title: Online Action Tube Detection via Resolving the Spatio-temporal Context Pattern

abstract: At present, spatio-temporal action detection in the video is still a challenging problem, considering the complexity of the background, the variety of the action or the change of the viewpoint in the unconstrained environment. Most of current approaches solve the problem via a two-step processing: first detecting actions at each frame; then linking them, which neglects the continuity of the action and operates in an offline and batch processing manner. In this paper, we attempt to build an online action detection model that introduces the spatio-temporal coherence existed among action regions when performing action category inference and position localization. Specifically, we seek to represent the spatio-temporal context pattern via establishing an encoder-decoder model based on the convolutional recurrent network. The model accepts a video snippet as input and encodes the dynamic information of the action in the forward pass. During the backward pass, it resolves such information at each time instant for action detection via fusing the current static or motion cue. Additionally, we propose an incremental action tube generation algorithm, which accomplishes action bounding-boxes association, action label determination and the temporal trimming in a single pass. Our model takes in the appearance, motion or fused signals as input and is tested on two prevailing datasets, UCF-Sports and UCF-101. The experiment results demonstrate the effectiveness of our method which achieves a performance superior or comparable to compared existing approaches.


title: Heterogeneous Graph Neural Networks for Malicious Account Detection

abstract: We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our approach, inspired from a connected subgraph approach, adaptively learns discriminative embeddings from heterogeneous account-device graphs based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. For the heterogeneous graph consists of various types of nodes, we propose an attention mechanism to learn the importance of different types of nodes, while using the sum operator for modeling the aggregation patterns of nodes in each type. Experiments show that our approaches consistently perform promising results compared with competitive methods over time.


title: Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism

abstract: In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing features and using ROI-Pooling to obtain individual features for each target. Some online learned target-specific CNN layers are used for adapting the appearance model for each target. In the framework, we introduce spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets. The visibility map of the target is learned and used for inferring the spatial attention map. The spatial attention map is then applied to weight the features. Besides, the occlusion status can be estimated from the visibility map, which controls the online updating process via weighted loss on training samples with different occlusion statuses in different frames. It can be considered as temporal attention mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on challenging MOT15 and MOT16 benchmark dataset respectively.


title: Surgical and interventional robotics: part III [Tutorial]

abstract: Part I of this tutorial described two broad paradigms of interventional assistance: surgical computer-aided design (CAD)/computer-aided manufacturing (CAM) and surgical assistance. Part II focused on the underlying concepts of surgical CAD/CAM, with a particular emphasis on percutaneous procedures. This final installment of our three-part tutorial series discusses surgical assistance. In this section, we introduce the basic concepts of a surgical workstation and briefly review several core robotic technologies used in surgical workstations.


title: Generalized Two-Way Two-Section Dual-Band Wilkinson Power Divider With Two Absorption Resistors and Its Miniaturization

abstract: A generalized two-way two-section dual-band Wilkinson power divider with two absorption resistors and its size-reduced model are presented. Design equations for dual-band and arbitrary power division are derived from modified even- and odd-mode analysis. The frequency ratio of the lower and upper band frequencies must be lower than three. The channel bandwidth is presented as a function of frequency ratio with mathematical expressions. A coupled line section is introduced to reduce circuit size based on the assumptions of linear phase characteristics and equal phase velocities of the even and odd modes. Two two-way dual-band power dividers, one three-way planar dual-band power divider, and one size-reduced dual-band power divider were fabricated. Experimental results were in good agreement with predicted values.


title: Design and nonlinear modeling of a large-displacement XYZ flexure parallel mechanism with decoupled kinematic structure

abstract: This article proposes an XYZ flexure parallel mechanism (FPM) with large displacement and decoupled kinematic structure. The motion range of the large-displacement FPM using notch hinges is more than 1mm and is achieved by assembling the typical large-displacement prismatic joints and a new large-displacement prismatic joint proposed in this article. Moreover, the decoupled XYZ stage has small cross-axis error and small parasitic rotation. An exact nonlinear modeling method for the typical prismatic joint and the new type of prismatic joint is presented. Using the proposed nonlinear method, the stiffness and dynamics of the XYZ FPM are studied. Comparison between the pseudo-rigid-body and the proposed nonlinear methods is performed on the prismatic joints and the XYZ FPM. To verify the proposed nonlinear modeling method, experiments are conducted to study the linear stiffness of the prismatic joint. The stiffness and the natural frequency of the XYZ FPM are also obtained from the experimental data. The cr...


title: Almost optimal exploration in multi-armed bandits

abstract: We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest setting of identifying the best arm, there remains a logarithmic multiplicative gap between the known lower and upper bounds for the number of arm pulls required for the task. This extra logarithmic factor is quite meaningful in nowadays large-scale applications. We present two novel, parameter-free algorithms for identifying the best arm, in two different settings: given a target confidence and given a target budget of arm pulls, for which we prove upper bounds whose gap from the lower bound is only doubly-logarithmic in the problem parameters. We corroborate our theoretical results with experiments demonstrating that our algorithm outperforms the state-of-the-art and scales better as the size of the problem increases.


title: A novel approach for process mining based on event types

abstract: Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the ?-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.


title: Insider threat detection model for the cloud

abstract: Cloud computing is a revolutionary technology that is changing the way people and organizations conduct business. It promises to help organizations save money on IT expenditure while increasing reliability, efficiency and productivity. However, despite the potential benefits that the cloud promises its users, it is facing some security challenges. Insider threats are some of the growing security concerns that are hindering the adoption of the cloud. Cloud providers are faced with a challenge of monitoring usage patterns of users so as to ensure that malicious insiders do not compromise the security of customer data and applications. Solutions are still needed to ensure that the data stored in the cloud is secure from malicious insiders of the cloud service provider. This paper presents an Insider Threat Detection Model that can be used to detect suspicious insider activities. An experimental system was designed to implement this model. This system uses sequential rule mining to detect malicious users by comparing incoming events against user profiles.


title: CP metasurfaced antennas excited by LP sources

abstract: A metasurface which is considered as a polarizer for microwave antennas is proposed in this paper. Preliminary results have shown that circularly polarized (CP) radiation can be obtained from linearly polarized (LP) source antennas with the use of metasurface, antenna performance such as realized gain, efficiency and bandwidth are improved simultaneously. The metasurfaced antennas have a succinct non-resonant cavity in sub-wavelength (1/13 λ 0 ) format.


title: Character-Level Chinese Dependency Parsing

abstract: Recent work on Chinese analysis has led to large-scale annotations of the internal structures of words, enabling characterlevel analysis of Chinese syntactic structures. In this paper, we investigate the problem of character-level Chinese dependency parsing, building dependency trees over characters. Character-level information can benefit downstream applications by offering flexible granularities for word segmentation while improving wordlevel dependency parsing accuracies. We present novel adaptations of two major shift-reduce dependency parsing algorithms to character-level parsing. Experimental results on the Chinese Treebank demonstrate improved performances over word-based parsing methods.


title: Configuration Debugging as Search: Finding the Needle in the Haystack

abstract: This work addresses the problem of diagnosing configuration errors that cause a system to function incorrectly. For example, a change to the local firewall policy could cause a network-based application to malfunction. Our approach is based on searching across time for the instant the system transitioned into a failed state. Based on this information, a troubleshooter or administrator can deduce the cause of failure by comparing system state before and after the failure. ::: ::: We present the Chronus tool, which automates the task of searching for a failure-inducing state change. Chronus takes as input a user-provided software probe, which differentiates between working and nonworking states. Chronus performs "time travel" by booting a virtual machine off the system's disk state as it existed at some point in the past. By using binary search, Chronus can find the fault point with effort that grows logarithmically with log size. We demonstrate that Chronus can diagnose a range of common configuration errors for both client-side and server-side applications, and that the performance overhead of the tool is not prohibitive.


title: Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis.

abstract: More than 4 decades of research and 9 meta-analyses have focused on the undermining effect: namely, the debate over whether the provision of extrinsic incentives erodes intrinsic motivation. This review and meta-analysis builds on such previous reviews by focusing on the interrelationship among intrinsic motivation, extrinsic incentives, and performance, with reference to 2 moderators: performance type (quality vs. quantity) and incentive contingency (directly performance-salient vs. indirectly performance-salient), which have not been systematically reviewed to date. Based on random-effects meta-analytic methods, findings from school, work, and physical domains (k = 183, N = 212,468) indicate that intrinsic motivation is a medium to strong predictor of performance (? = .21�45). The importance of intrinsic motivation to performance remained in place whether incentives were presented. In addition, incentive salience influenced the predictive validity of intrinsic motivation for performance: In a �crowding out� fashion, intrinsic motivation was less important to performance when incentives were directly tied to performance and was more important when incentives were indirectly tied to performance. Considered simultaneously through meta-analytic regression, intrinsic motivation predicted more unique variance in quality of performance, whereas incentives were a better predictor of quantity of performance. With respect to performance, incentives and intrinsic motivation are not necessarily antagonistic and are best considered simultaneously. Future research should consider using nonperformance criteria (e.g., well-being, job satisfaction) as well as applying the percent-of-maximum-possible (POMP) method in meta-analyses. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


title: Fast battery equalization with isolated bidirectional DC-DC converter for PHEV applications

abstract: Battery systems in plug-in hybrid electric vehicles (PHEV) suffer from voltage imbalance. This imbalance reduces the available capacity of certain charge/discharge cycles and shortens the battery life cycle. A new topology and smart balancing strategy for PHEV battery systems are proposed in this paper, which uses a bidirectional DC-DC converter to reduce balancing time and the number of switches needed. The simulation on a ten series connected battery cell is compared to a traditional switched capacitor equalizer.


title: DeepProp: extracting deep features from a single image for edit propagation

abstract: Edit propagation is a technique that can propagate various image edits (e.g., colorization and recoloring) performed via user strokes to the entire image based on similarity of image features. In most previous work, users must manually determine the importance of each image feature (e.g., color, coordinates, and textures) in accordance with their needs and target images. We focus on representation learning that automatically learns feature representations only from user strokes in a single image instead of tuning existing features manually. To this end, this paper proposes an edit propagation method using a deep neural network (DNN). Our DNN, which consists of several layers such as convolutional layers and a feature combiner, extracts stroke-adapted visual features and spatial features, and then adjusts the importance of them. We also develop a learning algorithm for our DNN that does not suffer from the vanishing gradient problem, and hence avoids falling into undesirable locally optimal solutions. We demonstrate that edit propagation with deep features, without manual feature tuning, can achieve better results than previous work.


title: Underground online music communities: exploring rules for membership

abstract: Purpose – This paper aims to investigate the rules and rituals for joining and operating within underground music file sharing communities as well as the members' motivations for joining.Design/methodology/approach – Actor‐network theory is combined with an ethnographic methodology to explore the structure, technology and rules of these communities from an actor‐oriented, member perspective. Empirical data include in‐depth interviews with three file sharers, and participant‐observations for 120 days within an online community.Findings – The paper provides an increased understanding of the structured and orderly nature of underground music file sharing communities and the perceived importance of strong rules and rituals for membership. Many communities use the same open‐source software.Research limitations/implications – Only a small number of file sharers (three) were interviewed. However they provide rich insights into this under‐researched topic.Practical implications – An understanding of these sophist...


title: Human identification using KnuckleCodes

abstract: The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.


title: Design of a transverse flux permanent magnet excited machine as a near-wheel motor for the use in electric vehicles

abstract: This paper presents the requirements on a city car and how they have been derived. In addition, the design of a transverse flux permanent magnet excited machine for the use in an electric vehicle is introduced. The drive concept of the city car bases on two near-wheel motors. Transverse flux machines are capable for direct drive applications because of their very high torque density. Their speed range matches the required speed range of electric vehicles' wheels. The paper deals with the design and the further development of a lamination concept for a transverse flux permanent magnet excited machine, whereby the designed form closure leads to a improved mechanical clamping which allows higher tolerances of the laminated steel sheets. The design is optimized considering torque density and the applicability to electric vehicles based on a finite element method simulation.


title: Optimization Design of a Segmented Halbach Permanent-Magnet Motor Using an Analytical Model

abstract: We present an optimization design of a slotless permanent-magnet brushless dc motor (400 W, 6000 rpm), with two-segment Halbach arrays. The optimization is based on a motor analytical model which is obtained by solving the magnetic scalar potential using the Fourier series. We show that the iron-cored Halbach configuration is better than the classical one and that the air-cored Halbach configuration is even better. This means that the rotor iron can be completely omitted. We verify the results by using the finite-element method.


title: Morse Theory of Attractors via Lyapunov Functions

abstract: This paper is concerned with the Morse theory of attractors for semiflows on complete metric spaces. First, we construct global Morse-Lyapunov functions for Morse decompositions of attractors. Then we extend some well known deformation results in the critical-point theory to Morse-Lyapunov functions which are only continuous. Based on these works, we finally introduce the concept of critical groups for Morse sets and establish Morse inequalities and Morse equations for attractors.


title: Parkinson's disease: clinical features and diagnosis

abstract: Objective: Parkinson’s disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. Methods: A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included “Parkinson’s disease”, “diagnosis” and “signs and symptoms”. Results: Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. Conclusions: A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.


title: Learning to Reason with Third-Order Tensor Products

abstract: We combine Recurrent Neural Networks with Tensor Product Representations to learn combinatorial representations of sequential data. This improves symbolic interpretation and systematic generalisation. Our architecture is trained end-to-end through gradient descent on a variety of simple natural language reasoning tasks, significantly outperforming the latest state-of-the-art models in single-task and all-tasks settings. We also augment a subset of the data such that training and test data exhibit large systematic differences and show that our approach generalises better than the previous state-of-the-art.


title: Audio augmentation for speech recognition.

abstract: Data augmentation is a common strategy adopted to increase the quantity of training data, avoid overfitting and improve robustness of the models. In this paper, we investigate audio-level speech augmentation methods which directly process the raw signal. The method we particularly recommend is to change the speed of the audio signal, producing 3 versions of the original signal with speed factors of 0.9, 1.0 and 1.1. The proposed technique has a low implementation cost, making it easy to adopt. We present results on 4 different LVCSR tasks with training data ranging from 100 hours to 1000 hours, to examine the effectiveness of audio augmentation in a variety of data scenarios. An average relative improvement of 4.3% was observed across the 4 tasks.


title: Assessing bradykinesia in Parkinson's disease using gyroscope signals

abstract: Parkinson's disease (PD) is a neurodegenerative brain disorder that slowly brings on the dopaminergic neurons death. The depletion of the dopaminergic signal causes the onset of motor symptoms such as tremor, bradykinesia and rigidity. Usually, neurologists regularly monitor motor symptoms and motor fluctuations using the MDS-UPDRS part III clinical scale. Nevertheless, to have a more objective and quantitative evaluation, it is possible to assess the cardinal motor symptoms of PD using wearable sensors and portable robotic devices. Unfortunately while there are several research papers on the use of these devices on PD patients, their use is not so common in clinical practice. In this work we recorded specific MDS-UPDRS motor tasks using magneto-inertial devices, worn by seven PD subjects and seven age-matched controls, in order to deeply analyze the kinematic and dynamic characteristics of goal-directed movements of upper limb, in addition to extract quantitative indices (peak velocity, smoothness, etc) useful for the assessment of motor symptoms. Using only gyroscope signals we looked at those parameters useful to assess bradykinesia. We observed parameters changes from OFF to ON phase congruent with the MDS-UPDRS changes, especially in the frequency domain. Our results suggest the prono-supination task is the more consistent to describe the bradykinesia symptom with the gyroscopes. Probably because of the amplitude of the movement performed. Moreover the peak power looks appropriate for bradykinesia symptom evaluation. We can conclude that, similar to the studies in which tremor symptom is evaluated, it is possible to monitor the bradykinesia using few wearable sensors and few simple parameters.


title: A 92GHz bandwidth SiGe BiCMOS HBT TIA with less than 6dB noise figure

abstract: A low-noise, broadband amplifier with resistive degeneration and transimpedance feedback is reported with 200 mV pp input linearity and less than 6 dB noise figure up to 88 GHz. The measured gain of 13 dB, noise figure, linearity, and group delay variation of ±1.5 ps are in excellent agreement with simulation. Eye diagram measurements were conducted up to 120 Gb/s and a dynamic range larger than 36 dB was obtained from eye diagram measurements at 40 Gb/s. The chip, which includes a 50Ω output buffer, occupies 0.138 mm2 and consumes 21 mA from a 2.3V supply.


title: DTN routing in body sensor networks with dynamic postural partitioning

abstract: This paper presents novel store-and-forward packet routing algorithms for Wireless Body Area Networks (WBAN) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. On-body DTN routing protocols are then developed using a stochastic link cost formulation, capturing multi-scale topological localities in human postural movements. Performance of the proposed protocols are evaluated experimentally and via simulation, and are compared with a number of existing single-copy DTN routing protocols and an on-body packet flooding mechanism that serves as a performance benchmark with delay lower-bound. It is shown that via multi-scale modeling of the spatio-temporal locality of on-body link disconnection patterns, the proposed algorithms can provide better routing performance compared to a number of existing probabilistic, opportunistic, and utility-based DTN routing protocols in the literature.


title: Converging Urban Agendas: Toward Healthy and Sustainable Communities

abstract: In light of recent developments such as the COP21 Paris climate agreement, the UN adoption of the Sustainable Development Goals for 2030, and the Habitat III Conference, there is increasing recognition of the role of human settlements as key components of both global challenges and global solutions. “Urban sustainability” under various names has matured over the last three decades not only in planning and related fields, but also in wider professional and popular discourse. In this paper we trace a historical overview of urban sustainability theory and practice, and explain why urban sustainability planning and development currently face limited and inconsistent application. We show that this lack of public uptake is due in part to monitoring, assessment, and decision-support frameworks and tools that do not engage citizens and their governments in a shared “strong sustainability” analysis and/or vision. We argue that urban sustainability today clearly needs to embrace equity, inclusion, and other social considerations; contribute to constructive societal mobilisation and compelling policy-making; advocate for development as a better alternative to growth; encourage the integration of human and environmental health interests; and encompass triple-bottom-line-inspired outcomes. Focusing on community capital productivity and regeneration may be the key to advancing healthy and sustainable communities.


title: Neurologic abnormalities in murderers.

abstract: Thirty-one individuals awaiting trial or sentencing for murder or undergoing an appeal process requested a neurologic examination through legal counsel. We attempted in each instance to obtain EEG, MRI or CT, and neuropsychological testing. Neurologic examination revealed evidence of "frontal" dysfunction in 20 (64.5%). There were symptoms or some other evidence of temporal lobe abnormality in nine (29%). We made a specific neurologic diagnosis in 20 individuals (64.5%), including borderline or full mental retardation (9) and cerebral palsy (2), among others. Neuropsychological testing revealed abnormalities in all subjects tested. There were EEG abnormalities in eight of the 20 subjects tested, consisting mainly of bilateral sharp waves with slowing. There were MRI or CT abnormalities in nine of the 19 subjects tested, consisting primarily of atrophy and white matter changes. Psychiatric diagnoses included paranoid schizophrenia (8), dissociative disorder (4), and depression (9). Virtually all subjects had paranoid ideas and misunderstood social situations. There was a documented history of profound, protracted physical abuse in 26 (83.8%) and of sexual abuse in 10 (32.3%). It is likely that prolonged, severe physical abuse, paranoia, and neurologic brain dysfunction interact to form the matrix of violent behavior.


title: Hybrid Machine-Crowd Approach for Fake News Detection

abstract: The rapid growth of fake news, especially in social media has become a challenging problem that has negative social impacts on a global scale. In contrast to fake news which intend to deceive and manipulate the reader, satirical stories are designed to entertain the reader by ridiculing or criticizing a social figure. Due to its serious threats of misleading information, researchers, governments, journalists and fact-checking volunteers are working together to address the fake news issue and increase the accountability of digital media. The automatic fake news detection systems enable identification of deceptive news. Low accuracy remains the main drawback of these systems. The automatic detection using only news' content is a technically challenging task as the language used in these articles is made to bypass the fake news detectors. This becomes even more complicated when the task is to differentiate the satirical stories from fake news. On the other side, human cognitive skills have shown to overperform machine-based systems when it comes to such tasks. In this paper, we address the fake news and satire detection by proposing a method that uses a hybrid machine-crowd approach for detection of potentially deceptive news. This system combines the human factor with the machine learning approach and a decision-making model that estimates the classification confidence of algorithms and decides whether the task needs human input or not. Our approach achieves reasonably higher accuracy compared to the reported baseline results, in exchange of cost and latency of using the crowdsourcing service.


title: Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples With Applications to Neuroimaging

abstract: We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provides a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.


title: Convolutional Neural Network for Trajectory Prediction

abstract: Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.


title: A Novel Broadband Dual-Polarization Antenna Utilizing Strong Mutual Coupling

abstract: A novel broadband dual-polarization antenna utilizing strong mutual coupling with high isolation is presented in this communication. The antenna is composed of two orthogonally situated dipoles which have square-loop shape arms (SLSAs). And to shape the radiation pattern of the antenna, a metal reflector is placed under the antenna. By elaborately utilizing the strong mutual coupling between the two dipoles, very good impedance match and sufficiently high isolation can be achieved simultaneously, the reasons for which are analyzed in this communication. Both the simulated and the measured results show that a more than 57.5% impedance bandwidth with SWR 31 dB can be achieved for the proposed antenna. As an important advantage, the input impedance of the antenna can be controlled within certain limits by adjusting the configurations, thus the antenna can be fed directly by coaxial cables without using impedance transformers. Besides, the antenna structure is compact and simple.


title: Residual encoder-decoder network introduced for multisource SAR image despeckling

abstract: Synthetic Aperture Radar (SAR) image is disturbed by multiplicative noise known as speckle. In this paper, based on the power of deep fully convolutional network, an encoding-decoding framework is introduced for multisource SAR image despeckling. The network contains a series of convolution and deconvolution layers, forming an end-to-end non-linear mapping between noise and clean SAR images. With addition of skip connection, the network can keep image details and accomplish the strategy for residual learning which solves the notorious problem of vanishing gradients and accelerates convergence. The experimental results on simulated and real SAR images show that the introduced approach achieves improvements in both despeckling performance and time efficiency over the state-of-the-art despeckling methods.


title: Stock Price Forecasting with Support Vector Machines Based on Web Financial Information Sentiment Analysis

abstract: The stock price forecasting has always been considered as a difficult problem in time series prediction. Mass of financial Internet information play an important role in the financial markets,information sentiment is an important indicator reflecting the ideas and emotions of investors and traders. Most of the existing research use the stock’s historical price and technical indicators to predict future price trends of the stock without taking the impact of financial information into account. In this paper, we further explore the relationship between the Internet financial information and financial markets,including the relations between the Internet financial information content, Internet financial information sentimental value and stock price. We collect the news of three stocks in the Chinese stock market in the GEMin a few large portals, use the text sentiment analysis algorithm to calculate the sentimental value of the corresponding Internet financial information, combined with the stock price data, implantSupport Vector Machines to analyzes and forecasts on the stock price, the accuracy of the prediction of stock priceshas been improved.


Cross-modal search for fashion attributes


title: Fusion of VR and teleoperation for innovative near-presence laboratory experience in engineering education

abstract: Distance learning is becoming a popular mode of learning among individuals seeking to add new skills to their repertoire or acquire an advanced degree. The availability of online course materials from institutions in academia aid this process, lending standardized and validated information to the audience seeking such knowledge. Although the online system of learning is extremely convenient for acquiring theoretical knowledge, the scope of hands-on experience is limited to the use of simulation software or involves the purchase of expensive laboratory kits. The proliferation of new technology like Virtual Reality (VR), Fifth Generation cellular networks (5G), and industrial robotics can be leveraged to bridge this gap, however. This will enable effective delivery of traditional disciplines like Electrical, Computer, or Mechanical Engineering using the online mode. This paper discusses the enabling technologies for implementing such a system. An ongoing effort to establish a proof-of-concept system in the Electrical and Computer Engineering department of the University of Nebraska-Lincoln is presented.


title: A fog computing based smart grid model

abstract: Traditional electric generation based on fossil fuel consumption threatens the humanity with global warming, climate change, and increased carbon emission. Renewable resources such as wind or solar power are the solution to these problems. The smart grid is the only choice to integrate green power resources into the energy distribution system, control power usage, and balance energy load. Smart grids employ smart meters which are responsible for two-way flows of electricity information to monitor and manage the electricity consumption. In a large smart grid, smart meters produce tremendous amount of data that are hard to process, analyze and store even with cloud computing. Fog computing is an environment that offers a place for collecting, computing and storing smart meter data before transmitting them to the cloud. This environment acts as a bridge in the middle of the smart grid and the cloud. It is geographically distributed and overhauls cloud computing via additional capabilities including reduced latency, increased privacy and locality for smart grids. This study overviews fog computing in smart grids by analyzing its capabilities and issues. It presents the state-of-the-art in area, defines a fog computing based smart grid and, gives a use case scenario for the proposed model.


title: Design Methodology for Thin-Film Transistor Based Pseudo-CMOS Logic Array with Multi-Layer Interconnect Architecture

abstract: Thin-film transistor (TFT) circuits are important for flexible electronics which are promising in the area of wearable devices. However, most TFT technologies only have unipolar devices and the process variation and defective rate are relatively high, which impose challenges to TFT circuit design. In this paper, we propose a novel logic array based on pseudo-CMOS logic to address the problem of unipolar TFT circuit design. A multi-layer interconnect architecture and wire routing methodology are presented to improve the routability and meanwhile the area efficiency. The experimental results show that the proposed logic array reduces more than 80% area compared with transistor level scheme.


title: A Constant-on-Time Control DC–DC Buck Converter With the Pseudowave Tracking Technique for Regulation Accuracy and Load Transient Enhancement

abstract: Constant-on-time (COT) control with an additional current feedback path in conventional buck converters can remove the need of large equivalent series resistance (ESR) at the output capacitor but it induces degraded output voltage regulation accuracy and slow transient response owing to opposite reaction between the output voltage and the additional current feedback information. Therefore, this paper proposes the pseudowave tracking (PWT) technique to reduce the load-dependent dc offset voltage of output load regulation while keeping the fast transient response similar to conventional COT buck converter with large ESR. The PWT technique further improves the transient response instantly by extending on-time and off-time periods during light-to-heavy and heavy-to-light load changes, respectively. The test chip fabricated in 28 nm CMOS process has demonstrated the improvement of offset voltage from 42 to 4 mV, while the transient response can be improved to 4 and 5 μs with load changes from 0.3 to 1.7 A and vice versa, respectively.


title: Wireless power for mobile devices

abstract: Wireless power transfer allows a convenient, easy to use battery charging of mobile phones and other mobile devices. No hassle with cables and plugs, just place the device on a pad and that's it. Such a system even has the potential to become a standard charging solution. Where are the limits for such a solution and which are the side conditions to consider? What are the possibilities to realize such a system? To make the whole idea a success, it is definitely necessary to come to widely accepted standard. Therefore, in 2009 the Wireless Power Consortium was founded with meanwhile more than 80 international companies as members. The consortium recently released the first worldwide standard on wireless power for mobile devices of to 5W called “Qi”. The contribution presents details of this standard and the rationale behind.


title: Micromelanomas: A Review of Melanomas ≤2 mm and a Case Report

abstract: The ABCD acronym used to screen pigmented lesions for melanoma obviously was not designed to contend with melanomas that are under 2 mm in diameter. Previously, views ranged that such small lesions could not be melanomas until a few reports of such “micromelanomas” emerged. The author presents a 2 mm melanoma in situ presenting as an insignificant pigmented lesion in a 60-year-old patient with no previous history of melanoma or multiple nevi—which is usually the norm in cases of small melanoma. This paper reiterates the fact that when it comes to a melanoma, size does not matter. In this paper, the term “micromelanoma” is used by the author to represent melanomas under 2 mm. Dermatoscopy and histopathology findings are discussed in this case, along with a review of small melanomas.


title: Aggregate Constraints for Virtual Manipulation with Soft Fingers

abstract: Interactive dexterous manipulation of virtual objects remains a complex challenge that requires both appropriate hand models and accurate physically-based simulation of interactions. In this paper, we propose an approach based on novel aggregate constraints for simulating dexterous grasping using soft fingers. Our approach aims at improving the computation of contact mechanics when many contact points are involved, by aggregating the multiple contact constraints into a minimal set of constraints. We also introduce a method for non-uniform pressure distribution over the contact surface, to adapt the response when touching sharp edges. We use the Coulomb-Contensou friction model to efficiently simulate tangential and torsional friction. We show through different use cases that our aggregate constraint formulation is well-suited for simulating interactively dexterous manipulation of virtual objects through soft fingers, and efficiently reduces the computation time of constraint solving.


title: Real-time vehicle detection and tracking

abstract: Nowadays, the rapid increase in the number of the automobiles on the highway and urban roads have created many challenges regarding the proper management and control of the traffic. Detection and tracking of vehicles using the traffic surveillance system gives more promising way to manage and control the road traffic. Vehicle surveillance represents a challenging task of moving object segmentation in complex environment. The detection ratio of such algorithms depends upon the quality of the generated foreground mask. Therefore, the aim of this paper is to present an efficient method for detection and tracking of vehicles which focuses on the trajectory of motion of the objects. The proposed method preserves the group of pixels in foreground which can be probable vehicles and discards the rest as noise. Therefore, it selectively rejects the objects which cannot be vehicles at the same time consolidate the candidate vehicles. Here, the foreground mask generation process is improved so that the quality of generated foreground mask better consequently increases the detection ratio. The performance of the proposed method is evaluated by comparing it with other standard methods qualitatively as well as quantitatively. The experimental results have established the superior performance of the proposed method.


title: Hand Pose Estimation via Latent 2.5D Heatmap Regression

abstract: Estimating the 3D pose of a hand is an essential part of human-computer interaction. Estimating 3D pose using depth or multi-view sensors has become easier with recent advances in computer vision, however, regressing pose from a single RGB image is much less straightforward. The main difficulty arises from the fact that 3D pose requires some form of depth estimates, which are ambiguous given only an RGB image. In this paper we propose a new method for 3D hand pose estimation from a monocular image through a novel 2.5D pose representation. Our new representation estimates pose up to a scaling factor, which can be estimated additionally if a prior of the hand size is given. We implicitly learn depth maps and heatmap distributions with a novel CNN architecture. Our system achieves the state-of-the-art estimation of 2D and 3D hand pose on several challenging datasets in presence of severe occlusions.


title: GPU-based segmentation of retinal blood vessels

abstract: In this paper a fast and accurate technique for retinal vessel tree extraction is proposed. It consists of a hybrid strategy based on global image filtering and contour tracing. With the aim of increasing the computation speed, the algorithm has been tailored for efficient execution on commodity graphics processing units achieving low execution times and high speedups over the CPU execution. The performance of the proposed method was tested on publicly available databases, STARE and DRIVE, based on standard measures such as accuracy, sensitivity and specificity. Results reveal an average accuracy comparable to that reported for state-of-the art techniques. Our method performs the vascular tree segmentation of the images in the DRIVE and the STARE databases in an average of 14 ms and 18 ms, respectively. To the best of our knowledge, the proposal features the highest accuracy/performance rate in the retinal blood vessel extraction domain.


title: Contrastive rhetoric in context: A dynamic model of L2 writing

abstract: Abstract The notion of contrastive rhetoric was first proposed as a pedagogical solution to the problem of L2 organization, and the subsequent development in research has generated, among other valuable insights, three explanations for the organizational structures of L2 texts, including linguistic, cultural, and educational explanations. However, the contribution of contrastive rhetoric to the teaching of ESL writing has been limited because of the underlying assumptions that has guided the early pedagogical approaches. This study identifies a static theory of L2 writing that has been widely used in teaching organizational structures and considers how the pedagogical application of insights from contrastive rhetoric studies have been limited by this theory. To overcome the limitations of the static theory, an alternative model of L2 writing is proposed, and its implications for further research and the teaching of L2 writing are discussed.


title: Analysing the Use of Interactive Technology to Implement Interactive Teaching

abstract: Recent policy initiatives in England have focused on promoting ‘interactive’ teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world. At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students' use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT's affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.


title: An Efficient Approach for Latent Fingerprint Recognition

abstract: The online fingerprints by biometric system is not widely used now a days and there is less scope as user is friendly with the system. This paper represents a framework and applying the latent fingerprints obtained from the crime scene. These prints would be matched with our database and we identify the criminal. For this process we have to get the fingerprints of all the citizens. This technique may reduce the crime to a large extent. Latent prints are different from the patent prints. These fingerprints are found at the time of crime and these fingerprints are left accidentally. By this approach we collect these fingerprints by chemicals, powder, lasers and other physical means. Sometimes, fingerprints have a broken curve and it is not so clear due to low pressure. We apply the M_join algorithm to join the curve to achieve better results. Thus, our proposed approach eliminates the pseudo minutiae and joins the broken curves in fingerprints.


title: An overview of workflow management: From process modeling to workflow automation infrastructure

abstract: Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area.


A Probabilistic Approach for Detection and Analysis of Cognitive Flow


title: Mobility-Aware Caching for Content-Centric Wireless Networks: Modeling and Methodology

abstract: As mobile services are shifting from connection- centric communications to content-centric communications, content-centric wireless networking emerges as a promising paradigm to evolve the current network architecture. Caching popular content at the wireless edge, including base stations and user terminals, provides an effective approach to alleviate the heavy burden on backhaul links, as well as lower delays and deployment costs. In contrast to wired networks, a unique characteristic of content-centric wireless networks (CCWNs) is the mobility of mobile users. While it has rarely been considered by existing works on caching design, user mobility contains various helpful side information that can be exploited to improve caching efficiency at both BSs and user terminals. In this article, we present a general framework for mobility-aware caching in CCWNs. Key properties of user mobility patterns that are useful for content caching are first identified, and then different design methodologies for mobility-aware caching are proposed. Moreover, two design examples are provided to illustrate the proposed framework in detail, and interesting future research directions are identified.


title: Beamlets and multiscale image analysis

abstract: We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis.


title: Drone-assisted public safety wireless broadband network

abstract: A nationwide interoperable public safety wireless broadband network is being planned by the First Responder Network Authority (FirstNet) under the auspices of the United States government. The public safety network shall provide the needed wireless coverage in the wake of an incident or a disaster. This paper proposes a drone-assisted multi-hop device-to-device (D2D) communication scheme as a means to extend the network coverage over regions where it is difficult to deploy a landbased relay. The resource are shared using either time division or frequency division scheme. Efficient algorithms are developed to compute the optimal position of the drone for maximizing the data rate, which are shown to be highly effective via simulations.


title: Learning from the hindsight plan — Episodic MPC improvement

abstract: Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the iterative learning setting, where the same task can be repeated several times, and propose a policy improvement scheme for MPC. The main idea is that between executions we can, offline, run MPC with a longer horizon, resulting in a hindsight plan. To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan. This effectively consolidates long-term reasoning into the short-horizon planning. We empirically evaluate our approach in contact-rich manipulation tasks both in simulated and real environments, such as peg insertion by a real PR2 robot.


title: The Hybrid Shopping List: Bridging the Gap between Physical and Digital Shopping Lists

abstract: Shopping is one of the most frequently occuring tasks in our daily lives, and creation and management of shopping lists is an important aspect of this task. Given the recent adoption of mobile devices, the process of writing lists is not only limited to the use of pen and paper, as a good number of digital tools and applications are available. The goal of this paper is to study and understand the transition between paper-based and digital shopping lists. We analyze how people interact with paper-based shopping lists and derive design implications for our own hybrid shopping support application, which combines paper-based lists with a mobile application. We contribute the study and the design and implementation of a hybrid (pen-and-paper-based UI and mobile GUI) application for the creation of shopping lists.


A Multi-media Approach to Cross-lingual Entity Knowledge Transfer


title: A Comparison of External Clustering Evaluation Indices in the Context of Imbalanced Data Sets

abstract: For highly imbalanced data sets, almost all the instances are labeled as one class, whereas far fewer examples are labeled as the other classes. In this paper, we present an empirical comparison of seven different clustering evaluation indices when used to assess partitions generated from highly imbalanced data sets. Some of the metrics are based on matching of sets (F-measure), information theory (normalized mutual information and adjusted mutual information), and pair of objects counting (Rand and adjusted Rand indices). We also investigate the BCubed metric, which takes into account the concepts of recall, precision, as well as counting pairs. Furthermore, in order to avoid the class size imbalance effect, we propose a modification to the Rand index, referred to as the normalized class size Rand (NCR) index. In terms of results, apart from NCR, our experiments indicate that all the other analyzed indices are not able to deal properly with the problem of class size imbalance.


title: Zero-Voltage Switching and Soft-Commutating Two-Transformer Full-Bridge PWM Converter Using the Voltage-Ripple

abstract: A zero-voltage switching (ZVS) and soft-commutating two-transformer full-bridge pulsewidth modulation converter using the voltage ripple is proposed in this paper. It is based on the phase-shift full-bridge with series-connected two transformers (TTFB) that act as an output inductor as well as a main transformer. Moreover, the TTFB converter achieves the ZVS of all the switches inherently even at a light load using the energy stored in the magnetizing inductance of each transformer. However, it has several drawbacks such as circulating energy during the freewheeling state, loss of duty cycle, and high voltage stress across the output rectifier diodes. To overcome these drawbacks, the proposed converter employs an output-voltage-doubler, allowing the output rectifier diode to softly commutate with less voltage stress, to reduce the freewheeling interval as well as the circulating energy. The operational principle, technical analysis, and design considerations are described. Finally, the experimental results from a 425 W, 385 Vdc /170 Vdc prototype are presented to confirm the operation, validity, and features of the proposed converter.


title: M-Umrah: An Android-Based Application to Help Pilgrims in Performing Umrah

abstract: Performing hajj and umrah is one of the five pillars of Islam. The ritual involves many rules and procedures, and to remember all these rules and procedures usually poses a problem for pilgrims. The advancement of IT can provide a solution to the problem mentioned above. Hence, we apply the Internet and smart phone technologies to develop the M - Umrah application for users. The target users of this application are the Malaysian hajj and umrah pilgrims. This application provides a step-by-step guide to performing umrah, starting from preparation at home up until the pilgrimage is completed. The application was developed on the Android platform, and the first version of the application has been released to the market. Up until this point, more than ten thousand users all around the world have downloaded the application.


title: A SLIM HORIZONTALLY POLARIZED OMNIDIRECTIONAL ANTENNA BASED ON TURNSTILE SLOT DIPOLE

abstract: A novel horizontally polarized (HP) antenna with omnidirectional pattern is presented in this paper. The proposed antenna applies the concept of rotating electric fleld method to conventional slot dipoles. Two CPW-fed slot dipoles are placed in a perpendicularly conjugate form. By properly arranging the magnitude and phase of input signals, the omnidirectional pattern can be synthesized at broadside. A prototype is developed at the 2.6GHz band, which ofiers a horizontally polarized omnidirectional radiation pattern with gain of 2.5{3.4dBi, and the measured antenna e-ciency is greater than 73% through the operating band (2.4{2.8GHz). Furthermore, a 20-dB polarization purity is achieved in this design. The overall volume of the proposed antenna is 22 £ 22 £ 90mm 3 (0.19‚0£0:19‚0£0:78‚0). Distinct from the other proposed HP antennas provided with planar geometry, this antenna is slim in shape, and it can be readily integrated with vertical dipoles to form a polarization diversity system in current wireless router and AP applications.


title: The Optimum Design of Gear Transmission Based on MATLAB

abstract: Gear mechanism is the most popular transmission mechanism, however, conventional design methods are complex and inaccurate. The optimum design solves the abovementioned problems effectively by means of applying to gear design. In numerous of optimization software, MATLAB holds obvious predominance on program and numerical calculation. Taking single gear transmission as an example, the math model of gear is founded, systematically, on the base of analyzing the foundation of goal function, the selection of design variables and the confirmation of restrictive conditions. The results solved by SQP algorithm prove out that the optimum design, by MATLAB, is effective, reliable and simple.


title: Lung nodule detection in CT images using neuro fuzzy classifier

abstract: Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.


title: Spline-Based Initialization of Monocular Visual–Inertial State Estimators at High Altitude

abstract: Due to the lack of direct distance measurements, robust and accurate state estimation at high altitude but GPS-denied environments is a challenging task. A possible solution is monocular visual–inertial estimators, in which visual and inertial measurements are properly fused to recover the metric estimates. However, these estimators suffer from initialization under poor numerical conditioning or even degeneration, due to difficulties in retrieving observations of visual features with sufficient parallax, and the excessive period of inertial measurement integration. In this letter, we introduce the joint formulation into the spline-based high altitude estimator initialization method for monocular visual–inertial navigation system, in which the fitting of the spline and the alignment of visual measurements and inertial measurements are jointly optimized to recover metric estimates. The method ensures that sufficient excitation is contained in the inertial measurements when initialized, which eliminates the numerical issues. Compared with the work of Liu and Shen, the joint formulation makes our initialization method insensitive to the choice of spline parameters. Thus, the adaptivity to various environments and motions is obtained, as well as higher accuracy. The method is applicable for both loosely coupled and tightly coupled visual–inertial estimators. Extensive experiments are conducted to validate our approach.


title: Identification of surface proteins on viable Plasmodium knowlesi merozoites.

abstract: Viable merozoites of Plasmodium knowlesi were isolated and the proteins that were labeled on intact merozoites by lactoperoxidase-catalyzed radioiodination were identified. Sodium dodecyl sulfate polyacrylamide gel electrophoresis and autoradiography of Triton soluble extracts of labeled merozoites demonstrated eight major bands ranging in apparent molecular weight from 150,000 D to 22,000 D. Exposure of intact merozoites to trypsin (10 microgram/ml) for 10 min resulted in the loss of the two highest molecular weight proteins (150,000 D and 105,000 D) and the appearance of two new bands at 70,000 D and 62,000 D. Trypsin treatment under these conditions also removed the receptor(s) for merozoite attachment to erythrocytes. Therefore, these high molecular weight proteins are candidates for the merozoite component that attaches to erythrocytes. There was no evidence that the labeled membrane components were serum or erythrocyte membrane components, two potential contaminants in the preparation. Anti-rhesus erythrocyte antibody did not precipitate labeled merozoite proteins. Furthermore, the immunoprecipitation of labeled merozoite proteins by rhesus anti-merozoite serum was not inhibited by erythrocyte ghosts.


Post-processing of Laser Scanning Point Clouds for the As-built Modeling of Petrochemical Installations


title: The Origin and Fate of Muscle Satellite Cells

abstract: Satellite cells represent the primary population of stem cells resident in skeletal muscle. These adult muscle stem cells facilitate the postnatal growth, remodeling, and regeneration of skeletal muscle. Given the remarkable regenerative potential of satellite cells, there is great promise for treatment of muscle pathologies such as the muscular dystrophies with this cell population. Various protocols have been developed which allow for isolation, enrichment, and expansion of satellite cell derived muscle stem cells. However, isolated satellite cells have yet to translate into effective modalities for therapeutic intervention. Broadening our understanding of satellite cells and their niche requirements should improve our in vivo and ex vivo manipulation of these cells to expedite their use for regeneration of diseased muscle. This review explores the fates of satellite cells as determined by their molecular signatures, ontogeny, and niche dependent programming.


title: Classification of household devices by electricity usage profiles

abstract: This paper investigates how to classify household items such as televisions, kettles and refrigerators based only on their electricity usage profile every 15 minutes over a fixed interval of time. We address this time series classification problem through deriving a set of features that characterise the pattern of usage and the amount of power used when a device is on. We evaluate a wide range of classifiers on both the raw data and the derived feature set using both a daily and weekly usage profile and demonstrate that whilst some devices can be identified with a high degree of accuracy, others are very hard to disambiguate with this granularity of data.


title: What is the effect of sensory discrimination training on chronic low back pain? A systematic review

abstract: Sensory discrimination training (SDT) for people with chronic low back pain (CLBP) is a novel approach based on theories of the cortical reorganization of the neural system. SDT aims to reverse cortical reorganization, which is observed in chronic pain patients. SDT is still a developing therapeutic approach and its effects have not been systematically reviewed. The aim of this systematic review was to evaluate if SDT decreases pain and improves function in people with CLBP. A systematic review was performed on the available literature to evaluate the effects of SDT. Randomised controlled trials compared the effectiveness of SDT on pain and function in people with CLBP with the effectiveness of other physiotherapy interventions, no treatment, or sham therapy. The methodological quality of the included studies and the clinical relevance of reported treatment effects were investigated. The original search revealed 42 records of which 6 fulfilled the inclusion criteria. The majority of studies showed that SDT caused statistically significant improvements in pain and function, but only two studies reported clinically relevant improvements. The applied SDT varied considerably with regard to dosage and content. The methodological quality of the included studies also varied, which hampered the comparability of results. Although SDT seems to improve pain and function in people with CLBP, study limitations render firm conclusions unsafe. Future studies should pay closer attention to power and sample selection as well as to the content and dosage of the SDT intervention. We recommend a large, well-powered, prospective randomized control study that uses a standardized SDT approach to address the hypothesis that SDT causes clinically relevant improvements in pain and function.


title: Showing user interface adaptivity by animated transitions

abstract: In order to reduce the inevitable end user disruption and cognitive perturbation induced by adapting a graphical user interface, the results of the adaptation could be conveyed to the end user by animating a transition scenario showing the evolution from the user interface before adaptation to the user interface after adaptation. A transition scenario consists of a sequence of adaptation operations (e.g., set/change a property of a widget, replace a widget by another, resize a widget) belonging to a catalogue of operations defined as an Extended Backus-Naur Form grammar. Each transition operation has a range from a single widget (e.g., this Ok button) to a selection of widgets based on a selector mechanism (e.g., all validation widgets of this family of interfaces). A transition scenario is built either automatically by any adaptation algorithm or interactively by a specific editor for designers. An animator then executes the animation scenario by parsing each adaptation operation one by one or in a grouped mode and by rendering them by an animated transition on a user interface model. The type (e.g., wipe, box in, box out) and parameters (e.g., animation speed, pace, direction) of each animated transition have been selected based on usability guidelines for animation. A user study suggests that a transition scenario reinforces understandability and trust, while still suffering from lag.


title: Using Universal Linguistic Knowledge to Guide Grammar Induction

abstract: We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies between pairs of syntactic categories that commonly occur across languages. During inference of the probabilistic model, we use posterior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also automatically refine the syntactic categories given in our coarsely tagged input. Across six languages our approach outperforms state-of-the-art unsupervised methods by a significant margin.


title: A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique

abstract: Most of the existing methods for bilingual word embedding only consider shallow context or simple co-occurrence information. In this paper, we propose a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived from a maximum complete sub-graph of pointwise mutual information based graph over bilingual corpus. In this way, we treat source and target words equally and a separated bilingual projection processing that have to be used in most existing works is not necessary any more. Several dimension reduction methods are evaluated to summarize the BSC-word relationship. The proposed method is evaluated on bilingual lexicon translation tasks and empirical results show that bilingual sense embedding methods outperform existing bilingual word embedding methods.


title: Intense pain soon after wrist fracture strongly predicts who will develop complex regional pain syndrome: prospective cohort study.

abstract: UNLABELLED ::: Complex regional pain syndrome (CRPS) is a distressing and difficult-to-treat complication of wrist fracture. Estimates of the incidence of CRPS after wrist fracture vary greatly. It is not currently possible to identify who will go on to develop CRPS after wrist fracture. In this prospective cohort study, a nearly consecutive sample of 1,549 patients presenting with wrist fracture to 1 of 3 hospital-based fracture clinics and managed nonsurgically was assessed within 1 week of fracture and followed up 4 months later. Established criteria were used to diagnose CRPS. The incidence of CRPS in the 4 months after wrist fracture was 3.8% (95% confidence interval = 2.9-4.8%). A prediction model based on 4 clinical assessments (pain, reaction time, dysynchiria, and swelling) discriminated well between patients who would and would not subsequently develop CRPS (c index .99). A simple assessment of pain intensity (0-10 numerical rating scale) provided nearly the same level of discrimination (c index .98). One in 26 patients develops CRPS within 4 months of nonsurgically managed wrist fracture. A pain score of ≥5 in the first week after fracture should be considered a "red flag" for CRPS. ::: ::: ::: PERSPECTIVE ::: This study shows that excessive baseline pain in the week after wrist fracture greatly elevates the risk of developing CRPS. Clinicians can consider a rating of greater than 5/10 to the question "What is your average pain over the last 2 days?" to be a "red flag" for CRPS.


title: Summarizing Source Code using a Neural Attention Model

abstract: High quality source code is often paired with high level summaries of the computation it performs, for example in code documentation or in descriptions posted in online forums. Such summaries are extremely useful for applications such as code search but are expensive to manually author, hence only done for a small fraction of all code that is produced. In this paper, we present the first completely datadriven approach for generating high level summaries of source code. Our model, CODE-NN , uses Long Short Term Memory (LSTM) networks with attention to produce sentences that describe C# code snippets and SQL queries. CODE-NN is trained on a new corpus that is automatically collected from StackOverflow, which we release. Experiments demonstrate strong performance on two tasks: (1) code summarization, where we establish the first end-to-end learning results and outperform strong baselines, and (2) code retrieval, where our learned model improves the state of the art on a recently introduced C# benchmark by a large margin.


DESIGN AND IMPLEMENTATION OF A PLANAR 4 × 4 BUTLER MATRIX IN SIW TECHNOLOGY FOR WIDE BAND HIGH POWER APPLICATIONS


title: Restoring the self: positive affect helps improve self-regulation following ego depletion

abstract: Previous work has shown that acts of self-regulation appear to deplete a psychological resource, resulting in poorer self-regulation subsequently. Four experiments using assorted manipulations and measures found that positive mood or emotion can counteract ego depletion. After an initial act of self-regulation, participants who watched a comedy video or received a surprise gift self-regulated on various tasks as well as non-depleted participants and significantly better than participants who experienced a sad mood induction, a neutral mood stimulus, or a brief rest period.


title: A pipeline for structured light bathymetric mapping

abstract: This paper details a methodology for using structured light laser imaging to create high resolution bathymetric maps of the sea floor. The system includes a pair of stereo cameras and an inclined 532nm sheet laser mounted to a remotely operated vehicle (ROV). While a structured light system generally requires a single camera, a stereo vision set up is used here for in-situ calibration of the laser system geometry by triangulating points on the laser line. This allows for quick calibration at the survey site and does not require precise jigs or a controlled environment. A batch procedure to extract the laser line from the images to sub-pixel accuracy is also presented. The method is robust to variations in image quality and moderate amounts of water column turbidity. The final maps are constructed using a reformulation of a previous bathymetric Simultaneous Localization and Mapping (SLAM) algorithm called incremental Smoothing and Mapping (iSAM). The iSAM framework is adapted from previous applications to perform sub-mapping, where segments of previously visited terrain are registered to create relative pose constraints. The resulting maps can be gridded at one centimeter and have significantly higher sample density than similar surveys using high frequency multibeam sonar or stereo vision. Results are presented for sample surveys at a submerged archaeological site and sea floor rock outcrop.


title: Fano's inequality for random variables

abstract: We extend Fano's inequality, which controls the average probability of (disjoint) events in terms of the average of some Kullback-Leibler divergences, to work with arbitrary [0,1]-valued random variables. Our simple two-step methodology is general enough to cover the case of an arbitrary (possibly continuously infinite) family of distributions as well as [0,1]-valued random variables not necessarily summing up to 1. Several novel applications are provided, in which the consideration of random variables is particularly handy. The most important applications deal with the problem of Bayesian posterior concentration (minimax or distribution-dependent) rates and with a lower bound on the regret in non-stochastic sequential learning. We also improve in passing some earlier fundamental results: in particular, we provide a simple and enlightening proof of the refined Pinsker's inequality of Ordentlich and Weinberger and derive a sharper Bretagnolle-Huber inequality.


title: Library Publishing for the 99%: Why Neoliberalism and Scholarly Publishing Need a Divorce

abstract: In this presentation, I will argue that the “serials crisis” and the subsequent need for library ::: publishing are best understood not by focusing on the singular actions of individuals or publishers, ::: but through an analysis of neoliberalism—the nation’s predominant economic ideology. In a ::: discourse of critique, I will analyze scholarly journal publishing using the following major elements ::: of neoliberalism: the primacy of the “free market,” privatization, deregulation, and using ::: individualism to justify cuts to social services and to discredit notions of the public good. Critics ::: argue that neoliberalism, a set of political and economic policies that have become widespread over ::: the past 35 years, is a pernicious system that disproportionately benefits a small number of major ::: corporate shareholders. Its effects, which include widening inequality and being prone to crises, are ::: masked by the public’s ignorance, historical amnesia, propaganda, indifference, and austerity. ::: ::: In a discourse of possibility, I will discuss the transformative potential of library-publishers creating ::: and participating in cooperatives. Library publishing can attain greater sustainability and impact ::: through a more robust system of state and regional partnerships based on two major types of ::: cooperatives: worker cooperatives, where the creators of goods and services collectively decide the ::: fate of their work, and purchasing cooperatives, where members mutually benefit by acquiring in ::: solidarity with each other. Cooperative economics is increasingly seen as a viable alternative to ::: neoliberalism, and there are successful stories across the globe. Additionally, the transition by several ::: Latin American nations away from neoliberalism by democratizing their economics and emphasizing ::: the public good and cooperation will be explored. ::: ::: Attendees will be able to contextualize the so-called serials crisis with greater clarity, advocate for ::: library publishing more compellingly, identify more impactful solutions, and have an increased sense ::: of individual and collective agency.


Integral Structure Perfection Diagnostics of Single Crystals Using Two Wavelengths of X-Ray Spectrum


title: Metaheuristics—the metaphor exposed

abstract: In recent years, the field of combinatorial optimization has witnessed a true tsunami of “novel” metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together – it seems that no idea is too far-fetched to serve as inspiration to launch yet another metaheuristic. In this paper, we will argue that this line of research is threatening to lead the area of metaheuristics away from scientific rigor. We will examine the historical context that gave rise to the increasing use of metaphors as inspiration and justification for the development of new methods, discuss the reasons for the vulnerability of the metaheuristics field to this line of research, and point out its fallacies. At the same time, truly innovative research of high quality is being performed as well. We conclude the paper by discussing some of the properties of this research and by pointing out some of the most promising research avenues for the field of metaheuristics.


title: Free Space Computation Using Stochastic Occupancy Grids and Dynamic

abstract: The computation of free space available in an environment is an essential task for many intelligent automotive and robotic applications. This paper proposes a new approach, which builds a stochastic occupancy grid to address the free space problem as a dynamic programming task. Stereo measurements are integrated over time reducing disparity uncertainty. These integrated measurements are entered into an occupancy grid, taking into account the noise properties of the measurements. In order to cope with real-time requirements of the application, three occupancy grid types are proposed. Their applicabilities and implementations are also discussed. Experimental results with real stereo sequences show the robustness and accuracy of the method. The current implementation of the method runs on off-the-shelf hardware at 20 Hz.


title: Multi-agent team cooperation: A game theory approach

abstract: The main goal of this work is to design a team of agents that can accomplish consensus over a common value for the agents' output using cooperative game theory approach. A semi-decentralized optimal control strategy that was recently introduced by the authors is utilized that is based on minimization of individual cost using local information. Cooperative game theory is then used to ensure team cooperation by considering a combination of individual cost as a team cost function. Minimization of this cost function results in a set of Pareto-efficient solutions. Among the Pareto-efficient solutions the Nash-bargaining solution is chosen. The Nash-bargaining solution is obtained by maximizing the product of the difference between the costs achieved through the optimal control strategy and the one obtained through the Pareto-efficient solution. The latter solution results in a lower cost for each agent at the expense of requiring full information set. To avoid this drawback some constraints are added to the structure of the controller that is suggested for the entire team using the linear matrix inequality (LMI) formulation of the minimization problem. Consequently, although the controller is designed to minimize a unique team cost function, it only uses the available information set for each agent. A comparison between the average cost that is obtained by using the above two methods is conducted to illustrate the performance capabilities of our proposed solutions.


title: Three-Dimensional Printing of Multifunctional Nanocomposites: Manufacturing Techniques and Applications.

abstract: The integration of nanotechnology into three-dimensional printing (3DP) offers huge potential and opportunities for the manufacturing of 3D engineered materials exhibiting optimized properties and multifunctionality. The literature relating to different 3DP techniques used to fabricate 3D structures at the macro- and microscale made of nanocomposite materials is reviewed here. The current state-of-the-art fabrication methods, their main characteristics (e.g., resolutions, advantages, limitations), the process parameters, and materials requirements are discussed. A comprehensive review is carried out on the use of metal- and carbon-based nanomaterials incorporated into polymers or hydrogels for the manufacturing of 3D structures, mostly at the microscale, using different 3D-printing techniques. Several methods, including but not limited to micro-stereolithography, extrusion-based direct-write technologies, inkjet-printing techniques, and popular powder-bed technology, are discussed. Various examples of 3D nanocomposite macro- and microstructures manufactured using different 3D-printing technologies for a wide range of domains such as microelectromechanical systems (MEMS), lab-on-a-chip, microfluidics, engineered materials and composites, microelectronics, tissue engineering, and biosystems are reviewed. Parallel advances on materials and techniques are still required in order to employ the full potential of 3D printing of multifunctional nanocomposites.


title: A Dialogue Intervention Simulation Framework to Facilitate Psychotherapy Training

abstract: Intensive Short-Term Dynamic Psychotherapy (ISTDP) is a form of psychotherapy that evokes emotional experiences with the patient to facilitate a corrective emotional experience. During ISDTP, verbal and non-verbal communication skills play an essential role in how the dialogue is directed to achieve successful treatment. In an attempt to enhance non-verbal communication skills, we present a framework that allows for the simple creation of a dialogue between the user and a virtual avatar representing the patient. The patient avatar incorporates facial expressions to express specific non-verbal communication thus expose psychotherapy trainees to better simulated nonverbal cues.


title: Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling

abstract: Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we explore LSTM RNN architectures for large scale acoustic modeling in speech recognition. We recently showed that LSTM RNNs are more effective than DNNs and conventional RNNs for acoustic modeling, considering moderately-sized models trained on a single machine. Here, we introduce the first distributed training of LSTM RNNs using asynchronous stochastic gradient descent optimization on a large cluster of machines. We show that a two-layer deep LSTM RNN where each LSTM layer has a linear recurrent projection layer can exceed state-of-the-art speech recognition performance. This architecture makes more effective use of model parameters than the others considered, converges quickly, and outperforms a deep feed forward neural network having an order of magnitude more parameters. Index Terms: Long Short-Term Memory, LSTM, recurrent neural network, RNN, speech recognition, acoustic modeling.


title: Refactoring - improving coupling and cohesion of existing code

abstract: Refactorings are widely recognised as ways to improve the internal structure of object-oriented software while maintaining its external behaviour. Unfortunately, refactorings concentrate on the treatment of symptoms (the so called code-smells), thus improvements depend a lot on the skills of the maintained coupling and cohesion on the other hand are quality attributes which are generally recognized as being among the most likely quantifiable indicators for software maintainability. Therefore, this paper analyzes how refactorings manipulate coupling/cohesion characteristics, and how to identify refactoring opportunities that improve these characteristics. As such we provide practical guidelines for the optimal usage of refactoring in a software maintenance process.


title: Nanofabrication at High Throughput and Low Cost

abstract: The task of nanofabrication can, in principle, be divided into two separate tracks: generation and replication of the patterned features. These two tracks are different in terms of characteristics, requirements, and aspects of emphasis. In general, generation of patterns is commonly achieved in a serial fashion using techniques that are typically slow, making this process only practical for making a small number of copies. Only when combined with a rapid duplication technique will fabrication at high-throughput and low-cost become feasible. Nanoskiving is unique in that it can be used for both generation and duplication of patterned nanostructures.


title: Parallel reinforcement learning with linear function approximation

abstract: In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by using parallel hardware. Our approach is based on agents using the SARSA(λ) algorithm, with value functions represented using linear function approximators. In our proposed method, each agent learns independently in a separate simulation of the single-agent problem. The agents periodically exchange information extracted from the weights of their approximators, accelerating convergence towards the optimal policy. We develop three increasingly efficient versions of this approach to parallel RL, and present empirical results for an implementation of the methods on a Beowulf cluster.


title: Good enough practices in scientific computing

abstract: Author summary Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.


title: Unlinkable minutiae-based fuzzy vault for multiple fingerprints

abstract: The ‘fuzzy vault scheme’ is a cryptographic primitive being considered for storing fingerprint minutiae protected. A well-known problem of the fuzzy vault scheme is its vulnerability against correlation attack-based cross-matching thereby conflicting with the ‘unlinkability requirement’ and ‘irreversibility requirement’ of effective biometric information protection. Yet, it has been demonstrated that in principle a minutiae-based fuzzy vault can be secured against the correlation attack by passing the to-be-protected minutiae through a quantisation scheme. Unfortunately, single fingerprints seem not to be capable of providing an acceptable security level against offline attacks. To overcome the aforementioned security issues, this study shows how an implementation for multiple fingerprints can be derived on basis of the implementation for single finger thereby making use of a Guruswami–Sudan algorithm-based decoder for verification. The implementation, for which public C++ source code can be downloaded, is evaluated for single and various multi-finger settings using the MCYT-Fingerprint-100 database and provides security-enhancing features such as the possibility of combination with password and a slow-down mechanism.


Eye Tracking in Human-Computer Interaction and Usability Research : Current Status and Future Prospects


Zoon Vulvitis Treated Successfully With Platelet-Rich Plasma: First Case Reported


title: Resistance to anti-EGFR therapy in colorectal cancer: from heterogeneity to convergent evolution.

abstract: The EGFR-targeted antibodies cetuximab and panitumumab are used to treat metastatic colorectal cancers. Mutations in KRAS , NRAS , and BRAF and amplification of ERBB2 and MET drive primary ( de novo ) resistance to anti-EGFR treatment. Recently, the emergence of alterations in the same genes was detected in patients who responded to EGFR blockade and then relapsed. These results illuminate a striking overlap between genes that, when mutated, drive primary and secondary resistance to anti-EGFR antibodies. Remarkably, although the mechanisms of resistance are genetically heterogeneous, they biochemically converge on key signaling pathways. This knowledge is being translated in the rational design of additional lines of therapy. ::: ::: Significance: Anti–EGFR-targeted therapies are used for the treatment of metastatic colorectal cancer. Molecular heterogeneity impairs their efficacy by fuelling de novo and acquired resistance. In this review, we highlight how genetically distinct resistance mechanisms biochemically converge on a limited number of signaling pathways that can be therapeutically intercepted. Cancer Discov; 4(11); 1–12. ©2014 AACR.


title: Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.

abstract: Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.