title: Enhanced image classification with a fast-learning shallow convolutional neural network

abstract: We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and the absence of iteratively-tuned parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods.


title: A 0.4-to-1 V Voltage Scalable $\Delta \Sigma $ ADC With Two-Step Hybrid Integrator for IoT Sensor Applications in 65-nm LP CMOS

abstract: This brief presents a two-step hybrid integrator (TSHI) that can operate at a wide supply voltage range, which is demonstrated with a third-order ${\Delta \Sigma }$ analog-to-digital converter (ADC). The proposed TSHI consists of a zero-crossing-detector (ZCD)-based integrator and an inverter-based integrator. In the coarse-integration step, the ZCD-based integrator performs a fast integration without concern for overshoot or detection delay issues. In the fine-integration step, the inverter-based integrator performs the residual integration with high accuracy. Hence, the TSHI provides fast and accurate integration process. In addition, the TSHI supports trade-off between voltage-scalable bandwidth and power consumption for an energy efficient operation of Internet-of-Things sensor nodes, owing to the scalable operation of the ZCD and inverter. The proposed ${\Delta \Sigma }$ ADC is fabricated in a 65-nm LP CMOS process, and the active area is 0.38 mm2. The fabricated ADC operates at supply voltages from 0.4 to 1 V. Depending on the supply voltage and sampling frequency, the power consumption and bandwidth of the ADC can be scaled from 12.7 to $948~{\mu }{\mathrm{W}}$ and from 7.5 to 400 kHz, respectively. The ADC maintains an SNDR higher than 60 dB within the operating supply range.


title: Molecular analysis of isolates of the cestode Rodentolepis nana from the great gerbil, Rhombomys opimus.

abstract: Rodentolepis nana (syn. Hymenolepis nana) is a cyclophyllidean zoonotic enteric parasite with worldwide distribution. In humans, it is more prevalent in children, especially in temperate zones. A morphologically similar species of R. nana occurs in different rodents, including gerbils. In the present study molecular characterization of five isolates of R. nana from Rhombomys opimus in the Golestan Province (n= 2) and Razavi Khorasan Province (n= 3), both in north-eastern Iran, were analysed. After DNA extraction, the internal transcribed spacer 2 (ITS2) region of the R. nana isolates was amplified and sequenced, and genetic variation was analysed. Accordingly, two isolates from the Razavi Khorasan Province showed ITS2 signatures that differed from the isolates from the Golestan Province. The third isolate from Razavi Khorasan did not reveal these specific signatures, but exhibited sparse nucleotide polymorphisms in the ITS2 sequence. The ITS gene is conserved, and variation in this fragment could be an interesting subject for further inspection of existing variation in the genome of R. nana, among different domestic and wildlife host species and from different areas.


title: Acupuncture for patients with migraine: a randomized controlled trial.

abstract: CONTEXT ::: Acupuncture is widely used to prevent migraine attacks, but the available evidence of its benefit is scarce. ::: ::: ::: OBJECTIVE ::: To investigate the effectiveness of acupuncture compared with sham acupuncture and with no acupuncture in patients with migraine. ::: ::: ::: DESIGN, SETTING, AND PATIENTS ::: Three-group, randomized, controlled trial (April 2002-January 2003) involving 302 patients (88% women), mean (SD) age of 43 (11) years, with migraine headaches, based on International Headache Society criteria. Patients were treated at 18 outpatient centers in Germany. ::: ::: ::: INTERVENTIONS ::: Acupuncture, sham acupuncture, or waiting list control. Acupuncture and sham acupuncture were administered by specialized physicians and consisted of 12 sessions per patient over 8 weeks. Patients completed headache diaries from 4 weeks before to 12 weeks after randomization and from week 21 to 24 after randomization. ::: ::: ::: MAIN OUTCOME MEASURES ::: Difference in headache days of moderate or severe intensity between the 4 weeks before and weeks 9 to 12 after randomization. ::: ::: ::: RESULTS ::: Between baseline and weeks 9 to 12, the mean (SD) number of days with headache of moderate or severe intensity decreased by 2.2 (2.7) days from a baseline of 5.2 (2.5) days in the acupuncture group compared with a decrease to 2.2 (2.7) days from a baseline of 5.0 (2.4) days in the sham acupuncture group, and by 0.8 (2.0) days from a baseline if 5.4 (3.0) days in the waiting list group. No difference was detected between the acupuncture and the sham acupuncture groups (0.0 days, 95% confidence interval, -0.7 to 0.7 days; P = .96) while there was a difference between the acupuncture group compared with the waiting list group (1.4 days; 95% confidence interval; 0.8-2.1 days; P<.001). The proportion of responders (reduction in headache days by at least 50%) was 51% in the acupuncture group, 53% in the sham acupuncture group, and 15% in the waiting list group. ::: ::: ::: CONCLUSION ::: Acupuncture was no more effective than sham acupuncture in reducing migraine headaches although both interventions were more effective than a waiting list control.


title: RC-loaded bow-tie antenna for improved pulse radiation

abstract: In this paper, a loading technique for improving pulse radiation from bow-tie antennas is introduced. This technique allows transmission of short transient pulses with very small late-time ringing and relatively high radiation efficiency. It makes use of a combination of a constant resistive loading along the antenna and a capacitive loading with linearly increasing reactance toward the antenna ends. The constant resistive loading is applied using volumetric microwave absorbers to cover one side of the antenna and the linear capacitive loading is realized by constructing narrow slots on the antenna surface. Relatively high radiation efficiency is achieved by choosing the location of the slot nearest to the feed point in such a way that radiation from it combines constructively with radiation from the feed point. Using a 0.8-ns monocycle for excitation, the technique results in a level of late-time ringing of lower than -40 dB and at the same time the peak value of the transmitted pulse is 54% higher than that of the same antenna without loading.


title: Definitions of groove and hollowness of the infraorbital region and clinical treatment using soft-tissue filler

abstract: Clarification is needed regarding the definitions and classification of groove and hollowness of the infraorbital region depending on the cause, anatomical characteristics, and appearance. Grooves in the infraorbital region can be classified as nasojugal grooves (or folds), tear trough deformities, and palpebromalar grooves; these can be differentiated based on anatomical characteristics. They are caused by the herniation of intraorbital fat, atrophy of the skin and subcutaneous fat, contraction of the orbital part of the orbicularis oculi muscle or squinting, and malar bone resorption. Safe and successful treatment requires an optimal choice of filler and treatment method. The choice between a cannula and needle depends on various factors; a needle is better for injections into a subdermal area in a relatively safe plane, while a cannula is recommended for avoiding vascular compromise when injecting filler into a deep fat layer and releasing fibrotic ligamentous structures. The injection of a soft-tissue filler into the subcutaneous fat tissue is recommended for treating mild indentations around the orbital rim and nasojugal region. Reducing the tethering effect of ligamentous structures by undermining using a cannula prior to the filler injection is recommended for treating relatively deep and fine indentations. The treatment of mild prolapse of the intraorbital septal fat or broad flattening of the infraorbital region can be improved by restoring the volume deficiency using a relatively firm filler.


Neurobiological Mechanism of Acupuncture for Relieving Visceral Pain of Gastrointestinal Origin


title: Cryptocash, cryptocurrencies, and cryptocontracts

abstract: One of the central challenges for mathematical cryptography is to create a payment system that provides the advantages of cash in a digital world. In this expository article we describe two very different solutions to this problem. The first is an elliptic-curve-based version of a construction of Brands, and the second is Bitcoin. We also discuss a generalization of Bitcoin that supports peer-to-peer contracts.


title: Explaining Deep Learning Models - A Bayesian Non-parametric Approach

abstract: Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.


title: Deep Siamese Network with Multi-level Similarity Perception for Person Re-identification

abstract: Person re-identification (re-ID), which aims at spotting a person of interest across multiple camera views, has gained more and more attention in computer vision community. In this paper, we propose a novel deep Siamese architecture based on convolutional neural network (CNN) and multi-level similarity perception. According to the distinct characteristics of diverse feature maps, we effectively apply different similarity constraints to both low-level and high-level feature maps, during training stage. Therefore, our network can efficiently learn discriminative feature representations at different levels, which significantly improves the re-ID performance. Besides, our framework has two additional benefits. Firstly, classification constraints can be easily incorporated into the framework, forming a unified multi-task network with similarity constraints. Secondly, as similarity comparable information has been encoded in the network's learning parameters via back-propagation, pairwise input is not necessary at test time. That means we can extract features of each gallery image and build index in an off-line manner, which is essential for large-scale real-world applications. Experimental results on multiple challenging benchmarks demonstrate that our method achieves splendid performance compared with the current state-of-the-art approaches.


title: 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

abstract: Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease diagnostics based on a convolutional neural network and sMRI and DTI modalities fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). Comparison with a single modality approach shows promising results. We also propose our own method of data augmentation for balancing classes of different size and analyze the impact of the ROI size on the classification results as well.


title: Towards SDN-based fog computing: MQTT broker virtualization for effective and reliable delivery

abstract: Performance of data analytics in Internet of Things (IoTs) depends on effective transport services offered by the underlying network. Fog computing enables independent data-plane computational features at the edge-switches, which serves as a platform for performing certain critical analytics required at the IoT source. To this end, in this paper, we implement a working prototype of Fog computing node based on Software-Defined Networking (SDN). Message Queuing Telemetry Transport (MQTT) is chosen as the candidate IoT protocol that transports data generated from IoT devices (a:k:a: MQTT publishers) to a remote host (called MQTT broker). We implement the MQTT broker functionalities integrated at the edge-switches, that serves as a platform to perform simple message-based analytics at the switches, and also deliver messages in a reliable manner to the end-host for post-delivery analytics. We mathematically validate the improved delivery performance as offered by the proposed switch-embedded brokers.


title: A Framework for Projected Clustering of High Dimensional Data Streams

abstract: The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams. ::: ::: In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.


title: The molecular and cellular biology of enhanced cognition

abstract: Most molecular and cellular studies of cognitive function have focused on either normal or pathological states, but recent research with transgenic mice has started to address the mechanisms of enhanced cognition. These results point to key synaptic and nuclear signalling events that can be manipulated to facilitate the induction or increase the stability of synaptic plasticity, and therefore enhance the acquisition or retention of information. Here, we review these surprising findings and explore their implications to both mechanisms of learning and memory and to ongoing efforts to develop treatments for cognitive disorders. These findings represent the beginning of a fundamental new approach in the study of enhanced cognition.


title: Data aggregators, consumer data, and responsibility online: Who is tracking consumers online and should they stop?

abstract: The goal of this article is to examine the strategic choices of firms collecting consumer data online and to identify the roles and obligations of the actors within the current network of online tracking. In doing so, the focus shifts from placing the onus on individuals to make an informed choice, to justifying the roles and responsibilities of firms when gathering, aggregating, and using consumers' interests or behavior online. Firms online are uniquely positioned to undercut or to respect privacy expectations within three possible roles: as a member of a supply chain of information traders, within a network of surveillance online, and as an arm of law enforcement. These firms benefit from aggregating and analyzing consumer data and have an associated responsibility to not only minimize the harm to consumers but also to enact change where the firm is in the most knowledgeable and powerful position.


title: Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs

abstract: This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs) are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%.


title: InfoGather+: semantic matching and annotation of numeric and time-varying attributes in web tables

abstract: Users often need to gather information about "entities" of interest. Recent efforts try to automate this task by leveraging the vast corpus of HTML tables; this is referred to as "entity augmentation". The accuracy of entity augmentation critically depends on semantic relationships between web tables as well as semantic labels of those tables. Current techniques work well for string-valued and static attributes but perform poorly for numeric and time-varying attributes. In this paper, we first build a semantic graph that (i) labels columns with unit, scale and timestamp information and (ii) computes semantic matches between columns even when the same numeric attribute is expressed in different units or scales. Second, we develop a novel entity augmentation API suited for numeric and time-varying attributes that leverages the semantic graph. Building the graph is challenging as such label information is often missing from the column headers. Our key insight is to leverage the wealth of tables on the web and infer label information from semantically matching columns of other web tables; this complements "local" extraction from column headers. However, this creates an interdependence between labels and semantic matches; we address this challenge by representing the task as a probabilistic graphical model that jointly discovers labels and semantic matches over all columns. Our experiments on real-life datasets show that (i) our semantic graph contains higher quality labels and semantic matches and (ii) entity augmentation based on the above graph has significantly higher precision and recall compared with the state-of-the-art.


title: Configuration Management Concept for Lyee Software

abstract: A configuration management concept is presented for software projects using Lyee methodology. To show this concept an introduction in configuration management is given. Then, the structure of Lyee programs is redefined by sets and their dependencies. From this redefined structure, the actual configuration management concept is derivated and discussed.


title: A survey of intelligent assistants for data analysis

abstract: Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically nontrivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data. This article provides a first survey to almost 30 years of research on intelligent discovery assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ideal future IDA as well as the challenges on the road ahead.


title: Exo-Glove PM: An Easily Customizable Modularized Pneumatic Assistive Glove

abstract: Customization is an important issue for assistive gloves because it affects glove performance. In this letter, we propose an assembly based customizable soft pneumatic assistive glove, named Exo-Glove PM. Pneumatic soft robots generally consist of a single structure with embedded actuators. However, when assembled, the region where the assemblies are connected easily undergoes large stress concentration that leads to failure. To overcome this issue, we have developed a hybrid actuator module that combines a soft actuation structure and rigid joining methods. In general, rigid joining methods require a bulky design to enable easy assembly, but Exo-Glove PM is designed to create pathways for assembly tools to access the bolt heads by bending certain parts. This novel way of joining assemblies allows soft robots to be built of multiple parts while minimizing the volume. It ensures small module size and enables modules to cover a wide range of hand sizes; the distance between each module is matched to the length of the user's phalanges using spacers. This approach allows users to customize the glove by assembly of standardized modules and maximizes the comfort and the performance of the glove without custom manufacturing and, thus, reduces costs.


title: Building Natural Language Interfaces to Web APIs

abstract: As the Web evolves towards a service-oriented architecture, application program interfaces (APIs) are becoming an increasingly important way to provide access to data, services, and devices. We study the problem of natural language interface to APIs (NL2APIs), with a focus on web APIs for web services. Such NL2APIs have many potential benefits, for example, facilitating the integration of web services into virtual assistants. We propose the first end-to-end framework to build an NL2API for a given web API. A key challenge is to collect training data, i.e., NL command-API call pairs, from which an NL2API can learn the semantic mapping from ambiguous, informal NL commands to formal API calls. We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands. We optimize the crowdsourcing process to further reduce the cost. More specifically, we propose a novel hierarchical probabilistic model for the crowdsourcing process, which guides us to allocate budget to those API calls that have a high value for training NL2APIs. We apply our framework to real-world APIs, and show that it can collect high-quality training data at a low cost, and build NL2APIs with good performance from scratch. We also show that our modeling of the crowdsourcing process can improve its effectiveness, such that the training data collected via our approach leads to better performance of NL2APIs than a strong baseline.


title: Availability Analysis and Optimization in CoMP and CA-enabled HetNets

abstract: Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets.


Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network


title: EMI Reduction via Spread Spectrum in DC/DC Converters: State of the Art, Optimization, and Tradeoffs

abstract: Spread spectrum is a technique introduced for mitigating electromagnetic interference (EMI) problems in many class of circuits. In this paper, with particular emphasis on switching DC/DC converters, we consider the most common and most efficient known spreading techniques, looking for spreading parameters that ensure the highest EMI reduction and the lowest performance reduction in the circuit where the spreading is applied. The result is an interesting tradeoff not only between EMI reduction and performance drop, but also on the EMI reduction itself when considering different EMI victim models. The proposed analysis is supported by measurements on two switching DC/DC converters: 1) based on pulse-width modulation and 2) based on the resonant converter class.


title: A multi-channel Rx for 76.5GHz automotive radar applications with 55dB IF channel-to-channel isolation

abstract: A multi-channel RX for automotive radar applications at 76.5GHz is presented. The chip uses a LO input signal at 38.25GHz which is multiplied on chip. The single-ended IF outputs show a noise density of −143dBm/Hz at 100kHz with a typical conversion gain of 18dB. The large signal IF channel-to-channel isolation is ≫ 55dB. The 14mm2 chip consumes 520mA from +3.3V supply in a 200GHz f T SiGe BiCMOS process.


title: Shakeout: A New Approach to Regularized Deep Neural Network Training

abstract: Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines $L_{0}$ , $L_{1}$ and $L_{2}$ regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.


title: Certifying and constructing minimally rigid graphs in the plane

abstract: We study minimally rigid graphs in the plane or plane isostatic graphs. These graphs (also called Laman graphs) admit characterizations based on decomposition into trees (Crapo's theorem and Recski's theorem). Tree partitions can be viewed as certificates of plane isostatic graphs. Unfortunately, they require Ω(n2) time to verify their validity where n is the number of vertices in the graph. We present a new construction (which can be viewed as a hierarchical decomposition of the graph) called red-black hierarchy that (i) is a certificate for plane isostatic graphs, and (ii) can be verified in linear time. We also show that it can be computed in O(n2) time.A classical result in Rigidity Theory by Henneberg [9] states that the plane isostatic graphs can be constructed incrementally by special vertex insertions. We study the following computational problem: given a Laman graph G, compute a sequence of Henneberg insertions that yields G. We show that the red-bl ack hierarchy can be used to compute a Henneberg construction in O(n2) time. Applied to planar graphs our algorithm can speed up a recent algorithm by Haas et al. [8] for embedding a planar Laman graph as a pointed pseudo-triangulation by a factor of O(n).


BosphorusSign: A Turkish Sign Language Recognition Corpus in Health and Finance Domains.


title: A Passive Filter Building Block for Input or Output Current Ripple Cancellation in a Power Converter

abstract: This paper analyzes a coupled inductor based $LL$ – $LC$ network as a building block to achieve input or output current ripple cancellation in power converters. The proposed current ripple cancellation technique works on the principle of inductance matching. It also provides reduced current stress on the switching devices of the converter compared with the other current ripple cancellation networks discussed in the literature. The current ripple cancellation technique is verified in achieving input and output current ripple minimization in different converters. The effect of inductance mismatch, inductor and capacitor tolerances, and circuit parasitic is also studied. Some of the published research works are demonstrated to be a special case of the proposed idea. The effectiveness of the proposed method is validated by demonstrating the zero-ripple input current using a 31 V dc to 60 V dc, 240 W boost converter prototype and a 31 V dc to 115 V (rms) ac, 320 W current-fed-switched inverter prototype, and the zero-ripple output current using a 62 V to 42 V dc, 450 W buck converter prototype.


title: Java Concurrency in Practice

abstract: "I was fortunate indeed to have worked with a fantastic team on the design and implementation of the concurrency features added to the Java platform in Java 5.0 and Java 6. Now this same team provides the best explanation yet of these new features, and of concurrency in general. Concurrency is no longer a subject for advanced users only. Every Java developer should read this book." --Martin Buchholz JDK Concurrency Czar, Sun Microsystems"For the past 30 years, computer performance has been driven by Moore's Law; from now on, it will be driven by Amdahl's Law. Writing code that effectively exploits multiple processors can be very challenging. Java Concurrency in Practice provides you with the concepts and techniques needed to write safe and scalable Java programs for today's--and tomorrow's--systems." --Doron Rajwan Research Scientist, Intel Corp"This is the book you need if you're writing--or designing, or debugging, or maintaining, or contemplating--multithreaded Java programs. If you've ever had to synchronize a method and you weren't sure why, you owe it to yourself and your users to read this book, cover to cover." --Ted Neward Author of Effective Enterprise Java"Brian addresses the fundamental issues and complexities of concurrency with uncommon clarity. This book is a must-read for anyone who uses threads and cares about performance." --Kirk Pepperdine CTO, JavaPerformanceTuning.com"This book covers a very deep and subtle topic in a very clear and concise way, making it the perfect Java Concurrency reference manual. Each page is filled with the problems (and solutions!) that programmers struggle with every day. Effectively exploiting concurrency is becoming more and more important now that Moore's Law is delivering more cores but not faster cores, and this book will show you how to do it." --Dr. Cliff Click Senior Software Engineer, Azul Systems"I have a strong interest in concurrency, and have probably written more thread deadlocks and made more synchronization mistakes than most programmers. Brian's book is the most readable on the topic of threading and concurrency in Java, and deals with this difficult subject with a wonderful hands-on approach. This is a book I am recommending to all my readers of The Java Specialists' Newsletter, because it is interesting, useful, and relevant to the problems facing Java developers today." --Dr. Heinz Kabutz The Java Specialists' Newsletter"I've focused a career on simplifying simple problems, but this book ambitiously and effectively works to simplify a complex but critical subject: concurrency. Java Concurrency in Practice is revolutionary in its approach, smooth and easy in style, and timely in its delivery--it's destined to be a very important book." --Bruce Tate Author of Beyond Java"Java Concurrency in Practice is an invaluable compilation of threading know-how for Java developers. I found reading this book intellectually exciting, in part because it is an excellent introduction to Java's concurrency API, but mostly because it captures in a thorough and accessible way expert knowledge on threading not easily found elsewhere." --Bill Venners Author of Inside the Java Virtual MachineThreads are a fundamental part of the Java platform. As multicore processors become the norm, using concurrency effectively becomes essential for building high-performance applications. Java SE 5 and 6 are a huge step forward for the development of concurrent applications, with improvements to the Java Virtual Machine to support high-performance, highly scalable concurrent classes and a rich set of new concurrency building blocks. In Java Concurrency in Practice, the creators of these new facilities explain not only how they work and how to use them, but also the motivation and design patterns behind them.However, developing, testing, and debugging multithreaded programs can still be very difficult; it is all too easy to create concurrent programs that appear to work, but fail when it matters most: in production, under heavy load. Java Concurrency in Practice arms readers with both the theoretical underpinnings and concrete techniques for building reliable, scalable, maintainable concurrent applications. Rather than simply offering an inventory of concurrency APIs and mechanisms, it provides design rules, patterns, and mental models that make it easier to build concurrent programs that are both correct and performant.This book covers: Basic concepts of concurrency and thread safety Techniques for building and composing thread-safe classes Using the concurrency building blocks in java.util.concurrent Performance optimization dos and don'ts Testing concurrent programs Advanced topics such as atomic variables, nonblocking algorithms, and the Java Memory Model


title: Therminator: a thermal simulator for smartphones producing accurate chip and skin temperature maps

abstract: Maintaining safe chip and device skin temperatures in small form-factor mobile devices (such as smartphones and tablets) while continuing to add new functionalities and provide higher performance has emerged as a key challenge. This paper presents Therminator, an early stage, fast, full-device thermal analyzer, which generates accurate steady-state temperature maps of the entire smartphone starting from the Application Processor and other key device components, extending to the skin of the device itself. The thermal analysis is sensitive to detailed device specifications (including its material composition and 3-D layout) as well as different use cases (each case specifying the set of active device components and their activity levels). Therminator considers all major components within the device, builds a corresponding compact thermal model for each component and the whole device, and produces their steady-state temperature maps. Temperature results obtained by using Therminator have been validated against a commercial computational fluid dynamics-based tool, i.e., Autodesk Simulation CFD, and thermocouple measurements on a Qualcomm Mobile Developer Platform. A case study on a Samsung Galaxy S4 using Therminator is provided to relate the device performance to the skin temperature and investigate the thermal path design.


title: Developing Computational Thinking through a Virtual Robotics Programming Curriculum

abstract: Computational thinking describes key principles from computer science that are broadly generalizable. Robotics programs can be engaging learning environments for acquiring core computational thinking competencies. However, few empirical studies evaluate the effectiveness of a robotics programming curriculum for developing computational thinking knowledge and skills. This study measures pre/post gains with new computational thinking assessments given to middle school students who participated in a virtual robotics programming curriculum. Overall, participation in the virtual robotics curriculum was related to significant gains in pre- to posttest scores, with larger gains for students who made further progress through the curriculum. The success of this intervention suggests that participation in a scaffolded programming curriculum, within the context of virtual robotics, supports the development of generalizable computational thinking knowledge and skills that are associated with increased problem-solving performance on nonrobotics computing tasks. Furthermore, the particular units that students engage in may determine their level of growth in these competencies.


title: Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps

abstract: Objective Accurate endoscopic differentiation would enable to resect and discard small and diminutive colonic lesions, thereby increasing cost-efficiency. Current classification systems based on narrow band imaging (NBI), however, do not include neoplastic sessile serrated adenomas/polyps (SSA/Ps). We aimed to develop and validate a new classification system for endoscopic differentiation of adenomas, hyperplastic polyps and SSA/Ps Design We developed the Workgroup serrAted polypS and Polyposis (WASP) classification, combining the NBI International Colorectal Endoscopic classification and criteria for differentiation of SSA/Ps in a stepwise approach. Ten consultant gastroenterologists predicted polyp histology, including levels of confidence, based on the endoscopic aspect of 45 polyps, before and after participation in training in the WASP classification. After 6 months, the same endoscopists predicted polyp histology of a new set of 50 polyps, with a ratio of lesions comparable to daily practice. Results The accuracy of optical diagnosis was 0.63 (95% CI 0.54 to 0.71) at baseline, which improved to 0.79 (95% CI 0.72 to 0.86, p Conclusions We developed and validated the first integrative classification method for endoscopic differentiation of small and diminutive adenomas, hyperplastic polyps and SSA/Ps. In a still image evaluation setting, introduction of the WASP classification significantly improved the accuracy of optical diagnosis overall as well as SSA/P in particular, which proved to be sustainable after 6 months.


title: Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware

abstract: As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of today, the discussion really turns from how the massive data sets are collected to how value can be derived from them, i.e., how to extract knowledge out of such (big) data. IoT devices are used in an ever-growing number of application domains (see Figure 1), ranging from sports gadgets (e.g., Fitbits and Apple Watches) or more serious medical devices (e.g., pacemakers and biochips) to smart homes, cities, and self-driving cars, to predictive maintenance in missioncritical systems (e.g., in nuclear power plants or airplanes). Such applications introduce endless possibilities for better understanding, learning, and informedly acting (i.e., situational awareness and actionable information in government lingo). Although rapid expansion of devices and sensors brings terrific opportunities for taking advantage of terabytes of machine data, the mind-boggling task of understanding growth of data remains and heavily relies on artificial intelligence and machine learning [1], [2].


title: Point cloud compression based on hierarchical point clustering

abstract: In this work we propose an algorithm for compressing the geometry of a 3D point cloud (3D point-based model). The proposed algorithm is based on the hierarchical clustering of the points. Starting from the input model, it performs clustering to the points to generate a coarser approximation, or a coarser level of detail (LOD). Iterating this clustering process, a sequence of LODs are generated, forming an LOD hierarchy. Then, the LOD hierarchy is traversed top down in a width-first order. For each node encountered during the traversal, the corresponding geometric updates associated with its children are encoded, leading to a progressive encoding of the original model. Special efforts are made in the clustering to maintain high quality of the intermediate LODs. As a result, the proposed algorithm achieves both generic topology applicability and good ratedistortion performance at low bitrates, facilitating its applications for low-end bandwidth and/or platform configurations.


title: Statistical body height estimation from a single image

abstract: We address the problem of estimating a person's body height from a single uncalibrated image. The novelty of our work lies in that we handle two difficult cases not previously addressed in the literature: (i) the image contains no reference length in the background scene to indicate absolute scale, (ii) the image contains the upper body part only. In a nutshell, our method combines well-known ideas from projective geometry and single-view metrology with prior probabilistic/statistical knowledge of human anthropometry, in a Bayesian-like framework. The method is demonstrated with synthetic (randomly generated) data as well as a dataset of 96 frontal images.


title: Causes vs Benefits in the Evolution of Prey Grouping

abstract: The presence of predators alters the evolutionary pressures acting on prey populations, often driving them to engage in new behaviors in order to avoid being the target of an attack. One common antipredator behavior is group formation, which can reduce the odds of any individual prey being the target of a given attack, typically scaling inversely with the number of prey in the group. This “dilution effect” is often hypothesized to be the primary driver of prey group formation as an anti-predator strategy. However, groups may help with predator avoidance in other ways as well. For example, prey behaviors or physical characteristics that visually confuse predators may reduce their ability to target an individual and make a kill. Indeed, some have suggested that the “predator confusion” effect alone is sufficient to drive the evolution of grouping in prey. Here we examine coevolving populations of predators and prey using the Avida digital evolution platform. We evaluate the relative importance of these two potential drivers of the evolution of prey grouping and show that the dilution effect, an inherent property of most prey groups, readily creates the pressures necessary for the evolution of prey grouping. In contrast, we found no evidence that predator confusion plays a significant role in prey group formation. Instead, the dilution effect alone is indicated as the primary driver of antipredator prey grouping strategies.


title: PTL-and clock-pulse circuit driven novel shift register architecture

abstract: In this paper, low power and area efficient 16-bit SISO(Serial In Serial Out) shift register is proposed. In this proposed design, the master-slave flip-flops are replaced by pulsed latches called SSASPL(Static differential Sense-Amp Shared Pulsed Latch)in order to reduce the area and power consumption. The timing issue of the pulsed latch is overcome by generating multiple non-overlap delayed short pulsed clock signals instead of using single pulsed clock signal. The shift register is divided into sub shift registers to reduce the number of clock buffers. Two circuits of256-bitand a 16-bitshift register(driven by CMOS-AND based clock pulse generator) are also implemented for study and comparison with proposed design. The proposed 16-bit shift register is driven by PTL-AND based clock pulse generator and is implemented using CMOS 0.18μm technology in Cadence Virtuoso. The proposed 16-bit shift register using PTL-AND clock pulse circuit consumes 14% less power and 4% less area compared to CMOS-AND pulse generation circuit.


title: Sensor networks for medical care

abstract: Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and


title: Transforming Web Tables to a Relational Database

abstract: HTML tables represent a significant fraction of web data. The often complex headers of such tables are determined accurately using their indexing property. Isolated headers are factored to extract category hierarchies. Web tables are then transformed into a canonical form and imported into a relational database. The proposed processing allows for the formulation of arbitrary SQL queries over the collection of induced relational tables.


title: Combining ensemble methods and social network metrics for improving accuracy of OCSVM on intrusion detection in SCADA systems

abstract: Modern Supervisory Control and Data Acquisition SCADA systems used by the electric utility industry to monitor and control electric power generation, transmission and distribution are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasing numbers of widely distributed components. The presence of a real time intrusion detection mechanism, which can cope with di?erent types of attacks, is of great importance, in order to defend a system against cyber attacks This defense mechanism must be distributed, cheap and above all accurate, since false positive alarms, or mistakes regarding the origin of the intrusion mean severe costs for the system. Recently an integrated detection mechanism, namely IT-OCSVM was proposed, which is distributed in a SCADA network as a part of a distributed intrusion detection system (IDS), providing accurate data about the origin and the time of an intrusion. In this paper we also analyze the architecture of the integrated detection mechanism and we perform extensive simulations based on real cyber attacks in a small SCADA testbed in order to evaluate the performance of the proposed mechanism.


title: Causal Inference

abstract: This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.


title: Is Learning The n-th Thing Any Easier Than Learning The First?

abstract: This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.


title: Abstract Chronic social stress: effects on limbic brain structures

abstract: Different types of stressors are known to activate distinct neuronal circuits in the brain. Acute physiological stimuli that are life threatening and require immediate reactions lead to a rapid stimulation of brainstem and hypothalamus to activate efferent visceral pathways. In contrast, psychological stressors activate higher-order brain structures for further interpretations of the perceived endangerment. Common to the later multimodal stressors is that they need cortical processing and, depending on previous experience or ongoing activation, the information is assembled within limbic circuits connecting, e.g., the hippocampus, amygdala and prefrontal cortex to induce neuroendocrine and behavioral responses. In view of the fact that stressful life events often contribute to the etiology of psychopathologies such as depressive episodes, several animal models have been developed to study central nervous mechanisms that are induced by stress. The present review summarizes observations made in the tree shrew chronic psychosocial stress paradigm with particular focus on neurotransmitter systems and structural changes in limbic brain regions.


Fault-Tolerant Frequent Pattern Mining: Problems and Challenges


title: Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm

abstract: Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an Expectation-Maximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semi-supervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN. We share our source code at this https URL


title: Ant Algorithms for Discrete Optimization

abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.


title: Simple PPDB: A Paraphrase Database for Simplification

abstract: We release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains 4.5 million paraphrase rules, making it the largest available resource for lexical simplification.


title: On the Effectiveness of Least Squares Generative Adversarial Networks

abstract: Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson $\chi^2$ divergence. We also present a theoretical analysis about the properties of LSGANs and $\chi^2$ divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. For evaluating the image quality, we train LSGANs on several datasets including LSUN and a cat dataset, and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. Furthermore, we evaluate the stability of LSGANs in two groups. One is to compare between LSGANs and regular GANs without gradient penalty. We conduct three experiments, including Gaussian mixture distribution, difficult architectures, and a new proposed method --- datasets with small variance, to illustrate the stability of LSGANs. The other one is to compare between LSGANs with gradient penalty and WGANs with gradient penalty (WGANs-GP). The experimental results show that LSGANs with gradient penalty succeed in training for all the difficult architectures used in WGANs-GP, including 101-layer ResNet.


title: Multiple feature-enhanced synthetic aperture radar imaging

abstract: Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images.


title: Gender differences in perceptions of web-based shopping

abstract: Women have yet to welcome Web-based shopping as readily as men. A primary factor for this state is how men and women view shopping. Understanding those differences will help vendors address this vital pool of consumers.


title: Yield Trends Are Insufficient to Double Global Crop Production by 2050

abstract: Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.


Variance Reduction Methods II 1 Importance Sampling


title: Private memoirs of a smart meter

abstract: Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.


title: Effectiveness of Balancing Everyday Life (BEL) versus standard occupational therapy for activity engagement and functioning among people with mental illness – a cluster RCT study

abstract: Many with a mental illness have an impoverished everyday life with few meaningful activities and a sedentary lifestyle. The study aim was to evaluate the effectiveness of the 16-week Balancing Everyday Life (BEL) program, compared to care as usual (CAU), for people with mental illness in specialized and community-based psychiatric services. The main outcomes concerned different aspects of subjectively evaluated everyday activities, in terms of the engagement and satisfaction they bring, balance among activities, and activity level. Secondary outcomes pertained to various facets of well-being and functioning. It was hypothesized that those who received the BEL intervention would improve more than the comparison group regarding activity, well-being and functioning outcomes. BEL is a group and activity-based lifestyle intervention. CAU entailed active support, mainly standard occupational therapy. The BEL group included 133 participants and the CAU group 93. They completed self-report questionnaires targeting activity and well-being on three occasions – at baseline, after completed intervention (at 16 weeks) and at a six-month follow-up. A research assistant rated the participants’ level of functioning and symptom severity on the same occasions. Non-parametric statistics were used since these instruments produced ordinal data. The BEL group improved more than the CAU group from baseline to 16 weeks on primary outcomes in terms of activity engagement (p < 0.001), activity level (p = 0.036) and activity balance (p < 0.042). The BEL group also improved more on the secondary outcomes of symptom severity (p < 0.018) and level of functioning (p < 0.046) from baseline to 16 weeks, but not on well-being. High intra-class correlations (0.12–0.22) indicated clustering effects for symptom severity and level of functioning. The group differences on activity engagement (p = 0.001) and activity level (p = 0.007) remained at the follow-up. The BEL group also improved their well-being (quality of life) more than the CAU group from baseline to the follow-up (p = 0.049). No differences were found at that time for activity balance, level of functioning and symptom severity. The BEL program was effective compared to CAU in terms of activity engagement. Their improvements were not, however, greater concerning other subjective perceptions, such as satisfaction with daily activities and self-rated health, and clustering effects lowered the dependability regarding findings of improvements on symptoms and functioning. Although the CAU group had “caught up” at the follow-up, the BEL group had improved more on general quality of life. BEL appeared to be important in shortening the time required for participants to develop their engagement in activity and in attaining improved quality of life in a follow-up perspective. The study was registered with ClinicalTrial.gov. Reg. No. NCT02619318 ::: .


title: Particle swarm optimization for hyper-parameter selection in deep neural networks

abstract: Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.


title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

abstract: a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.


title: Presto: distributed machine learning and graph processing with sparse matrices

abstract: It is cumbersome to write machine learning and graph algorithms in data-parallel models such as MapReduce and Dryad. We observe that these algorithms are based on matrix computations and, hence, are inefficient to implement with the restrictive programming and communication interface of such frameworks. In this paper we show that array-based languages such as R [3] are suitable for implementing complex algorithms and can outperform current data parallel solutions. Since R is single-threaded and does not scale to large datasets, we have built Presto, a distributed system that extends R and addresses many of its limitations. Presto efficiently shares sparse structured data, can leverage multi-cores, and dynamically partitions data to mitigate load imbalance. Our results show the promise of this approach: many important machine learning and graph algorithms can be expressed in a single framework and are substantially faster than those in Hadoop and Spark.


title: Dude, where's my card?: RFID positioning that works with multipath and non-line of sight

abstract: RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.


title: Do developers care about code smells? An exploratory survey

abstract: Code smells are a well-known metaphor to describe symptoms of code decay or other issues with code quality which can lead to a variety of maintenance problems. Even though code smell detection and removal has been well-researched over the last decade, it remains open to debate whether or not code smells should be considered meaningful conceptualizations of code quality issues from the developer's perspective. To some extent, this question applies as well to the results provided by current code smell detection tools. Are code smells really important for developers? If they are not, is this due to the lack of relevance of the underlying concepts, due to the lack of awareness about code smells on the developers' side, or due to the lack of appropriate tools for code smell analysis or removal? In order to align and direct research efforts to address actual needs and problems of professional developers, we need to better understand the knowledge about, and interest in code smells, together with their perceived criticality. This paper reports on the results obtained from an exploratory survey involving 85 professional software developers.


title: Theory and evaluation of human robot interactions

abstract: Human-robot interaction (HRI) for mobile robots is still in its infancy. Most user interactions with robots have been limited to tele-operation capabilities where the most common interface provided to the user has been the video feed from the robotic platform and some way of directing the path of the robot. For mobile robots with semiautonomous capabilities, the user is also provided with a means of setting way points. More importantly, most HRI capabilities have been developed by robotics experts for use by robotics experts. As robots increase in capabilities and are able to perform more tasks in an autonomous manner we need to think about the interactions that humans will have with robots and what software architecture and user interface designs can accommodate the human in-the-loop. We also need to design systems that can be used by domain experts but not robotics experts. This paper outlines a theory of human-robot interaction and proposes the interactions and information needed by both humans and robots for the different levels of interaction, including an evaluation methodology based on situational awareness.


abstract: Gesture-recognition is an important component for many intelligent human---computer interaction applications. For example, a realtime sign-language recognition system would detect and interpret hand gestures. Many vision-based sign-language recognition methods have been proposed over the years with mix results of usability. Some system are limited to recognize only a few gestures, while others require the use of 3D camera to provides depth information to improve recognition accuracy. In this paper, a Kinect-based Taiwanese sign-language recognition system is proposed. Three main features are extracted from the signing gestures, namely hand positions, hand signing direction, and hand shapes. The hand positions are readily available through the input sensor. The signing direction is determined using HMM on trajectory of the hand movement, and a SVM is trained and used to recognize the hand shapes. Experimental results show that the proposed system achieved an 85.14 % recognition rate.


title: Flow Energy Harvesting Using Piezoelectric Cantilevers With Cylindrical Extension

abstract: We present a new piezoelectric flow energy harvester (PFEH) based on a piezoelectric cantilever (PEC) with a cylindrical extension. The flow-induced vibration of the cylindrical extension causes the PEC to vibrate at the natural frequency of the PFEH. The PFEH provides a low-cost, compact, and scalable power source for small electronics by harvesting energy from ambient flows such as wind and water streams. Prototypes were tested in both laminar and turbulent air flows, demonstrating the feasibility of the design. Turbulence excitation was found to be the dominant driving mechanism of the PFEH with additional vortex shedding excitation contribution in the lock-in region.


title: Power Analysis Resistant SRAM

abstract: The power consumption of a standard CMOS SRAM during read/write operations is dependent on the address applied, the data accessed, and the type of access (read/write). The power analysis resistant SRAM structure presented in this work reduces the dependency of power consumption on data and address 10 times compared to standard SRAM at the expense of higher power and silicon area consumption. Modifications on the primitive SRAM cell, address decoding logic and write operation were made to reduce the dependency.


REVIEW EVALUATION AND TREATMENT OF THE OVERACTIVE BLADDER


title: BIST for network-on-chip interconnect infrastructures

abstract: In this paper, we present a novel built-in self-test methodology for testing the inter-switch links of network-on-chip (NoC) based chips. This methodology uses a high-level fault model that accounts for crosstalk effects due to inter-wire coupling. The novelty of our approach lies in the progressive reuse of the NoC infrastructure to transport test data to its own components under test in a bootstrap manner, and in extensively exploiting the inherent parallelism of the data transport mechanism to reduce the test time and implicitly the test cost.


title: Amazon Elastic Compute Cloud (EC2) vs. In-House HPC Platform: A Cost Analysis

abstract: Since its advent in the middle of the 2000's, the Cloud Computing (CC) paradigm is increasingly advertised as THE solution to most IT problems. While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, several voices (most probably commercial ones) emit the wish that CC platforms could also serve HPC needs and eventually replace in-house HPC platforms. If we exclude the pure performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when submitted to an High Performance Computing (HPC) workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by evaluating the Total Cost of Ownership (TCO) of the in-house HPC facility we operate since 2007 within the University of Luxembourg (UL), and compare it with the investment that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is two-fold. First we propose a theoretical price - performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on our own cluster TCO and taking into account all the Operating Expense (OPEX), we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balance the common intuition in favor of Cloud Computing (CC) platforms, would they be provided by the reference Cloud provider worldwide.


T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition


title: Effective summarization method of text documents

abstract: In this paper, we propose text summarization method that creates text summary by definition of the relevance score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every sentence in the document with characteristic vector of words, which appear in the document, and calculation of relevance score for each sentence. The relevance score of sentence is determined through its comparison with all the other sentences in the document and with the document title by cosine measure. Prior to application of this method the scope of features is defined and then the weight of each word in the sentence is calculated with account of those features. The weights of features, influencing relevance of words, are determined using genetic algorithms.


A theory of the learnable


title: Modeling and Control of Dual-Stage High-Power Multifunctional PV System in d - Q - $o$ Coordinate

abstract: A high-power photovoltaic (PV) system based on dual-stage topology of boost converter plus paralleled four-leg inverter is presented, which can perform as both grid-connected inverter and active power filter. The system not only can allow a wide range of input voltage from PV arrays, but also can compensate the nonlinear and unbalanced load current. This paper first presents the small-signal model of the presented inverter with LCL filter in d -q-o coordinate; then, the unified control strategy which combines PV power injection and distortional current compensation is demonstrated; finally, a method to control the zero-sequence circulating current between paralleled inverters is proposed. Simulations and experimental results are carried out on a 100-kVA prototype to validate the practicability of the whole system.


title: Design and antagonistic control of a tendon-driven Minimally Invasive Surgical robotic tool

abstract: This article is concerned with the design, implementation and control of a redundant robotic tool for Minimal Invasive Surgical (MIS) operations. The robotic tool is modular, comprised of identical stages of dual rotational Degrees of Freedom (DoF). An antagonistic tendon-driven mechanism using two DC-motors in a puller-follower configuration is used for each DoF. The inherent Coulomb friction is compensated using an adaptive scheme while varying the follower's reaction. Preliminary experimental results are provided to investigate the efficiency of the robot in typical surgical manoeuvres.


title: Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010

abstract: PROBLEM/CONDITION ::: Autism spectrum disorder (ASD). ::: ::: ::: PERIOD COVERED ::: 2010. ::: ::: ::: DESCRIPTION OF SYSTEM ::: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system in the United States that provides estimates of the prevalence of ASD and other characteristics among children aged 8 years whose parents or guardians live in 11 ADDM sites in the United States. ADDM surveillance is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional providers in the community. Multiple data sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, most ADDM Network sites also review and abstract records of children receiving special education services in public schools. The second phase involves review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if a comprehensive evaluation of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides updated prevalence estimates for ASD from the 2010 surveillance year. In addition to prevalence estimates, characteristics of the population of children with ASD are described. ::: ::: ::: RESULTS ::: For 2010, the overall prevalence of ASD among the ADDM sites was 14.7 per 1,000 (one in 68) children aged 8 years. Overall ASD prevalence estimates varied among sites from 5.7 to 21.9 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and racial/ethnic group. Approximately one in 42 boys and one in 189 girls living in the ADDM Network communities were identified as having ASD. Non-Hispanic white children were approximately 30% more likely to be identified with ASD than non-Hispanic black children and were almost 50% more likely to be identified with ASD than Hispanic children. Among the seven sites with sufficient data on intellectual ability, 31% of children with ASD were classified as having IQ scores in the range of intellectual disability (IQ ≤70), 23% in the borderline range (IQ = 71-85), and 46% in the average or above average range of intellectual ability (IQ >85). The proportion of children classified in the range of intellectual disability differed by race/ethnicity. Approximately 48% of non-Hispanic black children with ASD were classified in the range of intellectual disability compared with 38% of Hispanic children and 25% of non-Hispanic white children. The median age of earliest known ASD diagnosis was 53 months and did not differ significantly by sex or race/ethnicity. ::: ::: ::: INTERPRETATION ::: These findings from CDC's ADDM Network, which are based on 2010 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD in multiple communities in the United States. Because the ADDM Network sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States population. Consistent with previous reports from the ADDM Network, findings from the 2010 surveillance year were marked by significant variations in ASD prevalence by geographic area, sex, race/ethnicity, and level of intellectual ability. The extent to which this variation might be attributable to diagnostic practices, underrecognition of ASD symptoms in some racial/ethnic groups, socioeconomic disparities in access to services, and regional differences in clinical or school-based practices that might influence the findings in this report is unclear. ::: ::: ::: PUBLIC HEALTH ACTION ::: ADDM Network investigators will continue to monitor the prevalence of ASD in select communities, with a focus on exploring changes within these communities that might affect both the observed prevalence of ASD and population-based characteristics of children identified with ASD. Although ASD is sometimes diagnosed by 2 years of age, the median age of the first ASD diagnosis remains older than age 4 years in the ADDM Network communities. Recommendations from the ADDM Network include enhancing strategies to address the need for 1) standardized, widely adopted measures to document ASD severity and functional limitations associated with ASD diagnosis; 2) improved recognition and documentation of symptoms of ASD, particularly among both boys and girls, children without intellectual disability, and children in all racial/ethnic groups; and 3) decreasing the age when children receive their first evaluation for and a diagnosis of ASD and are enrolled in community-based support systems.


title: Adaptive edge-based stereo block matching algorithm for a mobile Graphics Processing Unit

abstract: Nowadays most of the applications of the stereo vision are related to the mobile devices, especially in the field of robotics. It is crucial to develop efficient and robust algorithms that would allow real time operation in a wide range of environments. This paper presents an efficient adaptive algorithm of stereo matching that was applied and optimized for the mobile Graphics Processing Unit. It is a well known problem that most of the stereo vision algorithms are based on the dense stereo matching methods that in most of the cases are the main factor for a demanding computation cost. The presented method introduces a novel approach to the stereo matching problem by adaptively combining the cost function that can be computed efficiently for small matching window and sparse accumulated windows similar to those applied in convolutional neural networks. Adaptability of this method is based on detecting edges in the processed images. Such a solution allows obtaining precise subpixel results on highly textured regions of the image as well as to obtain stable results on weakly matchable texture-less regions while sustaining high efficiency and not causing problems related to the memory bandwidth bottleneck.


title: An underactuated propeller for attitude control in micro air vehicles

abstract: Traditional coaxial helicopter micro air vehicles use a large propeller motor in conjunction with two small servomotors to control thrust, pitch, and roll forces and moments. Quadrotors similarly generate these necessary forces and moments through the coordinated control of multiple actuators. We present a novel propeller architecture which allows a single motor and rotor to express such control by modulating the torque applied to one passively hinged, underactuated propeller. Flight tests of a two-motor coaxial helicopter demonstrate that such a system can provide active stability and control in a real flight system.


title: Electroactive polymer-based devices for e-textiles in biomedicine

abstract: This paper describes the early conception and latest developments of electroactive polymer (EAP)-based sensors, actuators, electronic components, and power sources, implemented as wearable devices for smart electronic textiles (e-textiles). Such textiles, functioning as multifunctional wearable human interfaces, are today considered relevant promoters of progress and useful tools in several biomedical fields, such as biomonitoring, rehabilitation, and telemedicine. After a brief outline on ongoing research and the first products on e-textiles under commercial development, this paper presents the most highly performing EAP-based devices developed by our lab and other research groups for sensing, actuation, electronics, and energy generation/storage, with reference to their already demonstrated or potential applicability to electronic textiles


title: A feature-based personalized recommender system for product-line configuration

abstract: Today’s competitive marketplace requires the industry to understand unique and particular needs of their customers. Product line practices enable companies to create individual products for every customer by providing an interdependent set of features. Users configure personalized products by consecutively selecting desired features based on their individual needs. However, as most features are interdependent, users must understand the impact of their gradual selections in order to make valid decisions. Thus, especially when dealing with large feature models, specialized assistance is needed to guide the users in configuring their product. Recently, recommender systems have proved to be an appropriate mean to assist users in finding information and making decisions. In this paper, we propose an advanced feature recommender system that provides personalized recommendations to users. In detail, we offer four main contributions: (i) We provide a recommender system that suggests relevant features to ease the decision-making process. (ii) Based on this system, we provide visual support to users that guides them through the decision-making process and allows them to focus on valid and relevant parts of the configuration space. (iii) We provide an interactive open-source configurator tool encompassing all those features. (iv) In order to demonstrate the performance of our approach, we compare three different recommender algorithms in two real case studies derived from business experience.


title: Oxidative Stress and Diabetic Complications

abstract: Oxidative stress plays a pivotal role in the development of diabetes complications, both microvascular and cardiovascular. The metabolic abnormalities of diabetes cause mitochondrial superoxide overproduction in endothelial cells of both large and small vessels, as well as in the myocardium. This increased superoxide production causes the activation of 5 major pathways involved in the pathogenesis of complications: polyol pathway flux, increased formation of AGEs (advanced glycation end products), increased expression of the receptor for AGEs and its activating ligands, activation of protein kinase C isoforms, and overactivity of the hexosamine pathway. It also directly inactivates 2 critical antiatherosclerotic enzymes, endothelial nitric oxide synthase and prostacyclin synthase. Through these pathways, increased intracellular reactive oxygen species (ROS) cause defective angiogenesis in response to ischemia, activate a number of proinflammatory pathways, and cause long-lasting epigenetic changes that drive persistent expression of proinflammatory genes after glycemia is normalized ("hyperglycemic memory"). Atherosclerosis and cardiomyopathy in type 2 diabetes are caused in part by pathway-selective insulin resistance, which increases mitochondrial ROS production from free fatty acids and by inactivation of antiatherosclerosis enzymes by ROS. Overexpression of superoxide dismutase in transgenic diabetic mice prevents diabetic retinopathy, nephropathy, and cardiomyopathy. The aim of this review is to highlight advances in understanding the role of metabolite-generated ROS in the development of diabetic complications.


title: 3D Surface Matching and Recognition Using Conformal Geometry

abstract: 3D surface matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition and classification. However, surface matching with noise, occlusion and clutter is a challenging problem. In this paper, we analyze a family of conformal geometric maps including harmonic maps, conformal maps and least squares conformal maps with regards to 3D surface matching. As a result, we propose a novel and computationally efficient surface matching framework that uses least squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D surface-matching problem to a 2D image-matching problem, by comparing the resulting 2D conformal geometric maps, which are stable, insensitive to resolution changes and robust to occlusion and noise. Therefore, highly accurate and efficient 3D surface matching algorithms can be achieved by using conformal geometric maps. Finally, the performance of conformal geometric maps is evaluated and analyzed comprehensively in 3D surface matching with occlusion, noise and resolution variation. We also provide a series of experiments on real 3D face data that achieve high recognition rates.


Extracting social networks from literary text with word embedding tools


How to effectively define and measure maintainability


title: Power efficient charge pump in deep submicron standard CMOS technology

abstract: A power efficient charge pump is proposed. The use of low voltage transistors and of a simple 2-phase clocking scheme allows the use of higher frequencies compared to conventional solutions, thus obtaining high current, high efficiency and small area. Measurements show good results for frequencies around 100MHz. Two testpatterns have been fabricated, one with three stages and one with five stages, in a 1.8V 0.18micro;m standard CMOS digital process (6 metals) with triple well. High voltage capacitors have been implemented using metal to metal parasitic capacitance.


title: Advanced control units' diagnostic based on Ethernet for smart cars

abstract: Numerous electronic systems have been added to the vehicle to provide more comfort and safety to the drivers. This has led to a rise in the number of ECUs (Electronic Control Units) and consequently an eminence in the number of connections and networks as well as the amount of transmitted information. With these innovations and advances in technology and their high requirements, Ethernet is introduced to the automotive industry to profit from its benefits. Among the applications that this protocol has occupied we found the analysis and diagnosis of network traffic flowing between the ECU of the car. To successfully analyze all the exchanged data between the ECUs, an efficient analysis system is required. Indeed, this system should optimally capture all Ethernet packets without any loss allowing the realization of filters, obtaining statistics and real-time display.


Object Recognition Using Hough-transform Clustering of SURF Features


title: The impact of classroom design on pupils' learning

abstract: Assessments have been made of 153 classrooms in 27 schools in order to identify the impact of the physical classroom features on the academic progress of the 3766 pupils who occupied each of those specific spaces. ::: ::: This study confirms the utility of the naturalness, individuality and stimulation (or more memorably, SIN) conceptual model as a vehicle to organise and study the full range of sensory impacts experienced by an individual occupying a given space. In this particular case the naturalness design principle accounts for around 50% of the impact on learning, with the other two accounting for roughly a quarter each. ::: ::: Within this structure, seven key design parameters have been identified that together explain 16% of the variation in pupils' academic progress achieved. These are Light, Temperature, Air Quality, Ownership, Flexibility, Complexity and Colour. The muted impact of the whole-building level of analysis provides some support for the importance of “inside-out design”. ::: ::: The identification of the impact of the built environment factors on learning progress is a major new finding for schools' research, but also suggests that the scale of the impact of building design on human performance and wellbeing in general, can be isolated and that it is non-trivial. It is argued that it makes sense to capitalise on this promising progress and to further develop these concepts and techniques.


title: Automatic Collocation Suggestion in Academic Writing

abstract: In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners' collocation usages in their academic writing with satisfactory results.


Climate Change Is Increasing the Risk of the Reemergence of Malaria in Romania


title: Gated Fusion Network for Single Image Dehazing

abstract: In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms.


title: Prioritized task scheduling in fog computing

abstract: Fog computing, similar to edge computing, has been proposed as a model to introduce a virtualized layer between the end users and the back-end cloud data centers. Fog computing has attracted much attention due to the recent rapid deployment of smart devices and Internet-of-Things (IoT) systems, which often requires real-time, stringent-delay services. The fog layer placed between client and cloud layers aims to reduce the delay in terms of transmission and processing times, as well as the overall cost. To support the increasing number of IoT, smart devices, and to improve performance and reduce cost, this paper proposes a task scheduling algorithm in the fog layer based on priority levels. The proposed architecture, queueing and priority models, priority assignment module, and the priority-based task scheduling algorithms are carefully described. Performance evaluation shows that, comparing with existing task scheduling algorithms, the proposed algorithm reduces the overall response time and notably decreases the total cost. We believe that this work is significant to the emerging fog computing technology, and the priority-based algorithm is useful to a wide range of application domains.


AN APPROACH FOR SEMANTIC BUSINESS PROCESS MODEL MATCHING USING SUPERVISED MACHINE LEARNING


INTERSEX AND GENDER IDENTITY DISORDERS Male Gender Identity in an XX Individual with Congenital Adrenal Hyperplasia