title: Mirror neurons: from discovery to autism

abstract: a report on asurprising set of neurons that we (Giuseppe Di Pellegrino,Luciano Fadiga, Leonardo Fogassi, Vittorio Gallese) hadfound in the ventral premotor cortex of the monkey. Thefundamental characteristic of these neurons was that theydischarged both when the monkey performed a certainmotor act (e.g., grasping an object) and when it observedanother individual (monkey or human) performing that or asimilar motor act (Di Pellegrino et al. 1992). These neuronsare now known as mirror neurons (Fig. 1).


title: Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

abstract: Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.


title: Continuous non-invasive blood pressure monitoring using photoplethysmography: A review

abstract: Blood pressure is an important vital sign of the human body. It is generally monitored using intravenous or cuff-based devices, both of which have their own drawbacks. Continuous and non-invasive blood pressure has significant clinical value however the measurement devices are generally costly and complicated to setup and operate especially in home-based environment. This paper investigates state of the art techniques that utilise one or more photoplethysmographs (PPG) to produce a device that continuously and non-invasively monitors a patient's blood pressure. Three different estimation methodologies are reviewed and compared in this paper.


Cerebral resuscitation: role of osmotherapy


title: Employee attitudes and job satisfaction

abstract: This article identifies three major gaps between HR practice and the scientific research in the area of employee attitudes in general and the most focal employee attitude in particular—job satisfaction: (1) the causes of employee attitudes, (2) the results of positive or negative job satisfaction, and (3) how to measure and influence employee attitudes. Suggestions for practitioners are provided on how to close the gaps in knowledge and for evaluating implemented practices. Future research will likely focus on greater understanding of personal characteristics, such as emotion, in defining job satisfaction and how employee attitudes influence organizational performance. © 2004 Wiley Periodicals, Inc.


title: Mitigation of Human Exposure to RF Fields in Downlink of Millimeter-Wave Cellular Systems

abstract: Out of the very few studies that paid proper attention to the harmful health impacts in millimeter-wave (mmW) communications, most of them are concerned about uplink cases due to closer contact with the human body. Our recent study revealed that even the human exposure to radio frequency (RF) fields in downlink mmW technology is not very minimum to be ignored. There were a few RF exposure mitigation techniques for uplinks, but the downlink scenario is hardly paid any attention. However, this paper proposes a downlink protocol for mmW cellular communications that achieves the maximum data rate while keeping the impacts on human health minimized. Our results show that the proposed technique lowers both power density (PD) and specific absorption rate (SAR) compared to the typical protocol, with only slight sacrifice in data rates.


title: BRVO: Predicting pedestrian trajectories using velocity-space reasoning

abstract: We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot's environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.


title: A 1.4 pJ/bit, Power-Scalable 16×12 Gb/s Source-Synchronous I/O With DFE Receiver in 32 nm SOI CMOS Technology

abstract: A power-scalable 2 Byte I/O operating at 12 Gb/s per lane is reported. The source-synchronous I/O includes controllable TX driver amplitude, flexible RX equalization, and multiple deskew modes. This allows power reduction when operating over low-loss, low-skew interconnects, while at the same time supporting higher-loss channels without loss of bandwidth. Transceiver circuit innovations are described including a low-skew transmission-line clock distribution, a 4:1 serializer with quadrature quarter-rate clocks, and a phase rotator based on current-integrating phase interpolators. Measurements of a test chip fabricated in 32 nm SOI CMOS technology demonstrate 1.4 pJ/b efficiency over 0.75” Megtron-6 PCB traces, and 1.9 pJ/b efficiency over 20” Megtron-6 PCB traces.


title: Head Up Games: combining the best of both worlds by merging traditional and digital play

abstract: Current pervasive games are mostly location-aware applications, played on handheld computing devices. Considering pervasive games for children, it is argued that the interaction paradigm existing games support limits essential aspects of outdoor play like spontaneous social interaction, physical movement, and rich face-to-face communication. We present a new genre of pervasive games conceived to address this problem, that we call "Head Up Games" (HUGs) to underline that they liberate players from facing down to attend to screen-based interactions. The article discusses characteristics of HUG and relates them to existing genres of pervasive games. We present lessons learned during the design and evaluation of three HUG and chart future challenges.


title: TUISTER: a tangible UI for hierarchical structures

abstract: Tangible user interfaces provide access to virtual information through intuitive physical manipulation. However, feedback is mostly provided by displays in the environment instead of the TUI itself. In this paper we describe the design of Tuister, a tangible user interface with multiple embedded displays and sensors. We explain how Tuister can be used to browse and access hierarchical structures and briefly describe the current state of a prototype we're building.


title: Multimedia dataembedding and watermarking technologies

abstract: We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.


title: Face Recognition by Computers and Humans

abstract: This article talks about how the study of how humans perceive faces can be used to help design practical systems for face recognition. Besides applications related to identification and verification-such as access control, law enforcement, ID and licensing, and surveillance-face recognition has also proven useful in applications such as human-computer interaction, virtual reality, database retrieval, multimedia, and computer entertainment. Continuing research into face recognition will provide scientists and engineers with many vital projects, in areas such as homeland security, human-computer interaction, and numerous consumer applications. The areas we are considering pursuing are recognition from unconstrained video sequences, incorporating familiarity into algorithms, modeling effects of aging, and developing biologically plausible models for human face recognition ability.


title: PERFORMANCE EVALUATION OF DEPENDENT TWO-STAGE SERVICES

abstract: In many scenarios services are provided in successive stages. While tandem queues appropriately reflect the structure of such scenarios, the typical assumption that service times at different stages are independent often does not fit to reality. We examine, via simulation, the impact of dependencies among service times on expected customer waiting times. The usual network simulation overhead caused by event list handling is avoided by an extension of the Lindley recursion to the two-stage case. Numerical results are presented for exponentially and uniformly distributed service times with different types of dependencies and varying server utilizations.


title: Numerical analysis of bifurcations in Duffing's equation with hysteretic functions

abstract: This paper analyzes numerically the bifurcation phenomenon in Duffing's equation containing a hysteretic function and reports on the result of investigation of the effect of the hysteretic characteristics on the bifurcation. ::: ::: ::: ::: First, a large number of steady hysteresis loops is derived by numerically integrating the state equation of the system with various initial conditions, and the sets of the steady periodic solutions are observed. Then, using the method already proposed by the authors, the bifurcation of the steady solution is analyzed while varying the parameters of the system. The effects of the hysteretic loss and the ohmic loss on the bifurcation of the system are compared, and the difference is discussed. An example of the generation of chaos caused by increasing the hysteretic loss is shown.


title: Cell formation in group technology: review, evaluation and directions for future research

abstract: Abstract This paper discusses and reviews a fundamental issue in cellular manufacturing—cell formation. This problem is of strategic and operational importance in that it affects the fundamental structure and the overall layout of a cellular manufacturing system. We first provide a comprehensive mathematical formulation of the cell formation problem and then propose a methodology-based classification of prior research. This classification is used in reviewing the most recent literature on the cell formation problem. Based on a comparison and critical evaluation, we highlight the shortcomings of current approaches and also outline directions for future research.


title: Psychodynamic Psychotherapy for Children and Adolescents: A Meta-Analysis of Short-Term Psychodynamic Models (vol 52, pg 863, 2013)

abstract: Objective: Psychodynamically based brief psychotherapy is frequently used in clinical practice for a range of common mental disorders in children and adolescents. To our knowledge, there have been no meta-analyses to evaluate the effectiveness of these therapies. Method: After a broad search, we meta-analyzed controlled outcome studies of short-term psychodynamic psychotherapies (STPP, 40 or fewer sessions). We also performed sensitivity analyses and evaluated the risk of bias in this body of studies. Results: We found 11 studies with a total of 655 patients covering a broad range of conditions including depression, anxiety disorders, anorexia nervosa, and borderline personality disorder. STPP did not separate from what were mostly robust treatment comparators, but there were some subgroup differences. Robust (g ¼ 1.07, 95% CI ¼ 0.80–1.34) within group effect sizes were observed suggesting the treatment may be effective. These effects increased in follow up compared to post treatment (overall, g ¼ 0.24, 95% CI ¼ 0.00–0.48), suggesting a tendency toward increased gains. Heterogeneity was high across most analyses, suggesting that these data need be interpreted with caution. Conclusion: This review suggests that STPP may be effective in children and adolescents across a range of common mental disorders. J. Am. Acad. Child Adolesc. Psychiatry, 2013;52(8):863–875. Key Words: anxiety, child, depression, psychodynamic, psychotherapy


title: Action Recognition by Learning Deep Multi-Granular Spatio-Temporal Video Representation

abstract: Recognizing actions in videos is a challenging task as video is an information-intensive media with complex variations. Most existing methods have treated video as a flat data sequence while ignoring the intrinsic hierarchical structure of the video content. In particular, an action may span different granularities in this hierarchy including, from small to large, a single frame, consecutive frames (motion), a short clip, and the entire video. In this paper, we present a novel framework to boost action recognition by learning a deep spatio-temporal video representation at hierarchical multi-granularity. Specifically, we model each granularity as a single stream by 2D (for frame and motion streams) or 3D (for clip and video streams) convolutional neural networks (CNNs). The framework therefore consists of multi-stream 2D or 3D CNNs to learn both the spatial and temporal representations. Furthermore, we employ the Long Short-Term Memory (LSTM) networks on the frame, motion, and clip streams to exploit long-term temporal dynamics. With a softmax layer on the top of each stream, the classification scores can be predicted from all the streams, followed by a novel fusion scheme based on the multi-granular score distribution. Our networks are learned in an end-to-end fashion. On two video action benchmarks of UCF101 and HMDB51, our framework achieves promising performance compared with the state-of-the-art.


title: A Design Procedure for All-Digital Phase-Locked Loops Based on a Charge-Pump Phase-Locked-Loop Analogy

abstract: In this brief, a systematic design procedure for a second-order all-digital phase-locked loop (PLL) is proposed. The design procedure is based on the analogy between a type-II second-order analog PLL and an all-digital PLL. The all-digital PLL design inherits the frequency response and stability characteristics of the analog prototype PLL


title: Bib2vec: An Embedding-based Search System for Bibliographic Information

abstract: We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results.


title: Building an IoT-aware healthcare monitoring system

abstract: Nowadays, ageing related diseases represent one of the most relevant challenges for developed countries. The use of healthcare remote technology may allow reducing most of the management of the chronic diseases meanwhile it may also contribute to the improvement of elderly people's quality of life. Unfortunately, despite the advent of Internet of things and the even decreasing price of sensors, current proposals are not extensible during runtime meaning that they need to be maintained offline by engineers. Therefore, in this paper we discuss how to build an ad-hoc extensible (during runtime) healthcare monitoring system by using low cost wireless sensors and already existent Internet of things technology as communication platform. Moreover, we present a prototype of a basic healthcare remote monitoring system, which alerts, in real time, patients' relatives or medical doctors that an elderly people is experienced a problem that could need medical attention or hospitalization.


title: Reconfiguring Boundary Relations: Robotic Innovations in Pharmacy Work

abstract: Robotics is a rapidly expanding area of digital innovation with important implications for organizational practice in multioccupational settings. This paper explores the influence of robotic innovations on the boundary dynamics of three different occupational groups—pharmacists, technicians, and assistants—working in a hospital pharmacy. We extend Pickering's tuning approach [Pickering, A. 1995. The Mangle of Practice: Time, Agency, and Science. University of Chicago Press, Chicago] to examine the temporally emergent process that entangled the mechanical elements and digital inscriptions of a dispensing robot with the everyday practices of hospital pharmacy work. We found that engagement with the robot's hybrid and dynamic materiality over time reconfigured boundary relations among the three occupational groups, with important and contradictory consequences for the pharmacy workers' skills, jurisdictions, status, and visibility.


title: Nuclear threat detection with mobile distributed sensor networks

abstract: The ability to track illicit radioactive source in an urban environment is critical in national security applications. To this end, two modes of operation are common: positioning individual portal monitors, and deploying a network of distributed sensors. We address here the use of multiple detectors, mounted on moving vehicles, for the purpose of detecting nuclear threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors’ positions are known in real-time as these are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile detector networks to identify and locate risks is modeled and formulated here. The problem is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enable its use in real time, and in areas containing a large number of deployed detectors. A simulation study, that takes into account false-positive and false-negatives reports from individual sensors, demonstrates the effectiveness of the algorithm in using the sensor network’s detection capabilities.


title: Single carrier FDMA for uplink wireless transmission

abstract: Single carrier frequency division multiple access (SC FDMA), a modified form of orthogonal FDMA (OFDMA), is a promising technique for high data rate uplink communications in future cellular systems. SC-FDMA has similar throughput performance and essentially the same overall complexity as OFDMA. A principal advantage of SC-FDMA is the peak-to-average power ratio (PARR), which is lower than that of OFDMA. SC FDMA is currently a strong candidate for the uplink multiple access scheme in the long term evolution of cellular systems under consideration by the third generation partnership project (3GPP). In this paper, we give an overview of SC-FDMA. We also analyze the effects of subcarrier mapping on throughput and PARR. Among the possible subcarrier mapping approaches, we find that localized FDMA (LFDMA) with channel-dependent scheduling (CDS) results in higher throughput than interleaved FDMA (JFDMA). However, the PARR performance of IFDMA is better than that of LFDMA. As in other communications systems there are complex tradeoffs between design parameters and performance in an SC-FDMA system


Safe Visual Navigation via Deep Learning and Novelty Detection


title: The Art of Signaling: Fifty Years of Coding Theory

abstract: In 1948 Shannon developed fundamental limits on the efficiency of communication over noisy channels. The coding theorem asserts that there are block codes with code rates arbitrarily close to channel capacity and probabilities of error arbitrarily close to zero. Fifty years later, codes for the Gaussian channel have been discovered that come close to these fundamental limits. There is now a substantial algebraic theory of error-correcting codes with as many connections to mathematics as to engineering practice, and the last 20 years have seen the construction of algebraic-geometry codes that can be encoded and decoded in polynomial time, and that beat the Gilbert-Varshamov bound. Given the size of coding theory as a subject, this review is of necessity a personal perspective, and the focus is reliable communication, and not source coding or cryptography. The emphasis is on connecting coding theories for Hamming and Euclidean space and on future challenges, specifically in data networking, wireless communication, and quantum information theory.


title: Heuristics and Exact Methods for Number partitioning

abstract: Number partitioning is a classical NP-hard combinatorial optimization problem, whose solution is challenging for both exact and approximative methods. This work presents a new algorithm for number partitioning, based on ideas drawn from tree search, breadth first search, and beam search. A new set of benchmark instances for this problem is also proposed. The behavior of the new method on this and other testbeds is analyzed and compared to other well known heuristics and exact algorithms.


title: Adoption of mobile technology in business : a fit-viability model

abstract: Purpose – This paper aims to study the adoption of mobile technology in business and its determinants. A diagnostic tool for proper adoption of mobile technology is developed.Design/methodology/approach – Grounded on the fit‐viability framework, the paper uses a multi‐case study via the fit and viability dimensions to examine the success or failure of mobile technology applications in business.Findings – By drawing upon multiple streams of theory building, the paper is able to develop a set of measurement instruments to assess the fit and viability in adopting mobile technology. The findings demonstrate that the fit‐viability model (FVM) provides useful guidelines for enterprises in their decisions on whether to adopt a mobile technology.Research limitations/implications – First, the theoretical generalizability of the FVM needs to be more carefully observed in future studies. Second, the findings are exploratory and more extensive studies may be necessary.Practical implications – Chief information office...


title: Energy cost and metabolic power in elite soccer: a new match analysis approach.

abstract: PURPOSE ::: Video match analysis is used for the assessment of physical performances of professional soccer players, particularly for the identification of "high intensities" considered as "high running speeds." However, accelerations are also essential elements setting metabolic loads, even when speed is low. We propose a more detailed assessment of soccer players' metabolic demands by video match analysis with the aim of also taking into account accelerations. ::: ::: ::: METHODS ::: A recent study showed that accelerated running on a flat terrain is equivalent to running uphill at constant speed, the incline being dictated by the acceleration. Because the energy cost of running uphill is known, this makes it possible to estimate the instantaneous energy cost of accelerated running, the corresponding instantaneous metabolic power, and the overall energy expenditure, provided that the speed (and acceleration) is known. Furthermore, the introduction of individual parameters makes it possible to customize performance profiles, especially as it concerns energy expenditure derived from anaerobic sources. Data from 399 "Serie-A" players (mean +/- SD; age = 27 +/- 4 yr, mass = 75.8 +/- 5.0 kg, stature = 1.80 +/- 0.06 m) were collected during the 2007-2008 season. ::: ::: ::: RESULTS ::: Mean match distance was 10,950 +/- 1044 m, and average energy expenditure was 61.12 +/- 6.57 kJ x kg(-1). Total distance covered at high power (>20 W x kg(-1)) amounted to 26% and corresponding energy expenditure to approximately 42% of the total. "High intensities" expressed as high-power output are two to three times larger than those based only on running speed. ::: ::: ::: CONCLUSIONS ::: The present approach for the assessment of top-level soccer players match performance through video analysis allowed us to assess instantaneous metabolic power, thus redefining the concept of "high intensity" on the basis of actual metabolic power rather than on speed alone.


title: Reflecting on Connecting: Meta-Analysis of Differences Between Computer-Mediated and Face-to-Face Self-Disclosure

abstract: Self-disclosure is a key concept in computer-mediated communication CMC theory and research, but disagreement exists about the impact of CMC, relative to face-to-face FtF communication, on self-disclosure. We conducted a meta-analysis of studies comparing self-disclosure in CMC and FtF communication to summarize and clarify existing research. We also examined potential moderators of this difference-measure of self-disclosure, study design survey or experiment, interaction context task or social, type of CMC text-based or video-based, and interaction length. Overall, self-disclosure was higher in FtF communication than in CMC. Measure of self-disclosure, study design, and type of CMC moderated this difference. Findings suggest mixed support for predictions derived from key CMC theories and a need for CMC theory to more explicitly address self-disclosure.


title: Design of Polarization Reconfigurable Antenna Using Metasurface

abstract: A planar polarization-reconfigurable metasurfaced antenna (PRMS) designed using metasurface (MS) is proposed. The PRMS antenna consists of a planar MS placed atop of and in direct contact with a planar slot antenna, both having a circular shape with a diameter of 78 mm (0.9 $\lambda_{0}$ ), making it compact and low profile. By rotating the MS around the center with respect to the slot antenna, the PRMS antenna can be reconfigured to linear polarization, left-hand and right-hand circular polarizations. An equivalent circuit is used to explain the reconfigurability of the antenna. The PRMS antenna is studied and designed to operate at around 3.5 GHz using computer simulation. For verification of simulation results, the PRMS antenna is fabricated and measured. The antenna performance, in terms of polarization reconfigurability, axial-ratio bandwidth, impedance bandwidth, realized boresight gain and radiation pattern, is presented. Results show that the PRMS antenna in circular polarizations achieves an operating bandwidth of 3.3–3.7 GHz (i.e., fractional bandwidth 11.4%), a boresight gain of above 5 dBi and high-polarization isolation of larger than 15 dB. While the PRMS antenna in linear polarization achieves a gain of above 7.5 dBi with cross-polarization isolation larger than 50 dB.


title: A fast and smooth walking pattern generator of biped robot using Jacobian inverse kinematics

abstract: In order to solve inverse kinematics of a multi- DOF (degree of freedom) mechanism, many methods have been proposed with the Jacobian linearization method. When solving inverse kinematics problems with this method, long computation time is needed because the Jacobian matrix should be updated in order to solve the configuration for each different end-effector trajectory knot. In this study, two smooth trajectories are generated as target positions, one for swing leg's ankle, and the other for center of mass. These generated knot points in the task space with appropriate distance to each other are used to solve inverse kinematics by the proposed modified Jacobian method-Fixed leg Jacobian. It can guarantee that only one iteration is needed to solve the configuration when it is away from singularity with a small position error (0.0712% of leg length). We propose an algorithm that can generate the gait in real time including singularity avoidance and joint limit avoidance. Simulations have been carried out. The results showed that the proposed method can generate a smooth gait for robot walking on real time implementation.


title: Biohashing : two factor authentication featuring fingerprint data and tokenised random number

abstract: Abstract Human authentication is the security task whose job is to limit access to physical locations or computer network only to those with authorisation. This is done by equipped authorised users with passwords, tokens or using their biometrics. Unfortunately, the first two suffer a lack of security as they are easy being forgotten and stolen; even biometrics also suffers from some inherent limitation and specific security threats. A more practical approach is to combine two or more factor authenticator to reap benefits in security or convenient or both. This paper proposed a novel two factor authenticator based on iterated inner products between tokenised pseudo-random number and the user specific fingerprint feature, which generated from the integrated wavelet and Fourier–Mellin transform, and hence produce a set of user specific compact code that coined as BioHashing. BioHashing highly tolerant of data capture offsets, with same user fingerprint data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both token with random data and user fingerprint feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. The BioHashing has significant functional advantages over solely biometrics i.e. zero equal error rate point and clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without suffering from increased occurrence of false reject rates.


title: Maxine: An approachable virtual machine for, and in, java

abstract: A highly productive platform accelerates the production of research results. The design of a Virtual Machine (VM) written in the Java™ programming language can be simplified through exploitation of interfaces, type and memory safety, automated memory management (garbage collection), exception handling, and reflection. Moreover, modern Java IDEs offer time-saving features such as refactoring, auto-completion, and code navigation. Finally, Java annotations enable compiler extensions for low-level “systems programming” while retaining IDE compatibility. These techniques collectively make complex system software more “approachable” than has been typical in the past. The Maxine VM, a metacircular Java VM implementation, has aggressively used these features since its inception. A co-designed companion tool, the Maxine Inspector, offers integrated debugging and visualization of all aspects of the VM's runtime state. The Inspector's implementation exploits advanced Java language features, embodies intimate knowledge of the VM's design, and even reuses a significant amount of VM code directly. These characteristics make Maxine a highly approachable VM research platform and a productive basis for research and teaching.


title: Personalized speech recognition on mobile devices

abstract: We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device-specific information, such as proper names and other context-dependent information, we inject vocabulary items into the decoder graph and bias the language model on-the-fly. Our system achieves 13.5% word error rate on an open-ended dictation task, running with a median speed that is seven times faster than real-time.


title: On the derivation of coseismic displacement fields using differential radar interferometry: The Landers earthquake

abstract: Presents a map of the coseismic displacement field resulting from the Landers, CA, June 28, 1992 earthquake derived using data acquired from an orbiting high resolution radar system. Data from the ERS-1 synthetic aperture radar instrument acquired in April, July, and August 1992 are used to generate a high resolution, wide area map of the displacements. The data represent the motion in the direction of the radar line of sight to cm level precision of each 30 m resolution element in a 113 by 00 km image. The coseismic displacement contour map gives a lobed pattern consistent with theoretical models of the displacement field from the earthquake. Comparison of these data with GPS and EDM survey data yield a correlation of 0.06, thus the radar measurements are a means to extend the point measurements acquired by traditional techniques to an area map format. The technique used is i) more automatic, ii) more precise, and iii) better validated than previous similar applications of differential radar interferometry. Since only remotely-sensed satellite data are required with no additional requirements for ancillary information, the technique is well suited for global seismic monitoring and analysis. >


title: Metric learning with spectral graph convolutions on brain connectivity networks

abstract: Abstract Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods.


title: Biblioranking fundamental physics

abstract: Abstract Counting of number of papers, of citations and the h-index are the simplest bibliometric indices of the impact of research. We discuss some improvements. First, we replace citations with individual citations, fractionally shared among co-authors, to take into account that different papers and different fields have largely different average number of co-authors and of references. Next, we improve on citation counting applying the PageRank algorithm to citations among papers. Being time-ordered, this reduces to a weighted counting of citation descendants that we call PaperRank. We compute a related AuthorRank applying the PageRank algorithm to citations among authors. These metrics quantify the impact of an author or paper taking into account the impact of those authors that cite it. Finally, we show how self- and circular-citations can be eliminated by defining a closed market of Citation-coins. We apply these metrics to the InSpire database that covers fundamental physics, presenting results for papers, authors, journals, institutes, towns, countries for all-time and in recent time periods.


title: E-waste: an assessment of global production and environmental impacts.

abstract: E-waste comprises discarded electronic appliances, of which computers and mobile telephones are disproportionately abundant because of their short lifespan. The current global production of E-waste is estimated to be 20-25 million tonnes per year, with most E-waste being produced in Europe, the United States and Australasia. China, Eastern Europe and Latin America will become major E-waste producers in the next ten years. Miniaturisation and the development of more efficient cloud computing networks, where computing services are delivered over the internet from remote locations, may offset the increase in E-waste production from global economic growth and the development of pervasive new technologies. E-waste contains valuable metals (Cu, platinum group) as well as potential environmental contaminants, especially Pb, Sb, Hg, Cd, Ni, polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs). Burning E-waste may generate dioxins, furans, polycyclic aromatic hydrocarbons (PAHs), polyhalogenated aromatic hydrocarbons (PHAHs), and hydrogen chloride. The chemical composition of E-waste changes with the development of new technologies and pressure from environmental organisations on electronics companies to find alternatives to environmentally damaging materials. Most E-waste is disposed in landfills. Effective reprocessing technology, which recovers the valuable materials with minimal environmental impact, is expensive. Consequently, although illegal under the Basel Convention, rich countries export an unknown quantity of E-waste to poor countries, where recycling techniques include burning and dissolution in strong acids with few measures to protect human health and the environment. Such reprocessing initially results in extreme localised contamination followed by migration of the contaminants into receiving waters and food chains. E-waste workers suffer negative health effects through skin contact and inhalation, while the wider community are exposed to the contaminants through smoke, dust, drinking water and food. There is evidence that E-waste associated contaminants may be present in some agricultural or manufactured products for export.


title: Mining user similarity based on routine activities

abstract: Mobile user similarity is significant for location-based social network services. With the pervasiveness of location-acquisition technologies, research on measuring mobile user similarity based on their trajectories has attracted a lot of attention. However, trajectories imply only short-term mobile regularities, and thus users' long-term activity similarity is difficult to be captured. In this paper, we address the problem of mining users' long-term activity similarity based on their trajectories. To solve this problem, we propose a two-stage approach. At the first stage, the notion of routine activity is proposed to capture users' long-term activity regularities. The routine activities of a user are extracted from his/her daily trajectories. At the second stage, user similarity is calculated hierarchically based on the extracted routine activities. Finally, we evaluated our approach based on both real and artificial datasets. The experimental results show that users with different profiles can be discriminated on the basis of our similarity metric, and thus demonstrate the effectiveness of our approach.


title: Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

abstract: During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.


Domainless Adaptation by Constrained Decoding on a Schema Lattice


title: Algorithms for within-cluster searches using inverted files

abstract: Information retrieval over clustered document collections has two successive stages: first identifying the best-clusters and then the best-documents in these clusters that are most similar to the user query. In this paper, we assume that an inverted file over the entire document collection is used for the latter stage. We propose and evaluate algorithms for within-cluster searches, i.e., to integrate the best-clusters with the best-documents to obtain the final output including the highest ranked documents only from the best-clusters. Our experiments on a TREC collection including 210,158 documents with several query sets show that an appropriately selected integration algorithm based on the query length and system resources can significantly improve the query evaluation efficiency.


title: Nonstate Actors and the Diffusion of Innovations: The Case of Suicide Terrorism

abstract: Studies of terrorism in general and suicide terrorism in particular tend to view terrorist groups independently+ However, what if the propensity for a terror- ist group to adopt suicide tactics depends in part on its external linkages and the relationship between the organizational capabilities required to adopt the innovation and the organizational capabilities of the group? This article shows that the organi- zational change requirements for adopting an innovation significantly influence the overall adoption pattern, along with interlinkages between groups+ Additionally, eval- uating the universe of terrorist groups, not only those groups that adopted suicide terrorism but those that did not, shows that Pape's key variable of interest, occupa- tion, does not significantly predict the adoption of suicide terrorism+ Thinking about suicide terrorism as a special case of diffusion in the military area—an innovation for nonstate groups—can help bring the study of suicide terrorism further into the mainstream and highlight how the phenomenon has not just differences, but similar- ities, to other innovations+


title: Development and verification of life-size humanoid with high-output actuation system

abstract: Life-size humanoids which have the same joint arrangement as humans are expected to help in the living environment. In this case, they require high load operations such as gripping and conveyance of heavy load, and holding people at the care spot. However, these operations are difficult for existing humanoids because of their low joint output. Therefore, the purpose of this study is to develop the highoutput life-size humanoid robot. We first designed a motor driver for humanoid with featuring small, water-cooled, and high output, and it performed higher joint output than existing humanoids utilizing. In this paper, we describe designed humanoid arm and leg with this motor driver. The arm is featuring the designed 2-axis unit and the leg is featuring the water-cooled double motor system. We demonstrated the arm's high torque and high velocity experiment and the leg's high performance experiment based on water-cooled double motor compared with air-cooled and single motor. Then we designed and developed a life-size humanoid with these arms and legs. We demonstrated some humanoid's experiment operating high load to find out the arm and leg's validity.


title: Development of a Wheel-Paddle Integrated Quadruped Robot for Rough Terrain and its Verification on Hybrid Mode

abstract: To enhance the motion performance of all-terrain robots, an eccentric paddle mechanism (ePaddle) based on planet gear mechanism was designed. This letter is on the design, control scheme, and implementation of a wheel-paddle integrated quadruped robot based on the ePaddle mechanism. To ensure the reliability and ruggedness in harsh environment, protection measures were added to both electrical system and crucial mechanical parts. To negotiate rough terrain effectively, hybrid mode was introduced, which is more efficient comparing with other legged modes and easier to transit among three supporting gestures according to the variation of terrain situation. Each joint has been measured to follow accurate trajectories of position. Indoor and outdoor tests were conducted with the described hybrid mode, which verified the whole design and the negotiability in rough terrain.


title: Service Migration Patterns -- Decision Support and Best Practices for the Migration of Existing Service-Based Applications to Cloud Environments

abstract: In many ways cloud computing is an extension of the service-oriented computing (SOC) approach to create resilient and elastic hosting environments and applications. Service-oriented Architectures (SOA), thus, share many architectural properties with cloud environments and cloud applications, such as the distribution of application functionality among multiple application components (services) and their loosely coupled integration to form a distributed application. Existing service-based applications are, therefore, ideal candidates to be moved to cloud environments in order to benefit from the cloud properties, such as elasticity or pay-per-use pricing models. In order for such an application migration and the overall restructuring of an IT application landscape to be successful, decisions have to be made regarding (i) the portion of the application stack to be migrated and (ii) the process to follow during the migration in order to guarantee an acceptable service level to application users. In this paper, we present best practices how we addressed these challenges in form of service migration patterns as well as a methodology how these patterns should be applied during the migration of a service-based application or multiples thereof. Also, we present an implementation of the approach, which has been used to migrate a web-application stack from Amazon Web Services to the T-Systems cloud offering Dynamic Services for Infrastructure (DSI).


title: The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors

abstract: Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.


A File System for Mobile Computing


title: Increased Spectrum Utilisation in a Cognitive Radio Network: An M#x002F;M#x002F;1-PS Queue Approach

abstract: A channel share scheme is proposed as a solution to the resource allocation problem in cognitive radio networks whereby two secondary users can occupy the same channel simultaneously with the primary user. The solution will increase utilisation thus freeing up much needed spectrum to accommodate many more users. Processor sharing techniques have been developed but have not been extensively applied in cognitive radio networks, specifically, the weighted head of line processor sharing scheme has the potential of improving spectrum usage in networks. In this paper, we propose using transmission power as the basis for the weighting parameter. We then develop the state transition matrices for the model using continuous time Markov chains. Queues are imposed on both secondary users. The model is evaluated through simulations done on various network conditions. The results show that the proposed model offers a significant improvement over an ordinary M#x002F;M#x002F;1 two priority system. The applications can lead to improved network efficiency by allowing more users to transmit within a network.


title: Building a Practically Useful Theory of Goal Setting and Task Motivation -- A 35-Year Odyssey

abstract: University of TorontoThe authors summarize 35 years of empirical research ongoal-setting theory. They describe the core ﬁndings of thetheory, the mechanisms by which goals operate, modera-tors of goal effects, the relation of goals and satisfaction,and the role of goals as mediators of incentives. Theexternal validity and practical signiﬁcance of goal-settingtheory are explained, and new directions in goal-settingresearch are discussed. The relationships of goal setting toother theories are described as are the theory’s limitations.


title: A Noise-Tolerant Algorithm for Robot-Sensor Calibration Using a Planar Disk of Arbitrary 3-D Orientation

abstract: In a 3-D scanning task, a robot-sensor system controls a robotic arm to move a laser sensor. In order to align the coordinate system of the robotic arm and laser sensor, prior calibration is required to derive the transformation between both coordinate systems. This paper proposes a new calibration method in three steps: manual data collection, sensing data calculation, and transformation solution. First, at least four data are required to be collected by the user. The sensing data are then calculated from the collected data and adopted to provide the desired transformation. The proposed algorithm has two features: arbitrary placement of planar disk and noise tolerant. Using a planar disk, the algorithm will automatically derive the angular relationship between the disk and the sensor plane, enabling arbitrary orientation placement. Noise tolerant is guaranteed by fitting ellipses during the sensing data calculation and using a single set of sensing data in transformation solution. Experiments and comparisons are given to demonstrate the efficiency of the proposed calibration algorithm. Note to Practitioners —This paper was motivated by the problem of calibrating a laser sensor and a positioning device (robot arm, CMM, etc.) in a robust and fast manner. Specifically, the calibration is to derive the transformation by aligning the sensors coordinate system to the positioning devices coordinate system. The proposed calibration procedure consists of two parts: manual data collection and automatic transformation calculation. During manual data collection, users only need to select four different data; whereby each data contains of two positions with the same orientation. Then, the desired transformation will be derived automatically. The calibration is designed in an efficient and robust way whereby: 1) data collection is done using a simple planar disk placed in arbitrary orientations; 2) minimum human interaction required; 3) tolerant to noise in the sensor data; and 4) easy implementation by following a proven and standard protocol.


title: Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

abstract: Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.


title: Segmentation of cam-type femurs from CT scans

abstract: We introduce a new way to accurately segment cam-type pathological femurs from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. In addition, the pathological femurs with femoral-acetabular impingements have a non-standard shape, which increases segmentation difficulty. We overcome these difficulties by (a) dividing the femur into two rounds of segmentation—one for the femur head and another for the body—(b) pre-processing the CT scan to reduce anatomical sources of error (c) two modes of segmentation—a rough estimation of a contour and another for fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam femoral–acetabular impingements. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. Femur models segmented with our method had an average volume overlap error of 2.71±0.44% and symmetric surface distance of 0.28±0.04 mm compared to ground truth models.


title: A Rule based Approach to Word Lemmatization

abstract: Lemmatization is the process of finding the normalized form of a word. It is the same as looking for a transformation to apply on a word to get its normalized form. The approach presented in this paper focuses on word endings: what word suffix should be removed and/or added to get the normalized form. This paper compares the results of two word lemmatization algorithms, one based on if-then rules and the other based on ripple down rules induction algorithms. It presents the problem of lemmatization of words from Slovene free text and explains why the Ripple Down Rules (RDR) approach is very well suited for the task. When learning from a corpus of lemmatized Slovene words the RDR approach results in easy to understand rules of improved classification accuracy compared to the results of rule learning achieved in previous work.


title: Label Propagation with Neural Networks

abstract: Label Propagation (LP) is a popular transductive learning method for very large datasets, in part due to its simplicity and ability to parallelize. However, it has limited ability to handle node features, and its accuracy can be sensitive to the number of iterations. We propose an algorithm called LPNN that solves these problems by a loose-coupling of LP with a feature-based classifier. We experimentally establish the effectiveness of LPNN.


title: Reconstruction and analysis of the pupil dilation signal: Application to a psychophysiological affective protocol

abstract: Pupil dilation (PD) dynamics reflect the interactions of sympathetic and parasympathetic innervations in the iris muscle. Different pupillary responses have been observed with respect to emotionally characterized stimuli. Evidences of the correlation between PD and respiration, heart rate variability (HRV) and blood pressure (BP) are present in literature, making the pupil dilation a candidate for estimating the activity state of the Autonomic Nervous System (ANS), in particular during stressful and/or emotionally characterized stimuli. The aim of this study is to investigate whether both slow and fast PD dynamics can be addressed to characterized different affective states. Two different frequency bands were considered: the classical autonomic band [0-0.45] Hz and a very high frequency (VHF) band [0.45-5] Hz. The pupil dilation signals from 13 normal subjects were recorded during a psychological protocol suitable to evoke particular affective states. An elaborate reconstruction of the missing data (blink events and artifacts) was performed to obtain a more reliable signal, particularly in the VHF band. Results show a high correlation between the arousal of the event and the power characteristics of the signal, in all frequencies. In particular, for the “Anger” condition, we can observe 10 indices out of 13 significantly different with respect to “Baseline” counterparts. These preliminary results suggest that both slow and fast oscillations of the PD can be used to characterize affective states.


title: Reasoning at the Right Time Granularity

abstract: Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representation by modeling the system as evolving continuously over time. The expectation-propagation (EP) inference algorithm of Nodelman et al. (2005) can then vary the inference granularity over time, but the granularity is uniform across all parts of the system, and must be selected in advance. In this paper, we provide a new EP algorithm that utilizes a general cluster graph architecture where clusters contain distributions that can overlap in both space (set of variables) and time. This architecture allows different parts of the system to be modeled at very different time granularities, according to their current rate of evolution. We also provide an information-theoretic criterion for dynamically re-partitioning the clusters during inference to tune the level of approximation to the current rate of evolution. This avoids the need to hand-select the appropriate granularity, and allows the granularity to adapt as information is transmitted across the network. We present experiments demonstrating that this approach can result in significant computational savings.


title: Integrated torque and speed controller for continuous strip processing rollers

abstract: A new integrated torque and speed control algorithm has been proposed for the load balancing of rollers in a continuous strip processing line. Using the proposed method, the output torque and speed can be controlled to follow the reference in spite of nonideal effects such as the speed reference error and/or the controller gain difference between rolls. This new algorithm can be easily implemented in a motor drive system of each roll as it does not require the torque feedback of the others. Through the simulation and experiments for a simple CSPL consists of four driven rolls, the load balancing performance of the proposed scheme is presented and compared with that of the conventional method.


title: A fast vein display device based on the camera-projector system

abstract: Venipuncture is now a common medical means. Several devices have been developed to aid the paramedics or physicians in locating the vein. There are two problems existing in vein display devices: (1) The vein cannot be shown directly on the skin; (2) The method of displaying the vein on the skin is complex. An approach based on the camera-projector system is proposed to help the paramedics or physicians quickly find the vein for diagnosis and treatment. The proposed approach can solve the two problems and it is easy to be accomplished. A simple system including IR light, an IR camera and a DLP projector is constructed to test and verify this approach. In the experiment, the vein is projected directly on the surface of the skin and the whole process takes only a short period of time. The result of the projection of the vein can meet the requirement of the venipuncture.


title: Improved Vector Space Model TF/IDF Using Lexical Relations

abstract: Current vector space model, for instance TF/IDF, has not yet taken into account the relations between terms; it only combines the term frequency in a document and the inverse document frequency in whole database to identify importance-score (weight) of a term respect with the document. Here we discover lexical relations among terms in the document to improve the vector space model TF/IDF. The weight generated from TF/IDF for each term, which is improved by lexical relations among related terms in the document. We evaluate the proposed method using documents selected from Wikipedia. The result shown that the proposed method is significant effective.


Two Properties of Catalan-Larcombe-French Numbers


title: The Case for Motivated Reasoning

abstract: It is proposed that motivation may affect reasoning through reliance on a biased set of cognitive processes—that is, strategies for accessing, constructing, and evaluating beliefs. The motivation to be accurate enhances use of those beliefs and strategies that are considered most appropriate, whereas the motivation to arrive at particular conclusions enhances use of those that are considered most likely to yield the desired conclusion. There is considerable evidence that people are more likely to arrive at conclusions that they want to arrive at, but their ability to do so is constrained by their ability to construct seemingly reasonable justifications for these conclusions. These ideas can account for a wide variety of research concerned with motivated reasoning. The notion that goals or motives affect reasoning has a long and controversial history in social psychology. The propositions that motives may affect perceptions (Erdelyi, 1974), attitudes (Festinger, 1957), and attributions (Heider, 1958) have been put forth by some psychologists and challenged by others. Although early researchers and theorists took it for granted that motivation may cause people to make self-serving attributions and permit them to believe what they want to believe because they want to believe it, this view, and the research used to uphold it, came under concentrated criticism in the 1970s. The major and most damaging criticism of the motivational view was that all research purported to demonstrate motivated reasoning could be reinterpreted in entirely cognitive, nonmotivational terms (Miller & Ross, 1975; Nisbett & Ross, 1980). Thus people could draw self-serving conclusions not because they wanted to but because these conclusions seemed more plausible, given their prior beliefs and expectancies. Because both cognitive and motivational accounts could be generated for any empirical study, some theorists argued that the hot versus cold cognition controversy could not be solved, at least in the attribution paradigm (Ross & Fletcher, 1985; Tetlock & Levi, 1982). One reason for the persistence of this controversy lies in the failure of researchers to explore the mechanisms underlying motivated reasoning. Recently, several authors have attempted to rectify this neglect (Kruglanski & Freund, 1983; Kunda, 1987; Pyszczynski & Greenberg, 1987; Sorrentino & Higgins, 1986). All these authors share a view of motivation as having its effects through cognitive processes: People rely on cognitive processes and representations to arrive at their desired conclusions, but motivation plays a role in determining which of these will be used on a given occasion.


title: Gender, genre, and writing style in formal written texts

abstract: This article explores differences between male and female writing in a large subset of the British National Corpus covering a range of genres. Several classes of simple lexical and syntactic features that differ substantially according to author gender are identified, both in fiction and in nonfiction documents. In particular, we find significant differences between male- and female-authored documents in the use of pronouns and certain types of noun modifiers: although the total number of nominals used by male and female authors is virtually identical, females use many more pronouns and males use many more noun specifiers. More generally, it is found that even in formal writing, female writing exhibits greater usage of features identified by previous researchers as 'involved' while male writing exhibits greater usage of features which have been identified as 'informational'. Finally, a strong correlation between the characteristics of male (female) writing and those of nonfiction (fiction) is demonstrated.


title: Deep Metric Learning for Visual Understanding: An Overview of Recent Advances

abstract: Metric learning aims to learn a distance function to measure the similarity of samples, which plays an important role in many visual understanding applications. Generally, the optimal similarity functions for different visual understanding tasks are task specific because the distributions for data used in different tasks are usually different. It is generally believed that learning a metric from training data can obtain more encouraging performances than handcrafted metrics [1]-[3], e.g., the Euclidean and cosine distances. A variety of metric learning methods have been proposed in the literature [2]-[5], and many of them have been successfully employed in visual understanding tasks such as face recognition [6], [7], image classification [2], [3], visual search [8], [9], visual tracking [10], [11], person reidentification [12], cross-modal matching [13], image set classification [14], and image-based geolocalization [15]-[17].


title: BRAIN ANATOMICAL FEATURE DETECTION BY SOLVING PARTIAL DIFFERENTIAL EQUATIONS ON GENERAL MANIFOLDS

abstract: One important problem in human brain mapping research is to locate ::: the important anatomical features. Anatomical features on the ::: cortical surface are usually represented by landmark curves, called ::: sulci/gyri curves. These landmark curves are important information ::: for neuroscientists to study brain disease and to match different ::: cortical surfaces. Manual labelling of these landmark curves is ::: time-consuming, especially when large sets of data have to be ::: analyzed. In this paper, we present algorithms to automatically ::: detect and match landmark curves on cortical surfaces to get an ::: optimized brain conformal parametrization. First, we propose an ::: algorithm to obtain a hypothesized landmark region/curves using the ::: Chan-Vese segmentation method, which solves a Partial Differential ::: Equation (PDE) on a manifold with global conformal parameterization. ::: This is done by segmentating the high mean curvature region. Second, ::: we propose an automatic landmark curve tracing method based on the ::: principal directions of the local Weingarten matrix. Based on the ::: global conformal parametrization of a cortical surface, our method ::: adjusts the landmark curves iteratively on the spherical or ::: rectangular parameter domain of the cortical surface along its ::: principal direction field, using umbilic points of the surface as ::: anchors. The landmark curves can then be mapped back onto the ::: cortical surface. Experimental results show that the landmark curves ::: detected by our algorithm closely resemble these manually labeled ::: curves. Next, we applied these automatically labeled landmark curves ::: to generate an optimized conformal parametrization of the cortical ::: surface, in the sense that homologous features across subjects are ::: caused to lie at the same parameter locations in a conformal grid. ::: Experimental results show that our method can effectively help in ::: automatically matching cortical surfaces across subjects.


title: Neural network model for time series prediction by reinforcement learning

abstract: Two important issues when constructing a neural network (NN) for time series prediction: proper selection of (1) the input dimension and (2) the time delay between the inputs. These two parameters determine the structure, computing complexity and accuracy of the NN. This paper is to formulate an autonomous data-driven approach to identify a parsimonious structure for the NN so as to reduce the prediction error and enhance the modeling accuracy. The reinforcement learning based dimension and delay estimator (RLDDE) is proposed. It involves a trial-error learning process to formulate a selection policy for designating the above-mentioned two parameters. The proposed method is evaluated by the prediction of the benchmark sunspot time series.


title: Multilevel Spin-Orbit Torque MRAMs

abstract: In this paper, we present two multilevel spin-orbit torque magnetic random access memories (SOT-MRAMs). A single-level SOT-MRAM employs a three-terminal SOT device as a storage element with enhanced endurance, close-to-zero read disturbance, and low write energy. However, the three-terminal device requires the use of two access transistors per cell. To improve the integration density, we propose two multilevel cells (MLCs): 1) series SOT MLC and 2) parallel SOT MLC, both of which store two bits per memory cell. A detailed analysis of the bit-cell suggests that the S-MLC is promising for applications requiring both high density and low write-error rate, and P-MLC is particularly suitable for high-density and low-write-energy applications. We also performed iso-bit-cell area comparison of our MLC designs with previously proposed MLCs that are based on spin-transfer torque MRAM and show 3– $16\times $ improvement in write energy.


title: Central pattern generation involved in oral and respiratory control for feeding in the term infant

abstract: Purpose of review ::: Drinking and eating are essential skills for survival and benefit from the coordination of several pattern generating networks and their musculoskeletal effectors to achieve safe swallows. Oral-pharyngo-esophageal motility develops during infancy and early childhood, and is influenced by various factors, including neuromuscular maturation, dietary and postural habits, arousal state, ongoing illnesses, congenital anomalies, and the effects of medical or surgical interventions. Gastroesophageal reflux is frequent in neonates and infants, and its role in neonatal morbidity including dysphagia, chronic lung disease, or apparent life-threatening events is not well understood. This review highlights recent studies aimed at understanding the development of oral feeding skills, and cross-system interactions among the brainstem, spinal, and cerebral networks involved in feeding.


title: Comparison of some diagnostic assays for bacterial ring rot of potato: A case study

abstract: The results of testing 48 stems and tuber samples, representing 15 seed lots from several different growers, for the presence of the causal organism of bacterial ring rot,Clavibacter michiganensis subsp.sepedonicus with the gram stain, latex agglutination (LAT), enzyme-linked immunosorbent assay (ELISA), and a DNA hybridization assay (DHA) were compared. The frequency with which positive tests were obtained varied significantly among assays and was associated with symptom expression in samples. Detection was highest with the serological assays and lowest for DHA. Use of the gram stain, in conjunction with either LAT, ELISA, or DHA reduced the number of positive diagnoses, but increased the agreement between results for different assay combinations. Based on a comparison of results for both individual samples and seed lots, the performance of LAT and ELISA for diagnostic purposes were equivalent. DHA was found to be less useful due to limitations in test sensitivity forC. m. sepedonicus, but it does give unequivocal diagnoses of samples where bacterial populations are sufficient to allow detection.


title: A novel optimisation method for the design of LCL filters for three-phase grid-tied inverters

abstract: Imprudent selection of passive LCL filter parameters may result in worse filtering and excessive power losses. Existing designs methods are only able to provide a range for LCL parameters to lie within and therefore are not able to guarantee performance before extensive trial and adjustments are carried out. To overcome this problem, a novel optimal filter design of LCL filters with passive damping is proposed in this paper. The method of this paper is based solving a multi-objective optimisation problem using genetic algorithm to obtain optimal parameters values for the LCL filter. The goal of the optimisation method is to achieve high harmonic attenuation performance, while at the same time achieve small switching frequency ripples. The design method is verified through simulation studies carried out on a three-phase grid-tied voltage source inverter (VSI) in a distributed generation (DG) system. The simulation results show that the new method can achieve ripple elimination and greater harmonic attenuation than existing methods.


title: A Quantitative Analysis Of Lexical Differences Between Genders In Telephone Conversations

abstract: In this work, we provide an empirical analysis of differences in word use between genders in telephone conversations, which complements the considerable body of work in sociolinguistics concerned with gender linguistic differences. Experiments are performed on a large speech corpus of roughly 12000 conversations. We employ machine learning techniques to automatically categorize the gender of each speaker given only the transcript of his/her speech, achieving 92% accuracy. An analysis of the most characteristic words for each gender is also presented. Experiments reveal that the gender of one conversation side influences lexical use of the other side. A surprising result is that we were able to classify male-only vs. female-only conversations with almost perfect accuracy.


title: Novel Phase-Shift Control Technique for Full-Bridge Converter to Reduce Thermal Imbalance Under Light-Load Condition

abstract: A new phase-shift control technique for the phase-shift full-bridge converter is proposed to reduce thermal imbalance of power devices. For the full-bridge converter with phase-shift control, zero-voltage switching (ZVS) can be realized. However, the power devices for the leading leg are with higher thermal temperature since the related current to achieve ZVS is much higher than that for the lagging leg. The proposed new phase-shift control technique is to swap the leading leg and the lagging leg with each other by either alternating control, temperature feedback, or pseudo random method. Experimental results derived from a digital signal processor-controlled full-bridge converter are presented for confirmation. It will be shown that thermal balance among power devices can be achieved, thereby confirming the effectiveness of the proposed control technique.


title: Live Repetition Counting

abstract: The task of counting the number of repetitions of approximately the same action in an input video sequence is addressed. The proposed method runs online and not on the complete pre-captured video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a convolutional neural network and the information is then integrated over time. The entropy of the network's predictions is used in order to automatically start and stop the repetition counter and to select the appropriate time scale. Coupled with a region of interest detection mechanism, the method is robust enough to handle real world videos, even when the camera is moving. A unique property of our method is that it is shown to successfully train on entirely unrealistic data created by synthesizing moving random patches.


Interactions on government Facebook pages: an empirical analysis


title: State-of-the-art in visual geo-localization

abstract: Large-scale visual geo-localization has recently gained a lot of attention in computer vision research and new methods are proposed steadily. However, surveys of visual geo-localization methods are rare and they focus mainly on city-scale localization methods. We present a comprehensive and balanced study of existing visual geo-localization domains, including city-scale, global approaches and methods for natural environments. We describe the methods to show their pros and cons, application domains, datasets, as well as evaluation techniques. We categorize the reviewed methods by two criteria. The first is the type of data the method uses for geo-location estimation. The second criterion is the target environment for which the method has been proposed and validated. Based on this categorization, we analyze important conditions that must be considered while solving geo-localization problems. Each category is in a different state of research—while city-scale image-based methods received a lot of attention, other categories such as natural environments using cross-domain data sources are still challenging problems under active research. Future research of large-scale visual geo-localization is discussed, primarily the challenging and new research category—geo-localization in natural environments.


title: PIN Skimming: Exploiting the Ambient-Light Sensor in Mobile Devices

abstract: The pervasive usage of mobile devices, i.e., smartphones and tablet computers, and their vast amount of sensors represent a plethora of side channels posing a serious threat to the user's privacy and security. In this paper, we propose a new type of side channel which is based on the ambient-light sensor employed in today's mobile devices. While recent advances in this area of research focused on the employed motion sensors and the camera as well as the sound, we investigate a less obvious source of information leakage, namely the ambient light. We successfully demonstrate that minor tilts and turns of mobile devices cause variations of the ambient-light sensor information. Furthermore, we show that these variations leak enough information to infer a user's personal identification number (PIN) input based on a set of known PINs. Our results clearly show that we are able to determine the correct PIN---out of a set of 50 random PINs---within the first ten guesses about 80% of the time. In contrast, the chance of finding the right PIN by randomly guessing ten PINs would be 20%. Since the data required to perform such an attack can be gathered without any specific permissions or privileges, the presented attack seriously jeopardizes the security and privacy of mobile-device owners.


title: One-Class Collaborative Filtering

abstract: Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF. One is based on weighted low rank approximation; the other is based on negative example sampling. The experimental results show that our approaches significantly outperform the baselines.


title: doi:10.1155/2009/302092 Review Article MIMO Technologies in 3GPP LTE and LTE-Advanced

abstract: 3rd Generation Partnership Project (3GPP) has recently completed the specification of the Long Term Evolution (LTE) standard. Majority of the world's operators and vendors are already committed to LTE deployments and developments, making LTE the market leader in the upcoming evolution to 4G wireless communication systems. Multiple input multiple output (MIMO) technologies introduced in LTE such as spatial multiplexing, transmit diversity, and beamforming are key components for providing higher peak rate at a better system efficiency, which are essential for supporting future broadband data service over wireless links. Further extension of LTE MIMO technologies is being studied under the 3GPP study item "LTE-Advanced" to meet the requirement of IMT-Advanced set by International Telecommunication Union Radiocommunication Sector (ITU-R). In this paper, we introduce various MIMO technologies employed in LTE and provide a brief overview on the MIMO technologies currently discussed in the LTE-Advanced forum.


title: High performance binary neural networks on the Xeon+FPGA™ platform

abstract: Convolutional neural networks (CNNs) are deployed in a wide range of image recognition, scene segmentation and object detection applications. Achieving state of the art accuracy in CNNs often results in large models and complex topologies that require significant compute resources to complete in a timely manner. Binarised neural networks (BNNs) have been proposed as an optimised variant of CNNs, which constrain the weights and activations to +1 or —1 and thus offer compact models and lower computational complexity per operation. This paper presents a high performance BNN accelerator on the Intel®Xeon+FPGA™ platform. The proposed accelerator is designed to take advantage of the Xeon+FPGA system in a way that a specialised FPGA architecture can be targeted for the most compute intensive parts of the BNN whilst other parts of the topology can be handled by the Xeon™ CPU. The implementation is evaluated by comparing the raw compute performance and energy efficiency for key layers in standard CNN topologies against an Nvidia Titan X Pascal GPU and other published FPGA BNN accelerators. The results show that our single-package integrated Arria™ 10 FPGA accelerator coupled with a high-end Xeon CPU can offer comparable performance and better energy efficiency than a high-end discrete Titan X GPU card. In addition, our solution delivers the best performance compared to previous BNN FPGA implementations.


title: maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

abstract: This paper describes maxDNN, a computationally efficient convolution kernel for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3% computational efficiency on typical deep learning network architectures. The design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We only address forward propagation (FPROP) operation of the network, but we believe that the same techniques used here will be effective for backward propagation (BPROP) as well.


title: SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis

abstract: Deriving prior polarity lexica for sentiment analysis – where positive or negative scores are associated with words out of context – is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called ‘ensemble method’). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords , a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.


title: Backside TSV protrusion induced by thermal shock and thermal cycling

abstract: This paper reports on thermal-mechanical failures of through-silicon-vias (TSVs), in particular, for the first time, the protrusions at the TSV backside, which is exposed after wafer bonding, thinning and TSV revealing. Temperature dependence of TSV protrusion is investigated based on wide-range thermal shock and thermal cycling tests. While TSV protrusion on the TSV frontside is not visible after any of the tests, protrusions on the backside are found after both thermal shock tests and thermal cycling tests at temperatures above 250°C. The average TSV protrusion height increases from ~0.1 μm at 250°C to ~0.5 μm at 400°C and can be fitted to an exponential function with an activation energy of ~0.6eV, suggesting a Cu grain boundary diffusion mechanism.


title: Fast switch bootstrapping for GS/s high-resolution analog-to-digital converter

abstract: This paper presents a novel bootstrapped switch to serve as an input frontend for high resolution ADCs operating in the GHz frequency range. The switch reduces the on-resistance value and keeps it constant, resulting in precise sampling of input signals and better tracking bandwidth. The proposed bootstrapped switch is designed and simulated in TSMC 28 nm Digital CMOS. Simulation results at 800 MSamples/s show an improved and relatively constant spurious-free dynamic range (SFDR) and total harmonic distortion (THD) up to an input frequency of 1.6 GHz, which makes it suitable for a Time-Interleaved configuration.


Utilizing Anaerobic Fungi for Two-stage Sugar Extraction and Biofuel Production from Lignocellulosic Biomass


Fastflow: high-level and efficient streaming on multi-core


title: Arabic Tokenization, Part-Of-Speech Tagging And Morphological Disambiguation In One Fell Swoop

abstract: We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including part-of-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.


title: Handshaking, gender, personality, and first impressions.

abstract: Although people's handshakes are thought to reflect their personality and influence our first impressions of them, these relations have seldom been formalLy investigated. One hundred twelve participants had their hand shaken twice by 4 trained coders (2 men and 2 women) and completed 4 personality measures. The participants' handshakes were stable and consistent across time and coders. There were also gender differences on most of the handshaking characteristics. A firm handshake was related positively to extraversion and emotional expressiveness and negatively to shyness and neuroticisin; it was also positively related to openness to experience, but only for women. Finally, handshake characteristics were •related to the impressions of the participants formed by the coders. These results demonstrate that personality traits, assessed through self-report, can predict specific behaviors assessed by trained observers. The pattern of relations among openness, gender, handshaking, and first impressions suggests that a firm handshake may be an effective form of self-promotio n for women. Handshaking is a common greeting behavior and is often one of the first observations that individuals make of each other upon meeting. Thus, the handshake may be a basis for some of the initial impressions that an individual forms about another. Although handshakes are anecdotally believed to communicate information about a person's personality, little systematic research has been done on the relation between handshaking and personality. Indeed, the extent to which handshaking is sufficiently stable across time and consistent across situations to reflect stable individual differences is largely unknown. Handshaking has also historically been more common among men than it has been among women or between men and women. However, we know little about gender differences in handshaking characteristics or about how gender may be involved in relations between personality, initial impressions, and handshaking. The purpose of the present research is to assess the generalizability of some characteristics of handshaking behavior across time and gender; to test some hypotheses about the relations among handshaking dimensions, personality, and gender; and to evaluate the relation between handshaking dimensions and initial impressions formed about strangers.


title: Detecting Crowdsourcing Click Fraud in Search Advertising Based on Clustering Analysis

abstract: This article analyzes the denseness, moderateness and concentricity of search advertising crowd sourcing click fraud. Base on this, it puts forward the click fraud detection model based on clustering analysis. The model includes three steps: preprocessing, group detecting and post-processing. In the preprocessing step, the query that is less likely to be fraudulently clicked is removed. In the group detection step, a crowd sourcing click fraud group is equivalent to a cluster. DP-Means clustering method is used to detect malicious groups. In the post-processing step, demand clicks checked by mistake are filtered. The convergence, scalability and accuracy are verified by the simulation data and one week click data of a search engine company.


title: The Girona 500, a multipurpose autonomous underwater vehicle

abstract: This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.


title: A Boosting Algorithm For Classification Of Semi-Structured Text

abstract: The focus of research in text classification has expanded from simple topic identification to more challenging tasks such as opinion/modality identification. Unfortunately, the latter goals exceed the ability of the traditional bag-of-word representation approach, and a richer, more structural representation is required. Accordingly, learning algorithms must be created that can handle the structures observed in texts. In this paper, we propose a Boosting algorithm that captures sub-structures embedded in texts. The proposal consists of i) decision stumps that use subtrees as features and ii) the Boosting algorithm which employs the subtree-based decision stumps as weak learners. We also discuss the relation between our algorithm and SVMs with tree kernel. Two experiments on opinion/modality classification confirm that subtree features are important.


title: The neuroscience of depression: Implications for assessment and intervention

abstract: Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is the single most burdensome disease worldwide. In attempting to understand the profound deficits that characterize MDD across multiple domains of functioning, researchers have identified aberrations in brain structure and function in individuals diagnosed with this disorder. In this review we synthesize recent data from human neuroimaging studies in presenting an integrated neural network framework for understanding the impairments experienced by individuals with MDD. We discuss the implications of these findings for assessment of and intervention for MDD. We conclude by offering directions for future research that we believe will advance our understanding of neural factors that contribute to the etiology and course of depression, and to recovery from this debilitating disorder.


title: FPGA implementation of hamming code for increasing the frame rate of CAN communication

abstract: Controller Area Network (CAN) protocol utilizes Cyclic Redundancy Check (CRC) code as a self-correcting method to detect and correct errors. The main objective of this algorithm is to use an alternative error correction scheme which is called as the Hamming code, replacing the conventional CRC code. Moreover, to possibly increase the CAN's frame rate of the system. The bit's positions of the redundant bits ‘r’ and the bit streams of the frames from the start-of-frame (SOF) to the control bit frames are determine. These bits will be fed into the redundant bit controller to compute for the necessary r. The redundant bit's positions are in power of 2, and will be calculated using modulo-2 operation. This proposed method is synthesized using Xilinx Virtex-5 FPGA. The simulation results shows a significant increase of CAN's frame rate and, it minimizes the bits stuffing payload and can be a better option for detecting and correcting error in CAN System.


title: Anthropomorphic Movement Analysis and Synthesis: A Survey of Methods and Applications

abstract: The anthropomorphic body form is a complex articulated system of links/limbs and joints, simultaneously redundant and underactuated, and capable of a wide range of sophisticated movement. The human body and its movement have long been a topic of study in physiology, anatomy, biomechanics, and neuroscience and have served as inspiration for humanoid robot design and control. This survey paper reviews the literature on robotics research using anthropomorphic design principles as an inspiration, at both the design and control levels. Next, anthropomorphic body modeling, motion analysis, and synthesis techniques are overviewed. Finally, key applications arising at the intersection of robotics and human movement science are introduced. The survey ends with a discussion of open research questions and directions for future work.


title: Origins of Missionary Periodicals: Form and Function of Three Moravian Publications

abstract: The nineteenth century was a boom time for the genre of missionary periodicals. Missionary periodicals were established by religious organizations, societies, and churches for their members, for the general public, for theologians, for women, for children, and for converted non-Europeans, and their growth reflected the expansion of missionary societies into the non-European colonial world. Despite the abundance and wide range of these publications, research on the origins, form, and function of missionary periodicals remains limited. This article examines the rationale behind the establishment of three Moravian Church missionary periodicals: the British Periodical Accounts (1790–1970); the German Missions-Blatt (1837–1941); and the North American The Little Missionary (1870–1920). The article elucidates both broader similarities and differences in missionary periodicals, as well as distinguishing how intentions behind the establishment of missionary periodicals differed from the practice of how religious organizations, societies, and churches utilized these periodicals in presenting themselves to the outside world.


title: A Single-Phase Single-Stage Switched-Boost Inverter With Four Switches

abstract: This paper proposes a new single-phase single-stage switched-boost inverter with four switches. Like the quasi-Z-source inverter and quasi-switched boost inverter (qSBI), the proposed inverter has the main features as continuous input current, buck/boost voltage with single-stage conversion, and shoot-through immunity. Compared to the qSBI, the proposed inverter uses one more capacitor and one less switch. This paper presents the operating principles, pulse-width modulation control strategy, parameter design guidelines, and simulation results for the proposed inverter. To verify the performance of the proposed inverter, an 800-W prototype was built with an 110 V/50 Hz output voltage in stand-alone and grid-connected modes. The simulation and experimental results matched those of the theoretical analysis.