title: Total alkaloids of Rubus aleaefolius Poir. inhibit the STAT3 signaling pathway leading to suppression of proliferation and cell cycle arrest in a mouse model of hepatocellular carcinoma.

abstract: Signal transducer and activator of transcription 3 (STAT3) plays a critical role in cell survival and proliferation and is constitutively activated in many types of human cancers including hepatocellular carcinoma (HCC). Therefore, it is a major focus in the development of anticancer agents. Rubus aleaefolius Poir. has been demonstrated to be effective in the treatment of HCC. However, the precise mechanism of its anticancer activity remains largely unknown. Using HepG2 cells and a HCC mouse xenograft model, in the present study we evaluated the effect of the total alkaloids of Rubus aleaefolius Poir. (TARAP) on tumor growth in vitro and in vivo and investigated the underlying molecular mechanisms. We found that TARAP inhibited the proliferation of HepG2 human HCC cells and blocked G1/S cell cycle progression. In addition, TARAP treatment suppressed STAT3 phosphorylation in tumor tissues. Consequently, the inhibitory effect of TARAP on STAT3 activation resulted in the inhibition of proliferation. Moreover, TARAP altered the expression of several important target genes of the STAT3 signaling pathway, such as decreased expression of cyclinD1, cyclinE, cyclin-dependent kinase (CDK) 4 and CDK2 as well as upregulated p21. These results suggest that suppression of the STAT3 signaling pathway leading to inhibition of proliferation and cell cycle arrest may be one of the mechanisms of the anticancer activity of TARAP against HCC.


title: Comprehensive Study of Single-Phase AC-DC Power Factor Corrected Converters With High-Frequency Isolation

abstract: Solid-state switch mode AC-DC converters having high-frequency transformer isolation are developed in buck, boost, and buck-boost configurations with improved power quality in terms of reduced total harmonic distortion (THD) of input current, power-factor correction (PFC) at AC mains and precisely regulated and isolated DC output voltage feeding to loads from few Watts to several kW. This paper presents a comprehensive study on state of art of power factor corrected single-phase AC-DC converters configurations, control strategies, selection of components and design considerations, performance evaluation, power quality considerations, selection criteria and potential applications, latest trends, and future developments. Simulation results as well as comparative performance are presented and discussed for most of the proposed topologies.


title: An Investigative Laboratory Course in Human Physiology Using Computer Technology and Collaborative Writing

abstract: FitzPatrick, Kathleen A. An investigative laboratory course in human physiology using computer technology and collaborative writing. Active investigative student-directed experiences in laboratory science are being encouraged by national science organizations. A growing body of evidence from classroom assessment supports their effectiveness. This study describes four years of implementation and assessment of an investigative laboratory course in human physiology for 65 second-year students in sports medicine and biology at a small private comprehensive college. The course builds on skills and abilities first introduced in an introductory investigations course and introduces additional higher-level skills and more complex human experimental models. In four multiweek experimental modules, involving neuromuscular, reflex, and cardiovascular physiology, by use of computerized hardware/software with a variety of transducers, students carry out self-designed experiments with human subjects and perform data collection and analysis, collaborative writing, and peer editing. In assessments, including standard course evaluations and the Salgains Web-based evaluation, student responses to this approach are enthusiastic, and gains in their skills and abilities are evident in their comments and in improved performance.


title: Improving classification with latent variable models by sequential constraint optimization

abstract: In this paper we propose a method to use multiple generative models with latent variables for classi cation tasks. The standard approach to use generative models for classi cation is to train a separate model for each class. A novel data point is then classi ed by the model that attributes the highest probability. The algorithm we propose modi es the parameters of the models to improve the classi cation accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The rst has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the e6ectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem. c


title: Antecedents of Children of Alcohol Abused Parents: Grounded Theory Analysis

abstract: This research endeavored to fill the knowledge gap in children of alcohol abuse parents by uncovering one of the five fundamental themes of children of alcohol abuse parent, that is, the antecedents that add to the working hypothesis or paradigm model of the phenomenon. In-depth interviews were conducted with 19 children and seven parents who were recruited through Alcohol Abuse and Family Support Agency. A Grounded Theory approach was adopted to analyze the transcribed interview data. Nvivo 10 software was used in analyzing the data by methodologically coding and categorizing the data in open, axial and selective coding. Five major themes emerged from the analysis, which is referred to as antecedents of parents’ alcohol abuse phenomenon, Description of alcohol abuse phenomenon, Contexts and prevailing conditions affecting alcohol abuse, coping strategies and consequences of alcohol abuse on children. Based on this, the study was set out to construct a grounded theory of the phenomena of children in the alcohol abused families in Nigeria rooted in the description of their own comprehensive experience. This study is planned to benefit all parties: parents, teachers, counselors and other stakeholders to minimize the effect of parent alcohol abuse on children.


Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference


title: Transition of electronic word-of-mouth services from web to mobile context: A trust transfer perspective

abstract: Success in web services cannot promise the success in corresponding mobile services. To understand the mobile service adoption behavior under the context of web-mobile service transition, this study, taking mobile eWOM services as an example, investigates the role of trust in mobile service adoption and empirically examines the trust transfer mechanism. Specifically, trust in web services and two relationship-relevant factors namely functional consistency and perceived entitativity are proposed as the predictors of trust in mobile services. A field survey with 235 mobile eWOM services users is conducted to test the research model and hypotheses. The key findings include (1) trust in mobile services positively influences intention to use mobile services; (2) trust in web services, functional consistency and perceived entitativity positively influence trust in mobile services; (3) functional consistency positively influences perceived entitativity. Limitations, theoretical and practical implications are also discussed.


title: Design considerations to maximize performance of an IPM motor for a wide flux-weakening region

abstract: Interior permanent magnet (IPM) synchronous machines result to be a valid motor topology in case of both high efficiency and high flux-weakening range. In industry applications the design of interior permanent magnet (IPM) synchronous machines requires to satisfy an increasingly number of limitations. For an IPM machine the key parameters that can be used for a performance maximization refer to many aspects: geometry, material property, cost, control strategy. According to the size and geometry limitations of an IPM motor for a very high flux-weakening speed range is required, the paper analyzes how to maximize the performance modifying the PM quantity. FE simulations, are firstly verified comparing the results with measurements on a prototype, and then they are used to evaluate the tradeoffs of the different cases.


title: Comparison of Glucose Concentration in Interstitial Fluid, and Capillary and Venous Blood During Rapid Changes in Blood Glucose Levels

abstract: The relationship between glucose concentrations in interstitial fluid (ISF) and blood has generated great interest due to its importance in minimally invasive and noninvasive techniques for measuring blood glucose. The relationship between glucose levels in dermal ISF, and capillary and venous blood was studied with the dermal ISF samples obtained using the suction blister technique. The study was conducted with intensely managed diabetics whose blood glucose levels were manipulated so as to induce rapid changes in blood glucose levels. Glucose levels in the three compartments exhibited high correlations both when individual subjects were considered separately and when data from all subjects were combined. No significant time lag during glucose excursions was observed among the ISF, and capillary and venous glucose levels.


title: SOLERES-HCI: Modelling a Human-Computer Interaction Framework for Open EMS

abstract: Organizations and administrations working on environmental information for specific behaviors of soil or planning management of natural resources need to have innovator systems of quality information in order to guarantee the success of theirs daily activities. Experts managing the system consider that not only it is important to have reliable and updated information in order to help to make the most suitable decisions. Due to the variety of final users that cooperate and interact with the decision making system (for instance, politicians, technicians, administrators, etc.) it is also important to have real-time query systems that facilitate the human-human and human-computer interaction and coordination not only with user interfaces that adapt to the users’ profiles habits, but also with intelligent software agents. In this work we advance some ideas about the HCI framework of the SOLERES project, an Environmental Management System (EMS) that fixes, defines and experiences solutions to problems previously described.


title: Human-robot collaborative manipulation planning using early prediction of human motion

abstract: In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. Multiple plans are computed in parallel, one for each robot task available at the current time, and the trajectory with the least cost is selected for execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. Our results show that our framework enables the robot to avoid the human while still accomplishing the robot's task, even in cases where the initial prediction of the human's motion is incorrect. We also show that taking into account the predicted human workspace occupancy in the robot's motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human's current configuration.


title: Learning to Detect Vehicles by Clustering Appearance Patterns

abstract: This paper studies efficient means in dealing with intracategory diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.


title: eTourism: A comparison of Online and Offline Bookings and the Importance of Hotel Attributes

abstract: Internet is an important tool for information searching and purchasing of products especially in tourism. This paper investigates the impact of demographic and travel characteristics of hotel guests on online and offline reservations. It also examines the importance of hotel attributes in selecting a hotel and the differences in priorities between guests who book their accommodation online and offline. Data was collected by surveys of guests in three hotels in Dubrovnik. The results of the study showed that the method of booking mostly depends on the nature of travel, and that the importance of hotel attributes does not differ much between different groups of guests.


title: Broadband printed slot antenna for the fifth generation (5G) mobile and wireless communications

abstract: In this paper, a broadband elliptical-shaped slot antenna for the future fifth generation (5G) wireless applications is proposed. The antenna has a compact size of 0.5λ 0 × 0.5λ 0 at 30 GHz. It consists of a circular shaped radiating patch fed by a 50-Ω microstrip line via proximity-feed technique. An elliptically shaped slot is etched in the ground plane to enhance the antenna bandwidth. A stub has been added to the microstrip line feed to achieve better impedance matching bandwidth of the antenna. Simulated results indicate that the proposed 5G antenna yields a broadband impedance bandwidth larger than 67% (from 20 GHz to beyond 40 GHz) for S11 less than −10 dB. The achieved bandwidth covers both future 5G bands (28/38 GHz). The proposed antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band excluding the rejected band.


title: Adaptive Hierarchical Siamese Network for Object Tracking

abstract: A new object tracking algorithm based on hierarchical siamese network is proposed to solve the challenges of target deformation and background interference in tracking tasks. The introduction of hierarchical features makes up for the lack of features information caused by too small target. A similarity measure mechanism is firstly learned through matching training of extracted hierarchical features on annotated datasets. Online target tracking is achieved by searching candidate images at multiple scales around the target center of the previous frame. The adaptive examplar and the first frame exemplar are used to measure the similarity of the current frame to obtain an accurate score map, which allows the algorithm to adapt to the change of the target. Our algorithm achieves real-time target tracking without online fine-tune and achieves long-term effective tracking on multiple challenging video sequences.


title: Predictors of postoperative binocularity in adult strabismus.

abstract: We evaluated postoperative binocularity in a retrospective study of 111 adult strabismus patients in order to identify the factors which influence the success of surgery for horizontal concomitant strabismus in adults. Selection criteria included minimum age of 15 years at time of surgery, and preoperative fusion impairment with both the Bagolini lens test and synoptophore. Logistic regression analysis was used to correlate patient factors and postoperative binocularity. We found that 52 (65%) of 80 patients with exotropia and 23 (74.2%) of 31 patients with esotropia achieved post operative fusion. Significant predictive factors in exotropia were absence of previous surgery; visual acuity of the deviating eye > 0.5; an increase in the spherical equivalent of the deviating eye, and normal retinal correspondence. Significant predictive factors in esotropia were fusion during prism adaptation, absence of infantile esotropia, and an increase in vertical deviation. The majority of adults with exotropia or esotropia can achieve binocularity after surgery for horizontal concomitant strabismus.


title: Phase Transitions for High Dimensional Clustering and Related Problems

abstract: Consider a two-class clustering problem where we observe $X_i = \ell_i \mu + Z_i$, $Z_i \stackrel{iid}{\sim} N(0, I_p)$, $1 \leq i \leq n$. The feature vector $\mu\in R^p$ is unknown but is presumably sparse. The class labels $\ell_i\in\{-1, 1\}$ are also unknown and the main interest is to estimate them. We are interested in the statistical limits. In the two-dimensional phase space calibrating the rarity and strengths of useful features, we find the precise demarcation for the Region of Impossibility and Region of Possibility. In the former, useful features are too rare/weak for successful clustering. In the latter, useful features are strong enough to allow successful clustering. The results are extended to the case of colored noise using Le Cam's idea on comparison of experiments. We also extend the study on statistical limits for clustering to that for signal recovery and that for hypothesis testing. We compare the statistical limits for three problems and expose some interesting insight. We propose classical PCA and Important Features PCA (IF-PCA) for clustering. For a threshold $t>0$, IF-PCA clusters by applying classical PCA to all columns of $X$ with an $L^2$-norm larger than $t$. We also propose two aggregation methods. For any parameter in the Region of Possibility, some of these methods yield successful clustering. We find an interesting phase transition for IF-PCA. Our results require delicate analysis, especially on post-selection Random Matrix Theory and on lower bound arguments.


Reinforcement Learning with Recurrent Neural Networks


title: Predicting Winning Price in Real Time Bidding with Censored Data

abstract: In the aspect of a Demand-Side Platform (DSP), which is the agent of advertisers, we study how to predict the winning price such that the DSP can win the bid by placing a proper bidding value in the real-time bidding (RTB) auction. We propose to leverage the machine learning and statistical methods to train the winning price model from the bidding history. A major challenge is that a DSP usually suffers from the censoring of the winning price, especially for those lost bids in the past. To solve it, we utilize the censored regression model, which is widely used in the survival analysis and econometrics, to fit the censored bidding data. Note, however, the assumption of censored regression does not hold on the real RTB data. As a result, we further propose a mixture model, which combines linear regression on bids with observable winning prices and censored regression on bids with the censored winning prices, weighted by the winning rate of the DSP. Experiment results show that the proposed mixture model in general prominently outperforms linear regression in terms of the prediction accuracy.


title: Numerical calculation of symmetric capacity of Rayleigh fading channel with BPSK/QPSK

abstract: In this letter, we develop a simple series representation for the symmetric capacity of a Rayleigh fading channel with BPSK/QPSK when perfect channel state information is available at the receiver. This series expansion provides a simple and numerically efficient way to calculate the capacity. We also note that the bounds on the capacity developed in the paper by Baccarelli and Fasano (2000) are loose under the cases considered in this letter.


title: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system

abstract: We model how to implement a rubric in latent semantic analysis.The proposed method change abstract dimensions into meaningful dimensions.The method allows to detect easily written contents.Inbuilt rubric method has been used to give feedback to 924 university students. The purpose of this article is to validate, through two empirical studies, a new method for automatic evaluation of written texts, called Inbuilt Rubric, based on the Latent Semantic Analysis (LSA) technique, which constitutes an innovative and distinct turn with respect to LSA application so far. In the first empirical study, evidence of the validity of the method to identify and evaluate the conceptual axes of a text in a sample of 78 summaries by secondary school students is sought. Results show that the proposed method has a significantly higher degree of reliability than classic LSA methods of text evaluation, and displays very high sensitivity to identify which conceptual axes are included or not in each summary. A second study evaluates the method's capacity to interact and provide feedback about quality in a real online system on a sample of 924 discursive texts written by university students. Results show that students improved the quality of their written texts using this system, and also rated the experience very highly. The final conclusion is that this new method opens a very interesting way regarding the role of automatic assessors in the identification of presence/absence and quality of elaboration of relevant conceptual information in texts written by students with lower time costs than the usual LSA-based methods.


title: Performance of RC Framed Structure by Using Pushover Analysis

abstract: Recent earthquakes in which many concrete structures have been severely damaged or collapsed, have indicated the need for evaluating the seismic adequacy of buildings. About 60% of the land area of our country is susceptible to damaging levels of seismic hazard. We can't avoid future earthquakes, but safe building construction practices can certainly reduce the extent of damage and loss. To evaluate the performance of framed building under future expected earthquakes, a non-linear static pushover analysis has been conducted. The main aim of this study is to understand the behavior of Reinforced Concrete framed structures by using nonlinear static procedure (NSP) or pushover analysis in finite element software "SAP2000".and the Comparative study made for different models in terms of base shear, displacement, performance point. To achieve this objective, two typical new R.C.C. buildings were taken for analysis: G+8 and G+12 to cover the broader spectrum medium and high rise building construction. Different modeling issues were incorporated through ten model for the same building were; bare frame ,having shear wall with and without considering soft storey, infill as X- type concrete bracing with and without considering soft storey.


title: Bayesian Landmark Learning for Mobile Robot Localization

abstract: To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.


title: Research and Experiments on a Unipolar Capacitive Voltage Sensor

abstract: Voltage sensors are an important part of the electric system. In service, traditional voltage sensors need to directly contact a high-voltage charged body. Sensors involve a large volume, complex insulation structures, and high design costs. Typically an iron core structure is adopted. As a result, ferromagnetic resonance can occur easily during practical application. Moreover, owing to the multilevel capacitor divider, the sensor cannot reflect the changes of measured voltage in time. Based on the electric field coupling principle, this paper designs a new voltage sensor; the unipolar structure design solves many problems of traditional voltage sensors like the great insulation design difficulty and high costs caused by grounding electrodes. A differential signal input structure is adopted for the detection circuit, which effectively restrains the influence of the common-mode interference signal. Through sensor modeling, simulation and calculations, the structural design of the sensor electrode was optimized, miniaturization of the sensor was realized, the voltage division ratio of the sensor was enhanced, and the phase difference of sensor measurement was weakened. The voltage sensor is applied to a single-phase voltage class line of 10 kV for testing. According to the test results, the designed sensor is able to meet the requirements of accurate and real-time measurement for voltage of the charged conductor as well as to provide a new method for electricity larceny prevention and on-line monitoring of the power grid in an electric system. Therefore, it can satisfy the development demands of the smart power grid.


title: CoReCast: Collision Resilient Broadcasting in Vehicular Networks

abstract: Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization. Due to low coherence time, the channel changes rapidly in vehicular networks. CoReCast embraces packet collisions and takes advantage of the channel dynamics to decode collided packets. The design of CoReCast is based on a preamble detection scheme that estimates channels from multiple transmitters without any prior information about them. The proposed scheme reduces the space and time requirement exponentially than the existing schemes. The system is evaluated through experiments with USRP N210 and GPS devices placed in vehicles driven on roads in different environments as well as using trace-driven simulations. It provides 15x and 2x lower delay than 802.11p and OCP (Omniscient Clustering Protocol), respectively. Reliability of CoReCast is 8x and 2x better than 802.11p and OCP, respectively.


title: Symmetric Embedding of Locally Regular Hyperbolic Tilings

abstract: Hyperbolic tilings of type {3,7}, {7,3}, {4,5}, and {5,4} are mapped onto closed 2-manifolds of genus 3 through 7 with as much symmetry as possible. All these maps exhibit local regularity which makes all vertices look equivalent, but most lack some of the global flag-transitive symmetries that are topologically possible. On the other hand, many of these mappings allow some nice symmetrical embeddings in 3D Euclidean space with tiles of limited deformation, which makes it possible to create attractive Escher-like tiling patterns on these surfaces of higher genus.


title: New Highly Stable Dimeric 3-Deoxyanthocyanidin Pigments from Sorghum bicolor Leaf Sheath

abstract: The growing interest in natural alternatives to synthetic petroleum-based dyes for food applications necessitates looking at nontraditional sources of natural colors. Certain sorghum varieties accumulate large amounts of poorly characterized pigments in their nongrain tissue. We used High Performance Liquid Chromatography-Tandem Mass Spectroscopy to characterize sorghum leaf sheath pigments and measured the stability of isolated pigments in the presence of bisulfite at pH 1.0 to 7.0 over a 4-wk period. Two new 3-deoxyanthocyanidin compounds were identified: apigeninidin-flavene dimer and apigenin-7-O-methylflavene dimer. The dimeric molecules had near identical UV-Vis absorbance profiles at pH 1.0 to 7.0, with no obvious sign of chalcone or quinoidal base formation even at the neutral pH, indicating unusually strong resistance to hydrophilic attack. The dimeric 3-deoxyanthocyanidins were also highly resistant to nucleophilic attack by SO2; for example, apigeninidin-flavene dimer lost less than 20% of absorbance, compared to apigeninidin monomer, which lost more than 80% of absorbance at λmax within 1 h in the presence of SO2. The increased molecular complexity of the dimeric 3-deoxyanthocyanidins compared to their monomers may be responsible for their unusual stability in the presence of bisulfite; these compounds present new interesting opportunities for food applications.


title: Limits of Static Analysis for Malware Detection

abstract: Malicious code is an increasingly important problem that threatens the security of computer systems. The traditional line of defense against malware is composed of malware detectors such as virus and spyware scanners. Unfortunately, both researchers and malware authors have demonstrated that these scanners, which use pattern matching to identify malware, can be easily evaded by simple code transformations. To address this shortcoming, more powerful malware detectors have been proposed. These tools rely on semantic signatures and employ static analysis techniques such as model checking and theorem proving to perform detection. While it has been shown that these systems are highly effective in identifying current malware, it is less clear how successful they would be against adversaries that take into account the novel detection mechanisms. The goal of this paper is to explore the limits of static analysis for the detection of malicious code. To this end, we present a binary obfuscation scheme that relies on the idea of opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value. Based on opaque constants, we build obfuscation transformations that obscure program control flow, disguise access to local and global variables, and interrupt tracking of values held in processor registers. Using our proposed obfuscation approach, we were able to show that advanced semantics-based malware detectors can be evaded. Moreover, our opaque constant primitive can be applied in a way such that is provably hard to analyze for any static code analyzer. This demonstrates that static analysis techniques alone might no longer be sufficient to identify malware.


title: Reflection Symmetry Detection Using Locally Affine Invariant Edge Correspondence

abstract: Reflection symmetry detection receives increasing attentions in recent years. The state-of-the-art algorithms mainly use the matching of intensity-based features (such as the SIFT) within a single image to find symmetry axes. This paper proposes a novel approach by establishing the correspondence of locally affine invariant edge-based features, which are superior to the intensity based in the aspects that it is insensitive to illumination variations, and applicable to textureless objects. The locally affine invariance is achieved by simple linear algebra for efficient and robust computations, making the algorithm suitable for detections under object distortions like perspective projection. Commonly used edge detectors and a voting process are, respectively, used before and after the edge description and matching steps to form a complete reflection detection pipeline. Experiments are performed using synthetic and real-world images with both multiple and single reflection symmetry axis. The test results are compared with existing algorithms to validate the proposed method.


title: R-Kleene: A High-Performance Divide-and-Conquer Algorithm for the All-Pair Shortest Path for Densely Connected Networks

abstract: We propose a novel divide-and-conquer algorithm for the solution of the all-pair shortest-path problem for directed and dense graphs with no negative cycles. We propose R-Kleene, a compact and in-place recursive algorithm inspired by Kleene's algorithm. R-Kleene delivers a better performance than previous algorithms for randomly generated graphs represented by highly dense adjacency matrices, in which the matrix components can have any integer value. We show that R-Kleene, unchanged and without any machine tuning, yields consistently between 1/7 and 1/2 of the peak performance running on five very different uniprocessor systems.


Training Stacked Denoising Autoencoders for Representation Learning


title: A Bayesian network framework for reject inference

abstract: Most learning methods assume that the training set is drawn randomly from the population to which the learned model is to be applied. However in many applications this assumption is invalid. For example, lending institutions create models of who is likely to repay a loan from training sets consisting of people in their records to whom loans were given in the past; however, the institution approved loan applications previously based on who was thought unlikely to default. Learning from only approved loans yields an incorrect model because the training set is a biased sample of the general population of applicants. The issue of including rejected samples in the learning process, or alternatively using rejected samples to adjust a model learned from accepted samples only, is called reject inference.The main contribution of this paper is a systematic analysis of different cases that arise in reject inference, with explanations of which cases arise in various real-world situations. We use Bayesian networks to formalize each case as a set of conditional independence relationships and identify eight cases, including the familiar missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) cases. For each case we present an overview of available learning algorithms. These algorithms have been published in separate fields of research, including epidemiology, econometrics, clinical trial evaluation, sociology, and credit scoring; our second major contribution is to describe these algorithms in a common framework.


title: A neural network based approach to automated e-mail classification

abstract: We present a neural network based system for automated e-mail filing into folders and anti-spam filtering. The experiments show that it is more accurate than several other techniques. We also investigate the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.


title: A survey on motion prediction and risk assessment for intelligent vehicles

abstract: With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.


title: Detecting Display Energy Hotspots in Android Apps

abstract: Summary ::: The energy consumption of mobile apps has become an important consideration for developers as the underlying mobile devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption—up to 60% of an app's total energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. This paper presents a technique for detecting display energy hotspots—user interfaces of a mobile app whose energy consumption is greater than optimal. The technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. The evaluation of the technique shows that it can predict display energy consumption to within 14% of the ground truth and accurately rank display energy hotspots. Furthermore, the approach found 398 display energy hotspots in a set of 962 popular Android apps, showing the pervasiveness of this problem. For these detected hotspots, the average power savings that could be realized through better user interface design was 30%. Taken together, these results indicate that the approach represents a potentially impactful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.


title: Identity management in e-Health: A case study of web of things application using OpenID connect

abstract: Providing identity management (IdM) in the scene of Web of Things (WoT) is an important requirement to ensure protection of user data made available or consumed by the medical devices in WoT. This work aims to purpose the use of a user-centric IdM system in an ambient assisted living (AAL) environment in the WoT scenario. The IdM system is based on OpenID Connect that attends some of the main security requirements of an AAL environment.


title: Merlin: specification inference for explicit information flow problems

abstract: The last several years have seen a proliferation of static and runtime analysis tools for finding security violations that are caused by explicit information flow in programs. Much of this interest has been caused by the increase in the number of vulnerabilities such as cross-site scripting and SQL injection. In fact, these explicit information flow vulnerabilities commonly found in Web applications now outnumber vulnerabilities such as buffer overruns common in type-unsafe languages such as C and C++. Tools checking for these vulnerabilities require a specification to operate. In most cases the task of providing such a specification is delegated to the user. Moreover, the efficacy of these tools is only as good as the specification. Unfortunately, writing a comprehensive specification presents a major challenge: parts of the specification are easy to miss, leading to missed vulnerabilities; similarly, incorrect specifications may lead to false positives. This paper proposes Merlin, a new approach for automatically inferring explicit information flow specifications from program code. Such specifications greatly reduce manual labor, and enhance the quality of results, while using tools that check for security violations caused by explicit information flow. Beginning with a data propagation graph, which represents interprocedural flow of information in the program, Merlin aims to automatically infer an information flow specification. Merlin models information flow paths in the propagation graph using probabilistic constraints. A naive modeling requires an exponential number of constraints, one per path in the propagation graph. For scalability, we approximate these path constraints using constraints on chosen triples of nodes, resulting in a cubic number of constraints. We characterize this approximation as a probabilistic abstraction, using the theory of probabilistic refinement developed by McIver and Morgan. We solve the resulting system of probabilistic constraints using factor graphs, which are a well-known structure for performing probabilistic inference. We experimentally validate the Merlin approach by applying it to 10 large business-critical Web applications that have been analyzed with CAT.NET, a state-of-the-art static analysis tool for .NET. We find a total of 167 new confirmed specifications, which result in a total of 322 additional vulnerabilities across the 10 benchmarks. More accurate specifications also reduce the false positive rate: in our experiments, Merlin-inferred specifications result in 13 false positives being removed; this constitutes a 15% reduction in the CAT.NET false positive rate on these 10 programs. The final false positive rate for CAT.NET after applying Merlin in our experiments drops to under 1%.


title: Does UML make the grade ? Insights from the software development community

abstract: The Unified Modeling Language (UML) has become the de facto standard for systems development and has been promoted as a technology that will help solve some of the longstanding problems in the software industry. However, there is still little empirical evidence supporting the claim that UML is an effective approach to modeling software systems. Indeed, there is much anecdotal evidence suggesting the contrary, i.e. that UML is overly complex, inconsistent, incomplete and difficult to learn. This paper describes an investigation into the adoption and use of UML in the software development community. A web-based survey was conducted eliciting responses from users of UML worldwide. Results indicate a wide diversity of opinion regarding UML, reflecting the relative immaturity of the technology as well as the controversy over its effectiveness. This paper discusses the results of the survey and charts of the course for future research in UML usage.


title: GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases

abstract: Graphs are being increasingly used to model a wide range of scientific data. Such widespread usage of graphs has generated considerable interest in mining patterns from graph databases. While an array of techniques exists to mine frequent patterns, we still lack a scalable approach to mine statistically significant patterns, specifically patterns with low p-values, that occur at low frequencies. We propose a highly scalable technique, called GraphSig, to mine significant subgraphs from large graph databases. We convert each graph into a set of feature vectors where each vector represents a region within the graph. Domain knowledge is used to select a meaningful feature set. Prior probabilities of features are computed empirically to evaluate statistical significance of patterns in the feature space. Following analysis in the feature space, only a small portion of the exponential search space is accessed for further analysis. This enables the use of existing frequent subgraph mining techniques to mine significant patterns in a scalable manner even when they are infrequent. Extensive experiments are carried out on the proposed techniques, and empirical results demonstrate that GraphSig is effective and efficient for mining significant patterns. To further demonstrate the power of significant patterns, we develop a classifier using patterns mined by GraphSig. Experimental results show that the proposed classifier achieves superior performance, both in terms of quality and computation cost, over state-of-the-art classifiers.


title: The Feedback PI controller for Buck-Boost converter combining KY and Buck converter

abstract: Most mobile equipments use battery as power source. The increasing use of low voltage portable devices and growing requirements of functionalities embedded into such devices. Thus, efficient power management techniques are needed for longer battery life for them. Given the highly variable nature of batteries systems often require supply voltages to be both higher and lower than the battery. This is most efficiently generated by a buck-boost switching converter. But here the converter efficiency is decreased since the power loss occurs in the storage devices. Here, we analyze and describe step by step process of designing, feedback control and simulation of a novel voltage-bucking/boosting converter, combining KY and synchronous buck converter for battery power applications. Unlike the traditional buck-boost converter, this converter has the positive output voltage, different from the negative output voltage of the traditional inverting buck-boost converter. Since, such a converter operates in continuous conduction mode. Also it possesses the non-pulsating output current, thereby not only decreasing the current stress on the output capacitor but also reducing the output voltage ripple. Both the KY converter and the synchronous buck converter, combined into a positive buck-boost converter, uses the same power switches. Thus, it makes the circuit to be compact and the corresponding cost to be down.


title: Overview of Missile Flight Control Systems

abstract: The flight control system is a key element that allows the missile to meet its system performance requirements. The objective of the flight control system is to force the missile to achieve the steering commands developed by the guidance system. The types of steering commands vary depending on the phase of flight and the type of interceptor. For example, in the boost phase the flight control system may be designed to force the missile to track a desired flight-path angle or attitude. In the midcourse and terminal phases the system may be designed to track acceleration commands to effect an intercept of the target. This article explores several aspects of the missile flight control system, including its role in the overall missile system, its subsystems, types of flight control systems, design objectives, and design challenges. Also discussed are some of APL's contributions to the field, which have come primarily through our role as Technical Direction Agent on a variety of Navy missile programs.


title: Link prediction in complex networks: a local na\"{\i}ve Bayes model

abstract: Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local na\"{\i}ve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network.


title: A Coaxial Line to Post-Wall Waveguide Transition for a Cost-Effective Transformer between a RF-Device and a Planar Slot-Array Antenna in 60-GHz Band

abstract: Interfaces between a coaxial structure and a post-wall waveguide are proposed as the essential components for cost-effective millimeter-wave modules. PTFE substrate is selected in terms of loss and manufacturability. The reflection and the transmission characteristics are investigated. The short-stepped and the short-taper-stepped feeding structures provide 14.7% and 13.2% bandwidths for the reflection smaller than -15 dB, respectively. The 46 x 40 mm 2 size antenna fed by the short-stepped structure in PTFE substrate gives 27.3 dBi with 58.2% efficiency at 60.0 GHz. Feeding structures in PTFE substrate fulfill electrical and manufacturing demands in millimeter-wave bands.


title: On some inequalities for the incomplete gamma function

abstract: Let p ¬= 1 be a positive real number. We determine all real numbers α = α(p) and β = β(p) such that the inequalities formula math. formula math. are valid for all x > 0. And, we determine all real numbers a and b such that - log(1 - e -ax ) ≤ √ x ∞ e-t/t ≤ - log(1 - e -bx ) hold for all > 0.


(1702-3501) COVERINGS, MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS


title: Nanoporous silica nanoparticles with spherical and anisotropic shape as fillers in dental composite materials

abstract: Abstract The objective of this study was to test whether nanoporous silica nanoparticles can be employed as fillers in dental composite materials to improve their mechanical properties. These nanoporous silica nanoparticles were synthesized using sol-gel methods, in part modified by silanization, and thoroughly characterized. The nanoporous nanoparticles were added to dental resins to form nanocomposites (resins impregnated with nanoparticles) and hybrid composites (containing in addition conventional microfillers). The incorporation of these nanoporous nanoparticles in dental resins or composites was characterized by investigation of the complex viscosity and double bond conversion as well as by determination of flexural strength and Young’s modulus. The dispersion of the nanofillers was examined by SEM and EDX imaging of fracture surfaces. Incorporation of small contents (1–3 wt%) of unmodified nanoporous particles leads to improved mechanical properties. However, the incorporation of larger contents results in particle agglomeration and declining mechanical properties. This effect is less pronounced when the surface of the particles is modified with methacrylate residues, resulting in a lower agglomeration tendency and a more homogeneous filler dispersion. Surface properties and, concomitantly, dispersibility of the nanoparticles have a strong influence on mechanical properties. But the incorporation of nanoporous instead of solid nanoparticles into dental composite materials is indeed a possibility to improve the mechanical behavior. However, modification of the surface is necessary and the key to achieving uniform dispersion and, thereby, improving mechanical properties.


title: Facile and green synthesis of (La0.95Eu0.05)2O2S red phosphors with sulfate-ion pillared layered hydroxides as a new type of precursor: controlled hydrothermal processing, phase evolution and photoluminescence

abstract: AbstractThis study presents a facile and green route for the synthesis of (La0.95Eu0.05)2O2S red phosphors of controllable morphologies, with the sulfate-type layered hydroxides of Ln2(OH)4SO4·2H2O (Ln = La and Eu) as a new type of precursor. The technique takes advantage of the fact that the precursor has had the exact Ln:S molar ratio of the targeted phosphor, thus saving the hazardous sulfurization reagents indispensable to traditional synthesis. Controlled hydrothermal processing at 120 °C yielded phase-pure Ln2(OH)4SO4·2H2O crystallites in the form of either nanoplates or microprisms, which can both be converted into Ln2O2S phosphor via a Ln2O2SO4 intermediate upon annealing in flowing H2 at a minimum temperature of ∼ 700 °C. The nanoplates collapse into relatively rounded Ln2O2S particles while the microprisms retain well their initial morphologies at 1 200 °C, thus yielding two types of red phosphors. Photoluminescence excitation (PLE) studies found two distinct charge transfer (CT) excitation band...


How Does Enterprise Architecture Add Value to Organisations?


title: NV-Heaps: making persistent objects fast and safe with next-generation, non-volatile memories

abstract: Persistent, user-defined objects present an attractive abstraction for working with non-volatile program state. However, the slow speed of persistent storage (i.e., disk) has restricted their design and limited their performance. Fast, byte-addressable, non-volatile technologies, such as phase change memory, will remove this constraint and allow programmers to build high-performance, persistent data structures in non-volatile storage that is almost as fast as DRAM. Creating these data structures requires a system that is lightweight enough to expose the performance of the underlying memories but also ensures safety in the presence of application and system failures by avoiding familiar bugs such as dangling pointers, multiple free()s, and locking errors. In addition, the system must prevent new types of hard-to-find pointer safety bugs that only arise with persistent objects. These bugs are especially dangerous since any corruption they cause will be permanent. We have implemented a lightweight, high-performance persistent object system called NV-heaps that provides transactional semantics while preventing these errors and providing a model for persistence that is easy to use and reason about. We implement search trees, hash tables, sparse graphs, and arrays using NV-heaps, BerkeleyDB, and Stasis. Our results show that NV-heap performance scales with thread count and that data structures implemented using NV-heaps out-perform BerkeleyDB and Stasis implementations by 32x and 244x, respectively, by avoiding the operating system and minimizing other software overheads. We also quantify the cost of enforcing the safety guarantees that NV-heaps provide and measure the costs of NV-heap primitive operations.


title: Feedback Loops Link Odor and Pheromone Signaling with Reproduction

abstract: Summary Pheromones can have profound effects on reproductive physiology and behavior in mammals. To investigate the neural circuits underlying these effects, we used a genetic transneuronal tracer to identify neurons that synapse with GnRH (LHRH) neurons, the key regulators of reproduction. We then asked whether the connected neurons are presynaptic or postsynaptic to GnRH neurons and analyzed their responses to chemosensory cues. Surprisingly, these experiments indicate that GnRH neurons receive pheromone signals from both odor and pheromone relays in the brain and may also receive common odor signals. Moreover, feedback loops are evident whereby GnRH neurons could influence both odor and pheromone processing. Remarkably, ∼800 GnRH neurons communicate with ∼50,000 neurons in 53 functionally diverse brain areas, with some connections exhibiting sexual dimorphism. These studies reveal a complex interplay between reproduction and other functions in which GnRH neurons appear to integrate information from multiple sources and modulate a variety of brain functions.


Adversarial Training for Relation Extraction


title: A compiler-level intermediate representation based binary analysis and rewriting system

abstract: This paper presents component techniques essential for converting executables to a high-level intermediate representation (IR) of an existing compiler. The compiler IR is then employed for three distinct applications: binary rewriting using the compiler's binary back-end, vulnerability detection using source-level symbolic execution, and source-code recovery using the compiler's C backend. Our techniques enable complex high-level transformations not possible in existing binary systems, address a major challenge of input-derived memory addresses in symbolic execution and are the first to enable recovery of a fully functional source-code. We present techniques to segment the flat address space in an executable containing undifferentiated blocks of memory. We demonstrate the inadequacy of existing variable identification methods for their promotion to symbols and present our methods for symbol promotion. We also present methods to convert the physically addressed stack in an executable (with a stack pointer) to an abstract stack (without a stack pointer). Our methods do not use symbolic, relocation, or debug information since these are usually absent in deployed executables. We have integrated our techniques with a prototype x86 binary framework called SecondWrite that uses LLVM as IR. The robustness of the framework is demonstrated by handling executables totaling more than a million lines of source-code, produced by two different compilers (gcc and Microsoft Visual Studio compiler), three languages (C, C++, and Fortran), two operating systems (Windows and Linux) and a real world program (Apache server).


title: Convolutional recurrent neural networks for bird audio detection

abstract: Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.


title: Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing

abstract: Consider the problem of testing k hypotheses simultaneously. In this article we discuss finite- and large-sample theory of stepdown methods that provide control of the familywise error rate (FWE). To improve on the Bonferroni method or on Holm's stepdown method, Westfall and Young made effective use of resampling to construct stepdown methods that implicitly estimate the dependence structure of the test statistics. However, their methods depend on an assumption known as “subset pivotality.” Our goal here is to construct general stepdown methods that do not require such an assumption. To accomplish this, we take a close look at what makes stepdown procedures work; a key component is a monotonicity requirement of critical values. By imposing monotonicity on estimated critical values (which is not an assumption on the model but rather is an assumption on the method), we show how to construct stepdown tests that can be applied in a stagewise fashion so that at most k tests need to be computed. Moreover, at ea...


title: Novel Design of Dual Band-Notched Monopole Antenna With Bandwidth Enhancement for UWB Applications

abstract: This letter proposes a novel printed monopole antenna for ultrawideband applications with dual band-notched function. The antenna consists of a square radiating patch with an inverted T-shaped ring slot, surrounded by a C-shaped slot, which provides a wide usable fractional bandwidth of more than 125% (2.71-12.06 GHz). In this structure, by cutting an inverted T-shaped slot, the impedance bandwidth is effectively improved at the upper frequency. In order to generate single band-notched characteristics, we use an inverted T-shaped slot, surrounded by a C-shaped slot, in the radiating patch. By adding an inverted T-shaped parasitic structure inside the inverted T-shaped slot on the radiating patch, a dual band-notched function is achieved, and also by inserting this parasitic structure, additional resonance is excited, and hence much wider impedance bandwidth can be produced, especially at the higher band. The measured results reveal that the presented dual band-notch monopole antenna offers a wide bandwidth with two notched bands, covering all the 5.2/5.8-GHz WLAN, 3.5/5.5-GHz WiMAX, and 4-GHz C-bands. The designed antenna has a small size of 12 × 18 × 0.8 mm3.


title: A literature review on phishing crime, prevention review and investigation of gaps

abstract: Phishing is a rapidly growing threat in cyber world and causing billions of dollars in damage every year to internet users. It is an unlawful activity which uses a group of social engineering and technology to collect an Internet user's sensitive information. The identification of phishing techniques can be performed in various methods of communications like email, instant messages, pop-up messages, or at web page level. Over the period, a number of research articles have published with different techniques and procedures but have failed to detect all associated risks and provide a comprehensive solution. This paper presents a theoretical model of CRI to study this threat in a systematic manner. While there is a common perception about the successful phishing attack involves creating an identical messages or website to deceive the internet user however this theory has not been utilized to evaluate this threat and investigate the gaps systematically. Our model attempts to evaluate this crime, review different research perspectives and approaches and investigate the gaps. In this sense, our literature review study is significant to generate attentiveness about phishing in order to boost thoughts and actions to improve the cyber security and gain internet users' confidence.


title: IMO forensics in Android and windows systems

abstract: Smartphone forensics is currently gaining more attention because of their widespread in the market. Smart devices are provisioned with wide range of applications that extend their capabilities and features. IMO Instant Messaging (IM) application is a mobile application that allows making audio and video calls, chat messages, share photos and videos, and group chatting. The increased usage of IM applications on Android phones has turned to be a source for mobile and computer forensic investigators. This paper aims to investigate the IMO App in Android and Windows systems from forensics perspective. We perform real chats and calls in different scenarios. We extract the artifacts from the NAND flash memory in Android and from the main memory (RAM) in Windows platform. Our results show that important data can still be recovered even after deleting chats and uninstalling the app.


title: The molecular mechanisms of signaling by cooperative assembly formation in innate immunity pathways

abstract: The innate immune system is the first line of defense against infection and responses are initiated by pattern recognition receptors (PRRs) that detect pathogen-associated molecular patterns (PAMPs). PRRs also detect endogenous danger-associated molecular patterns (DAMPs) that are released by damaged or dying cells. The major PRRs include the Toll-like receptor (TLR) family members, the nucleotide binding and oligomerization domain, leucine-rich repeat containing (NLR) family, the PYHIN (ALR) family, the RIG-1-like receptors (RLRs), C-type lectin receptors (CLRs) and the oligoadenylate synthase (OAS)-like receptors and the related protein cyclic GMP-AMP synthase (cGAS). The different PRRs activate specific signaling pathways to collectively elicit responses including the induction of cytokine expression, processing of pro-inflammatory cytokines and cell-death responses. These responses control a pathogenic infection, initiate tissue repair and stimulate the adaptive immune system. A central theme of many innate immune signaling pathways is the clustering of activated PRRs followed by sequential recruitment and oligomerization of adaptors and downstream effector enzymes, to form higher-order arrangements that amplify the response and provide a scaffold for proximity-induced activation of the effector enzymes. Underlying the formation of these complexes are co-operative assembly mechanisms, whereby association of preceding components increases the affinity for downstream components. This ensures a rapid immune response to a low-level stimulus. Structural and biochemical studies have given key insights into the assembly of these complexes. Here we review the current understanding of assembly of immune signaling complexes, including inflammasomes initiated by NLR and PYHIN receptors, the myddosomes initiated by TLRs, and the MAVS CARD filament initiated by RIG-1. We highlight the co-operative assembly mechanisms during assembly of each of these complexes.


title: Athlete: A cargo handling and manipulation robot for the moon

abstract: A robotic vehicle called ATHLETE—the All-Terrain Hex-Limbed, Extra-Terrestrial Explorer—is described, along with initial results of field tests of two prototype vehicles. This vehicle concept is capable of efficient rolling mobility on moderate terrain and walking mobility on extreme terrain. Each limb has a quick-disconnect tool adapter so that it can perform general-purpose handling, assembly, maintenance, and servicing tasks using any or all of the limbs. © 2007 Wiley Periodicals, Inc.


title: E-government and NPM: a perfect marriage?

abstract: E-Government and New Public Management (NPM) are two concepts that are often used in discussions about modernizing government. Both concepts refer to various forms of reform of the public sector and, at least to a certain degree, address the same problems public administration is nowadays confronted with (lack of accountability, underperformance and diminished level of legitimacy, to name a few).The fact that e-government and new public management address the same problems does not imply that the contents of these concepts indeed overlap. Both concepts comprise heterogeneous elements and multiple dimensions; intuitively, some of these elements may be understood as overlapping, but a clear conceptual framework indicating where new public management and E-government overlap and reinforce one another, is lacking.Based on theoretical and empirical evidence, a framework for classification of reforms is presented. The framework highlights various types of reforms, and identifies requirements for architectures and accountability routes in technological and institutional reforms.


title: Automatic diacritization of Arabic text using recurrent neural networks

abstract: This paper presents a sequence transcription approach for the automatic diacritization of Arabic text. A recurrent neural network is trained to transcribe undiacritized Arabic text with fully diacritized sentences. We use a deep bidirectional long short-term memory network that builds high-level linguistic abstractions of text and exploits long-range context in both input directions. This approach differs from previous approaches in that no lexical, morphological, or syntactical analysis is performed on the data before being processed by the net. Nonetheless, when the network is post-processed with our error correction techniques, it achieves state-of-the-art performance, yielding an average diacritic and word error rates of 2.09 and 5.82 %, respectively, on samples from 11 books. For the LDC ATB3 benchmark, this approach reduces the diacritic error rate by 25 %, the word error rate by 20 %, and the last-letter diacritization error rate by 33 % over the best published results.


title: Patch-Based Low-Rank Minimization for Image Denoising

abstract: Patch-based low-rank minimization for image processing attracts much attention in recent years. The minimization of the matrix rank coupled with the Frobenius norm data fidelity can be solved by the hard thresholding filter with principle component analysis (PCA) or singular value decomposition (SVD). Based on this idea, we propose a patch-based low-rank minimization method for image denoising. The main denoising process is stated in three equivalent way: PCA, SVD and low-rank minimization. Compared to recent patch-based sparse representation methods, experiments demonstrate that the proposed method is rather rapid, and it is effective for a variety of natural grayscale images and color images, especially for texture parts in images. Further improvements of this method are also given. In addition, due to the simplicity of this method, we could provide an explanation of the choice of the threshold parameter, estimation of PSNR values, and give other insights into this method.


title: Participant-dependent and participant-independent classification of emotions using EEG signals

abstract: Emotion recognition has gained importance and significant attention as a part of the human-computer interface (HCI), and is envisioned to provide an "emotional dimension" to HCI. In this paper, we propose a novel method for participant-dependent and participant-independent emotion recognition using electroencephalogram (EEG) signals. We aim to distinguish between positive and negative valence emotions on the valence-arousal graph, i.e., considering a binary classification problem. The EEG signals of DEAP dataset, where forty different videos were used to stimulate 32 participants is considered for the classification problem. Participant-dependent features are extracted in order to classify emotions for each participant, where a perfect classification score is shown to be achieved for one of the participants. Next, we consider generalization across participants and find participant-independent features. Applying the widely used or conventional method in this regard, we observe that the training time increases many fold with increase in participants. We propose a technique called averaging of features and show that the computational complexity of the proposed technique is significantly low as compared to the conventional method with increase in participants. The results obtained by detailed experiments and evaluation procedure indicate that averaging of features technique yields nearly the same performance as the conventional approach, but scarcely consumes computational time. The maximum classification accuracy for 32 participants was obtained to be 81.03% and 77.46% using conventional and averaging approaches respectively.


title: Behavioral tolerance to repeated apomorphine administration in parkinsonian monkeys

abstract: Four consecutive injections (s.c.) of apomorphine (Apo) were given to 5 parkinsonian monkeys after i.v. MPTP administration. The minimal effective dose (MED) of Apo was defined as that capable of reducing motor disability by 50% or more for a minimum period of 30 min. Repeated apomorphine injections were given with an interval of 30 min after the motor effect of the previous injection had worn off or with a separation of 3 h between injections. The doses used in different experiments were the MED (2.4 μg/kg), 4 MED and 8 MED. In every experiment the duration of motor benefit was longest with the first Apo injection. There was a decay in the duration of the response elicited by consecutive Apo injections when given 30 min after the previous effect had wanted. This was significant for the MED and 4 MED (ANOVA, P < 0.01). When Apo boluses were given with an interval of 3 h there was a significant reduction in the duration of the response elicited by the MED, 4 MED and 8 MED of apomorphine. For the MED the reduction in the duration of the motor response was significantly greater for injections given with a 30-min interval than 3-h interval. These findings indicate that behavioral hyposensitivity to repeated Apo administration in parkinsonian monkeys occurs preferentially when near threshold doses are given with short intervals.


title: A two-way time of flight ranging scheme for wireless sensor networks

abstract: Relative ranging between Wireless Sensor Network (WSN) nodes is considered to be an important requirement for a number of distributed applications. This paper focuses on a two-way, time of flight (ToF) technique which achieves good accuracy in estimating the point-to-point distance between two wireless nodes. The underlying idea is to utilize a two-way time transfer approach in order to avoid the need for clock synchronization between the participating wireless nodes. Moreover, by employing multiple ToF measurements, sub-clock resolution is achieved. A calibration stage is used to estimate the various delays that occur during a message exchange and require subtraction from the initial timed value. The calculation of the range between the nodes takes place on-node making the proposed scheme suitable for distributed systems. Care has been taken to exclude the erroneous readings from the set of measurements that are used in the estimation of the desired range. The two-way ToF technique has been implemented on commercial off-the-self (COTS) devices without the need for additional hardware. The system has been deployed in various experimental locations both indoors and outdoors and the obtained results reveal that accuracy between 1 m RMS and 2.5 m RMS in line-of-sight conditions over a 42 m range can be achieved.


title: Vessel-guided airway tree segmentation : A voxel classification approach

abstract: This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the airway tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage. When combining the airway appearance model with vessel orientation similarity, the improvement is even more significant (p<0.01) than only using the airway appearance model, with on average 7% increase in the total length of branches extracted correctly.


title: Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

abstract: This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.


title: SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features

abstract: This paper describes our submission to SemEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some preprocessing, including detection of MultiWord Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated for sub-strings and contrasting sub-string features are used to better capture complex phenomena like sarcasm. The resulting supervised system, using a Naive Bayes model, achieved high performance in classifying entire tweets, ranking 7th on the main set and 2nd when applied to sarcastic tweets.


title: Validation of species–climate impact models under climate change

abstract: Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climatechange scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past � 20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models’ predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.


Does String-Based Neural MT Learn Source Syntax?


title: Cross-Tenant Side-Channel Attacks in PaaS Clouds

abstract: We present a new attack framework for conducting cache-based side-channel attacks and demonstrate this framework in attacks between tenants on commercial Platform-as-a-Service (PaaS) clouds. Our framework uses the FLUSH-RELOAD attack of Gullasch et al. as a primitive, and extends this work by leveraging it within an automaton-driven strategy for tracing a victim's execution. We leverage our framework first to confirm co-location of tenants and then to extract secrets across tenant boundaries. We specifically demonstrate attacks to collect potentially sensitive application data (e.g., the number of items in a shopping cart), to hijack user accounts, and to break SAML single sign-on. To the best of our knowledge, our attacks are the first granular, cross-tenant, side-channel attacks successfully demonstrated on state-of-the-art commercial clouds, PaaS or otherwise.


Deceptive Reviews: The Influential Tail


title: Adversarial Message Passing For Graphical Models

abstract: Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn parameters of generators by fooling discriminators. Typically, GANs are considered to be models themselves and are not understood in the context of inference. Current techniques rely on inefficient global discrimination of joint distributions to perform learning, or only consider discriminating a single output variable. We overcome these limitations by treating GANs as a basis for likelihood-free inference in generative models and generalize them to Bayesian posterior inference over factor graphs. We propose local learning rules based on message passing minimizing a global divergence criterion involving cooperating local adversaries used to sidestep explicit likelihood evaluations. This allows us to compose models and yields a unified inference and learning framework for adversarial learning. Our framework treats model specification and inference separately and facilitates richly structured models within the family of Directed Acyclic Graphs, including components such as intractable likelihoods, non-differentiable models, simulators and generally cumbersome models. A key result of our treatment is the insight that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions. As a side-result, we discuss the link to likelihood maximization. These approaches hold promise to be useful in the toolbox of probabilistic modelers and enrich the gamut of current probabilistic programming applications.


title: Muscle buffer capacity and aerobic fitness are associated with repeated-sprint ability in women

abstract: In addition to a high aerobic fitness, the ability to buffer hydrogen ions (H+) may also be important for repeated-sprint ability (RSA). We therefore investigated the relationship between muscle buffer capacity (betamin vivo and betamin vitro) and RSA. Thirty-four untrained females [mean (SD): age 19 (1) years, maximum oxygen uptake (VO2peak) 42.3 (7.1) ml x kg(-1) x min(-1)] completed a graded exercise test (GXT), followed by a RSA cycle test (five 6-s sprints, every 30 s). Capillary blood was sampled during the GXT and before and after the RSA test to determine blood pH (pHb) and lactate concentration ([La-]b). Muscle biopsies were taken before (n=34) and after (n=23) the RSA test to determine muscle lactate concentration ([La-]i), hydrogen ion concentration ([H+]i) pHi, betamin vivo and betamin vitro. There were significant correlations between work decrement (%) and betamin vivo (r=-0.72, P<0.05), VO2peak (r=-0.62, P<0.05), lactate threshold (LT) (r=-0.56, P<0.05) and changes in [H+]i (r=0.41, P<0.05). There were however, no significant correlations between work decrement and betamin vitro, or changes in [La-]i, or [La-]b. There were also no significant correlations between total work (J x kg(-1)) during the RSA test and betamin vitro, betamin vivo, or changes in [La-]i, pHi, [La-]b, or pHb. There were significant correlations between total work (J x kg(-1)) and both VO2peak (r=0.60, P<0.05) and LT(r=0.54, P<0.05). These results support previous research, identifying a relationship between RSA and aerobic fitness. This study is the first to identify a relationship between betamin vivo and RSA. This suggests that the ability to buffer H+ may be important for maintaining performance during brief, repeated sprints.


title: An analytical review for event prediction system on time series

abstract: This Time series mining is a new area of research in temporal databases and has been an active area of research with its notable recent progress. Event prediction is one of the main goals of time series mining which have important roles for appropriate decision making in different application area. So far, different studies have been presented in the field of time series mining for meaningful events prediction, which have ample challenges. Lack of systematic identification of challenges causes some obstacles for the development of methods. In this paper, due to the abundance and diversity of challenges in event prediction system on time series, lack of a perfect platform for their systematic identification and removing barriers for the development of methods, a classification is proposed for challenging problems of event prediction system on time series. Also, reviewed and analyzed the application of data mining techniques for solving different challenges in event prediction system on time series. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a comparison structure that can map data mining techniques into the event prediction challenges. The proposed classification of this paper by introducing systematic challenges can help create different research pivots for the elimination of challenges in different areas of applying and developing methods. We think that this paper can help researchers in event prediction systems on time series for the future activities.


title: A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

abstract: Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.


title: Genetic algorithm-based design of the active damping for an LCL-filter three-phase active rectifier

abstract: The use of a LCL-filter mitigates the switching ripple injected in the grid by a three-phase active rectifier. However stability problems could arise in the current control loop. In order to overcome them a damping resistor can be inserted, at the price of a reduction of the efficiency. On the contrary the use of the active damping seems really attractive but it is often limited by the use of more sensors respect to the standard control and by the complex tuning procedure. This paper introduces a new active damping method that does not need the use of more sensors and that can be tuned using genetic algorithms. It consists of adding a filter on the reference voltage for the converter's modulator. The tuning process of this filter is easily done, for a wide range of sampling frequencies, with the use of genetic algorithms. This method is used only for the optimum choice of the parameters of the filter and an on-line implementation is not needed. Thus the resulting active damping solution does not need new sensors or complex calculations. Moreover, in the paper particular attention is devoted to the dynamics of the system due to the introduction of the active damping.


title: Implementing a Web Browser with Phishing Detection Techniques

abstract: Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker. Phishing has become the most popular practice among the criminals of the Web. Phishing attacks are becoming more frequent and sophisticated. The impact of phishing is drastic and significant since it can involve the risk of identity theft and financial losses. Phishing scams have become a problem for online banking and e-commerce users. In this paper we propose a novel approach to detect phishing attacks. We implemented a prototype web browser which can be used as an agent and processes each arriving email for phishing attacks. Using email data collected over a period time we demonstrate data that our approach is able to detect more phishing attacks than existing schemes.


title: Evaluation of elbow pain in adults.

abstract: The elbow is a complex joint designed to withstand a wide range of dynamic exertional forces. The location and quality of elbow pain can generally localize the injury to one of the four anatomic regions: anterior, medial, lateral, or posterior. The history should include questions about the onset of pain, what the patient was doing when the pain started, and the type and frequency of athletic and occupational activities. Lateral and medial epicondylitis are two of the more common diagnoses and often occur as a result of occupational activities. Patients have pain and tenderness over the affected tendinous insertion that are accentuated with specific movements. If lateral and medial epicondylitis treatments are unsuccessful, ulnar neuropathy and radial tunnel syndrome should be considered. Ulnar collateral ligament injuries occur in athletes participating in sports that involve overhead throwing. Biceps tendinopathy is a relatively common source of pain in the anterior elbow; history often includes repeated elbow flexion with forearm supination and pronation. Olecranon bursitis is a common cause of posterior elbow pain and swelling. It can be septic or aseptic, and is diagnosed based on history, physical examination, and bursal fluid analysis if necessary. Plain radiography is the initial choice for the evaluation of acute injuries and is best for showing bony injuries, soft tissue swelling, and joint effusions. Magnetic resonance imaging is the preferred imaging modality for chronic elbow pain. Musculoskeletal ultrasonography allows for an inexpensive dynamic evaluation of commonly injured structures.


title: Examining Schools’ Distributed Instructional Leadership Capacity: Comparison of Elementary and Secondary Schools

abstract: The purpose of this study is to elucidate the fundamental differences between elementary and secondary schools in distributed instructional leadership practices by comparing results determined by the Comprehensive Assessment of Leadership for Learning (CALL) survey. The data set used in this study was derived from the 2011-1013 CALL survey: a 360-degree, on-line, formative assessment and feedback system for K-12 school leadership. A total number of 4311 voluntary teachers, school administrators and other school staff participated in this study. The analysis included three integral steps. First, descriptive statistics were carried out to explain how distributed instructional leadership was rated on the CALL survey. Second, an independent-samples t-test was employed to compare the mean scores of elementary and secondary schools on the CALL survey. Third, ratios of elementary and secondary school staff in the top scoring 20 percentile and bottom scoring 20 percentile were compared to each other. The results suggested that all five distributed-instructional leadership domains which were addressed in the CALL had very close mean scores, indicating that none of the broad leadership areas was being ignored and elementary and secondary schools differ only in terms of the leadership practices related to monitoring teaching and learning.


title: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines

abstract: The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.


Sentiment Analysis of Customer Review Data using Big Data: A Survey


title: Influence of Resistance Training Frequency on Muscular Adaptations in Well-Trained Men

abstract: The purpose of this study was to investigate the effects of training muscle groups 1 day per week using a split-body routine (SPLIT) vs. 3 days per week using a total-body routine (TOTAL) on muscular adaptations in well-trained men. Subjects were 20 male volunteers (height = 1.76 ± 0.05 m; body mass = 78.0 ± 10.7 kg; age = 23.5 ± 2.9 years) recruited from a university population. Participants were pair matched according to baseline strength and then randomly assigned to 1 of the 2 experimental groups: a SPLIT, where multiple exercises were performed for a specific muscle group in a session with 2�3 muscle groups trained per session (n = 10) or a TOTAL, where 1 exercise was performed per muscle group in a session with all muscle groups trained in each session (n = 10). Subjects were tested pre- and poststudy for 1 repetition maximum strength in the bench press and squat, and muscle thickness (MT) of forearm flexors, forearm extensors, and vastus lateralis. Results showed significantly greater increases in forearm flexor MT for TOTAL compared with SPLIT. No significant differences were noted in maximal strength measures. The findings suggest a potentially superior hypertrophic benefit to higher weekly resistance training frequencies.


title: Sequential line search for efficient visual design optimization by crowds

abstract: Parameter tweaking is a common task in various design scenarios. For example, in color enhancement of photographs, designers tweak multiple parameters such as "brightness" and "contrast" to obtain the best visual impression. Adjusting one parameter is easy; however, if there are multiple correlated parameters, the task becomes much more complex, requiring many trials and a large cognitive load. To address this problem, we present a novel extension of Bayesian optimization techniques, where the system decomposes the entire parameter tweaking task into a sequence of one-dimensional line search queries that are easy for human to perform by manipulating a single slider. In addition, we present a novel concept called crowd-powered visual design optimizer, which queries crowd workers, and provide a working implementation of this concept. Our single-slider manipulation microtask design for crowdsourcing accelerates the convergence of the optimization relative to existing comparison-based microtask designs. We applied our framework to two different design domains: photo color enhancement and material BRDF design, and thereby showed its applicability to various design domains.


title: Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis

abstract: Abstract Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. Experimental results show that SVM gives the highest accuracy (97.13%) with lowest error rate. All experiments are executed within a simulation environment and conducted in WEKA data mining tool.


title: 150 years of Darwin’s theory of intercellular flow of hereditary information

abstract: In 1868, Charles Darwin published his Pangenesis theory, which proposed a mechanism for the flow of hereditary information between cells and generations. Pangenesis has since been disputed, but emerging evidence of cell-to-cell communication urges the reconsideration of this 150-year-old theory. Charles Darwin’s Pangenesis theory, which proposed an intercellular mechanism for the flow of hereditary information, is gaining new ground.


title: Compact Planar Microstrip Branch-Line Couplers Using the Quasi-Lumped Elements Approach With Nonsymmetrical and Symmetrical T-Shaped Structure

abstract: A class of the novel compact-size branch-line couplers using the quasi-lumped elements approach with symmetrical or nonsymmetrical T-shaped structures is proposed in this paper. The design equations have been derived, and two circuits using the quasi-lumped elements approach were realized for physical measurements. This novel design occupies only 29% of the area of the conventional approach at 2.4 GHz. In addition, a third circuit was designed by using the same formula implementing a symmetrical T-shaped structure and occupied both the internal and external area of the coupler. This coupler achieved 500-MHz bandwidth while the phase difference between S21 and S31 is within 90degplusmn1deg. Thus, the bandwidth is not only 25% wider than that of the conventional coupler, but occupies only 70% of the circuit area compared to the conventional design. All three proposed couplers can be implemented by using the standard printed-circuit-board etching processes without any implementation of lumped elements, bonding wires, and via-holes, making it very useful for wireless communication systems