel can be learned from data in an end-to-end fashion via error back-propagation. The proposed kernel pooling scheme is evaluated in terms of both kernel approximation error and visual recognition accuracy. Experimental evaluations demonstrate state-of-the-art performance on commonly used fine-grained recognition datasets.


title: Car Accident Detection and Notification System using Smartphone

abstract: Every day around the world, a large percentage of people die from traffic accident injuries. An influential indicator of survival rates after detecting the accident is the time between the occurrence of the accident and the arrival of emergency responders to the scene. Reductions in this time, in turn, may affect the numbers of fatalities, and this is achieved through using automatic traffic accident detection and notification systems which are either built-in the modern vehicles or available in the roads. This book highlights the state of arts for the existing automatic traffic accident detection and notification systems and reviews smartphone's interesting features that are motivated the researchers to develop smartphone based accident detection and notification systems, to provide an efficient solution in case the lack, complexity, cost, and the maintenance of other automatic accident detection systems. Most of the smartphone based accident detection systems rely on the high speed of the vehicle and the G-Force value to detect an accident. Hence, in addition to the high speed accident detection, this book concentrated on low speed car accident detection also.


title: A Partial Derandomization of PhaseLift using Spherical Designs

abstract: The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced algorithm for phase recovery that is computationally tractable, numerically stable, and comes with rigorous performance guarantees. PhaseLift is optimal in the sense that the number of amplitude measurements required for phase reconstruction scales linearly with the dimension of the signal. However, it specifically demands Gaussian random measurement vectors—a limitation that restricts practical utility and obscures the specific properties of measurement ensembles that enable phase retrieval. Here we present a partial derandomization of PhaseLift that only requires sampling from certain polynomial size vector configurations, called \(t\) -designs. Such configurations have been studied in algebraic combinatorics, coding theory, and quantum information. We prove reconstruction guarantees for a number of measurements that depends on the degree \(t\) of the design. If the degree is allowed to grow logarithmically with the dimension, the bounds become tight up to polylog-factors. Beyond the specific case of PhaseLift, this work highlights the utility of spherical designs for the derandomization of data recovery schemes.


title: Nutritional support for wound healing.

abstract: Healing of wounds, whether from accidental injury or surgical intervention, involves the activity of an intricate network of blood cells, tissue types, cytokines, and growth factors. This results in increased cellular activity, which causes an intensified metabolic demand for nutrients. Nutritional deficiencies can impede wound healing, and several nutritional factors required for wound repair may improve healing time and wound outcome. Vitamin A is required for epithelial and bone formation, cellular differentiation, and immune function. Vitamin C is necessary for collagen formation, proper immune function, and as a tissue antioxidant. Vitamin E is the major lipid-soluble antioxidant in the skin; however, the effect of vitamin E on surgical wounds is inconclusive. Bromelain reduces edema, bruising, pain, and healing time following trauma and surgical procedures. Glucosamine appears to be the rate-limiting substrate for hyaluronic acid production in the wound. Adequate dietary protein is absolutely essential for proper wound healing, and tissue levels of the amino acids arginine and glutamine may influence wound repair and immune function. The botanical medicines Centella asiatica and Aloe vera have been used for decades, both topically and internally, to enhance wound repair, and scientific studies are now beginning to validate efficacy and explore mechanisms of action for these botanicals. To promote wound healing in the shortest time possible, with minimal pain, discomfort, and scarring to the patient, it is important to explore nutritional and botanical influences on wound outcome.


title: Cognitive Tutor: Applied research in mathematics education

abstract: For 25 years, we have been working to build cognitive models of mathematics, which have become a basis for middle- and high-school curricula. We discuss the theoretical background of this approach and evidence that the resulting curricula are more effective than other approaches to instruction. We also discuss how embedding a well specified theory in our instructional software allows us to dynamically evaluate the effectiveness of our instruction at a more detailed level than was previously possible. The current widespread use of the software is allowing us to test hypotheses across large numbers of students. We believe that this will lead to new approaches both to understanding mathematical cognition and to improving instruction.


title: An overlapping module identification method in protein-protein interaction networks

abstract: Background ::: Previous studies have shown modular structures in PPI (protein-protein interaction) networks. More recently, many genome and metagenome investigations have focused on identifying modules in PPI networks. However, most of the existing methods are insufficient when applied to networks with overlapping modular structures. In our study, we describe a novel overlapping module identification method (OMIM) to address this problem.


title: Multimodal Task-Driven Dictionary Learning for Image Classification

abstract: Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages of feature-level fusion based on the joint sparse representation of the multimodal inputs. In this paper, we propose a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information. In this task-driven formulation, the multimodal dictionaries are learned simultaneously with their corresponding classifiers. The resulting multimodal dictionaries can generate discriminative latent features (sparse codes) from the data that are optimized for a given task such as binary or multiclass classification. Moreover, we present an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level. The efficacy of the proposed algorithms for multimodal classification is illustrated on four different applications—multimodal face recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared with the counterpart reconstructive-based dictionary learning algorithms, the task-driven formulations are more computationally efficient in the sense that they can be equipped with more compact dictionaries and still achieve superior performance.


title: ZeroTouch: an optical multi-touch and free-air interaction architecture

abstract: ZeroTouch (ZT) is a unique optical sensing technique and architecture that allows precision sensing of hands, fingers, and other objects within a constrained 2-dimensional plane. ZeroTouch provides tracking at 80 Hz, and up to 30 concurrent touch points. Integration with LCDs is trivial. While designed for multi-touch sensing, ZT enables other new modalities, such as pen+touch and free-air interaction. In this paper, we contextualize ZT innovations with a review of other flat-panel sensing technologies. We present the modular sensing architecture behind ZT, and examine early diverse uses of ZT sensing.


title: Truss Weight Minimization Using Hybrid Harmony Search and Big Bang–Big Crunch Algorithms

abstract: Weight minimization of truss structures is an important topic of structural engineering. Metaheuristic optimization methods can explore the whole design space by performing a limited number of structural analyses. “Second generation” algorithms like harmony search (HS) and Big Bang–Big Crunch (BB–BC) are very efficient in truss optimization problems but are computationally expensive. To overcome this limitation, this chapter presents two novel hybrid formulations of HS and BB–BC where metaheuristic search is hybridized by including gradient/pseudogradient information as the criterion to accept or reject new trial designs or to perform new explosions. Each trial design is formed by combining a set of descent directions and then eventually corrected to improve it further. An improved local one-dimensional search derived from simulated annealing is included in the optimization process.


CaseSummarizer: A System for Automated Summarization of Legal Texts


title: Rewriting Ontological Queries into Small Nonrecursive Datalog Programs

abstract: We consider the setting of ontological database access, where an A-box is given in form of a relational database D and where a Boolean conjunctive query q has to be evaluated against D modulo a T-box Σ formulated in DL-Lite or Linear Datalog±. It is well-known that (Σ, q) can be rewritten into an equivalent nonrecursive Datalog program P that can be directly evaluated over D. However, for Linear Datalog± or for DL-Lite versions that allow for role inclusion, the rewriting methods described so far result in a nonrecursive Datalog program P of size exponential in the joint size of Σ and q. This gives rise to the interesting question of whether such a rewriting necessarily needs to be of exponential size. In this paper we show that it is actually possible to translate (Σ, q) into a polynomially sized equivalent nonrecursive Datalog program P.


ITS-WP-04-07 Monitoring Traffic and Emissions by Floating Car Data


title: PM-tree: Pivoting Metric Tree for Similarity Search in Multimedia Databases

abstract: In this paper we introduce the Pivoting M-tree (PM-tree), a metric access method combining M-tree with the pivot-based approach. While in M-tree a metric region is represented by a hyper-sphere, in PM-tree the shape of a metric region is determined by intersection of the hyper-sphere and a set of hyper-rings. The set of hyper-rings for each metric region is related to a fixed set of pivot objects. As a consequence, the shape of a metric region bounds the indexed objects more tightly which, in turn, significantly improves the overall efficiency of similarity search. We present basic algorithms on PM-tree and two cost models for range query processing. Finally, the PM-tree efficiency is experimentally evaluated on large synthetic as well as real-world datasets.


title: Mesh saliency

abstract: Research over the last decade has built a solid mathematical foundation for representation and analysis of 3D meshes in graphics and geometric modeling. Much of this work however does not explicitly incorporate models of low-level human visual attention. In this paper we introduce the idea of mesh saliency as a measure of regional importance for graphics meshes. Our notion of saliency is inspired by low-level human visual system cues. We define mesh saliency in a scale-dependent manner using a center-surround operator on Gaussian-weighted mean curvatures. We observe that such a definition of mesh saliency is able to capture what most would classify as visually interesting regions on a mesh. The human-perception-inspired importance measure computed by our mesh saliency operator results in more visually pleasing results in processing and viewing of 3D meshes. compared to using a purely geometric measure of shape. such as curvature. We discuss how mesh saliency can be incorporated in graphics applications such as mesh simplification and viewpoint selection and present examples that show visually appealing results from using mesh saliency.


title: Towards Asynchronous Distributed MCMC Inference for Large Graphical Models

abstract: With increasingly cheap availability of computational resources such as storage and bandwidth, access to large amount of data has become commonplace. There is a significant need to organize and extract the latent information from such datasets, much of which cannot be achieved by incorporating only local dependencies. Recent work in information extraction, such as [1, 2], represent uncertainty over these large datasets as graphical models. To perform inference over these millions of variables, there is a need to distribute the inference; however the dense, loopy structure with long-range dependencies makes the problem non-trivial.


title: Epidermal Permeability Barrier Defects and Barrier Repair Therapy in Atopic Dermatitis

abstract: Atopic dermatitis (AD) is a multifactorial inflammatory skin disease perpetuated by gene-environmental interactions and which is characterized by genetic barrier defects and allergic inflammation. Recent studies demonstrate an important role for the epidermal permeability barrier in AD that is closely related to chronic immune activation in the skin during systemic allergic reactions. Moreover, acquired stressors (e.g., Staphylococcus aureus infection) to the skin barrier may also initiate inflammation in AD. Many studies involving patients with AD revealed that defective skin barriers combined with abnormal immune responses might contribute to the pathophysiology of AD, supporting the outside-inside hypothesis. In this review, we discuss the recent advances in human and animal models, focusing on the defects of the epidermal permeability barrier, its immunologic role and barrier repair therapy in AD.


title: Automatic Lens Distortion Correction Using One-Parameter Division Models

abstract: We present a method to automatically correct the radial distortion caused by wide-angle lenses using the distorted lines generated by the projection of 3D straight lines onto the image. Lens distortion is estimated by the division model using one parameter, which allows to state the problem into the Hough transform scheme by adding a distortion parameter to better extract straight lines from the image. This paper describes an algorithm which applies this technique, providing all the details of the design of an improved Hough transform. We perform experiments using calibration patterns and real scenes showing a strong distortion to illustrate the performance of the proposed method. Source Code The source code, the code documentation, and the online demo are accessible at the IPOL web page of this article 1 . In this page, an implementation is available for download. Compilation and usage instructions are included in the README.txt file of the archive.


title: Efficient Disk-Based Directed Graph Processing: A Strongly Connected Component Approach

abstract: Recently, there have been many disk-based systems proposed for iterative graph processing. In the popular vertex/edge-centric systems, an iterative directed graph algorithm needs to reprocess many partitions so as to update their vertices’ states according to other non-convergent vertices for the unawareness of their dependencies. As a result, it induces high data access cost and a long time to converge. To tackle this problem, we propose a novel system for iterative directed graph processing with taking advantage of the strongly connected component (SCC) structure. It stores a directed graph into a directed acyclic graph (DAG) sketch, with each node representing a SCC in the original data graph. During execution, the SCCs are loaded into memory for processing in a parallel way according to the topological order of the DAG sketch, and the vertices in each SCC are tried to be handled along the directed paths. In this way, each SCC is able to reach convergence in order and needs to be loaded into the main memory for exactly once, getting much lower data access cost and faster convergence. Besides, the vertices of each SCC need fewer updates for convergence. We further develop a lightweight approach to maintain the DAG sketch and handle SCCs in an incremental way for evolving graphs. Compared with the state-of-the-art methods, experimental results show that our approach achieves a performance improvements of 1.46-8.37 times for static graphs, and can reduce the execution time by 61.4-72.7 percent for evolving graphs.


title: Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network

abstract: Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologically-inspired deep network, called shuttleNet\footnote{Our code is available at \url{this https URL}}. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of cells and states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain's feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.


title: High-Precision Sensorless Drive for High-Speed BLDC Motors Based on the Virtual Third Harmonic Back-EMF

abstract: In order to improve the performance of the high-speed brushless direct current motor drives, a novel high-precision sensorless drive has been developed. It is well known that the inevitable voltage pulses, which are generated during the commutation periods, will impact the rotor position detecting accuracy, and further impact the performance of the overall sensorless drive, especially in the higher speed range or under the heavier load conditions. For this reason, the active compensation method based on the virtual third harmonic back electromotive force incorporating the SFF-SOGI-PLL (synchronic-frequency filter incorporating the second-order generalized integrator based phase-locked loop) is proposed to precise detect the commutation points for sensorless drive. An experimental driveline system used for testing the electrical performance of the developed magnetically suspended motor is built. The mathematical analysis and the comparable experimental results have been shown to validate the effectiveness of the proposed sensorless drive algorithm.


title: Analysis of fog model considering computing and communication latency in 5G cellular networks

abstract: Challenging 5G requirements have been proposed to enable the provision of more satisfactory services to users. One of the most critical 5G enhancements is extremely low latency compared with that of 4G. Various 5G applications will be supported by Cloud computing services. However, Cloud services sometimes have a long communication distance because of the physical location of data centers. Fog computing architecture is being introduced in 5G cellular networks to shorten the latency arising from the location of data centers. The communication delay as well as the computing delay must be considered for latency-sensitive applications. A Fog server serving many users will probably have a longer computing delay than a Fog server with a lighter workload. To clarify the computing delay and communication delay in Fog architecture, we define a mathematical model of a Fog network and the important related parameters. We also analyze results from a model used to evaluate three different policies for selecting the target Fog server for each task. We conducted simulations to evaluate this model.


title: Credit scoring using support vector machines with direct search for parameters selection

abstract: Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.


title: CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

abstract: The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.


title: Efficient Graph-Based Image Segmentation

abstract: This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.


title: Predicting Which Topics You Will Join in the Future on Social Media

abstract: Every day, social media users send millions of microblogs on every imaginable topics. If we could predict which topics a user will join in the future, it would be easy to determine what topics will become popular and what kinds of users a topic may attract. It also can be of great interest for many applications. In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. We introduce a novel deep convolutional neural network with external neural memory and attention mechanism to perform this problem. User's posting history and topics were modeled with an external neural memory architecture. The convolutional neural network based matching methods were used to construct the relations between users and topics. Final decisions were made based on these matching results. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter. The experimental results demonstrated that the proposed method could perform significantly better than other methods. Comparing to the state-of-the-art deep neural networks, our approach achieves a relative improvement of 18.2\% in F1-score and 28.9\% in MAP@10.


title: GANs for Biological Image Synthesis

abstract: In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.


Formalizing Schoenberg’s Fundamentals of Musical Composition through Petri Nets


title: Measurement of Driver's Consciousness by Image Processing -A Method for Presuming Driver's Drowsiness by Eye-Blinks coping with Individual Differences -

abstract: Nowadays, driving support systems, such as car navigation systems, are getting common, and they support drivers in several aspects. It is important for driving support systems to detect status of driver's consciousness. Particularly, detecting driver's drowsiness could prevent drivers from collisions caused by drowsy driving. In this paper, we discuss a system and a method for detecting driver's drowsiness, which use a property of blinks waveforms that driver's eye-closing time is strongly connected to their drowsiness. Moreover, our system detects the drowsiness robustly for individual differences by using three factors extracted from the individual blink waveform.


title: Item-Level Indoor Localization With Passive UHF RFID Based on Tag Interaction Analysis

abstract: Radio-frequency identification (RFID) with a received signal strength indicator (RSSI) is a low-cost and low-complexity approach for item-level indoor localization. Although RSSI-based algorithms suffer from multipath effect and other environmental factors, reference tags and RSSI changes can be utilized to further improve the localization accuracy. However, the current algorithms lack deep analysis of the influence of tag interaction on localization accuracy and faces the challenge of simultaneously locating multiple close targets. In this paper, we propose an analysis method about how tag interaction affects a tag antenna radiation pattern and an RSSI change. The tag interaction analysis guides us to improve the design of RSSI-based localization algorithms. We take the k-nearest neighbor (k-NN) algorithm and the Simplex algorithm as two examples. The experimental results show that the revised k-NN and the revised Simplex algorithms are robust to different numbers, spacing, and materials of target objects, and they are superior to other RFID localization schemes, considering cost, capability of simultaneous localization of multiple targets, and location estimation errors.


abstract: Novel control technique of single-phase PWM rectifier by compensating output ripple voltage is presented. Output ripple voltage is cancelled before feeding to voltage controller in order to increase system bandwidth while supply current is still nearly sinusoidal and maintains unity power factor. Output ripple voltage estimator is calculated by the information of peak value of supply voltage and current. Peak value estimator of sinusoidal signal is introduced with the help of a simple trigonometric rule. Additionally, feed forward current controller is also presented so as to improve tracking performance of supply current. The proposed scheme is simulated in order to compare with conventional scheme. The simulation results show that the proposed scheme is working effectively both in rectifying mode and regenerating mode


Framing in Social Media: How the U.S. Congress uses Twitter hashtags to frame political issues Introduction


title: A psychometric evaluation of the PedsQL™ Family Impact Module in parents of children with sickle cell disease

abstract: BackgroundCaring for a child with a chronic condition, such as sickle cell disease, can have a significant impact on parents and families. In order to provide comprehensive care and support to these families, psychometrically sound instruments are needed as an initial step in measuring the impact of chronic diseases on parents and families. We sought to evaluate the psychometric properties of the PedsQL™ Family Impact Module in populations of children with and without sickle cell disease. In addition, we sought to determine the correlation between parent's well being and their proxy report of their child's health-related quality of life (HRQL).MethodsWe conducted a cross-sectional study of parents of children with and without sickle cell disease who presented to an urban hospital-based sickle cell disease clinic and an urban primary care clinic. We assessed the HRQL and family functioning of both groups of parents utilizing the PedsQL™ Family Impact Module. The reliability, validity and factor structure of the instrument were determined and scores from the instrument were correlated with scores from parent-proxy report of their child's HRQL using the PedsQL™ 4.0 Generic Core Scales.ResultsParents of 170 children completed the module (97 parents of children with sickle cell disease and 73 parents of children without sickle cell disease). The Family Impact Module had high ceiling effects but was reliable (Cronbach's alpha > 0.80 in all scales). The empirical factor structure was generally consistent with the theoretical factor structure and supported construct validity. The Family Impact Module discriminated between parents of children with severe sickle cell disease from parents of children with mild disease or no disease in the areas of communication and worry. There were no significant differences across any of the subscales between parents of children with mild sickle cell disease and those with no disease. Parents with higher scores, representing better HRQL and family functioning, generally reported higher HRQL scores for their children.ConclusionThe PedsQL™ Family Impact module was reliable, however it displayed large ceiling effects and did not discriminate well between parents of children with and without sickle cell disease. Future research to evaluate the psychometric properties of the Family Impact Module for parents of healthy children may be helpful.


title: A Forward-Secure Digital Signature Scheme

abstract: We describe a digital signature scheme in which the public key is fixed but the secret signing key is updated at regular intervals so as to provide a forward security property: compromise of the current sccret key does not enable an adversary to forge signatures pertaining to the past. This can be useful to mitigate the damage caused by key exposure without requiring distribution of keys Our construction uses ideas from the Fiat-Shamir and Ong-Schnorr identification and signature schemes, and is proven to be forward secure based on the hardness of factoring, in the random oracle model The construction is also quite efficient.


title: Multimodal interaction in the car: combining speech and gestures on the steering wheel

abstract: Implementing controls in the car becomes a major challenge: The use of simple physical buttons does not scale to the increased number of assistive, comfort, and infotainment functions. Current solutions include hierarchical menus and multi-functional control devices, which increase complexity and visual demand. Another option is speech control, which is not widely accepted, as it does not support visibility of actions, fine-grained feedback, and easy undo of actions. Our approach combines speech and gestures. By using speech for identification of functions, we exploit the visibility of objects in the car (e.g., mirror) and simple access to a wide range of functions equaling a very broad menu. Using gestures for manipulation (e.g., left/right), we provide fine-grained control with immediate feedback and easy undo of actions. In a user-centered process, we determined a set of user-defined gestures as well as common voice commands. For a prototype, we linked this to a car interior and driving simulator. In a study with 16 participants, we explored the impact of this form of multimodal interaction on the driving performance against a baseline using physical buttons. The results indicate that the use of speech and gesture is slower than using buttons but results in a similar driving performance. Users comment in a DALI questionnaire that the visual demand is lower when using speech and gestures.


title: Reconciling Contradictions of Open Data Regarding Transparency, Privacy, Security and Trust

abstract: While Open Data initiatives are diverse, they aim to create and contribute to public value. Yet several potential contradictions exist between public values, such as trust, transparency, privacy, and security, and Open Data policies. To bridge these contradictions, we present the notion of precommitment as a restriction of one's choices. Conceptualized as a policy instrument, precommitment can be applied by an organization to restrict the extent to which an Open Data policy might conflict with public values. To illustrate the use of precommitment, we present two case studies at two public sector organizations, where precommitment is applied during a data request procedure to reconcile conflicting values. In this procedure, precommitment is operationalized in three phases. In the first phase, restrictions are defined on the type and the content of the data that might be requested. The second phase involves the preparation of the data to be delivered according to legal requirements and the decisions taken in phase 1. Data preparation includes amongst others the deletion of privacy sensitive or other problematic attributes. Finally, phase 3 pertains to the establishment of the conditions of reuse of the data, limiting the use to restricted user groups or opening the data for everyone.


title: Dual methods for nonconvex spectrum optimization of multicarrier systems

abstract: The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations


title: Interventions for treating obesity in children.

abstract: BACKGROUND ::: Child and adolescent obesity is increasingly prevalent, and can be associated with significant short- and long-term health consequences. ::: ::: ::: OBJECTIVES ::: To assess the efficacy of lifestyle, drug and surgical interventions for treating obesity in childhood. ::: ::: ::: SEARCH STRATEGY ::: We searched CENTRAL on The Cochrane Library Issue 2 2008, MEDLINE, EMBASE, CINAHL, PsycINFO, ISI Web of Science, DARE and NHS EED. Searches were undertaken from 1985 to May 2008. References were checked. No language restrictions were applied. ::: ::: ::: SELECTION CRITERIA ::: We selected randomised controlled trials (RCTs) of lifestyle (i.e. dietary, physical activity and/or behavioural therapy), drug and surgical interventions for treating obesity in children (mean age under 18 years) with or without the support of family members, with a minimum of six months follow up (three months for actual drug therapy). Interventions that specifically dealt with the treatment of eating disorders or type 2 diabetes, or included participants with a secondary or syndromic cause of obesity were excluded. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two reviewers independently assessed trial quality and extracted data following the Cochrane Handbook. Where necessary authors were contacted for additional information. ::: ::: ::: MAIN RESULTS ::: We included 64 RCTs (5230 participants). Lifestyle interventions focused on physical activity and sedentary behaviour in 12 studies, diet in 6 studies, and 36 concentrated on behaviorally orientated treatment programs. Three types of drug interventions (metformin, orlistat and sibutramine) were found in 10 studies. No surgical intervention was eligible for inclusion. The studies included varied greatly in intervention design, outcome measurements and methodological quality.Meta-analyses indicated a reduction in overweight at 6 and 12 months follow up in: i) lifestyle interventions involving children; and ii) lifestyle interventions in adolescents with or without the addition of orlistat or sibutramine. A range of adverse effects was noted in drug RCTs. ::: ::: ::: AUTHORS' CONCLUSIONS ::: While there is limited quality data to recommend one treatment program to be favoured over another, this review shows that combined behavioural lifestyle interventions compared to standard care or self-help can produce a significant and clinically meaningful reduction in overweight in children and adolescents. In obese adolescents, consideration should be given to the use of either orlistat or sibutramine, as an adjunct to lifestyle interventions, although this approach needs to be carefully weighed up against the potential for adverse effects. Furthermore, high quality research that considers psychosocial determinants for behaviour change, strategies to improve clinician-family interaction, and cost-effective programs for primary and community care is required.


title: GPGPU Processing in CUDA Architecture

abstract: The future of computation is the Graphical Processing Unit, i.e. the GPU. The promise that the graphics cards have shown in the field of image processing and accelerated rendering of 3D scenes, and the computational capability that these GPUs possess, they are developing into great parallel computing units. It is quite simple to program a graphics processor to perform general parallel tasks. But after understanding the various architectural aspects of the graphics processor, it can be used to perform other taxing tasks as well. In this paper, we will show how CUDA can fully utilize the tremendous power of these GPUs. CUDA is NVIDIA’s parallel computing architecture. It enables dramatic increases in computing performance, by harnessing the power of the GPU. This paper talks about CUDA and its architecture. It takes us through a comparison of CUDA C/C++ with other parallel programming languages like OpenCL and DirectCompute. The paper also lists out the common myths about CUDA and how the future seems to be promising for CUDA.


title: Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts

abstract: Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While smart contracts inherit the availability and other security assurances of blockchains, however, they are impeded by blockchains' lack of confidentiality and poor performance. We present Ekiden, a system that addresses these critical gaps by combining blockchains with Trusted Execution Environments (TEEs). Ekiden leverages a novel architecture that separates consensus from execution, enabling efficient TEE-backed confidentiality-preserving smart-contracts and high scalability. Our prototype (with Tendermint as the consensus layer) achieves example performance of 600x more throughput and 400x less latency at 1000x less cost than the Ethereum mainnet. Another contribution of this paper is that we systematically identify and treat the pitfalls arising from harmonizing TEEs and blockchains. Treated separately, both TEEs and blockchains provide powerful guarantees, but hybridized, though, they engender new attacks. For example, in naive designs, privacy in TEE-backed contracts can be jeopardized by forgery of blocks, a seemingly unrelated attack vector. We believe the insights learned from Ekiden will prove to be of broad importance in hybridized TEE-blockchain systems.


title: Ouroboros Praos: An Adaptively-Secure, Semi-synchronous Proof-of-Stake Blockchain

abstract: We present “Ouroboros Praos”, a proof-of-stake blockchain protocol that, for the first time, provides security against fully-adaptive corruption in the semi-synchronous setting: Specifically, the adversary can corrupt any participant of a dynamically evolving population of stakeholders at any moment as long the stakeholder distribution maintains an honest majority of stake; furthermore, the protocol tolerates an adversarially-controlled message delivery delay unknown to protocol participants.


title: PV panel model based on datasheet values

abstract: This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell/panel in Standard Test Conditions (STC)1 are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence. Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested.


title: Data hiding watermarking for halftone images

abstract: In many printer and publishing applications, it is desirable to embed data in halftone images. We proposed some novel data hiding methods for halftone images. For the situation in which only the halftone image is available, we propose data hiding smart pair toggling (DHSPT) to hide data by forced complementary toggling at pseudo-random locations within a halftone image. The complementary pixels are chosen to minimize the chance of forming visually undesirable clusters. Our experimental results suggest that DHSPT can hide a large amount of hidden data while maintaining good visual quality. For the situation in which the original multitone image is available and the halftoning method is error diffusion, we propose the modified data hiding error diffusion (MDHED) that integrates the data hiding operation into the error diffusion process. In MDHED, the error due to the data hiding is diffused effectively to both past and future pixels. Our experimental results suggest that MDHED can give better visual quality than DHSPT. Both DHSPT and MDHED are computationally inexpensive.


abstract: We present Dynamic Sampling Convolutional Neural Networks (DSCNN), where the position-specific kernels learn from not only the current position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. And the kernels are further factorized to reduce parameters. The multiple sampling strategy enlarges the effective receptive fields significantly without requiring more parameters. While DSCNNs inherit the advantages of DFN, namely avoiding feature map blurring by position-specific kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard back-propagation. We demonstrate the merits of our DSCNNs on both sparse and dense prediction tasks involving object detection and flow estimation. Our results show that DSCNNs enjoy stronger recognition abilities and achieve 81.7% in VOC2012 detection dataset. Also, DSCNNs obtain much sharper responses in flow estimation on FlyingChairs dataset compared to multiple FlowNet models' baselines.


title: Image demosaicing: a systematic survey

abstract: Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array (CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source codes/executables in response to the principle of reproducible research. In this article, we provide a systematic survey of over seventy published works in this field since 1999 (complementary to previous reviews 22,67 ). Our review attempts to address important issues to demosaicing and identify fundamental differences among competing approaches. Our findings suggest most existing works belong to the class of sequential demosaicing - i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered luminance information. We report our comparative study results with a collection of eleven competing algorithms whose source codes or executables are provided by the authors. Our comparison is performed on two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging). While most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on the IMAX one (images with varying-hue and high-saturation edges) degrades significantly. Such observation suggests the importance of properly addressing the issue of mismatch between assumed model and observation data in demosaicing, which calls for further investigation on issues such as model validation, test data selection and performance evaluation.


title: Social Support in eSports: Building Emotional and Esteem Support from Instrumental Support Interactions in a Highly Competitive Environment

abstract: In this paper we report an empirical study of how and what types of social support eSports players can experience from their gameplay. Specifically, we interviewed 26 eSports players and sought out their first-person descriptions of experiences of social support. We found that even though most players started out as strangers, the context of eSports facilitated frequent acts of helping through both tangible and intangible means within the game. Such in-game informational and instrumental support often led to emotional and esteem support, and these different types of support functions not only remained within the context of the game but also "bled" out into in-person interactions and relationships. We contribute to CSCW and HCI by both confirming and augmenting existing theories of mediated social support in this underexplored context. Our findings regarding how emotional and esteem support are built from instrumental support interactions in eSports not only provide a rich description of players' experiences in highly competitive digital environments and the consequences of their social interactions but also suggest a number of future research opportunities.


title: Analysis of Subthreshold Finfet Circuits for Ultra-Low Power Design

abstract: In this paper, we first explore sub-threshold Fin-FET circuits design space, finding their optimal power supply point for minimum energy consumption. We then study soft error vulnerability in sub-threshold region. Our experiments indicate that the energy consumption in sub-threshold region can achieve 4 orders of magnitude energy saving. Compared to bulk CMOS technology, FinFET circuits have lower functional power supply and lower optimal energy consumption in sub-threshold region. In addition, FinFET has better soft error immunity in sub-threshold region.


title: Gamification in intelligent tutoring systems

abstract: Gamification is the use of game mechanics and strategies in nongame contexts. Currently, according to various reports, is an emerging trend in many application fields, including education. It seeks to encourage people, behavior change and improve troubleshooting. Furthermore, mobile and ubiquitous learning make possible to extend the teaching-learning beyond formal contexts such as classrooms. Moreover, while the intelligent tutoring systems have been shown to improve student achievement and enhance learning, they still have major problems in their use and need further investigation. Among them, one of the main problems found is the inappropriate use of these systems by the students, perhaps due to boredom, lack of interest or motivation and monotony, which among other factors, cause the student to behave incorrectly when interacting with these kind systems. For this reason, in this paper we present a conceptual architecture proposal for an Intelligent Tutorial System (ITS) known as EMATIC (Mathematics Education through ICT) that includes gamification elements as key components of the system.


title: Reduced Left Ventricular Torsion Early After Myocardial Infarction Is Related to Left Ventricular Remodeling

abstract: BACKGROUND ::: Left ventricular (LV) torsion is emerging as a sensitive parameter of LV systolic myocardial performance. The aim of the present study was to explore the effects of acute myocardial infarction (AMI) on LV torsion and to determine the value of LV torsion early after AMI in predicting LV remodeling at 6-month follow-up. ::: ::: ::: METHODS AND RESULTS ::: A total of 120 patients with a first ST-segment elevation AMI (mean+/-SD age, 59+/-10 years; 73% male) were included. All patients underwent primary percutaneous coronary intervention. After 48 hours, speckle-tracking echocardiography was performed to assess LV torsion; infarct size was assessed by myocardial contrast echocardiography. At 6-month follow-up, LV volumes and LV ejection fraction were reassessed to identity patients with LV remodeling (defined as a > or =15% increase in LV end-systolic volume). Compared with control subjects, peak LV torsion in AMI patients was significantly impaired (1.54+/-0.64 degrees /cm vs 2.07+/-0.27 degrees /cm, P<0.001). By multivariate analysis, only LV ejection fraction (beta=0.36, P<0.001) and infarct size (beta=-0.47, P<0.001) were independently associated with peak LV torsion. At 6-month follow-up, 19 patients showed LV remodeling. By multivariate analysis, only peak LV torsion (odds ratio=0.77; 95% CI, 0.65-0.92; P=0.003) and infarct size (odds ratio=1.04; 95% CI, 1.01-1.07; P=0.021) were independently related to LV remodeling. Peak LV torsion provided modest but significant incremental value over clinical, echocardiographic, and myocardial contrast echocardiography variables in predicting LV remodeling. By receiver-operating characteristics curve analysis, peak LV torsion < or =1.44 degrees /cm provided the highest sensitivity (95%) and specificity (77%) to predict LV remodeling. ::: ::: ::: CONCLUSIONS ::: LV torsion is significantly impaired early after AMI. The amount of impairment of LV torsion predicts LV remodeling at 6-month follow-up.


title: The Miniaturization of a Partially 3-D Printed Quadrifilar Helix Antenna

abstract: In this paper, the design and implementation of a quadrifilar helix antenna are presented where the four arms are 3-D printed. The antenna structure is miniaturized by resorting to a combination of two approaches. The first approach is based on loading the tips of the four helical arms with circular conductive disks. The size and location of these elements directly affect the antenna’s operating frequency. The second miniaturization approach is based on incorporating an FR-4 dielectric material into the vacant space between the four arms of the quadrifilar helix. As a result, the length of the antenna is reduced by a ratio of 43%. The quadrifilar helix antenna is also designed on top of a ground plane with an optimized conical shape and topology. The antenna is partially fabricated using 3-D printing additive technology, where measurements show great agreement with simulated results.


Transforming the LEGO Group for the Digital Economy


title: Efficient geometric graph matching using vertex embedding

abstract: For many applications such as road network analysis and image processing, it is critical to study spatial properties of objects in addition to object relationships. Geometric graphs provide a suitable modeling framework for such applications, where vertices are located in some 2D space. For applications where the similarity between the structures of different graphs plays an important role, typically, inexact graph matching algorithms are employed. However, graph matching algorithms face many problems such as scalability with respect to graph size and less tolerance to changes in graph structure or labels. In this paper, we propose a solution to the problem of inexact graph matching for geometric graphs in the 2D space. Our approach allows to effectively answer subgraph and common subgraph queries for geometric graphs that differ in structure, spatial properties, and labels. Initially, a spatial feature is extracted from each vertex, and string edit distance is used to find the distance between pairs of vertices. To speed up graph matching, we propose vertex embedding into the Euclidean space. Based on this, the distance between two vertices can be computed using the Euclidean distance in constant time. To answer subgraph and common subgraph queries, we introduce an iterative matching algorithm that matches two graphs using their similarity in the Euclidean space. Such an algorithm merges highly similar vertices to create similar connected subgraphs. Using representative geometric graphs extracted from road networks, we show that our approach outperforms existing graph matching approaches in terms of matching quality and runtime.


title: Using Online Social Networks to Build Healthy Communities: A Design-based Research Investigation

abstract: This paper discusses the design and evaluation of an online social network used within twenty- two established after school programs across three major urban areas in the Northeastern United States. The overall goal of this initiative is to empower students in grades K-8 to prevent obesity through healthy eating and exercise. The online social network was designed to support communication between program participants. Results from the related evaluation indicate that the online social network has potential for advancing awareness and community action around health related issues; however, greater attention is needed to professional development programs for program facilitators, and design features could better support critical thinking, social presence, and social activity.


title: Comparison of Clinical Trial and Systematic Review Outcomes for the 4 Most Prevalent Eye Diseases

abstract: Importance Suboptimal overlap in outcomes reported in clinical trials and systematic reviews compromises efforts to compare and summarize results across these studies. Objectives To examine the most frequent outcomes used in trials and reviews of the 4 most prevalent eye diseases (age-related macular degeneration [AMD], cataract, diabetic retinopathy [DR], and glaucoma) and the overlap between outcomes in the reviews and the trials included in the reviews. Design, Setting, and Participants This cross-sectional study examined all Cochrane reviews that addressed AMD, cataract, DR, and glaucoma; were published as of July 20, 2016; and included at least 1 trial and the trials included in the reviews. For each disease, a pair of clinical experts independently classified all outcomes and resolved discrepancies. Outcomes (outcome domains) were then compared separately for each disease. Main Outcomes and Measures Proportion of review outcomes also reported in trials and vice versa. Results This study included 56 reviews that comprised 414 trials. Although the median number of outcomes per trial and per review was the same (n = 5) for each disease, the trials included a greater number of outcomes overall than did the reviews, ranging from 2.9 times greater (89 vs 30 outcomes for glaucoma) to 4.9 times greater (107 vs 22 outcomes for AMD). Most review outcomes, ranging from 14 of 19 outcomes (73.7%) (for DR) to 27 of 29 outcomes (93.1%) (for cataract), were also reported in the trials. For trial outcomes, however, the proportion also named in reviews was low, ranging from 19 of 107 outcomes (17.8%) (for AMD) to 24 of 89 outcomes (27.0%) (for glaucoma). Only 1 outcome (visual acuity) was consistently reported in greater than half the trials and greater than half the reviews. Conclusions and Relevance Although most review outcomes were reported in the trials, most trial outcomes were not reported in the reviews. The current analysis focused on outcome domains, which might underestimate the problem of inconsistent outcomes. Other important elements of an outcome (ie, specific measurement, specific metric, method of aggregation, and time points) might have differed even though the domains overlapped. Inconsistency in trial outcomes may impede research synthesis and indicates the need for disease-specific core outcome sets in ophthalmology.


title: Specification of the neural crest occurs during gastrulation and requires Pax7

abstract: The neural crest is a stem population critical for development of the vertebrate craniofacial skeleton and peripheral ganglia. Neural crest cells originate along the border between the neural plate and epidermis, migrate extensively and generate numerous derivatives, including neurons and glia of the peripheral nervous system, melanocytes, bone and cartilage of the head skeleton. Impaired neural crest development is associated with human defects, including cleft palate. Classically, the neural crest has been thought to form by interactions at the border between neural and non-neural ectoderm or mesoderm, and defined factors such as bone morphogenetic proteins (BMPs) and Wnt proteins have been postulated as neural crest-inducers. Although competence to induce crest cells declines after stage 10 (ref. 14), little is known about when neural crest induction begins in vivo. Here we report that neural crest induction is underway during gastrulation and well before proper neural plate appearance. We show that a restricted region of chick epiblast (stage 3–4) is specified to generate neural crest cells when explanted under non-inducing conditions. This region expresses the transcription factor Pax7 by stage 4 + and later contributes to neural folds and migrating neural crest. In chicken embryos, Pax7 is required for neural crest formation in vivo, because blocking its translation inhibits expression of the neural crest markers Slug, Sox9, Sox10 and HNK-1. Our results indicate that neural crest specification initiates earlier than previously assumed, independently of mesodermal and neural tissues, and that Pax7 has a crucial function during neural crest development.


title: A novel cache architecture with enhanced performance and security

abstract: Caches ideally should have low miss rates and short access times, and should be power efficient at the same time. Such design goals are often contradictory in practice. Recent findings on efficient attacks based on information leakage in caches have also brought the security issue up front. Design for security introduces even more restrictions and typically leads to significant performance degradation. This paper presents a novel cache architecture that can simultaneously achieve the above goals. Specifically, cache miss rates are reduced with dynamic remapping and longer cache indices, access-time overhead overcome with astute low-level circuit design, and information leakage thwarted by a security-aware cache replacement algorithm together with the performance enhancing mechanisms. We present both theoretical analysis and experimental results, using the SPEC2000 suite to evaluate the cache miss behavior, and CACTI and HSPICE to validate the circuit design. Our results show that the proposed cache architecture has low miss rates comparable to a highly associative cache and short access times and power efficiency close to that of a direct-mapped cache. At the same time it can thwart cache-based software side-channel attacks, providing both legacy and security-enhanced software a much higher degree of security. Additional benefits that the proposed cache architecture can bring, like fault tolerance and hot-spot mitigation, are also discussed briefly.


title: Design considerations of time constant mismatch problem for inductor DCR current sensing method

abstract: This paper identifies advantages and disadvantages of several commonly used current sensing methods such as dedicated sense resistor sensing, MOSFET R/sub DS(ON)/ current sensing, and inductor DC resistance (DCR) current sensing. Among these current sense methods, inductor DCR current sense will become dominant one in the future. The mismatching issue between the time constant made by the current sensing RC network and the one formed with output inductor and its DC resistance is addressed. A small signal model of a buck converter using inductor DCR current sensing with mismatched time constants is presented, and the modeling has been verified experimentally.


title: Design Methodology for High Efficient Inductive Power Transfer Systems With High Coil Positioning Flexibility

abstract: With contactless inductive power transfer (IPT), it is possible to transfer electrical energy to stationary or movable consumers without contacts, cables, or slip rings. To reduce the very high development effort of new contactless inductive energy supplies, a new systematic and modular design methodology is presented in the paper. This methodology includes new methods to increase the transfer efficiency and the positioning flexibility of the consumer device and is particularly implemented into a simulation software tool. The positioning tolerance is improved by the optimization of the coil and ferrite geometry. Thereby, the influence of physical and geometrical parameters on the magnetic coupling and on the electrical transfer characteristics is investigated. As a result of the design methodology, a new IPT system for household appliances in the output power range of 1 kW at an overall efficiency of more than 90% and with a high positioning tolerance is presented.


title: Predictable routing

abstract: Predictable routing is the concept of using prespecified patterns to route a net. By doing this, we allow an more accurate prediction mechanism for metrics such as congestion and wirelength earlier in the design flow. Additionally, we can better plan the routes, insert buffers and perform wire sizing earlier. With comparable routing quality, we show that we can predictably route up to 80% of a selected subset of nets. Also, we introduce methods for finding a group of nets which can be predictably routed.


title: Windows driver memory analysis: A reverse engineering methodology

abstract: In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7. Manipulation (DKOM), tcpip.sys, Windows 7, Windows Vista. 2000 MSC: 60, 490.


title: A body-shadowing model for off-body and body-to-body communications

abstract: This paper presents a simple model for body-shadowing in off-body and body-to-body channels. The model is based on a body shadowing pattern associated with the on-body antenna, represented by a cosine function whose amplitude parameter is calculated from measurements. This parameter, i.e the maximum body-shadowing loss, is found to be linearly dependent on distance. The model was evaluated against a set of off-body channel measurements at 2.45 GHz in an indoor office environment, showing a good fit. The coefficient of determination obtained for the linear model of the maximum body-shadowing loss is greater than 0.6 in all considered scenarios, being higher than 0.8 for the ones with a static user.