title: Building Discriminative User Profiles for Large-scale Content Recommendation

abstract: Content recommendation systems are typically based on one of the following paradigms: user based customization, or recommendations based on either collaborative filtering or low rank matrix factorization methods, or with systems that impute user interest profiles based on content browsing behavior and retrieve items similar to the interest profiles. All of these systems have a distinct disadvantage, namely data sparsity and cold-start on items or users. Furthermore, very few content recommendation solutions explicitly model the wealth of information in implicit negative feedback from the users. In this paper, we propose a hybrid solution that makes use of a latent factor model to infer user interest vectors. The hybrid approach enables us to overcome both the data sparsity and cold-start problems. Our proposed method is learned purely on implicit user feedback, both positive and negative. Exploiting the information in the negative feedback allows the user profiles generated to be discriminative. We also provide a Map/Reduce framework based implementation that enables scaling our solution to real-world recommendation problems. We demonstrate the efficacy of our proposed approach with both offline experiments and A/B tests on live traffic on Yahoo properties.


title: ArtiSketch: A System for Articulated Sketch Modeling Technion- Israel Institute of Technology

abstract: We present ArtiSketch - a system which allows the conversion of a wealth of existing 2D content into 3D content by users who do not necessarily possess artistic skills. Using ArtiSketch, a novice user may describe a 3D model as a set of articulated 2D sketches of a shape from different viewpoints. ArtiSketch then automatically converts the sketches to an articulated 3D object. Using common interactive tools, the user provides an initial estimate of the 3D skeleton pose for each frame, which ArtiSketch refines to be consistent between frames. This skeleton may then be manipulated independently to generate novel poses of the 3D model.


title: Parent Praise to Toddlers Predicts Fourth Grade Academic Achievement via Children’s Incremental Mindsets

abstract: In a previous study, parent-child praise was observed in natural interactions at home when children were 1, 2, and 3 years of age. Children who received a relatively high proportion of process praise (e.g., praise for effort and strategies) showed stronger incremental motivational frameworks, including a belief that intelligence can be developed and a greater desire for challenge, when they were in 2nd or 3rd grade (Gunderson et al., 2013). The current study examines these same children's (n = 53) academic achievement 1 to 2 years later, in 4th grade. Results provide the first evidence that process praise to toddlers predicts children's academic achievement (in math and reading comprehension) 7 years later, in elementary school, via their incremental motivational frameworks. Further analysis of these motivational frameworks shows that process praise had its effect on fourth grade achievement through children's trait beliefs (e.g., believing that intelligence is fixed vs. malleable), rather than through their learning goals (e.g., preference for easy vs. challenging tasks). Implications for the socialization of motivation are discussed. (PsycINFO Database Record


title: FPGP: Graph Processing Framework on FPGA A Case Study of Breadth-First Search

abstract: Large-scale graph processing is gaining increasing attentions in many domains. Meanwhile, FPGA provides a power-efficient and highly parallel platform for many applications, and has been applied to custom computing in many domains. In this paper, we describe FPGP (FPGA Graph Processing), a streamlined vertex-centric graph processing framework on FPGA, based on the interval-shard structure. FPGP is adaptable to different graph algorithms and users do not need to change the whole implementation on the FPGA. In our implementation, an on-chip parallel graph processor is proposed to both maximize the off-chip bandwidth of graph data and fully utilize the parallelism of graph processing. Meanwhile, we analyze the performance of FPGP and show the scalability of FPGP when the bandwidth of data path increases. FPGP is more power-efficient than single machine systems and scalable to larger graphs compared with other FPGA-based graph systems.


title: Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search

abstract: Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods. In particular, adversarial training, which is employed in recent studies, seems to be a key ingredient to success. In this paper, we show that simple convolutional autoencoders (CAEs) built upon only standard network components, i.e., convolutional layers and skip connections, can outperform the state-of-the-art methods which employ adversarial training and sophisticated loss functions. The secret is to employ an evolutionary algorithm to automatically search for good architectures. Training optimized CAEs by minimizing the $\ell_2$ loss between reconstructed images and their ground truths using the ADAM optimizer is all we need. Our experimental results show that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB provided by the former state-of-the-art methods, respectively.


title: GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

abstract: Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of conditional molecule generation.


title: Wideband microstrip‐fed printed bow‐tie antenna for phased‐array systems

abstract: A parametric study of the feeding structure is conducted using FDTD code in order to assess the influence of the dimension of the slope-strip feed line on the antenna performance and to find the optimal impedance bandwidth. In the design process, a rectangular radiating patch with dimensions 25 25 mm is chosen. The dimensions of the slope-strip feeding structure are determined by the parameters H, h, and t. The parameter t is the horizontal offset of the free end of the slope-strip feed line from the center location of the patch. Here, h (the distance between the free end of the slope-strip feed line and the radiation patch) is selected to be 0.5 mm. The simulated results demonstrate that the parameters H and t are two important variables which affect the antenna’s impedance-bandwidth characteristics. Figure 2 illustrates the normalized impedance (normalized to 50) and return loss of the antenna for various heights H of the foam substrate with t 0.0 mm and for various values of t with H 8 mm. It is seen that, for the case with H 8 mm and t 0, the optimal bandwidth performances are achieved. Using the abovementioned design dimensions, a prototype of the proposed antenna was successfully implemented and measured. Figure 3 illustrates the experimental return loss of the constructed prototype with H 8 mm, h 0.5 mm, and t 0 mm, which shows that the simulated results in general agree with the measured data. The impedance bandwidth, determined from 10-dB return loss, reaches 2.26 GHz (3.10 –5.36 GHz) or about 53.4%, referenced to the center frequency of 4.23 GHz. The simulated and experimental results show that the proposed antenna structure has a similar radiation characteristic with that of the L-strip-fed microstrip patch antenna in [5]. 3. CONCLUSION A slope-strip line-feeding technique for a microstrip antenna has been introduced. An antenna fed by the shaped microstrip has been investigated, fabricated, and tested. Experimental data show that the antenna achieves optimal impedance characteristics with a bandwidth of 53.4% for less than 10-dB return loss. This new antenna may find potential applications in broadband antennaarray designs and corresponding wireless-communication systems. ACKNOWLEDGMENT


title: Personalized E-learning recommendation model based on psychological type and learning style models

abstract: E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized, and to help overcome “one-size-fits-all” learning model. Each learner has a different learning style and different individual needs. This study proposes a data-driven recommendation model which uses the student's personality and learning style in order to recommend the learning course presentation or objects way. The data model identifies both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI) theory. The proposed model utilizes data from student engagement with the learning management system (Moodle) and the social network, Facebook. The model helps students become aware of their personality, which in turn makes them more efficient in their study habits. The model also provides vital information for educators, equipping them with a better understanding of each student's personality. The predicted personality preference was used to match it with the corresponding learning styles from Kolb's model. An experiment of the recommendation model was tested on a sample of students, and at the end a t-test was applied on some collected behavior from our student sample dataset to validate the model. The results indicate an improvement in the students' engagement and commitment to the course after applying the research data-driven model on the e-learning system.


title: Cloud Computing: Assessing Azure, Amazon EC2, Google App Engine and Hadoop for IT Decision Making and Developer Career Growth

abstract: This book is an industry-leading primer on cloud computing: its background, the purpose it serves, how the cloud can be best utilized, which platforms offer which features, and how to get started. Cloud computing is one of todays most interesting technologies, but remains a bit mercurial and mysterious. It can mean a lot of different things to different people. By tracing several real-world scenariosfrom the requirement gathering through the design, coding, and deployment phasesthis book will give you a clear understanding of what it takes to build and deploy a successful, scalable application that leverages cloud computing platforms and tools. Furthermore, this book emphasizes the critical differences between legacy IT approaches, and explains the methodologies and features available through a cloud-based model. Frameworks and tools such as Amazon EC2, Azure, Google App Engine, Hadoop, CouchDB, Memcache, and JavaSpaces are introduced, illustrating how each technology is best utilized within the context of cloud computing. In the end, this book clears the air and empowers youwhether youre an IT decision maker or a career progressionminded developerwith the information and knowledge necessary to fully consider the implications of migrating to, developing, and/or using cloud-driven applications. What youll learn How to take the vagueness out of cloud computing The core concepts and considerations of cloud computing When and when not to go to the cloud How to weigh the varying factors that go into ROI-based decisions of going into the cloud How to use various cloud computing platforms such as Amazon, Azure, and Google App Engine How to use cloud-based techniques and strategies Who is this book for? This book is intended for anyone interested in the shift currently occurring in the IT industry, in which IT resources and CPU utilization are becoming standard commodities. This book covers both general theoretical concepts and hands-on, real-world examplestargeting this book to both businesspeople and developers alike.


title: Information Flow within a Dispersed Agile Team: A Distributed Cognition Perspective

abstract: One of the hallmarks of a co-located agile team is the simple and open flow of information between its members. In a co-located setting, peripheral awareness, osmotic communication and simple information radiators support agile principles such as collective ownership, minimal documentation and simple design, and facilitate smooth collaboration. However in a dispersed agile team, where individual team members are distributed across several sites, these mechanisms are not available and information sharing has to be more explicit. Research into distributed software development has been tackling similar issues, but little work has been reported into dispersed agile teams. This paper reports on a field study of one successful partially dispersed agile team. Using a distributed cognition analysis which focuses on information propagation and transformation within the team we investigate how the team collaborates and compare our findings with those from co-located teams.


title: Specific demands for developing ICT systems for shared home care : a user centred approach

abstract: How can we develop usable and work process-oriented ICT systems for shared homecare?Shared homecare involves different professionals, consists of mobile work and requires immediate and ubiquitous access to patient-oriented information, supporting an integrated view on the care process.This thesis presents a new collaborative design method for user needs analysis and requirements specification in the context of health information systems development; the Multi-disciplinary Thematic Seminar (MdTS) method. The thesis also describes the MdTS method’s application and two different usability evaluations of the developed system.The MdTS addresses a significant problem with health information technologies; they tend to support collaborative work of healthcare professionals poorly, sometimes leading to a fragmentation of workflow and disruption of healthcare processes. Based on human-computer interaction methods, MdTS implies a multiple-user needs analysis by thorough investigation of the entire interdisciplinary cooperative work and its transformation into technical specifications in order to develop appropriate information and communication technology (ICT) for the users’ differing work situations.Application of the MdTS resulted in a prototype, the OLD@HOME Virtual Health Record (VHR), adapted to the specific demands in shared homecare. Through mobile devices each care professional accessed patient information in profession-specific views from an integrated platform.This thesis provides an interesting case, illustrating how mobile ICT can support shared homecare, thereby bridging health and social care activities and improving knowledge about joint work processes.Results from the usability evaluations were overall positive. Information needed at point of care was available on mobile devices and presented in an understandable manner. However, the evaluations also indicated that it is difficult to transfer results from one homecare setting to another due to differences in operational routines.In conclusion, application of the MdTS method, in this study, succeeded in elicitation of correct user needs and in transferring correct requirements specifications to system developers for implementation.


title: Forensic Issues in Suicidal Single Gunshot Injuries to the Chest

abstract: This study presents a case series of suicides carried out by self-inflicted gunshot wounds to the chest-a relatively uncommon means of suicide. The retrospective autopsy study performed included all cases of single suicidal gunshot injuries to the chest during a 20-year period and which were committed by the use of a handgun. The sample included 67 deceased persons that were an average of 44.4 ± 19.1 years old (range, 12-89 years; 58 men and 9 women). The most common region of the entrance wound was the left side of the chest (54/67), followed by the sternum (10/67), and the right side of the chest (3/67). For 9 subjects, the range of fire could not be determined, as well as whether the shot went through their clothing. In the remaining 58 subjects, only contact or near-contact wounds were found. Of the 58, only 3 subjects had their clothing removed between the chest wall and the muzzle. Three directions of the internal bullet paths were those most frequently found: downward right-to-left (27/67), downward left-to-right (20/67), and downward parallel (10/67) (χ = 101.045, P = 0.000). Also, most bullet paths were directed downward (57/67, χ = 32.970, P = 0.000). The most frequently injured organ was the heart (47/67), and the immediate causes of death were exsanguination (49/67), heart disruption (14/67), and tamponade (4/67). Language: en


title: Breath carbon monoxide concentration in cigarette and bidi smokers in India.

abstract: OBJECTIVE ::: To measure and compare the breath carbon monoxide (CO) levels in cigarette and bidi smokers in India. ::: ::: ::: METHODS ::: Breath CO was measured in 389 smokers (241 cigarette smokers,148 bidi smokers) using portable breath CO analyser (Bedfont-England, Smokelyzer). Tobacco contents and length of single stick of different brands of cigarette and bidi were also measured. ::: ::: ::: RESULTS ::: Their mean age was 38.7 +/- 13.4 years. The average duration of smoking was 18.2 +/- 13.0 years. Average breath CO levels were 15.6 +/- 7.0 ppm in smokers and 4.07 +/- 1.16 ppm in non-smokers. Average breath CO level was significantly higher in bidi smokers (18.9 +/- 7.7 ppm) compared to cigarette smokers (13.6 +/- 5.8 ppm) when total consumption of cigarette/bidi was more than five pack-years (p = 0.002). Average tobacco weight of bidi (216.8 mg) was significantly less than cigarette (696 mg). ::: ::: ::: CONCLUSIONS ::: Bidi is equally or more harmful than cigarette smoking. One bidi may be considered to one cigarette for calculating "pack-years" of smoking.


title: Detecting Stable Clusters Using Principal Component Analysis

abstract: Clustering is one of the most commonly used tools in the analysis of gene expression data (1, 2) . The usage in grouping genes is based on the premise that co-expression is a result of co-regulation. It is thus a preliminary step in extracting gene networks and inference of gene function (3, 4) . Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3, 5, 6) , including sensitivity to drugs (7) , and can also detect artifacts of experimental conditions (8) . Clustering and its applications in biology are presented in greater detail in the chapter by Zhao and Karypis (see also (9) ). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well. Clustering is a form of unsupervised learning, i.e. no information on the class variable is assumed, and the objective is to find the “natural” groups in the data. However, most clustering algorithms generate a clustering even if the data has no inherent cluster structure, so external validation tools are required. Given a set of partitions of the data into an increasing number of clusters (e.g. by a hierarchical clustering algorithm, or k-means), such a validation tool will tell the user the number of clusters in the data (if any). Many methods have been proposed in the literature to address this problem (10–15) . Recent studies have shown the advantages of sampling-based methods (12, 14) . These methods are based on the idea that when a partition has captured the structure in the data, this partition should be stable with respect to perturbation of the data. Bittner et al. (16) used a similar approach to validate clusters representing gene expression of melanoma patients. The emergence of cluster structure depends on several choices: data representation and normalization, the choice of a similarity measure and clustering algorithm. In this chapter we extend the stability-based validation of cluster structure, and propose stability as a figure of merit that is useful for comparing clustering solutions, thus helping in making these choices. We use this framework to demonstrate the ability of Principal Component Analysis (PCA) to extract features relevant to the cluster structure. We use stability as a tool for simultaneously choosing the number of principal components and the number of clusters; we compare the performance of different similarity measures and normalization schemes. The approach is demonstrated through a case study of yeast gene expression data from Eisen et al. (1) . For yeast, a functional classification of a large number of genes is known, and we use this classification for validating the results produced by clustering. A method for comparing clustering solutions specifically applicable to gene expression data was introduced in (17) . However, it cannot be used to choose the number of clusters, and is not directly applicable in choosing the number of principal components. The results of clustering are easily corrupted by the addition of noise: even a few


title: An Approach for Detecting Spam in Arabic Opinion Reviews

abstract: For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although, it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. Therefore, there is a need to develop a new approach that works to identify spam in opinion reviews. We have some in English; we need one in Arabic language in order to identify Arabic spam reviews. To the best of our knowledge, there is still no published study to detect spam in Arabic reviews. In this research, we propose a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Our work is based on the state-of-the-art achievements in the Latin-based spam detection techniques keeping in mind the specific nature of the Arabic language. In addition; we overcome the drawbacks of the class imbalance problem by using sampling techniques. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved to 99.59%.


title: Secure accumulators from euclidean rings without trusted setup

abstract: Cryptographic accumulators are well-known to be useful in many situations. However, the most efficient accumulator (the RSA accumulator) it is not secure against a certificate authority who has herself selected the RSA modulus n. We generalize previous work and define the root accumulator in modules over Euclidean rings. We prove that the root accumulator is secure under two different pairs of assumptions on the module family and on the used hash function. Finally, we propose a new instantiation of the root accumulator, based on class groups of imaginary quadratic order, that combines the best properties of previous solutions. It has short (non)membership proofs like the RSA accumulator, and at the same time it is secure against a malicious certificate authority. Up to this point, this seems to be the only unique application of class groups of imaginary quadratic orders, and we hope that this paper will motivate more research on cryptography in the said groups.


title: Deep Learning Strong Parts for Pedestrian Detection

abstract: Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (e.g. ImageNet). Although these features are able to handle variations such as poses, viewpoints, and lightings, they may fail when pedestrian images with complex occlusions are present. Occlusion handling is one of the most important problem in pedestrian detection. Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors. DeepParts has several appealing properties. First, DeepParts can be trained on weakly labeled data, i.e. only pedestrian bounding boxes without part annotations are provided. Second, DeepParts is able to handle low IoU positive proposals that shift away from ground truth. Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal. Extensive experiments in Caltech dataset demonstrate the effectiveness of DeepParts, which yields a new state-of-the-art miss rate of 11:89%, outperforming the second best method by 10%.


title: Development and experimental testing of a portable hand exoskeleton

abstract: In this paper, the development and the experimental testing phases of an innovative assistive device for hand disabilities are presented. This robotic device is based on an parallel kinematic chain applied to a single phalanx architecture and it is designed to be extremely portable to easily assist people with physical disabilities in their everyday lives. Focusing on patients with hand opening disabilities, the authors have developed a model-based methodology which starting from the motion capture of the patient fingertip trajectories is able to define the mechanism that better fits with such trajectories. The authors have validated the proposed innovative mechanism by developing a portable Hand Exoskeleton System (HES) prototype and performing a suitable preliminary testing campaign. The testing phase of the real prototype on a group of patients is currently ongoing.


title: Understanding autonomous drone maneuverability for Internet of Things applications

abstract: Increasing sensing and communication capabilities combined with falling prices have made drones very attractive for Internet of Things applications. A key requirement of these applications is that the drones should be autonomously maneuvered by computer programs. It is therefore important to understand the practical limitations of autonomous drone maneuverability to ensure that target application performance is met. In this paper, we first analyze drone maneuverability using theory to shed light on the tradeoff between the flying speed and the turning agility of the drone. To investigate the practical maneuverability performance, we then emulate as well as fly a commercial drone under the control of an Android program. We reveal some practical maneuverability factors that must be considered for the applications that require frequent changes of direction for the drone.


title: Contribution of the serotoninergic system to anxious and depressive traits that may be partially responsible for the phenotypical variability of bulimia nervosa

abstract: Abstract Eating disorders (ED), such as anorexia nervosa (AN) and bulimia nervosa (BN), are complex psychiatric phenotypes influenced by both genetic and environmental factors. We investigated the genetic contribution of four single nucleotide polymorphisms (SNPs) within the serotonin receptor 5HT2C and two sequence variants within the serotonin transporter SLC6A4 to different ED-related psychopathological symptoms in a total sample of 82 ED patients. All patients were diagnosed according to DSM-IV criteria and underwent diagnostic and psychopathological assessments by means of structured clinical interviews and rating scales. We detected significant evidence of association between the −995A/−759T/−697C/Cys23 haplotype of the 5HT2C gene and different anxious and depressive subscales of the SCL90-R instrument, that included Somatization (p = 0.029), Obsessive-Compulsiveness (p = 0.021), Depression (p = 0.032), Anxiety (p = 0.004), Hostility (p = 0.028), Phobic Anxiety (p = 0.029) and Paranoid Ideation (p = 0.008), in BN patients. We also observed a strong association between the 5HTTLPR polymorphism of the SLC6A4 gene and Anxiety in the same group of BN patients (p = 0.004). However, no epistatic effects between the 5HT2C and SLC6A4 genes on the different anxious and depressive subscales were observed. Our preliminary data suggest that the serotoninergic system contributes to the different psychopathological symptoms that may be partially responsible for the phenotypical variability within the bulimic phenotype.


abstract: Objective: Review the topical usage of systemic drugs in dermatological diseases. Data Sources: From previous Literatures, reviews and studies as well as medical websites (PubMed, MD consult, Medscape) and Scientific Journals data- bases were searched from the start date of each data- base. Study Selection: Selection was done by supervisors for studying new advancement in topical use of systemic drugs in dermatology and studies that addressed topical therapy in dermatology and systemic drugs used topically in dermatology. Data Extraction: Data from published studies were manually extracted and summarized. Study quality assessment included whether ethical approval was gained, prospective design, eligibility criteria specified, appropriate controls used, adequate follow-up achieved and defined outcome measures. Data synthesis: In this review the data found that several studies of the topical usage of systemic drugs in dermatological diseases to know which systemic drug can used topically. Findings: A total of 44 studies were included in the review as they were deemed eligible by fulfilling the inclusion criteria. Of these 44 articles, included in this review, 32 were Topical usage of systemic drugs in dermatological diseases and 12 were topical therapy. Studies indicate that some of systemic drugs can used topically in the treatment of some dermatological diseases. Conclusion: Extemporaneous compounding helps physicians to individualize treatment to the patient's specific needs and to create topical preparations that are not otherwise commercially available. However, comparative effectiveness studies are needed to determine whether or not topical use of systemic therapeutics is more beneficial than existing therapies. (Mostafa Ahmed Hammamand Heba Abd Elmordy. Topical Use of Systemic Drugs in Dermatology. Life Sci J 2015;12(4):105-110). (ISSN:1097-8135). http://www.lifesciencesite.com . 13


title: The impact of organisational structure on enterprise architecture deployment

abstract: Purpose – In many organisations, the alignment between information technology (IT) and business is viewed as an important catalyst for the organisation's effectiveness and efficiency towards achieving a competitive advantage. Yet, a shared understanding between business and IT (the EA included) remains an issue in many organisations. The organisational structure affects the alignment between business and IT units. Conversely, a lack of alignment of both components (IT and business units) impedes organisations' capacity to increase and improve their productivity. Many studies have been conducted in the area of IT, EA and the organisational structure. There is, however, little evidence of work conducted in exploring the interface between organisational structure and EA prior to this study. The paper aims to discuss these issues. Design/methodology/approach – A case-study research approach was employed to investigate the impact of organisational structure in the deployment of EA in organisations. Qualitative...


FlashFusion: Real-time Globally Consistent Dense 3D Reconstruction using CPU Computing


title: Radiation Blockage Reduction in Antennas Using Radio-Frequency Cloaks [Antenna Applications Corner]

abstract: This article presents an experimental demonstration of antenna blockage reduction based on the concept of radio-frequency (RF) cloaking. The proposed cloaking configuration is validated by studying the antenna characteristics in the presence of obstacles that are covered with and without the cloaking surface. The known concept of thin cloaks used for reducing the scattering width is extended in this article to mitigate the blockage in antennas. An attempt is made to improve the degraded radiation characteristics of the antenna due to the presence of metal obstacles loaded in front of the aperture. This is accomplished by covering the obstacles with a cloaking surface made up of a periodic arrangement of interconnected patches and microstrip lines. The optimizations are carried out for various scenarios by varying the thickness of the substrate and the length of the interconnecting microstrip line to achieve an optimum cloaking performance. It is demonstrated through measurements that the RF cloaking could be achieved between the frequencies 3 and 3.3 GHz. At 3.2 GHz, the gain of the horn antenna in free space is measured to be 17.2 dBi, and the gain of the horn antenna with uncloaked obstacles at the aperture is 3.73 dBi. The same configuration with cloaked obstacles exhibits a gain of 12.13 dBi.


title: Region-dependent and stage-specific effects of stress, environmental enrichment, and antidepressant treatment on hippocampal neurogenesis

abstract: Chronic stress and depression are associated with decreased levels of hippocampal neurogenesis. On the other hand, anti- depressants as well as environmental enrichment may rely in part on their pro-neurogenic effects to improve cognition and mood. Because a functional heterogeneity has been consistently reported along the septo- temporal axis of the hippocampus, regional changes in neurogenesis could differentially contribute to these effects and affect distinct hippo- campal functions. Mapping these regional changes could therefore pro- vide a better understanding of the function of newborn neurons. While some studies report region-specific effects of stress and antidepressants on neurogenesis, it is unclear whether these changes affect distinct pop- ulations of newborn neurons according to their developmental stage in a region-specific manner. By using endogenous markers and BrdU label- ing we quantified the regional changes in cell proliferation and survival as well as in the number of neuronal progenitors and immature neurons following unpredictable chronic mild stress (UCMS), environmental enrichment (EE) and chronic fluoxetine (20 mg/kg/day) treatment along the septo-temporal axis of the hippocampus. EE promoted cell prolifera- tion and survival of 4-week-old newborn cells as well as increased the number and proportion of post-mitotic immature neurons specifically within the septal hippocampus. By contrast, UCMS uniformly decreased cell proliferation, survival and immature newborn neurons but differen- tially affected progenitor cells with a decrease restricted to the tempo- ral regions of the hippocampus. Whereas fluoxetine treatment in control mice affected proliferation and survival specifically in the tem- poral hippocampus, it reversed most of the UCMS-induced alterations all along the septo-temporal axis. These results highlight that different factors known for exerting a mood improving effect differentially regu- late neurogenesis along the septo-temporal axis of the hippocampus. Such region and stage specific effects may correlate to distinct func- tional properties of newborn neurons along the septo-temporal axis of the hippocampus which may contribute differently to the pathophysiol- ogy of affective disorders. V C 2013 Wiley Periodicals, Inc.


title: Combining multiple depth-based descriptors for hand gesture recognition

abstract: The hand is reliably extracted from the scene by jointly using color and depth data.Features extracted from depth data allow a reliable hand gesture recognition.Multiple features capturing different properties of the gestures are combined together.The proposed approach is able to obtain a very high accuracy in real-time. Depth data acquired by current low-cost real-time depth cameras provide a more informative description of the hand pose that can be exploited for gesture recognition purposes. Following this rationale, this paper introduces a novel hand gesture recognition scheme based on depth information. The hand is firstly extracted from the acquired data and divided into palm and finger regions. Then four different sets of feature descriptors are extracted, accounting for different clues like the distances of the fingertips from the hand center and from the palm plane, the curvature of the hand contour and the geometry of the palm region. Finally a multi-class SVM classifier is employed to recognize the performed gestures. Experimental results demonstrate the ability of the proposed scheme to achieve a very high accuracy on both standard datasets and on more complex ones acquired for experimental evaluation. The current implementation is also able to run in real-time.


title: Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives

abstract: The fourth generation (4G) mobile communication systems are offering service worldwide steadily. Although 4G systems could be loaded with much more services and data than previous systems, there is still a dramatic gap between the people’s practical requirements and what can be offered by the 4G technologies. Consequently, the research and development for the fifth generation (5G) systems have already been started. This article presents an overview of potential network architecture and highlights several promising techniques which could be employed in the future 5G systems. These techniques include non-orthogonal multiple access (NOMA), massive multiple input and multiple output (MIMO), cooperative communications and network coding, full duplex (FD), device-to-device (D2D) communications, millimeter wave communications, automated network organization, cognitive radio (CR), and green communications. The state-of-art and implementation issue of these techniques are also addressed.


title: Post-quantum cryptography - dealing with the fallout of physics success

abstract: Cryptography is essential for the security of Internet communication, cars, and implanted medical devices. However, many commonly used cryptosystems will be completely broken once big quantum computers exist


title: Story Generation After TALE-SPIN

abstract: TALE-SPIN, the last major AI attempt at story generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR is a program (under development) which generates atoriea as a creative reasoner in pursuit of her own narrative goals. It is thus intended to simulate an author's mind as ahe makes up a story, rather than the world as things happen in it. The four major forces driving the story generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal.


title: SNIP: Single-shot Network Pruning based on Connection Sensitivity

abstract: Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility. In this work, we present a new approach that prunes a given network once at initialization prior to training. To achieve this, we introduce a saliency criterion based on connection sensitivity that identifies structurally important connections in the network for the given task. This eliminates the need for both pretraining and the complex pruning schedule while making it robust to architecture variations. After pruning, the sparse network is trained in the standard way. Our method obtains extremely sparse networks with virtually the same accuracy as the reference network on the MNIST, CIFAR-10, and Tiny-ImageNet classification tasks and is broadly applicable to various architectures including convolutional, residual and recurrent networks. Unlike existing methods, our approach enables us to demonstrate that the retained connections are indeed relevant to the given task.


title: Learning with Partially Absorbing Random Walks

abstract: We propose a novel stochastic process that is with probability αi being absorbed at current state i, and with probability 1 – αi follows a random edge out of it. We analyze its properties and show its potential for exploring graph structures. We prove that under proper absorption rates, a random walk starting from a set S of low conductance will be mostly absorbed in S. Moreover, the absorption probabilities vary slowly inside S, while dropping sharply outside, thus implementing the desirable cluster assumption for graph-based learning. Remarkably, the partially absorbing process unifies many popular models arising in a variety of contexts, provides new insights into them, and makes it possible for transferring findings from one paradigm to another. Simulation results demonstrate its promising applications in retrieval and classification.


Robust and Fast Side View Vehicle Detection and Counting


title: Detecting and Aligning Faces by Image Retrieval

abstract: Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. The same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.


title: Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation

abstract: In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness. We build upon the work on causal models and fairness in machine learning, and we express the problem of combining multiple models within the framework of opinion pooling. We propose two simple algorithms, grounded in the theory of counterfactual fairness and causal judgment aggregation, that are guaranteed to generate aggregated probabilistic causal models respecting the criterion of fairness, and we compare their behaviors on a toy case study.


title: A minature 70 W quasi-MMIC PA block suitable for highly integrated X-band pulsed SSPA schemes

abstract: A compact X-band (8.4 GHz to 9.8 GHz) single stage power amplifier (PA) is demonstrated using a discrete 0.25 μm discrete GaN power bar with 20 mm of total gate periphery and low-cost passive-only GaAs MMIC technology for the input/output matching. The PA, operated at 30V in pulsed conditions, provides more than 13 dB of linear gain and nearly 70W of output power with peak power added efficiency of 48% at 4 dB of gain compression. Its overall dimension of 6.42 × 4.56 mm2 makes it very suitable to be cascaded and power combined in highly integrated X-band SSPA schemes. An example of a possible SSPA output stage exploiting two PAs is given, providing a configuration a peak output power of 127 W with 38% of PAE was measured in pulsed conditions in the same frequency band.


Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks


title: Miniaturization of Branch-Line Coupler Using Composite Right/Left-Handed Transmission Lines with Novel Meander-shaped-slots CSSRR

abstract: A novel compact-size branch-line coupler using composite right/left-handed transmission lines is proposed in this paper. In order to obtain miniaturization, composite right/left-handed transmission lines with novel complementary split single ring resonators which are realized by loading a pair of meander-shaped-slots in the split of the ring are designed. This novel coupler occupies only 22.8% of the area of the conventional approach at 0.7 GHz. The proposed coupler can be implemented by using the standard printed-circuit-board etching processes without any implementation of lumped elements and via-holes, making it very useful for wireless communication systems. The agreement between measured and stimulated results validates the feasible configuration of the proposed coupler.


title: Processing and Nutritive Value of Mango Seed Kernel Flour

abstract: Processing of ripe mango fruit, generates its peel and seed as waste, which is approximately 40-50 % of the total fruit weight. Present study was undertaken to process mango seed kernel and to study its nutritional value. Mango seeds of Totapuri variety were procured from local food processing industry. Kernels were separated from seeds and processed into flour through various processing steps. Particle size distribution of the flour was studied. The Mango Kernel Flour (MKF) was subjected for chemical analysis. The nutrients analyzed for mango kernel flour were moisture, protein, fat, crude fibre, ash, calcium, magnesium, potassium, sodium, manganese, copper, zinc and iron by standard methods. Carbohydrate and energy contents were computed. Mango kernel oil was extracted by solvent extraction method. Results revealed that recovery per cent of mango kernel flour was 80.6 and maximum flour particles passed through 60 mesh. MKF is good source of protein (7.53 g/100g), fat (11.45 g/100 g) and energy (421 k.cal /100g). It also contains appreciable levels of calcium (170 mg/100g), magnesium (210 mg/100g) and potassium (368 mg/100g) which are important macro minerals required for vital functions of the body. Oil yield from mango kernel flour was found to be 11.5 per cent which was studied for selected physical and chemical properties.


title: Ammoxidation activity of in situ synthesized ammonium salt of molybdophosphoric acid on VOPO4 catalysts

abstract: Abstract Two isomorphous VOPO4 samples were synthesized by means of well-known aqueous and organic preparation methods. These materials are further used for the in situ generation of ammonium salt of 12-molybdophosphoric acid (AMPA) from their solid phase phosphate components. Formation of AMPA was confirmed by XRD, FTIR, Raman and XPS analyses. The activity of the catalysts was tested in a fixed bed tubular glass reactor at atmospheric pressure for the selective ammoxidation of 2-methylpyrazine (MP) to 2-cyanopyrazine (CP). The reaction was carried out in the temperature range of 360–420 °C. Between the two mono-phosphates tested, the α-VOPO4 is observed to show somewhat better activity compared to β-VOPO4. Interestingly, in situ synthesized AMPA catalysts displayed significantly better performance compared to their corresponding parent VOPO4 solids. Among all catalysts tested, AMPA-α-VOPO4 exhibited the best performance (conversion (MP) = ca. 90%, selectivity (CP) = 65% at T = 420 °C). The differences in catalytic performance of the tested catalysts are ascribed to the structural differences of the used VOPO4 solids.


title: Automatic generation of mobile app signatures from traffic observations

abstract: There are network management, traffic engineering, and security practices adopted in today's networking that rely on the knowledge about what applications' traffic is passing through the networks. These practices might fail with mobile apps whose identity remains hidden in generic HTTP traffic. The main reason is that unlike traditional applications, most mobile apps do not use specific protocols or IP ports with distinctive features. Many enterprises and service providers are in a great need of regaining control over their networks that increasingly carry mobile traffic. In this paper we propose FLOWR, a system that automatically identifies mobile apps by continually learning the apps' distinguishing features via traffic analysis. FLOWR focuses solely on key-value pairs in HTTP headers and intelligently identifies the pairs suitable for app signatures. Our system employs a custom supervised learning approach that leverages a very limited knowledge of app-signature seeds and autonomously grows its capacity for app identification. The approach is motivated by a simple but effective hypothesis that unknown app-identifying features should co-occur with the known signatures. Our experimental results show a significant growth in flow identification coverage provided by FLOWR. Specifically, we show that FLOWR can achieve identification of 86–95% of flows related to their generating apps.


title: Propose but verify : Fast mapping meets cross-situational word learning

abstract: We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances [e.g., Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via crosssituational statistics. Psychological Science, 18(5), 414–420]. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing – more a ‘fast mapping’ procedure than a gradual statistical one. We provide experimental evidence for this propose-but-verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge ‘gradualist’ accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language.


title: FoodNet: Recognizing Foods Using Ensemble of Deep Networks

abstract: In this letter, we propose a protocol for an automatic food recognition system that identifies the contents of the meal from the images of the food. We developed a multilayered convolutional neural network (CNN) pipeline that takes advantages of the features from other deep networks and improves the efficiency. Numerous traditional handcrafted features and methods are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.


Patchmatch stereo - stereo matching with slanted support windows


title: Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

abstract: Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.


title: The Relationship Between Brand Related UGC and CBBE : An Internet Meme Experiment

abstract: Background: The modern day era of the Internet gave birth to the growing phenomenon of Internet memes (IM), a type of online user generated content (UGC) (Gangadharbatla, 2008). Now marketers have ...


title: Towards a Cyber Conflict Taxonomy

abstract: This paper seeks to create a practical taxonomy to describe cyber conflict events and the actors involved in them in a manner that is useful to security practitioners and researchers working in the domain of cyber operations. The proposed Cyber Conflict Taxonomy is an extensible network taxonomy organized as a plex data structure. Subjects of the taxonomy are entered as either Events or Entities and are then categorized using the categories and subcategories of Actions or Actors. Each of these categories is further subdivided into increasingly specific subcategories used to describe the defining characteristics of each subject and labeled lateral linkages are used to illustrate the associative relationships between Entities and Events. The categories are organized in both a hierarchical and associative manner to illustrate the relationships between subjects and categories. A prototype of this taxonomy was developed and tested using a test set of recent cyber conflict events and used to explore the relationship and connections between these events and the states, groups or individuals that participated in them. Furthermore, this taxonomy can potentially identify actors across different events based on their similar method of operation, toolsets and target sets.


title: NADPH oxidase, Nramp1 and nitric oxide synthase 2 in the host antimicrobial response.

abstract: Using highly conserved, complex enzyme systems, leukocytes utilize the toxic nature of free radical intermediates, derived from oxygen and nitrogen, to control microbial pathogens as part of the innate immune response. Upon activation, NADPH oxidase generates superoxide anion radicals, which in turn give rise to further reactive oxygen intermediates. Similarly, activated nitric oxide synthase 2 catalyses the production of nitric oxide radicals, which leads to the formation of reactive nitrogen intermediates. Nitrogen- and oxygen-centered reactive intermediates can interact to form further reactive species. In addition, presence of the cationic transporter, Nrampl, may exacerbate the effects of these toxic compounds on invading microbes. While each of these antimicrobial systems can operate independently, the combination of their activities is synergistic in the successful containment of almost all invading pathogens. These systems are activated and modulated by microbial products and a series of temporally expressed cytokines. They also feed directly into the initiation of the adaptive immune response, which culminates in lasting specific immunity. The effector molecules, generated in the early innate immune response, are not specific to the invading pathogen and may also cause damage to the host. It is the critical balance of these processes in the initial stages of infection that determines the outcome of infectious disease.


title: Generating Images from Captions with Attention

abstract: Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset.


title: Philosophical conceptions of the self: implications for cognitive science

abstract: Abstract Several recently developed philosophical approaches to the self promise to enhance the exchange of ideas between the philosophy of the mind and the other cognitive sciences. This review examines two important concepts of self: the ‘minimal self', a self devoid of temporal extension, and the ‘narrative self', which involves personal identity and continuity across time. The notion of a minimal self is first clarified by drawing a distinction between the sense of self-agency and the sense of self-ownership for actions. This distinction is then explored within the neurological domain with specific reference to schizophrenia, in which the sense of self-agency may be disrupted. The convergence between the philosophical debate and empirical study is extended in a discussion of more primitive aspects of self and how these relate to neonatal experience and robotics. The second concept of self, the narrative self, is discussed in the light of Gazzaniga's left-hemisphere ‘interpreter' and episodic memory. Extensions of the idea of a narrative self that are consistent with neurological models are then considered. The review illustrates how the philosophical approach can inform cognitive science and suggests that a two-way collaboration may lead to a more fully developed account of the self.


title: Metabolism and functions of glutathione in brain

abstract: The tripeptide glutathione is the thiol compound present in the highest concentration in cells of all organs. Glutathione has many physiological functions including its involvement in the defense against reactive oxygen species. The cells of the human brain consume about 20% of the oxygen utilized by the body but constitute only 2% of the body weight. Consequently, reactive oxygen species which are continuously generated during oxidative metabolism will be generated in high rates within the brain. Therefore, the detoxification of reactive oxygen species is an essential task within the brain and the involvement of the antioxidant glutathione in such processes is very important. The main focus of this review article will be recent results on glutathione metabolism of different brain cell types in culture. The glutathione content of brain cells depends strongly on the availability of precursors for glutathione. Different types of brain cells prefer different extracellular glutathione precursors. Glutathione is involved in the disposal of peroxides by brain cells and in the protection against reactive oxygen species. In coculture astroglial cells protect other neural cell types against the toxicity of various compounds. One mechanism for this interaction is the supply by astroglial cells of glutathione precursors to neighboring cells. Recent results confirm the prominent role of astrocytes in glutathione metabolism and the defense against reactive oxygen species in brain. These results also suggest an involvement of a compromised astroglial glutathione system in the oxidative stress reported for neurological disorders.


title: Deep Learning Face Attributes in the Wild

abstract: Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.


title: Unable to resist temptation: How self-control depletion promotes unethical behavior

abstract: Across four experimental studies, individuals who were depleted of their self-regulatory resources by an initial act of self-control were more likely to "impulsively cheat" than individuals whose self-regulatory resources were intact. Our results demonstrate that individuals depleted of self-control resources were more likely to behave dishonestly (Study 1). Depletion reduced people's moral awareness when they faced the opportunity to cheat, which, in turn, was responsible for heightened cheating (Study 2). Individuals high in moral identity, however, did not show elevated levels of cheating when they were depleted (Study 3), supporting our hypothesis that self-control depletion increases cheating when it robs people of the executive resources necessary to identify an act as immoral or unethical. Our results also show that resisting unethical behavior both requires and depletes self-control resources (Study 4). Taken together, our findings help to explain how otherwise ethical individuals predictably engage in unethical behavior.


title: Encapsulation of Iron in Liposomes Significantly Improved the Efficiency of Iron Supplementation in Strenuously Exercised Rats

abstract: To investigate the effect of iron liposome supplementation, a rat model of exercise-associated anemia was established by subjecting the animals to high-intensity running exercises for 4 weeks. Rats with confirmed anemia were strenuously exercised for another 2 weeks while receiving iron supplements by intragastric administration of ferric ammonium citrate (FAC) liposomes or heme iron liposomes. Control groups were administered equivalent amounts of FAC, heme iron, or blank liposomes. Subsequently, complete blood count (CBC), serum iron, and liver iron levels were tested to determine the efficiency of iron liposomes in relieving anemia. Superoxide dismutase (SOD) and malonyldialdehyde (MDA) were also detected to determine potential side effects of iron supplementation. The CBC, as well as serum iron and liver iron contents, significantly increased and reached much higher levels in anemic rats treated with iron liposomes, compared with those of control groups. The increase of SOD and decrease of MDA levels were also observed after supplementation with iron liposomes. These results demonstrate that iron liposomes can efficiently relieve the iron deficiency in strenuously exercised rats and may potentially be used as a supplement for the treatment of exercise-associated iron deficiency anemia with minimal side effects.


title: Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets

abstract: Several recent authors have advocated for financial markets to move from continuous clearing to discrete or batched clearing, as a way to defeat the latency arms race: the never-ending quest for small advantages in time to access markets. How frequently should such a modern batch auction clear? We conduct a systematic simulation-based investigation on the relationship between clearing frequency and metrics of market quality, such as allocative efficiency, comparing the performance of discrete and continuous auction mechanisms under empirical equilibrium behavior of all participating traders. In effect we perform empirical mechanism design on frequent batch auctions. We find that in a wide array of environments, equilibrium efficiency is improved for small positive intervals but falls off dramatically when there are too few opportunities to trade. The result is a large range of batch frequencies that are near optimally efficient; this range is wider in thick markets.


title: Motion Feature Network: Fixed Motion Filter for Action Recognition

abstract: Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.


title: Static noise margin variation for sub-threshold SRAM in 65-nm CMOS

abstract: The increased importance of lowering power in memory design has produced a trend of operating memories at lower supply voltages. Recent explorations into sub-threshold operation for logic show that minimum energy operation is possible in this region. These two trends suggest a meeting point for energy-constrained applications in which SRAM operates at sub-threshold voltages compatible with the logic. Since sub-threshold voltages leave less room for large static noise margin (SNM), a thorough understanding of the impact of various design decisions and other parameters becomes critical. This paper analyzes SNM for sub-threshold bitcells in a 65-nm process for its dependency on sizing, V DD , temperature, and local and global threshold variation. The V T variation has the greatest impact on SNM, so we provide a model that allows estimation of the SNM along the worst-case tail of the distribution.


title: Pricing American options with the SABR model

abstract: We introduce a simple and flexible method to price derivative securities on assets with volatilities which are stochastic. As a special case we treat the SABR model in more detail. Our approach is based on the construction of recombining trees using interpolation methods on probability measures, and this makes it very suitable for the application of parallel computing techniques. We show how one can easily incorporate features which are characteristic for practical option pricing problems, such as a term structure of interest, early exercise possibilities and the payment of cash dividends.


title: Modern Trends in Inductive Power Transfer for Transportation Applications

abstract: Inductive power transfer (IPT) has progressed to be a power distribution system offering significant benefits in modern automation systems and particularly so in stringent environments. Here, the same technology may be used in very dirty environments and in a clean room manufacture. This paper reviews the development of simple factory automation (FA) IPT systems for both today's complex applications and onward to a much more challenging application-IPT roadway. The underpinning of all IPT technology is two strongly coupled coils operating at resonance to transfer power efficiently. Over time the air-gap, efficiency, coupling factor, and power transfer capability have significantly improved. New magnetic concepts are introduced to allow misalignment, enabling IPT systems to migrate from overhead monorails to the floor. However, the demands of IPT roadway bring about significant challenges. Here, compared with the best FA practice, air-gaps need to be 100 times larger, power levels greater than ten times, system losses ten times lower to meet efficiency requirements, and systems from different manufacturers must be interoperable over the full range of operation. This paper describes how roadway challenges are being met and outlines the problems that still exist and the solutions designers are finding to them.


Understanding and Simplifying One-Shot Architecture Search


title: Pupillometry A Window to the Preconscious

abstract: The measurement of pupil diameter in psychology (in short, "pupillometry") has just celebrated 50 years. The method established itself after the appearance of three seminal studies (Hess & Polt ...


title: Deformation capture and modeling of soft objects

abstract: We present a data-driven method for deformation capture and modeling of general soft objects. We adopt an iterative framework that consists of one component for physics-based deformation tracking and another for spacetime optimization of deformation parameters. Low cost depth sensors are used for the deformation capture, and we do not require any force-displacement measurements, thus making the data capture a cheap and convenient process. We augment a state-of-the-art probabilistic tracking method to robustly handle noise, occlusions, fast movements and large deformations. The spacetime optimization aims to match the simulated trajectories with the tracked ones. The optimized deformation model is then used to boost the accuracy of the tracking results, which can in turn improve the deformation parameter estimation itself in later iterations. Numerical experiments demonstrate that the tracking and parameter optimization components complement each other nicely. Our spacetime optimization of the deformation model includes not only the material elasticity parameters and dynamic damping coefficients, but also the reference shape which can differ significantly from the static shape for soft objects. The resulting optimization problem is highly nonlinear in high dimensions, and challenging to solve with previous methods. We propose a novel splitting algorithm that alternates between reference shape optimization and deformation parameter estimation, and thus enables tailoring the optimization of each subproblem more efficiently and robustly. Our system enables realistic motion reconstruction as well as synthesis of virtual soft objects in response to user stimulation. Validation experiments show that our method not only is accurate, but also compares favorably to existing techniques. We also showcase the ability of our system with high quality animations generated from optimized deformation parameters for a variety of soft objects, such as live plants and fabricated models.


title: Toward a Formal Model of Cognitive Synergy

abstract: "Cognitive synergy" refers to a dynamic in which multiple cognitive processes, cooperating to control the same cognitive system, assist each other in overcoming bottlenecks encountered during their internal processing. Cognitive synergy has been posited as a key feature of real-world general intelligence, and has been used explicitly in the design of the OpenCog cognitive architecture. Here category theory and related concepts are used to give a formalization of the cognitive synergy concept. ::: A series of formal models of intelligent agents is proposed, with increasing specificity and complexity: simple reinforcement learning agents; "cognit" agents with an abstract memory and processing model; hypergraph-based agents (in which "cognit" operations are carried out via hypergraphs); hypergraph agents with a rich language of nodes and hyperlinks (such as the OpenCog framework provides); "PGMC" agents whose rich hypergraphs are endowed with cognitive processes guided via Probabilistic Growth and Mining of Combinations; and finally variations of the PrimeAGI design, which is currently being built on top of OpenCog. ::: A notion of cognitive synergy is developed for cognitive processes acting within PGMC agents, based on developing a formal notion of "stuckness," and defining synergy as a relationship between cognitive processes in which they can help each other out when they get stuck. It is proposed that cognitive processes relating to each other synergetically, associate in a certain way with functors that map into each other via natural transformations. Cognitive synergy is proposed to correspond to a certain inequality regarding the relative costs of different paths through certain commutation diagrams. ::: Applications of this notion of cognitive synergy to particular cognitive phenomena, and specific cognitive processes in the PrimeAGI design, are discussed.


title: A 10 GHz low-power multi-modulus frequency divider using Extended True Single-Phase Clock (E-TSPC) Logic

abstract: This paper presents a multi-modulus frequency divider (MMD) based on the Extended True Single-Phase Clock (E-TSPC) Logic. The MMD consists of four cascaded divide-by-2/3 E-TSPC cells. The basic functionality of the MMD and the E-TSPC 2/3 divider are explained. The whole design was implemented in an [0.13] m CMOS process from IBM. Simulation and measurement results of the MMD are shown. Measurement results indicates a maximum operating frequency of [10] GHz and a power consumption of [4] mW for each stage. These results are compared to other state of the art dual modulus E-TSPC dividers, showing the good position of this design relating to operating frequency and power consumption.


title: A Unified Bellman Equation for Causal Information and Value in Markov Decision Processes

abstract: The interaction between an artificial agent and its environment is bi-directional. The agent extracts relevant information from the environment, and affects the environment by its actions in return to accumulate high expected reward. Standard reinforcement learning (RL) deals with the expected reward maximization. However, there are always information-theoretic limitations that restrict the expected reward, which are not properly considered by the standard RL. In this work we consider RL objectives with information-theoretic limitations. For the first time we derive a Bellman-type recursive equa- tion for the causal information between the environment and the agent, which is combined plausibly with the Bellman recursion for the value function. The unified equitation serves to explore the typical behavior of artificial agents in an infinite time horizon.


title: Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017

abstract: We report on the Wikidata vandalism detection task at the WSDM Cup 2017. The task received five submissions for which this paper describes their evaluation and a comparison to state of the art baselines. Unlike previous work, we recast Wikidata vandalism detection as an online learning problem, requiring participant software to predict vandalism in near real-time. The best-performing approach achieves a ROC-AUC of 0.947 at a PR-AUC of 0.458. In particular, this task was organized as a software submission task: to maximize reproducibility as well as to foster future research and development on this task, the participants were asked to submit their working software to the TIRA experimentation platform along with the source code for open source release.


Conflict Resolution Paradigms and their Influences to the Shona Societies in Zimbabwe


title: Multi-phase Offline Signature Verification System Using Deep Convolutional Generative Adversarial Networks

abstract: We present an offline signature verification system with new architecture, using Deep Convolutional Generative Adversarial Networks (DCGANs) to learn features unsupervised instead of using hand-crafted features. The advantage of this architecture is that system has a robust generalization ability. Besides, aiming at the conflict between convenience and accuracy, a hybrid Writer-Independent-Writer-Dependent classifier is used, which is an approach compromise two different kind of classifier. We test our method in GPDSsyntheticSignature database, which is an updated version of GPDS-960. In order to make it's convenient to compare with other works, we also use GPDS-960 to test our method. Our experimental results show that, the accuracy of proposed method is satisfactory. Due to this kind of architecture, the more query samples are tested and enrolled into the system, the more accurate our system will be conceptually.


title: Discovering telecom fraud situations through mining anomalous behavior patterns

abstract: In this paper we tackle the problem of superimposed fraud detection in telecommunication systems. We propose two anomaly detection methods based on the concept of signatures. The first method relies on a signature deviation-based approach while the second on a dynamic clustering analysis. Experiments carried out with real data, voice call records from an entire week, corresponding to approximately 2.5 millions of CDRs and 700 thousand of signatures processed per day, allowed us to detect several anomalous situations. The frauds analysts provide us a small list of 12 customers for whom a fraudulent behavior was detected during this week. Thus, 9 and 11 fraud situations were discovered from each method respectively. Preliminary results and discussion with fraud analysts has already proved that our methods are a valuable tool to assist them in fraud detection.


title: Exploring IOT Application Using Raspberry Pi

abstract: There are thousands of sensors in an industry with different usage, such as, pressure transmitters, flow meter, temperature transmitters, level transmitters, and so on. Wired networks are mainly used to transfer data to base station by connecting sensor. It brings advantage as it provides reliable and stable communication system for instruments and controls. However, the cost of cables necessary is very costly. Therefore, recently low cost wireless networks are strongly required by customers, for example, temporary instrument networks and/or some non-critical permanent sites which require low data rate and longer battery life. In client/server model, file server act as a parent's node which allow multiple child node to connect with it. It is responsible for central storage and data management so that other computers enable to access the file under the same network. This article explores the use of Raspberry Pi to function as a server in which several laptops are connected to it to copy, store and delete the file over network. IT requires authentication for user login before granting access to the file to ensure data integrity and security. File server is widely used in many areas, for example in education for uploading study note into the serve and student immediate downloading it into their computer. Moreover this work also explores the use of Raspberry Pi B+ model and XBee (ZigBee module) to demonstrate wireless communication data transmission, proving the validity of usage as a mobile low-power wireless network communication. The main goal of the research is to explore the use of Raspberry Pi for client-server communication using various wireless communication scenario such as Wi-Fi and ZigBee.


title: One-Class Kernel Spectral Regression for Outlier Detection

abstract: The paper introduces a new efficient nonlinear one-class classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilbert subspace, minimises the scatter of target distribution along an optimal projection direction while at the same time keeping projections of positive observations distant from the mean of the negative class. We provide a graph embedding view of the problem which can then be solved efficiently using the spectral regression approach. In this sense, unlike previous similar methods which often require costly eigen-computations of dense matrices, the proposed approach casts the problem under consideration into a regression framework which is computationally more efficient. In particular, it is shown that the dominant complexity of the proposed method is the complexity of computing the kernel matrix. Additional appealing characteristics of the proposed one-class classifier are: 1-the ability to be trained in an incremental fashion (allowing for application in streaming data scenarios while also reducing the computational complexity in a non-streaming operation mode); 2-being unsupervised, but providing the option for refining the solution using negative training examples, when available; Last but not least, 3-the use of the kernel trick which facilitates a nonlinear mapping of the data into a high-dimensional feature space to seek better solutions.


title: An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data

abstract: The number of Non-Performing Loans has increased in recent years, paralleling the current financial crisis, thus increasing the importance of credit scoring models. This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro Fuzzy. The proposed model’s performance was compared with conventional and commonly utilized models. The credit scoring models are tested using a 10-fold cross-validation process with the credit card data of an international bank operating in Turkey. Results demonstrate that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network (ANN) approaches, in terms of average correct classification rate and estimated misclassification cost. As with ANN, the proposed model has learning ability; unlike ANN, the model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications are rejected.


title: The Importance of Sleep for Athletic Performance

abstract: ABSTRACTENSURING THAT ATHLETES FUNCTION OPTIMALLY THROUGHOUT TRAINING IS AN IMPORTANT FOCUS FOR THE STRENGTH AND CONDITIONING COACH. SLEEP IS AN INFLUENTIAL FACTOR THAT AFFECTS THE QUALITY OF TRAINING, GIVEN ITS IMPLICATIONS ON THE RECOVERY PROCESS. INTENSE TRAINING MAY PREDISPOSE ATHLETES TO RISK F


title: Alignment of dynamic networks

abstract: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done.For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface.http://nd.edu/∼cone/DynaMAGNA++/ .tmilenko@nd.edu.Supplementary data are available at Bioinformatics online.


title: Accuracy of heart rate variability estimation by photoplethysmography using an smartphone: Processing optimization and fiducial point selection

abstract: This work compares several fiducial points to ::: detect the arrival of a new pulse in a photoplethysmographic ::: signal using the built-in camera of smartphones or a ::: photoplethysmograph. Also, an optimization process for the ::: signal preprocessing stage has been done. Finally we ::: characterize the error produced when we use the best cutoff ::: frequencies and fiducial point for smartphones and ::: photopletysmograph and compare if the error of smartphones ::: can be reasonably be explained by variations in pulse transit ::: time. The results have revealed that the peak of the first ::: derivative and the minimum of the second derivative of the ::: pulse wave have the lowest error. Moreover, for these points, ::: high pass filtering the signal between 0.1 to 0.8 Hz and low pass ::: around 2.7 Hz or 3.5 Hz are the best cutoff frequencies. Finally, ::: the error in smartphones is slightly higher than in a ::: photoplethysmograph


title: What does this emoji mean? A vector space skip-gram model for twitter emojis

abstract: Comunicacio presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovenia.


title: Temporal Stream Algebra

abstract: Data stream management systems (DSMS) so far focus on ::: event queries and hardly consider combined queries to both ::: data from event streams and from a database. However, ::: applications like emergency management require combined ::: data stream and database queries. Further requirements are ::: the simultaneous use of multiple timestamps after different ::: time lines and semantics, expressive temporal relations between multiple time-stamps and ::: exible negation, grouping ::: and aggregation which can be controlled, i. e. started and ::: stopped, by events and are not limited to fixed-size time ::: windows. Current DSMS hardly address these requirements. ::: This article proposes Temporal Stream Algebra (TSA) so ::: as to meet the afore mentioned requirements. Temporal ::: streams are a common abstraction of data streams and data- ::: base relations; the operators of TSA are generalizations of ::: the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally. ::: In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent ::: blocking expressions on a "syntactical" level.


title: Early experiences with a 3D model search engine

abstract: New acquisition and modeling tools make it easier to create 3D models, and affordable and powerful graphics hardware makes it easier to use them. As a result, the number of 3D models available on the web is increasing rapidly. However, it is still not as easy to find 3D models as it is to find, for example, text documents and images. What is needed is a \3D model search engine," a specialized search engine that targets 3D models. We created a prototype 3D model search engine to investigate the design and implementation issues. Our search engine can be partitioned into three main components: (1) acquisition: 3D models have to be collected from the web, (2) analysis: they have to be analyzed for later matching, and (3) query processing and matching: an online system has to match user queries to the collected 3D models. Our site currently indexes over 36,000 models, of which about 31,000 are freely available. In addition to a text search interface, it offers several 3D and 2D shape-based query interfaces. Since it went online one year ago (in November 2001), it has processed over 148,000 searches from 37,800 hosts in 103 different countries. Currently 20--25% of the about 1,000 visitors per week are returning users. This paper reports on our initial experiences designing, building, and running the 3D model search engine.


title: Three-Dimensional Displays: A Review and Applications Analysis

abstract: S. Benton published a definitive taxonomy of the first one hundred and seventy years of 3D displays covering the field up to the year 2000. In this article we review how display technologies have advanced in the last ten years and update Benton's taxonomy to include the latest additions. Our aim is to produce a display taxonomy suitable for content producers highlighting which displays have common requirements for image delivery. We also analyze key technical characteristics of 3D displays and use these characteristics to suggest the future applications for each category of display.


title: W-band, 5W solid-state power amplifier/combiner

abstract: This paper reports a W-band solid-state power amplifier with an output power of 5.2W at 95 GHz and greater than 3 watts over the 94 to 98.5 GHz band. These SOA results were achieved by combining 12 GaN MMICs in a low-loss radial-line combiner network. The 12-way combiner demonstrates an overall combining efficiency of 87.5%, and excluding combiner conductor losses (0.3 dB), exhibits a combining efficiency of 93.7% at 95GHz. The size of the 12-way amplifier/combiner is only 2.39″ dia. × 1.5″ length. This work represents the first application of the radial-line combiner configuration to applications at W-band frequencies, and establishes new levels of performance for solid-state power amplifiers operating at these frequencies.


title: Multisampling Maximum Power Point Tracker (MS-MPPT) to Compensate Irradiance and Temperature Changes

abstract: This paper presents a maximum power point tracking (MPPT) method that compensates fast, though smooth, irradiance (or temperature) changes. The proposed method belongs to the family of perturb and observe (P&O) methods as the photovoltaic (PV) voltage is intentionally perturbed to induce changes on the PV power. In consequence, the slope of the $pv$ -characteristic curve can be detected, which reveals the MPP position. The perturbation is usually an increment with varying direction, which is used to update the next PV voltage reference. However, changes on irradiance (or temperature) may also cause changes on the PV power, which mix with the algorithm's intentional increment effect on the PV power. This combination of effects confuses the slope detection, which may entail a wrong decision on the next increment direction, and a considerable deviation from the MPP. The scheme presented here proposes the use of more samplings and an associated combination of increments directions before taking a decision. This additional information is used to separate the effect of irradiance (or temperature) changes from the algorithm's intentional perturbation effect. Thus, the proposed multisampling MPPT can provide effective MPP tracking despite of irradiance (or temperature) changes. The proposed scheme is experimentally tested and compared to the conventional P&O under irradiance changes.


title: Accurate viscous free surfaces for buckling, coiling, and rotating liquids

abstract: We present a fully implicit Eulerian technique for simulating free surface viscous liquids which eliminates artifacts in previous approaches, efficiently supports variable viscosity, and allows the simulation of more compelling viscous behaviour than previously achieved in graphics. Our method exploits a variational principle which automatically enforces the complex boundary condition on the shear stress at the free surface, while giving rise to a simple discretization with a symmetric positive definite linear system. We demonstrate examples of our technique capturing realistic buckling, folding and coiling behavior. In addition, we explain how to handle domains whose boundary comprises both ghost fluid Dirichlet and variational Neumann parts, allowing correct behaviour at free surfaces and solid walls for both our viscous solve and the variational pressure projection of Batty et al. [BBB07].


title: Depixelizing pixel art

abstract: We describe a novel algorithm for extracting a resolution-independent vector representation from pixel art images, which enables magnifying the results by an arbitrary amount without image degradation. Our algorithm resolves pixel-scale features in the input and converts them into regions with smoothly varying shading that are crisply separated by piecewise-smooth contour curves. In the original image, pixels are represented on a square pixel lattice, where diagonal neighbors are only connected through a single point. This causes thin features to become visually disconnected under magnification by conventional means, and creates ambiguities in the connectedness and separation of diagonal neighbors. The key to our algorithm is in resolving these ambiguities. This enables us to reshape the pixel cells so that neighboring pixels belonging to the same feature are connected through edges, thereby preserving the feature connectivity under magnification. We reduce pixel aliasing artifacts and improve smoothness by fitting spline curves to contours in the image and optimizing their control points.


title: Security enhancement in image steganography for medical integrity verification system

abstract: Nowadays image steganography has major role in confidential medical image communication. When the medical image is transmitted through insecure public network, there is a chance for tampering medical images. Therefore, it is crucial to check the integrity of medical images to prevent any unauthorized modification. To check the integrity we calculate cryptographic hash function of ROI (Region Of Interest) by using SHA algorithm. The hash value (H1) will be embedded in the RONI using discrete wavelet transform. By comparing the hash value at receiver side, we can check the integrity of medical image. If any tampering occurs, the hash function does not match. This paper proposes a new method to improve the security. The modified medical image is embedded in an ordinary looking image by spatial reversible steganography method. It helps to conceal the existence of secret medical data. It ensures that the eavesdroppers will not have any suspicion that medical image is hidden in that image. This combined approach will give enhanced security.


title: Reference Architecture for Cryptocurrency in Banking

abstract: The Internet has now become an environment and source for innovation development that has impacted a wide range of industries. The platform has enabled entrepreneurs to create groundbreaking solutions that have had a disruptive and transformative effect on the lives of many people. While banking had dealt with several innovations such as internet payment providers, more recently a virtual alternative to cash has begun to emerge. Although the notion of true electronic cash has been considered for some time, the availability of a practical electronic currency has only recently gained actual adoption that is forcing many financial institutions to reconsider the potential long-term impact. We provide some insight into the latest trends in cryptocurrency and propose a reference architecture for adopting this form of financial asset in a central banking scenario; i.e. a Fiat based cryptocurrency. The presently used cryptocurrency are based on a peer to peer scheme and hence we outline an IT solution that will accommodate a centrally issued electronic currency.


title: FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review

abstract: Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not possible before. In particular, convolution neural networks (CNNs) have demonstrated their effectiveness in image detection and recognition applications. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve desired performance levels. Consequently, hardware accelerators that use application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and graphic processing units (GPUs) have been employed to improve the throughput of CNNs. More precisely, FPGAs have been recently adopted for accelerating the implementation of deep learning networks due to their ability to maximize parallelism as well as due to their energy efficiency. In this paper, we review recent existing techniques for accelerating deep learning networks on FPGAs. We highlight the key features employed by the various techniques for improving the acceleration performance. In addition, we provide recommendations for enhancing the utilization of FPGAs for CNNs acceleration. The techniques investigated in this paper represent the recent trends in FPGA-based accelerators of deep learning networks. Thus, this review is expected to direct the future advances on efficient hardware accelerators and to be useful for deep learning researchers.