title: Mendeley: Recommendations for Researchers

abstract: For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.


title: Demographics and Automation

abstract: We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change—corresponding to an increasing ratio of older to middle-aged workers—is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.


title: Comprehensive Dataset of Broadcast Soccer Videos

abstract: In the absence of a rich dataset, sports video analysis remains a challenging task. Shot analysis, event detection, and player tracking are important aspects of soccer game research, however currently available datasets focus mainly on single-type annotations, and have several limitations when applied to full video analysis; these include a lack of training samples, temporal annotations, and rich labels of different types. In this paper, we focus on broadcast soccer videos and present a comprehensive dataset for analysis. In its current version, the dataset contains 222 broadcast soccer videos, totaling 170 video hours. The dataset covers three annotation types: (1) A shot boundary with two shot transition types, and shot type annotations with five shot types; (2) Event annotations with 11 event labels, and a story annotation with 15 story labels at coarser granularity; and (3) bounding boxes of the players under analysis in a subset of 19908 video frames. We hope that the introduction of this dataset will enable the research community to develop soccer video analysis techniques.


title: Handling large-scale action space in deep Q network

abstract: Deep reinforcement learning (DRL) is a new topic in recent years. Deep Q Network is a popular DRL implement. It is a well-studied technique and has achieved significant improvement on several challenging tasks such as Atari 2600 games. However, in some kinds of games, there are a large number of possible actions. Thus the output layer in DQN could be complicated due to the large-scale action space, which could harm the performance of DQN. In this paper, we proposed a variant structure of DQN to handle this problem. We could reduce the size of output layer in DQN. The experimental results show that our method improves significantly in some tasks with large-scale action space.


title: DeepRailway: A Deep Learning System for Forecasting Railway Traffic

abstract: Urban railway transit is of great significance in the daily lives of Metropolitan residents. Therefore, forecasting rail- way traffic is fundamental to urban management. However, very few research has been focused on collectively forecast railway transit in a citywide scale. With the development of location based service, the huge volume of GPS trajectory data make it possible for a citywide prediction of railway traffic. In this paper, we propose a deep-learning-based system named DeepRailway to predict and simulate rail- way traffic through heterogeneous data sources. Our data sources include huge volume of trajectory data and rail- way network. In our system, we firstly match the trajectory points to the railway network. And then the patterns of these trajectories are found using a network-based kernel density estimation (KDE), which converts the forecasting task into a sequence prediction problem. An LSTM recurrent neu- ral network model is built to predict the densities through- out the whole network. We evaluate our system in differ- ent timespan and prediction steps to verify its performance against other prediction methods.


title: An improved deep neural network model for job matching

abstract: Job matching which benefit job seekers, employees and employers is very important today. In this work, a deep neural network model is proposed to predict an employee's future career details, which includes position name, salary and company scale based on the online resume data. Like most NLP tasks, the input features are multi-field, non-sparse, discrete and categorical, while their dependencies are mostly unknown. Previous works were mostly focused on engineering, which resulted in a large feature space and heavy computation. To solve this task, we use embedding layers to explore feature interactions and merge two automatically learned features extracted from the resumes. Experimental results on over 70,000 real-word online resumes show that our model outperforms shallow models, like SVM and Random Forests, in effectiveness and accuracy.


title: The effect of argon laser trabeculoplasty on the blood-aqueous barrier and intraocular pressure in human glaucomatous eyes treated with diclofenac 0.1%

abstract: • Background: We studied the effect of argon laser trabeculoplasty (ALT) on the bloodaqueous barrier (BAB) in 41 eyes of 41 patients with primary open-angle glaucoma, pseudoexfoliation glaucoma, or pigment dispersion glaucoma using the Fluorotron Master II. • Methods: Fluorophotometry was performed the day before ALT and on the 3rd day after surgery at 30 and 60 min after intravenous injection of 7 mg/kg body weight sodium fluorescein 10%. Intraocular pressure (IOP) was measured using Goldmann applanation tonometry on the day before surgery and at 3rd days and 1 year (mean) after ALT. Patients were treated with argon laser by one surgeon (180°, 0.1 s, 50 μm 0.6–1.0 W 56 laser burns). Eyes were randomly assigned to either diclofenac-sodium 0.1 % eye drops or vehicle. Eye drops were applied six times 1 h before ALT into the operated eyes and five times daily for 3 days postoperatively. • Results: On the 3rd day after ALT there was significant disruption of the BAB in the placebo-treated eyes compared to the diclofenac 0.1%-treated eyes. In the placebo-treated eyes as well as in diclofenac-sodium 0.1 %-treated eyes there was a significant decrease of IOP postoperatively for up to 1 year. There was no significant difference concerning the IOP reduction after 1 year. Diclofenac-sodium 0.1 % eye drops significantly stabilized the BAB on the 3rd day after ALT, compared to placebo, in this model. • Conclusion: Diclofenac-sodium 0.1 % significantly stabilized the disruption of the blood-aqueous barrier on the 3rd day after ALT. Concerning the IOP-lowering effect of ALT, the postoperative application of steroids should be avoided.


Challenges for Securing Cyber Physical Systems


title: Adaptive Classification for Prediction Under a Budget

abstract: We propose a novel adaptive approximation approach for test-time resource-constrained prediction motivated by Mobile, IoT, health, security and other applications, where constraints in the form of computation, communication, latency and feature acquisition costs arise. We learn an adaptive low-cost system by training a gating and prediction model that limits utilization of a high-cost model to hard input instances and gates easy-to-handle input instances to a low-cost model. Our method is based on adaptively approximating the high-cost model in regions where low-cost models suffice for making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.


title: Mining and Analyzing the Future Works in Scientific Articles

abstract: Future works in scientific articles are valuable for researchers and they can guide researchers to new research directions or ideas. In this paper, we mine the future works in scientific articles in order to 1) provide an insight for future work analysis and 2) facilitate researchers to search and browse future works in a research area. First, we study the problem of future work extraction and propose a regular expression based method to address the problem. Second, we define four different categories for the future works by observing the data and investigate the multi-class future work classification problem. Third, we apply the extraction method and the classification model to a paper dataset in the computer science field and conduct a further analysis of the future works. Finally, we design a prototype system to search and demonstrate the future works mined from the scientific papers. Our evaluation results show that our extraction method can get high precision and recall values and our classification model can also get good results and it outperforms several baseline models. Further analysis of the future work sentences also indicates interesting results.


title: Big Data Analytics: Applications, Prospects and Challenges

abstract: In the era of the fourth industrial revolution (Industry 4.0), big data has major impact on businesses, since the revolution of networks, platforms, people and digital technology have changed the determinants of firms’ innovation and competitiveness. An ongoing huge hype for big data has been gained from academics and professionals, since big data analytics leads to valuable knowledge and promotion of innovative activity of enterprises and organizations, transforming economies in local, national and international level. In that context, data science is defined as the collection of fundamental principles that promote information and knowledge gaining from data. The techniques and applications that are used help to analyze critical data to support organizations in understanding their environment and in taking better decisions on time. Nowadays, the tremendous increase of data through the Internet of Things (continuous increase of connected devices, sensors and smartphones) has contributed to the rise of a “data-driven” era, where big data analytics are used in every sector (agriculture, health, energy and infrastructure, economics and insurance, sports, food and transportation) and every world economy. The growing expansion of available data is a recognized trend worldwide, while valuable knowledge arising from the information come from data analysis processes. In that context, the bulk of organizations are collecting, storing and analyzing data for strategic business decisions leading to valuable knowledge. The ability to manage, analyze and act on data (“data-driven decision systems”) is very important to organizations and is characterized as a significant asset. The prospects of big data analytics are important and the benefits for data-driven organizations are significant determinants for competitiveness and innovation performance. However, there are considerable obstacles to adopt data-driven approach and get valuable knowledge through big data.


title: Folded Strip/Slot Antenna With Extended Bandwidth for WLAN Application

abstract: A compact and novel printed antenna for 2.4-GHz WLAN/Zigbee/Bluetooth applications is proposed in this letter. To extend the bandwidth, a strip mode and a slot mode are combined together, which generate a dual-mode response inside the radiating band. To miniaturize the overall size, the structure is folded in order to fully utilize the available antenna space. The antenna is fabricated on a 1-mm FR4 board and excited by an SMA connector using a short microstrip line. It achieves a -10-dB bandwidth of 5.09% with a compact size of 0.073 λ ::: 0 ::: × 0.052 λ ::: 0 ::: × 0.008 λ ::: 0 ::: (9.2 × 6.5 × 1 mm ::: 3 ::: ) and an in-band radiation efficiency above 73%. It is small and low-profile, which can be fabricated at a low cost using the standard printed circuit board (PCB) process. It shows good radiation performance and therefore is suitable for applications in Internet of Things and smart home devices.


title: Gaussian Processes for Machine Learning (GPML) Toolbox

abstract: The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace's method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.


title: Conceptualizing e-Governance

abstract: In this paper, we aimed at conceptualizing the notion of e-governance, as there is a need to explore the new configurations and knowledge issues having emerged out of such pressures as globalization, economic competitiveness, the evolution of the State and the development of information and communication technologies (ICTs). In fact, we postulate that there is a constant and necessary readjustment to be made between the trend towards technological innovation and the need for regulation and institutional acknowledgement and transformation. This co-evolutionary process goes far beyond the mere extension of government action onto non -State actors, as e-governance is too often understood. We see this process rather as a collective problem-solving dynamics, involving several types of stakeholders, among which, often, the State and its diverse representatives, but not only and by far, have to operate in a multi-level and open-ended space ICTs, in that type of process, are multi-faceted, sometimes just tools for productivity or communication, but sometimes also an industrial sector of its own, that has to be supported and regulated, as part of a critical series of structural change factors impacting our society. In this evolution, administrative services have been thought of, too often, as the main objective of e-governance, but we see now the need for an enhanced capability framework in policy-making and regulation, involving a broader mindset when it comes to use and regulate ICTs.


title: A Strong RSA-based and Certificateless-based Signature Scheme

abstract: The certificateless-based signature system allows people to verify the signature without the certificate. For this reason, we do not need the certificate authority (CA) to store and manage users' certificates and public keys. Certificateless-based signature can also overcome the certificate management problem and the key escrow problem of the traditional signature system. In 2012, Zhang and Mao first designed the certificateless-based signature scheme based on RSA operations; however, their scheme still has latent vulnerabilities. To overcome these short- comings, we propose an improved version to make the RSA-based certificateless scheme stronger and more secure. Besides, we reduce the computational cost to make our scheme more efficient.


title: Fuzzy C-Means Clustering With Local Information and Kernel Metric For Image Segmentation

abstract: Image segmentation is defined as action of splitting an image into band of pixels. Aim of this process is to group pixels into regions. This is used in locating objects in satellite images, face recognition, iris recognition, agricultural imaging, medical imaging. Clustering is process that make group of similar objects and is widely used technique for brain tumour detection. We proposed a new method Modified FCM algorithm based on the Distance metric for segmentation of images that have been corrupted by intensity inhomogeneities and noise. We define a new trade-off weighted fuzzy factor to adaptively control the local neighbor relationship.


title: Deep transfer metric learning

abstract: Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.


title: Novel frequency selective surfaces designing by modification of Jerusalem cross slot

abstract: This research introduces an efficient method to obtain three novel frequency selective surface (FSS) slots. The method is based on the modification of Jerusalem cross (JC) slot which is called modified Jerusalem cross (MJC). The modifications of JC slot consist of the element type and geometry, the substrate and superstrate parameters, and inter-element spacing; however the FSSs have same dimension of unit cells and periodicity(Ltlambda). The array of slots which acts as a bandpass filter, were positioned normal to incidence plane wave. The array of metallic patches constitutes a capacitive surface and the inductive surface, which together act as a resonant structure in the path of an incident plane wave. The Si- parameters, resonance frequency, bandwidth and null between two adjacent resonance frequencies for each structure have been compared with the JC FSS. Three type of MJC have been designed and compared with JC slot. The result shows MJC1 has higher resonance frequency and higher bandwidth, MJC2 in the first resonance frequency works at higher resonance frequency and higher bandwidth but in second resonance frequency the bandwidth is lower and resonance frequency is higher than JC, and MJC3 has approximately same resonance frequency and higher band. This work provides new viewpoints to design novel structures for special purposes.


title: Facebook Involvement, Objectified Body Consciousness, Body Shame, and Sexual Assertiveness in College Women and Men

abstract: Given the heightened attention to visual impression management on social media websites, previous research has demonstrated an association between Facebook use and objectified body consciousness among adolescent girls and young women in various Western countries, including the U.S. (e.g., Meier and Gray 2013). The current study aimed to test whether both young women and men using social networking sites are vulnerable to objectified body consciousness, and to extend this line of research to sexual health outcomes. We tested a path model of Facebook involvement, objectified body consciousness, body shame, and sexual assertiveness and examined whether the negative health consequences of objectified body consciousness were greater in magnitude for women than men. Participants in this study were U.S. college students in the Midwest, 467 women and 348 men, who on average reported using social networking sites for 6 years. They completed survey measures assessing their involvement in Facebook, body surveillance, appearance self-worth, and enjoyment of sexualization. They also reported on feelings of body shame and sexual assertiveness. For both women and men, Facebook involvement predicted objectified body consciousness, which in turn predicted greater body shame and decreased sexual assertiveness. The link between objectified body consciousness and body shame was greater in magnitude for women, but no gender difference was found in the association between body shame and sexual assertiveness. We suggest that social media foster a heightened experience of the self from an observer’s point of view, which has consequences for body image and sexual agency among women as well as men.


title: Exploitation of unlabeled sequences in hidden markov models

abstract: This paper presents a method for effectively using unlabeled sequential data in the learning of hidden Markov models (HMMs). With the conventional approach, class labels for unlabeled data are assigned deterministically by HMMs learned from labeled data. Such labeling often becomes unreliable when the number of labeled data is small. We propose an extended Baum-Welch (EBW) algorithm in which the labeling is undertaken probabilistically and iteratively so that the labeled and unlabeled data likelihoods are improved. Unlike the conventional approach, the EBW algorithm guarantees convergence to a local maximum of the likelihood. Experimental results on gesture data and speech data show that when labeled training data are scarce, by using unlabeled data, the EBW algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach.


title: Characterization of a 7.6-Mb germline deletion encompassing the NF1 locus and about a hundred genes in an NF1 contiguous gene syndrome patient

abstract: We describe a large germline deletion removing the NF1 locus, identified by heterozygosity mapping based on microsatellite markers, in an 8-year-old French girl with a particularly severe NF1 contiguous gene syndrome. We used gene-dose mapping with sequence-tagged site real-time PCR to locate the deletion end points, which were precisely characterized by means of long-range PCR and nucleotide sequencing. The deletion is located on chromosome arm 17q and is exactly 7 586 986 bp long. It encompasses the entire NF1 locus and about 100 other genes, including numerous chemokine genes, an attractive in silico-selected cerebrally expressed candidate gene (designated NUFIP2, for nuclear fragile X mental retardation protein interacting protein 2; NM_020772) and four microRNA genes. Interestingly, the centromeric breakpoint is located in intron 4 of the PIPOX gene (pipecolic acid oxidase; NM_016518) and the telomeric breakpoint in intron 5 of the GGNBP2 gene (gametogenetin binding protein 2; NM_024835) coding a transcription factor. As PIPOX and GGNBP2 have the same transcriptional orientation, we postulated, and then confirmed, the existence of a chimeric transcript. This transcript, and/or haploinsufficiency of one or several deleted genes, could explain the clinical severity of the syndrome in this patient.


title: Fitted Learning: Models with Awareness of their Limits

abstract: Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge. Problems such as fooling, adding new classes over time, and the need to retrain learning models only for small changes to the original problem all point to a potential shortcoming in the classic classification regime, where a comprehensive a priori knowledge of the possible classes or concepts is critical. Without such knowledge, classifiers misjudge the limits of their knowledge and overgeneralization therefore becomes a serious obstacle to consistent performance. In response to these challenges, this paper extends the classic regime by reframing classification instead with the assumption that concepts present in the training set are only a sample of the hypothetical final set of concepts. To bring learning models into this new paradigm, a novel elaboration of standard architectures called the competitive overcomplete output layer (COOL) neural network is introduced. Experiments demonstrate the effectiveness of COOL by applying it to fooling, separable concept learning, one-class neural networks, and standard classification benchmarks. The results suggest that, unlike conventional classifiers, the amount of generalization in COOL networks can be tuned to match the problem.


title: A neural attention model for speech command recognition

abstract: This paper introduces a convolutional recurrent network with attention for speech command recognition. Attention models are powerful tools to improve performance on natural language, image captioning and speech tasks. The proposed model establishes a new state-of-the-art accuracy of 94.1% on Google Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition task), while still keeping a small footprint of only 202K trainable parameters. Results are compared with previous convolutional implementations on 5 different tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35 word recognition (V1) and left-right (V1)). We show detailed performance results and demonstrate that the proposed attention mechanism not only improves performance but also allows inspecting what regions of the audio were taken into consideration by the network when outputting a given category.


title: XPMap-Based Irregular Pyramids for Image Segmentation

abstract: An improved means for grounding the electrical service in a building or for grounding motors and the like consisting of an electrical ground fixture comprising a metal pipe section and a securing means for a ground wire integrally associated with the metal pipe section. The securing means comprises a metal casing adapted to receive the ground wire, the casing being integrally connected with and positioned exteriorly on the pipe section. A clamp, operatively associated with the casing and movable with respect thereto is adapted to secure the ground wire in the casing. When the ground wire is secured, a ground path is provided between the wire and the casing and metal pipe section. The pipe section is incorporated into an appropriate part of the water system of the building. Difficulties of installation, corrosion problems and crimping of the pipe occurring with previously used screw-secured plate members fitted about a pipe are avoided according to the present invention.


title: Multi-Predicate Semantic Role Labeling

abstract: The current approaches to Semantic Role Labeling (SRL) usually perform role classification for each predicate separately and the interaction among individual predicate’s role labeling is ignored if there is more than one predicate in a sentence. In this paper, we prove that different predicates in a sentence could help each other during SRL. In multi-predicate role labeling, there are mainly two key points: argument identification and role labeling of the arguments shared by multiple predicates. To address these issues, in the stage of argument identification, we propose novel predicate-related features which help remove many argument identification errors; in the stage of argument classification, we adopt a discriminative reranking approach to perform role classification of the shared arguments, in which a large set of global features are proposed. We conducted experiments on two standard benchmarks: Chinese PropBank and English PropBank. The experimental results show that our approach can significantly improve SRL performance, especially in Chinese PropBank.


title: An object-oriented data model for a time series management system

abstract: The analysis of time series is a central issue in economic research and many other scientific applications. However, the data management functionality for this field is not provided by general-purpose DBMSs. Therefore, we propose a data model of a specialized time series management system (TSMS) which accounts for these needs. The model is centered around an object-oriented kernel that offers the classes and value types needed for the target applications. The model provides base classes for multivariate time series and for groups as a means to hierarchically partition the time series space. The system offers a computationally complete data manipulation language including capabilities to query time series and groups. An elaborate array model is supported to account for the functional needs of statistical computations. Furthermore, a customizable calendar system providing a variety of predefined calendars is included. >


title: Cyberbullying: Youngsters' Experiences and Parental Perception

abstract: ABSTRACT This paper describes a survey on the prevalence and nature of cyberbullying of and by youngsters and the parental perceptions. Two questionnaires, one for youngsters and one for their parents, were sent to 1,211 final-year pupils of primary schools and first-year pupils of all levels of secondary schools and their parents. Pupils completed the questionnaires in their classroom, and parents completed them at home. The results show that about 16% of the youngsters had engaged in bullying via the Internet and text messages, while about 23% of the youngsters had been victims of cyberbullying. Cyberbullying seems to be a rather anonymous, individualistic activity, which primarily takes place at home. The most frequently used nature of harassments were name-calling and gossiping. Youngsters mostly react to cyberbullying by pretending to ignore it, by really ignoring it, or by bullying the bully. Most parents set rules for their children about the way to they should use the Internet but are not really c...


title: Experimental identification and analytical modelling of human walking forces: Literature review

abstract: Abstract Dynamic forces induced by humans walking change simultaneously in time and space, being random in nature and varying considerably not only between different people but also for a single individual who cannot repeat two identical steps. Since these important aspects of walking forces have not been adequately researched in the past, the corresponding lack of knowledge has reflected badly on the quality of their mathematical models used in vibration assessments of pedestrian structures such as footbridges, staircases and floors. To develop better force models which can be used with more confidence in the structural design, an adequate experimental and analytical approach must be taken to account for their complexity. This paper is the most comprehensive review published to date, of 270 references dealing with different experimental and analytical characterizations of human walking loading. The source of dynamic human-induced forces is in fact in the body motion. To date, human motion has attracted a lot of interest in many scientific branches, particularly in medical and sports science, bioengineering, robotics, and space flight programs. Other fields include biologists of various kinds, physiologists, anthropologists, computer scientists (graphics and animation), human factors and ergonomists, etc. It resulted in technologically advanced tools that can help understanding the human movement in more detail. Therefore, in addition to traditional direct force measurements utilizing a force plate and an instrumented treadmill, this review also introduces methods for indirect measurement of time-varying records of walking forces via combination of visual motion tracking (imaging) data and known body mass distribution. The review is therefore an interdisciplinary article that bridges the gaps between biomechanics of human gait and civil engineering dynamics. Finally, the key reason for undertaking this review is the fact that human–structure dynamic interaction and pedestrian synchronization when walking on more or less perceptibly moving structures are increasingly giving serious cause for concern in vibration serviceability design. There is a considerable uncertainty about how excessive structural vibrations modify walking and hence affect pedestrian-induced forces, significantly in many cases. Modelling of this delicate mechanism is one of the challenges that the international civil structural engineering community face nowadays and this review thus provides a step toward understanding better the problem.


title: Evaluation of microstructure and mechanical properties of TiC/TiC-steel composite coating produced by gas tungsten arc (GTA) coating process

abstract: Abstract TiC/TiC–steel composite layer was produced on AISI 304 steel by gas tungsten arc (GTA) coating process to improve its hardness and wear resistance properties. Effect of current and scan speed on the coating geometry and corresponding microstructure was investigated from the SEM images. Pin-on-disc type abrasive wear test was performed to assess the tribological behavior of the coating. At low GTA current and high scan speed, a distinguished layer of TiC deposited that exhibits significantly higher hardness and high wear resistance than the steel substrate. Conversely, at high current and low scan speed produced TiC–steel composite layer offers relatively low hardness and lower wear resistance. The wear resistance improved five to fifty times depending on the coating microstructure.


title: High productivity thermal compression bonding for 3D-IC

abstract: The evaluation result of 4 layer stacked IC which was bonded using thermal compression bonder (TCB) is reported. The throughput can be remarkably improved because chips of multi-layer can be pre bonded by using non-conductive film (NCF) which is pre-applied adhesive and can be thermally pressed at a time. To realize this process, we stacked the 4 chips having through silicon via (TSV) on a Si substrate and evaluated the connectibility. As the evaluation after bonding, wettability of a solder by cross-section observation and a void in NCF layer by constant depth mode scanning acoustic microscope (C-SAM) observation were confirmed. As a result, it was confirmed that the voidless and good solder joints were possible by reducing the temperature difference in a stacking direction. For the evaluation, we used the TEG of 6 mm × 6 mm × 0.05 mm size which has more than 15,000 bumps of 12 µm height and 15 µm diameter. It was also demonstrated that gang bonding for a plurality of pre bonded chips formed on a substrate was possible by using the novel bonding attachment which accepts the thicknesses difference of 5 µm.


title: Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays

abstract: In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.


title: mActive: A Randomized Clinical Trial of an Automated mHealth Intervention for Physical Activity Promotion

abstract: BACKGROUND ::: We hypothesized that a fully automated mobile health (mHealth) intervention with tracking and texting components would increase physical activity. ::: ::: ::: METHODS AND RESULTS ::: mActive enrolled smartphone users aged 18 to 69 years at an ambulatory cardiology center in Baltimore, Maryland. We used sequential randomization to evaluate the intervention's 2 core components. After establishing baseline activity during a blinded run-in (week 1), in phase I (weeks 2 to 3), we randomized 2:1 to unblinded versus blinded tracking. Unblinding allowed continuous access to activity data through a smartphone interface. In phase II (weeks 4 to 5), we randomized unblinded participants 1:1 to smart texts versus no texts. Smart texts provided smartphone-delivered coaching 3 times/day aimed at individual encouragement and fostering feedback loops by a fully automated, physician-written, theory-based algorithm using real-time activity data and 16 personal factors with a 10 000 steps/day goal. Forty-eight outpatients (46% women, 21% nonwhite) enrolled with a mean±SD age of 58±8 years, body mass index of 31±6 kg/m(2), and baseline activity of 9670±4350 steps/day. Daily activity data capture was 97.4%. The phase I change in activity was nonsignificantly higher in unblinded participants versus blinded controls by 1024 daily steps (95% confidence interval [CI], -580 to 2628; P=0.21). In phase II, participants receiving texts increased their daily steps over those not receiving texts by 2534 (95% CI, 1318 to 3750; P<0.001) and over blinded controls by 3376 (95% CI, 1951 to 4801; P<0.001). ::: ::: ::: CONCLUSIONS ::: An automated tracking-texting intervention increased physical activity with, but not without, the texting component. These results support new mHealth tracking technologies as facilitators in need of behavior change drivers. ::: ::: ::: CLINICAL TRIAL REGISTRATION ::: URL: http://ClinicalTrials.gov/. Unique identifier: NCT01917812.


title: Recent advancement in biosensors technology for animal and livestock health management.

abstract: The term biosensors encompasses devices that have the potential to quantify physiological, immunological and behavioural responses of livestock and multiple animal species. Novel biosensing methodologies offer highly specialised monitoring devices for the specific measurement of individual and multiple parameters covering an animal's physiology as well as monitoring of an animal's environment. These devices are not only highly specific and sensitive for the parameters being analysed, but they are also reliable and easy to use, and can accelerate the monitoring process. Novel biosensors in livestock management provide significant benefits and applications in disease detection and isolation, health monitoring and detection of reproductive cycles, as well as monitoring physiological wellbeing of the animal via analysis of the animal's environment. With the development of integrated systems and the Internet of Things, the continuously monitoring devices are expected to become affordable. The data generated from integrated livestock monitoring is anticipated to assist farmers and the agricultural industry to improve animal productivity in the future. The data is expected to reduce the impact of the livestock industry on the environment, while at the same time driving the new wave towards the improvements of viable farming techniques. This review focusses on the emerging technological advancements in monitoring of livestock health for detailed, precise information on productivity, as well as physiology and well-being. Biosensors will contribute to the 4th revolution in agriculture by incorporating innovative technologies into cost-effective diagnostic methods that can mitigate the potentially catastrophic effects of infectious outbreaks in farmed animals.


title: Improving Topic Coherence with Regularized Topic Models

abstract: Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. However, when dealing with small collections or noisy text (e.g. web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. To overcome this, we propose two methods to regularize the learning of topic models. Our regularizers work by creating a structured prior over words that reflect broad patterns in the external data. Using thirteen datasets we show that both regularizers improve topic coherence and interpretability while learning a faithful representation of the collection of interest. Overall, this work makes topic models more useful across a broader range of text data.


title: Self-Supervised Neural Aggregation Networks for Human Parsing

abstract: In this paper, we present a Self-Supervised Neural Aggregation Network (SS-NAN) for human parsing. SS-NAN adaptively learns to aggregate the multi-scale features at each pixel "address". In order to further improve the feature discriminative capacity, a self-supervised joint loss is adopted as an auxiliary learning strategy, which imposes human joint structures into parsing results without resorting to extra supervision. The proposed SS-NAN is end-to-end trainable. SS-NAN can be integrated into any advanced neural networks to help aggregate features regarding the importance at different positions and scales and incorporate rich high-level knowledge regarding human joint structures from a global perspective, which in turn improve the parsing results. Comprehensive evaluations on the recent Look into Person (LIP) and the PASCAL-Person-Part benchmark datasets demonstrate the significant superiority of our method over other state-of-the-arts.


title: Design of novel multiplexer based thermometer to binary code encoder for 4 bit flash ADC

abstract: In this paper, a novel multiplexer based thermometer to binary Code Encoder For 4 Bit Flash ADC is proposed. The proposed encoder design uses 2:1 multiplexes (MUX) and 2 input XOR gates for converting thermometer code to binary code. Initially 2:1 MUX are used to convert the thermometer code to equivalent gray code. The resulting gray code is then converted to the binary code using XOR gates. Low power consumption is achieved in the proposed design with the grounding concept and avoiding the additional inverters compared to the conventional encoder design. The proposed encoder is implemented in 180 nm CMOS, N-well technology with 1.8 V supply voltage and was simulated using Cadence Spectre simulator. Simulation results shows reduction in power up to 60% when compared with conventional encoder architectures. The proposed design has a figure-of-merit (FOM) of 13.28 fJ with the propagation delay of 0.5180 ns.


title: Inferring Light Fields from Shadows

abstract: We present a method for inferring a 4D light field of a hidden scene from 2D shadows cast by a known occluder on a diffuse wall. We do this by determining how light naturally reflected off surfaces in the hidden scene interacts with the occluder. By modeling the light transport as a linear system, and incorporating prior knowledge about light field structures, we can invert the system to recover the hidden scene. We demonstrate results of our inference method across simulations and experiments with different types of occluders. For instance, using the shadow cast by a real house plant, we are able to recover low resolution light fields with different levels of texture and parallax complexity. We provide two experimental results: a human subject and two planar elements at different depths.


title: Beam Tilting Antenna Using Integrated Metamaterial Loading

abstract: This communication presents a technique to re-direct the radiation beam from a planar antenna in a specific direction with the inclusion of metamaterial loading. The beam-tilting approach described here uses the phenomenon based on phase change resulting from an EM wave entering a medium of different refractive index. The metamaterial H-shaped unit-cell structure is configured to provide a high refractive index which was used to implement beam tilting in a bow-tie antenna. The fabricated unit-cell was first characterized by measuring its S-parameters. Hence, a two dimensional array was constructed using the proposed unit-cell to create a region of high refractive index which was implemented in the vicinity bow-tie structure to realize beam-tilting. The simulation and experimental results show that the main beam of the antenna in the E-plane is tilted by 17 degrees with respect to the end-fire direction at 7.3, 7.5, and 7.7 GHz. Results also show unlike conventional beam-tilting antennas, no gain drop is observed when the beam is tilted; in fact there is a gain enhancement of 2.73 dB compared to the original bow-tie antenna at 7.5 GHz. The reflection-coeflicient of the antenna remains <; - 10 dB in the frequency range of operation.


title: Comparing traditional and integrated curricula on physiology knowledge retention and application

abstract: Introduction: The Faculty of Medical Sciences revised its curriculum from traditional, subject-based, to an integrated, system-based one in 2007. This study aimed to assess and compare the ability to retain and apply knowledge in Physiology, among the last batch of students following the traditional curriculum and the first batch following the integrated curriculum. Methods: Twenty true/false type applied Physiology questions from the 2nd year examinations of the two batches were administered separately when the students were in the final year. The marks were compared with those obtained for the same questions at the 2nd year examination. The difference between the two was considered the retention score. A SAQ comprising applied Physiology questions was also administered to assess application ability and the scores were compared. Results: A total of 152 and 132 participated from the batches following the traditional and integrated curricula respectively. Both batches displayed a significant loss of knowledge by the final year (negative retention score). The knowledge loss was lower in academically average and high performing students of the integrated curriculum. Students following the integrated curriculum showed significantly higher ability to apply Physiology knowledge to clinical problems. Conclusion: The students following the integrated curriculum were better in applying physiology knowledge to clinical scenarios and the retention of knowledge was also better among the average and high performing students following the integrated curriculum.


title: EEG signal analysis for BCI application using fuzzy system

abstract: An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.


title: The "self digest": Self-knowledge serving self-regulatory functions.

abstract: Self-knowledge is conceptualized as a self digest that summarizes one's relations to the world and the personal consequences of these relations. It is a handy sourcebook that serves self-regulatory functions. It is distinguished from the classic notion that self-knowledge contains one descriptive actual self. The self digest contains information about three kinds of actual selves that differ in self-regulatory function: (a) an instrumental self, (b) an expectant self, and (c) a monitored self. It represents not only the actual self but desired (and undesired ) selves that reflect different kinds of self-regulatory focus (i.e., promotion or prevention). It represents not only one's own standpoint but also the standpoint of others whose beliefs one is motivated to take into account. This self-regulatory perspective is used to reconsider self-esteem, self-enhancement, self-consistency, self-presentation, and cross-cultural differences in the self.


title: A 60-GHz RLSA fed by butler matrix carrying three OAM modes

abstract: We propose a 60-GHz radial line slot antenna (RLSA), which is fed by a Butler matrix, for orbital angular momentum (OAM) applications. The Butler matrix is placed at the lower layer and it is based on a post-wall waveguide structure. It has four input ports and it can launch different progressive phase states in the RLSA parallel plate waveguide (PPW). The RLSA in the upper layer is fed by the Butler matrix through four posts. The simulated results show that, at the design frequency, the proposed antenna can generate three OAM-mode states: l = 0, −1 and +1.


title: Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?

abstract: Face recognition performance improves rapidly with the recent deep learning technique developing and underlying large training dataset accumulating. In this paper, we report our observations on how big data impacts the recognition performance. According to these observations, we build our Megvii Face Recognition System, which achieves 99.50% accuracy on the LFW benchmark, outperforming the previous state-of-the-art. Furthermore, we report the performance in a real-world security certification scenario. There still exists a clear gap between machine recognition and human performance. We summarize our experiments and present three challenges lying ahead in recent face recognition. And we indicate several possible solutions towards these challenges. We hope our work will stimulate the community's discussion of the difference between research benchmark and real-world applications.


title: GREW - a scalable frequent subgraph discovery algorithm

abstract: Existing algorithms that mine graph datasets to discover patterns corresponding to frequently occurring subgraphs can operate efficiently on graphs that are sparse, contain a large number of relatively small connected components, have vertices with low and bounded degrees, and contain well-labeled vertices and edges. However, for graphs that do not share these characteristics, these algorithms become highly unscalable. In this paper we present a heuristic algorithm called GREW to overcome the limitations of existing complete or heuristic frequent subgraph discovery algorithms. GREW is designed to operate on a large graph and to find patterns corresponding to connected subgraphs that have a large number of vertex-disjoint embeddings. Our experimental evaluation shows that GREW is efficient, can scale to very large graphs, and find non-trivial patterns.


title: A beamforming method enabling easy packaging of scalable architecture phased arrays

abstract: Today's large phased arrays-especially the ones used in radars-are most of the time based on scalable architectures which have the so called tile, tray or brick type sub-array structures as their building blocks. A common challenge regarding these types of arrays is the integration of different sub-array blocks with each other. The main reason behind this, is the difficulty in maintaining the uniformity of the array at the sub-array boundaries despite the mechanical interfaces. In this work, a method is proposed which enables the array designer to use non-uniformities at the sub-array boundaries without any impact on the array performance. The technique is based on disabling array elements at the boundaries given that some special element spacing conditions are satisfied creating a rank deficiency in the visible portion of the beam space of the array.


title: GRAVITY-ASSIST: A series elastic body weight support system with inertia compensation

abstract: We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.


A Simple End-to-End Question Answering Model for Product Information


title: Self-Adaptive Systems in Organic Computing: Strategies for Self-Improvement

abstract: With the intensified use of intelligent things, the demands on the technological systems are increasing permanently. A possible approach to meet the continuously changing challenges is to shift the system integration from design to run-time by using adaptive systems. Diverse adaptivity properties, so-called self-* properties, form the basis of these systems and one of the properties is self-improvement. It describes the ability of a system not only to adapt to a changing environment according to a predefined model, but also the capability to adapt the adaptation logic of the whole system. In this paper, a closer look is taken at the structure of self-adaptive systems. Additionally, the systems' ability to improve themselves during run-time is described from the perspective of Organic Computing. Furthermore, four different strategies for self-improvement are presented, following the taxonomy of self-adaptation suggested by Christian Krupitzer et al.


title: Making effective use of healthcare data using data-to-text technology

abstract: Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs, and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial, and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover, and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports, or via e-health platforms by their doctors. Unfortunately, such text is the outcome of a highly labor-intensive process if it is done by healthcare professionals. It is also prone to incompleteness and subjectivity and hard to scale up to different domains, wider audiences, and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.


Bacterial Cellulose Production from Industrial Waste and by-Product Streams


title: Lithium Batteries Take To The Road

abstract: Lithium-ion cells are poised to take an increasing share of the auto battery market, just as electric drive seems set to begin a long, slow climb to become, at last, a serious power-train option. But what's rarely understood is how much that second revolution depends on the first.


title: Intrusion detection using deep belief networks

abstract: With the advent of digital technology, security threats for computer networks have increased dramatically over the last decade being much bolder and brazen. There is a great need for an effective Intrusion Detection System (IDS) which are intelligent specialized system designed to interpret the intrusion attempts in incoming network traffic. Deep belief neural (DBN) networks proved to be the most influential deep neural nets and generative neural networks that stack Restricted Boltzmann Machines. In this paper, we explore the capabilities of DBN's performing intrusion detection through series of experiments after training it with NSL-KDD dataset. The trained DBN network now identifies any kind of unknown attack in dataset supplied to it and to the best of our knowledge this is first comprehensive paper performing intrusion detection using deep belief nets. The proposed system not only detect attacks but also classify them in five groups with the accuracy of identifying and classifying network activity based on limited, incomplete, and nonlinear data sources. The proposed system achieved detection accuracy about 97.5% for only fifty iterations that is state of art performance compare to the existing system till today for intrusion detection.


title: Face matching between near infrared and visible light images

abstract: In many applications, such as E-Passport and driver's license, the enrollment of face templates is done using visible light (VIS) face images. Such images are normally acquired in controlled environment where the lighting is approximately frontal. However, Authentication is done in variable lighting conditions. Matching of faces in VIS images taken in different lighting conditions is still a big challenge. A recent development in near infrared (NIR) image based face recognition [1] has well overcome the difficulty arising from lighting changes. However, it requires that enrollment face images be acquired using NIR as well. ::: ::: In this paper, we present a new problem, that of matching a face in an NIR image against one in a VIS images, and propose a solution to it. The work is aimed to develop a new solution for meeting the accuracy requirement of face-based biometric recognition, by taking advantages of the recent NIR face technology while allowing the use of existing VIS face photos as gallery templates. Face recognition is done by matching an NIR probe face against a VIS gallery face. Based on an analysis of properties of NIR and VIS face images, we propose a learning-based approach for the different modality matching. A mechanism of correlation between NIR and VIS faces is learned from NIR → VIS face pairs, and the learned correlation is used to evaluate similarity between an NIR face and a VIS face. We provide preliminary results of NIR → VIS face matching for recognition under different illumination conditions. The results demonstrate advantages of NIR → VIS matching over VIS → VIS matching.


title: HeMIS: Hetero-Modal Image Segmentation

abstract: We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.


Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift


title: The Acute Management of Intracerebral Hemorrhage

abstract: Abstract Intracerebral hemorrhage (ICH) is a devastating disease with high rates of mortality and morbidity. The major risk factors for ICH include chronic arterial hypertension and oral anticoagulation. After the initial hemorrhage, hematoma expansion and perihematoma edema result in secondary brain damage and worsened outcome. A rapid onset of focal neurological deficit with clinical signs of increased intracranial pressure is strongly suggestive of a diagnosis of ICH, although cranial imaging is required to differentiate it from ischemic stroke. ICH is a medical emergency and initial management should focus on urgent stabilization of cardiorespiratory variables and treatment of intracranial complications. More than 90% of patients present with acute hypertension, and there is some evidence that acute arterial blood pressure reduction is safe and associated with slowed hematoma growth and reduced risk of early neurological deterioration. However, early optimism that outcome might be improved by the early administration of recombinant factor VIIa (rFVIIa) has not been substantiated by a large phase III study. ICH is the most feared complication of warfarin anticoagulation, and the need to arrest intracranial bleeding outweighs all other considerations. Treatment options for warfarin reversal include vitamin K, fresh frozen plasma, prothrombin complex concentrates, and rFVIIa. There is no evidence to guide the specific management of antiplatelet therapy-related ICH. With the exceptions of placement of a ventricular drain in patients with hydrocephalus and evacuation of a large posterior fossa hematoma, the timing and nature of other neurosurgical interventions is also controversial. There is substantial evidence that management of patients with ICH in a specialist neurointensive care unit, where treatment is directed toward monitoring and managing cardiorespiratory variables and intracranial pressure, is associated with improved outcomes. Attention must be given to fluid and glycemic management, minimizing the risk of ventilator-acquired pneumonia, fever control, provision of enteral nutrition, and thromboembolic prophylaxis. There is an increasing awareness that aggressive management in the acute phase can translate into improved outcomes after ICH.


title: Cuckoo++ Hash Tables: High-Performance Hash Tables for Networking Applications

abstract: Hash tables are essential data-structures for networking applications (e.g., connection tracking, firewalls, network address translators). Among these, cuckoo hash tables provide excellent performance by processing lookups with very few memory accesses (2 to 3 per lookup). Yet, they remain memory bound and each memory access impacts performance. In this paper, we propose algorithmic improvements to cuckoo hash tables to eliminate unnecessary memory accesses, without altering the properties of the original cuckoo hash table so that all existing theoretical analysis remain applicable. We also present an implementation tailored to run efficiently on Intel Xeon processors, thus supporting NFV and softwarization trends and compare it to the optimized implementation of DPDK. On a single core, our implementation achieves 37M positive lookups per second (i.e., when the key looked up is present in the table), and 60M negative lookups per second, a 45% to 70% improvement over DPDK.


title: THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS

abstract: Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.


title: Supercoiled loops and the organization of replication and transcription in eukaryotes

abstract: The nuclear DNA of eukaryotes is organized into a series of loops each topologically anchored by elements of the nuclear matrix. Evidence is reviewed which indicates that the anchorage points of the loops are formed by transcriptionally active genes and that individual loops function as replicons. The data suggests a specific model for coupling of DNA replication and transcription in eukaryotes.


title: Winding and air gap configurations for power inductors to reduce near magnetic field emission

abstract: This paper first developed magnetic reluctance models for magnetic inductors. The near magnetic field emission of the inductors with different winding and air gap configurations are analyzed based on the developed models. Several inductor structures are proposed to reduce the near magnetic field emission. Simulation and experiments are conducted to validate the developed techniques.


Digital Applications for Cultural and Heritage Institutions


title: Detecting Data Semantic: A Data Leakage Prevention Approach

abstract: Data leakage prevention systems (DLPSs) are increasingly being implemented by organizations. Unlike standard security mechanisms such as firewalls and intrusion detection systems, DLPSs are designated systems used to protect in use, at rest and in transit data. DLPSs analytically use the content and surrounding context of confidential data to detect and prevent unauthorized access to confidential data. DLPSs that use content analysis techniques are largely dependent upon data fingerprinting, regular expressions, and statistical analysis to detect data leaks. Given that data is susceptible to change, data fingerprinting and regular expressions suffer from shortcomings in detecting the semantics of evolved confidential data. However, statistical analysis can manage any data that appears fuzzy in nature or has other variations. Thus, DLPSs with statistical analysis capabilities can approximate the presence of data semantics. In this paper, a statistical data leakage prevention (DLP) model is presented to classify data on the basis of semantics. This study contributes to the data leakage prevention field by using data statistical analysis to detect evolved confidential data. The approach was based on using the well-known information retrieval function Term Frequency-Inverse Document Frequency (TF-IDF) to classify documents under certain topics. A Singular Value Decomposition (SVD) matrix was also used to visualize the classification results. The results showed that the proposed statistical DLP approach could correctly classify documents even in cases of extreme modification. It also had a high level of precision and recall scores.


title: Local Ca2+ detection and modulation of synaptic release by astrocytes

abstract: Astrocytes communicate with synapses by means of intracellular calcium ([Ca(2+)](i)) elevations, but local calcium dynamics in astrocytic processes have never been thoroughly investigated. By taking advantage of high-resolution two-photon microscopy, we identify the characteristics of local astrocyte calcium activity in the adult mouse hippocampus. Astrocytic processes showed intense activity, triggered by physiological transmission at neighboring synapses. They encoded synchronous synaptic events generated by sparse action potentials into robust regional (∼12 μm) [Ca(2+)](i) elevations. Unexpectedly, they also sensed spontaneous synaptic events, producing highly confined (∼4 μm), fast (millisecond-scale) miniature Ca(2+) responses. This Ca(2+) activity in astrocytic processes is generated through GTP- and inositol-1,4,5-trisphosphate-dependent signaling and is relevant for basal synaptic function. Thus, buffering astrocyte [Ca(2+)](i) or blocking a receptor mediating local astrocyte Ca(2+) signals decreased synaptic transmission reliability in minimal stimulation experiments. These data provide direct evidence that astrocytes are integrated in local synaptic functioning in adult brain.


title: Goal emulation and planning in perceptual space using learned affordances

abstract: In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 7-10 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans.


title: Classification of Emoji Categories from Tweet Based on Deep Neural Networks

abstract: In this paper, we describe the sentiment analysis method from tweets based on emoji's category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method.


title: Blockchain technology in healthcare: The revolution starts here

abstract: Blockchain technology has shown its considerable adaptability in recent years as a variety of market sectors sought ways of incorporating its abilities into their operations. While so far most of the focus has been on the financial services industry, several projects in other service related areas such as healthcare show this is beginning to change. Numerous starting points for Blockchain technology in the healthcare industry are the focus of this report. With examples for public healthcare management, user-oriented medical research and drug counterfeiting in the pharmaceutical sector, this report aims to illustrate possible influences, goals and potentials connected to this disruptive technology.


title: A PFC-Based BLDC Motor Drive Using a Canonical Switching Cell Converter

abstract: This paper presents a power factor correction (PFC)-based canonical switching cell (CSC) converter-fed brushless dc motor (BLDCM) drive for low-power household applications. The speed of BLDCM is controlled by varying the dc-bus voltage of voltage source inverter (VSI). The BLDCM is electronically commutated for reduced switching losses in VSI due to low-frequency switching. A front-end CSC converter operating in discontinuous inductor current mode (DICM) is used for dc-bus voltage control with unity power factor at ac mains. A single sensor for dc-bus voltage sensing is used for the development of the proposed drive, which makes it a cost-effective solution. A prototype of the proposed configuration is developed, and its performance is validated with test results for the control of speed over a wide range with a unity power factor at universal ac mains.


title: GEOMETRICAL ACCURACY OF BAYER PATTERN IMAGES

abstract: Modern digital still cameras sample the color spectrum using a color filter array coated to the CCD array such that each pixel samples only one color channel. The result is a mosaic of color samples which is used to reconstruct the full color image by taking the information of the pixels’ neighborhood. This process is called demosaicking. While standard literature evaluates the performance of these reconstruction algorithms by comparison of a ground-truth image with a reconstructed Bayer pattern image in terms of grayscale comparison, this work gives an evaluation concept to asses the geometrical accuracy of the resulting color images. Only if no geometrical distortions are created during the demosaicking process, it is allowed to use such images for metric calculations, e.g. 3D reconstruction or arbitrary metrical photogrammetric processing.


title: BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

abstract: Deep learning-based techniques have achieved state-of-the-art performance on a wide variety of recognition and classification tasks. However, these networks are typically computationally expensive to train, requiring weeks of computation on many GPUs; as a result, many users outsource the training procedure to the cloud or rely on pre-trained models that are then fine-tuned for a specific task. In this paper we show that outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a \emph{BadNet}) that has state-of-the-art performance on the user's training and validation samples, but behaves badly on specific attacker-chosen inputs. We first explore the properties of BadNets in a toy example, by creating a backdoored handwritten digit classifier. Next, we demonstrate backdoors in a more realistic scenario by creating a U.S. street sign classifier that identifies stop signs as speed limits when a special sticker is added to the stop sign; we then show in addition that the backdoor in our US street sign detector can persist even if the network is later retrained for another task and cause a drop in accuracy of {25}\% on average when the backdoor trigger is present. These results demonstrate that backdoors in neural networks are both powerful and---because the behavior of neural networks is difficult to explicate---stealthy. This work provides motivation for further research into techniques for verifying and inspecting neural networks, just as we have developed tools for verifying and debugging software.


title: Reinforcement Learning in Energy Trading Game Among Smart Microgrids

abstract: Reinforcement learning (RL) is essential for the computation of game equilibria and the estimation of payoffs under incomplete information. However, it has been a challenge to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is a priori available and the strategy chosen by each microgrid is private to opponents, even trading partners. This paper proposes a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue. By establishing the relationship between the average utility maximization and the best strategy, two learning-automaton-based algorithms are developed for seeking the Nash equilibria to accommodate the variety of situations. The novelty of the proposed algorithms is related to the incorporation of a normalization procedure into the classical linear reward–inaction scheme to provide a possibility to operate any bounded utility of a stochastic character. Finally, a numerical example is given to demonstrate the effectiveness of the algorithms.


title: Information Centric Networking in the IoT: Experiments with NDN in the Wild

abstract: This paper explores the feasibility, advantages, and challenges of an ICN-based approach in the Internet of Things. We report on the first NDN experiments in a life-size IoT deployment, spread over tens of rooms on several floors of a building. Based on the insights gained with these experiments, the paper analyses the shortcomings of CCN applied to IoT. Several interoperable CCN enhancements are then proposed and evaluated. We significantly decreased control traffic (i.e., interest messages) and leverage data path and caching to match IoT requirements in terms of energy and bandwidth constraints. Our optimizations increase content availability in case of IoT nodes with intermittent activity. This paper also provides the first experimental comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.


Gait control in a soft robot by sensing interactions with the environment using self-deformation


title: Contrast Enhancement-Based Forensics in Digital Images

abstract: As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques.


title: The Past, Present, and Future of Cyberdyne

abstract: Cyberdyne is a distributed system that discovers vulnerabilities in third-party, off-the-shelf binary programs. It competed in all rounds of DARPA’s Cyber Grand Challenge (CGC). In the qualifying event, Cyberdyne was the second most effective bug-finding system. In the final event, it was the bug-finding arm of the fourth-place team. Since then, Cyberdyne has been successfully applied during commercial code audits. The first half of this article describes the evolution and implementation of Cyberdyne and its bug-finding tools. The second half of the article looks at what it took to have Cyberdyne audit real applications and how we performed the first paid automated security audit for the Mozilla Secure Open Source Fund. We conclude with a discussion about the future of automated security audits.


title: DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet

abstract: Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.


title: End-to-End Dense Video Captioning with Masked Transformer

abstract: Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods on dense video captioning tackle this problem by building two models, i.e. an event proposal and a captioning model, for these two sub-problems. The models are either trained separately or in alternation. This prevents direct influence of the language description to the event proposal, which is important for generating accurate descriptions. To address this problem, we propose an end-to-end transformer model for dense video captioning. The encoder encodes the video into appropriate representations. The proposal decoder decodes from the encoding with different anchors to form video event proposals. The captioning decoder employs a masking network to restrict its attention to the proposal event over the encoding feature. This masking network converts the event proposal to a differentiable mask, which ensures the consistency between the proposal and captioning during training. In addition, our model employs a self-attention mechanism, which enables the use of efficient non-recurrent structure during encoding and leads to performance improvements. We demonstrate the effectiveness of this end-to-end model on ActivityNet Captions and YouCookII datasets, where we achieved 10.12 and 6.58 METEOR score, respectively.


title: Polyhierarchical Classifications Induced by Criteria Polyhierarchies, and Taxonomy Algebra

abstract: A new approach to the construction of general persistent polyhierarchical classifications is proposed. It is based on implicit description of category polyhierarchy by a generating polyhierarchy of classification criteria. Similarly to existing approaches, the classification categories are defined by logical functions encoded by attributive expressions. However, the generating hierarchy explicitly predefines domains of criteria applicability, and the semantics of relations between categories is invariant to changes in the universe composition, extending variety of criteria, and increasing their cardinalities. The generating polyhierarchy is an independent, compact, portable, and re-usable information structure serving as a template classification. It can be associated with one or more particular sets of objects, included in more general classifications as a standard component, or used as a prototype for more comprehensive classifications. The approach dramatically simplifies development and unplanned modifications of persistent hierarchical classifications compared with tree, DAG, and faceted schemes. It can be efficiently implemented in common DBMS, while considerably reducing amount of computer resources required for storage, maintenance, and use of complex polyhierarchies.


title: Sensitivity of a polymerase chain reaction-based assay to detect Onchocerca volvulus DNA in skin biopsies

abstract: A polymerase chain reaction (PCR) of a 150-bp tandem repeat of Onchocerca volvulus (O-150) combined with Southern-blot hybridization to species-specific DNA probes was employed for DNA detection. O-150 was amplified from parasites originating from Uganda, Benin, Cameroon, Liberia, Ghana, Burkina Faso, Mali, and Zaire and was successfully hybridized to digoxigenin-labeled oligonucleotides. To investigate the sensitivity of the PCR, 2 skin biopsies were taken from each of 227 persons from Uganda with proven O. volvulus infections but with low microfilaria (mf) densities due to ivermectin treatment. One biopsy was tested by PCR and the other was digested using collagenase to assess the total number of mf. The PCR revealed 76.2% of the samples to be positive, and the collagenase method showed that 78.9% were positive, indicating similar sensitivity for the two methods. It is probable that for both techniques the biopsy must contain at least one live mf or fragments of a dead mf. In this study, no free or circulating O. volvulus DNA could be detected in skin biopsies by PCR.


title: Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

abstract: Recent advances in automotive technology, such as, sensing and onboard computation, have resulted in the development of adaptive cruise control (ACC) algorithms that improve both comfort and safety. With a view towards developing advanced controllers for ACC, this paper presents an experimental platform for validation and demonstration of an online optimization based controller. Going beyond traditional PID based controllers for ACC that lack proof of safety, we construct a control framework that gives formal guarantees of correctness. In particular, safety constraints—maintaining a valid following distance from a lead car—are represented by control barrier functions (CBFs), and control objectives— achieving a desired speed—are encoded through control Lyapunov functions (CLFs). These different objectives can be unified through a quadtraic program (QP), with constraints dictated by CBFs and CLFs, that balances safety and the control objectives in an optimal fashion. This methodology is demonstrated on scale-model cars, for which the CBF-CLF based controller is implemented online, with the end result being the experimental validation of an advanced adaptive cruise controller.


title: Low Precision RNNs: Quantizing RNNs Without Losing Accuracy

abstract: Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in runtime efficiency on hardware, yet it often comes at the cost of reduced accuracy. This paper proposes a quantization approach that increases model size with bit-width reduction. This approach will allow networks to perform at their baseline accuracy while still maintaining the benefits of reduced precision and overall model size reduction.


title: The acceleration-velocity relationship: identification of normal and spastic upper extremity movement.

abstract: This study defines and evaluates a non-invasive technique that wi!l identify a spasm as an uncommanded movement (U) from a normally commanded movement (C). Upper arm function during a tracking task is recognized as non-linear and characterized by a phase plane with acceleration (second derivative of stick position) plotted on the ordinate and velocity (first derivative of stick position) plotted on the abscissa. An acceleration time constant (rA) is defined as (VA/A,) where A,,, is the maximal acceleration and V, is the velocity at A,. A deceleration time constant (ru) is also defined as (V&&,), where D, is the maximal deceleration and V, is the velocity at D,. Ten spastic subjects (S) were categorized into three groups of mildly spastic (M), intermediately spastic (I), and highly spastic (H) which were compared to five normal subjects (N). The results indicate that for C and U, both V, and A, are significantly lower for S at all three levels as compared to N. rA is significantly higher for M and I as compared to N. Both V, and mu are significantly higher for S at all three levels when compared to N. DM is significantly lower for S at all three levels of S when compared to N. Finally, A, and rr, significantly differentiate C vs U for M. V, and ru significantly differentiate for I. V,, AM as well as D,, ru significantly differentiate for H. The physical significance of these results is discussed with respect to the time course and magnitude of net muscle moment (M), and the reversal of the agonist-antagonist relationship during the acceleration and deceleration phases. Finally, a spasm identification algorithm is proposed. 0 1997 Elsevier Science Ltd.


title: Feature Selection using Attribute Ratio in NSL-KDD data

abstract: In Recent year, network traffic is quickly increasing due to the development of information and communication technology and the spread of smart device. Most of intrusion detection studies have focused on efficient intrusion detection method. Feature selection research is important because we detect intrusions or attacks after filtering irrelevant or redundant features in network traffic data. The purpose of this study is to identify important selected input features in building IDS that is computationally efficient and effective. We apply one of the efficient classifier decision tree algorithm for evaluating feature reduction method. We compare between proposed method and other standard methods.


title: A New Wireless Sensor System for Smart Diapers

abstract: This letter presents a new wireless sensor system for smart diaper application, which consists of an interrogator circuit, an antenna, and a passive LC resonating sensor tag. The wireless link between the interrogator circuit and the sensor tag is established through the inductive coupling. The wetting of the diaper shifts the resonating frequency of the sensor tag, changing the inductive link, and thus trigger an ldquoonrdquo or ldquooffrdquo condition reflected at the interrogator circuit. With suitable sensor tags, smart diapers can be achieved in a straightforward and low cost approach.


title: Stimulation of the caudal zona incerta is superior to stimulation of the subthalamic nucleus in improving contralateral parkinsonism

abstract: Deep brain stimulation (DBS) has an increasing role in the treatment of idiopathic Parkinson's disease. Although, the subthalamic nucleus (STN) is the commonly chosen target, a number of groups have reported that the most effective contact lies dorsal/dorsomedial to the STN (region of the pallidofugal fibres and the rostral zona incerta) or at the junction between the dorsal border of the STN and the latter. We analysed our outcome data from Parkinson's disease patients treated with DBS between April 2002 and June 2004. During this period we moved our target from the STN to the region dorsomedial/medial to it and subsequently targeted the caudal part of the zona incerta nucleus (cZI). We present a comparison of the motor outcomes between these three groups of patients with optimal contacts within the STN (group 1), dorsomedial/medial to the STN (group 2) and in the cZI nucleus (group 3). Thirty-five patients with Parkinson's disease underwent MRI directed implantation of 64 DBS leads into the STN (17), dorsomedial/medial to STN (20) and cZI (27). The primary outcome measure was the contralateral Unified Parkinson's Disease Rating Scale (UPDRS) motor score (off medication/off stimulation versus off medication/on stimulation) measured at follow-up (median time 6 months). The secondary outcome measures were the UPDRS III subscores of tremor, bradykinesia and rigidity. Dyskinesia score, L-dopa medication reduction and stimulation parameters were also recorded. The mean adjusted contralateral UPDRS III score with cZI stimulation was 3.1 (76% reduction) compared to 4.9 (61% reduction) in group 2 and 5.7 (55% reduction) in the STN ( P -value for trend <0.001). There was a 93% improvement in tremor with cZI stimulation versus 86% in group 2 versus 61% in group 1 ( P- value = 0.01). Adjusted ‘off–on’ rigidity scores were 1.0 for the cZI group (76% reduction), 2.0 for group 2 (52% reduction) and 2.1 for group 1 (50% reduction) ( P- value for trend = 0.002). Bradykinesia was more markedly improved in the cZI group (65%) compared to group 2 (56%) or STN group (59%) ( P -value for trend = 0.17). There were no statistically significant differences in the dyskinesia scores, L-dopa medication reduction and stimulation parameters between the three groups. Stimulation related complications were seen in some group 2 patients. High frequency stimulation of the cZI results in greater improvement in contralateral motor scores in Parkinson's disease patients than stimulation of the STN. We discuss the implications of this finding and the potential role played by the ZI in Parkinson's disease.


title: Multiserver queueing for supervisory control of autonomous vehicles

abstract: Widespread adoption of unmanned aerial vehicles has led to intense interest in increasing the vehicle-to-operator ratio in unmanned systems. We propose a multiserver queueing structure for the supervisory control of multiple autonomous unmanned vehicles and demonstrate that multiserver queueing can increase the vehicle-to-operator ratio. Analytical results are derived for M/M/c queues with identically and non-identically distributed service times, and vehicle-to-operator ratio increases of up to 50% are predicted for multiserver queues compared to single server queues. A human fatigue model is applied to determine the performance of fixed shift and staggered shift structures in the presence of operator fatigue. Results are extended to log-normally distributed service times in discrete event simulation.


title: Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

abstract: Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that have tried to either map representations between the two domains, or learn to extract features that are domain-invariant. In this work, we approach the problem in a new light by learning in an unsupervised manner a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.


title: Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks

abstract: Due to the spatially variant blur caused by camera shake and object motions under different scene depths, deblurring images captured from dynamic scenes is challenging. Although recent works based on deep neural networks have shown great progress on this problem, their models are usually large and computationally expensive. In this paper, we propose a novel spatially variant neural network to address the problem. The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). RNN is used as a deconvolution operator performed on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the weights for the RNN at every location. As a result, the RNN is spatially variant and could implicitly model the deblurring process with spatially variant kernels. The third CNN is used to reconstruct the final deblurred feature maps into restored image. The whole network is end-to-end trainable. Our analysis shows that the proposed network has a large receptive field even with a small model size. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size.