title: An adaptive localization of pupil degraded by eyelash occlusion and poor contrast

abstract: The inner boundary of iris represents the pupil’s edge. Hence, to work an Iris Recognition System (IRS) and the gaze tracking system expeditiously it is important to locate it as precisely as possible in a significant amout of time. In the presence of non-ideal constraints e.g. non-uniform illumination, poor contrast, eyelashes, hairs, glasses, off-angle orientation, these systems may not work well. In this paper we present an adaptive pupil localization method based on the roundness criteria. First, it applies a gray level inversion to suppress the reflections, then it performs Gray level co-occurrence matrix (GLCM) based contrast estimation. If this estimated contrast is lower than a certain threshold, the input image is made to undergo gamma correction to adjust the contrast. Subsequently, anisotropic diffusion filtering followed by log transformation is applied, which suppresses the effect of eyelash occlusion, limits the creation of small regions and highlight the dark pixels. Afterwards, a clean binary image with few regions is acquired using adaptive thresholding and some morphological operations. Finally, the roundness metric is computed for each of these regions and the region with largest roundness metric, also being greater than a prescribed threshold, declared as pupil. Experiments were carried out on few well known databases, NICE1, CASIA V3 lamp, MMU, WVU and IITD. The results are grounded upon subjective and objective evaluation; which in turn, indicate that our method outperforms a state-of-the-art approach and a deep learning approach in terms of localization capability in some unconstrained scenarios and shorter processing time. After assessing the performance of the proposed algorithm, it is manifested that it ensures a fast and robust localization of pupil in the presence of corneal reflection, poor contrast, glasses and eyelash occlusion.


title: Deep Convolutional Factor Analyser for Multivariate Time Series Modeling

abstract: Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.


title: Robust twin support vector machine for pattern classification

abstract: In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via second order cone programming formulations for classification, which can deal with data with measurement noise efficiently. Preliminary experiments confirm the robustness of the proposed method and its superiority to the traditional robust SVM in both computation time and classification accuracy. Remarkably, since there are only inner products about inputs in our dual problems, this makes us apply kernel trick directly for nonlinear cases. Simultaneously we does not need to solve the extra inverse of matrices, which is totally different with existing TWSVMs. In addition, we also show that the TWSVMs are the special case of our robust model and simultaneously give a new dual form of TWSVM by degenerating R-TWSVM, which successfully overcomes the existing shortcomings of TWSVM.


title: A Minimally Invasive Tendril Robot for In-Space Inspection

abstract: Current work at NASA's Johnson Space Center is focusing on the identification and design of novel robotic archetypes to fill roles complimentary to current space robots during in-space assembly and maintenance tasks. Tendril, NASA's latest robot designed for minimally invasive inspection, is one system born of this effort. Inspired by the biology of snakes, tentacles, and climbing plants, the Tendril robot is a long slender manipulator that can extend deep into crevasses and under thermal blankets to inspect areas largely inaccessible by conventional means. The design of the Tendril, with its multiple bending segments and 1 cm diameter, also serves as an initial step in exploring the whole body control known to continuum robots coupled with the small scale and dexterity found in medical and commercial minimally invasive devices. An overview of Tendril's design is presented along with preliminary results from testing that seeks to improve Tendril's performance through an iterative design process


title: The short- and long-term fates of memory items retained outside the focus of attention

abstract: When a test of working memory (WM) requires the retention of multiple items, a subset of them can be prioritized. Recent studies have shown that, although prioritized (i.e., attended) items are associated with active neural representations, unprioritized (i.e., unattended) memory items can be retained in WM despite the absence of such active representations, and with no decrement in their recognition if they are cued later in the trial. These findings raise two intriguing questions about the nature of the short-term retention of information outside the focus of attention. First, when the focus of attention shifts from items in WM, is there a loss of fidelity for those unattended memory items? Second, could the retention of unattended memory items be accomplished by long-term memory mechanisms? We addressed the first question by comparing the precision of recall of attended versus unattended memory items, and found a significant decrease in precision for unattended memory items, reflecting a degradation in the quality of those representations. We addressed the second question by asking subjects to perform a WM task, followed by a surprise memory test for the items that they had seen in the WM task. Long-term memory for unattended memory items from the WM task was not better than memory for items that had remained selected by the focus of attention in the WM task. These results show that unattended WM representations are degraded in quality and are not preferentially represented in long-term memory, as compared to attended memory items.


Mersenne Twister A Pseudo-Random Number Generator


title: Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

abstract: The Kuka LWR is equipped with torque sensors mounted into the actuated joints. Each torque sensor is calibrated separately before it is mounted on the robot. This needs a second calibration at the last stage of the assembling of the robot in order to take into account the effect of the robot structure through it's jacobian matrix. This final calibration is necessary to improve the accuracy of the estimation of the interaction wrench of the robot with its environment. However, the proposed calibration techniques are usually complicated, time-consuming, and must be carried out before assembling the sensors on the robot. In this paper, a simple and fast method for calibrating the sensors once they are assembled on the robot is presented. The method is based on the least squares solution of an over-determined linear system obtained with the robot inverse dynamic identification model in which are included the sensor gains. This model is calculated with available sensor measurement and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on the Kuka LWR4+ but can be applied to any similar kind of robot equipped with joint torque sensors.


title: Mining user behaviours: a study of check-in patterns in location based social networks

abstract: Understanding the patterns underlying human mobility is of an essential importance to applications like recommender systems. In this paper we investigate the behaviour of around 10,000 frequent users of Location Based Social Networks (LBSNs) making use of their full movement patterns. We analyse the metadata associated with the whereabouts of the users, with emphasis on the type of places and their evolution over time. We uncover patterns across different temporal scales for venue category usage. Then, focusing on individual users, we apply this knowledge in two tasks: 1) clustering users based on their behaviour and 2) predicting users' future movements. By this, we demonstrate both qualitatively and quantitatively that incorporating temporal regularities is beneficial for making better sense of user behaviour.


title: Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings

abstract: Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information of entities available both in a KB and a Web table. Our experimental evaluation is conducted using two existing benchmark data sets in addition to a new large-scale benchmark created using Wikipedia tables. Our results show that: (1) our novel lookup-based method outperforms state-of-the-art lookup-based methods, (2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and (3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation. As a result, we propose a hybrid method that significantly outperforms individual methods on all the benchmarks.


Southern-pine silvopasture systems: Forage characteristics, soil quality, and landscape utilization by cattle


title: Enhanced Armored Vehicle Fire Control System Design Modifications

abstract: This paper discusses a technique which promises to significantly enhance the capability of many existing armored vehicle fire control systems to better service maneuvering targets. The basic technique described in this paper allows for the transformation of existing driven reticle systems to emulate a gun director system by relatively simple signal flow changes which do not require any additional hardware. The net effect of decoupling the reticle servo from the weapon turret stabilization drive is to achieve the cumulative advantages of both systems.


title: The increased risk of developing tuberculosis in children with malignancy

abstract: SummaryTuberculosis (TB) remains a serious health problem in many developing countries. As impaired immunity is closely associated with the pathogenesis of TB, an increase in the incidence of this disease among patients with cancer is anticipated. This retrospective analysis of children from an area of high TB prevalence was done to determine the incidence of TB in children treated for cancer. Of 390 children admitted between 1983 and 1990, 278 were evaluated. Standardized morbidity ratios showed that children with malignancies had an 11-times greater chance of developing TB than children of the same age group in the general population. The highest incidence occurred in children with Wilms' tumour (5 of 41). In the majority of patients (77%), TB was diagnosed during the initial period of hospitalization and chest X-ray suggested post-primary TB. This suggests reactivation of quiescent mycobacteria during a period of impaired immunity. Children from an area with a high incidence of TB who develop malignanc...


title: Personalities of Russian amateur botany, 2. Alexander Mikhailovich Polilov (1869–?), a forgotten plant collector in Russian Lapland, and his collection of Hieracium pasense

abstract: Alexander Polilov collected many zoological and botanical specimens during his service as a medical doctor for the Arctic Ocean Hydrographic Expedition (1898–1915). Basic details of Polilov’s biography are presented. His botanical collections originated from the Kola Peninsula and are housed at LE. On the basis of a specimen collected by Polilov, Hieracium pasense was described as new to science from the north-western part of Murmansk Region, Russia. This species is related to H. gemellum from Sweden and is distributed in a small area between Kirkenes (Sor-Varanger, Norway) and Vichany Bay (Murmansk Region, Russia); its presence in Norway is documented with herbarium specimens at H. Hieracium eurofinmarkicum (as a synonym) and the Russian occurrence of H. gracilentipes (misidentification) are included into this species here, with an updated description and a distribution map. A lectotype of Hieracium gemellum subsp. eurofinmarkicum is designated. On the basis of its limited distribution area, the conservation status of the species is assessed as Endangered.


title: Review: False alarm minimization techniques in signature-based intrusion detection systems: A survey

abstract: A network based Intrusion Detection System (IDS) gathers and analyzes network packets and report possible low level security violations to a system administrator. In a large network setup, these low level and partial reports become unmanageable to the administrator resulting in some unattended events. Further it is known that state of the art IDS generate many false alarms. There are techniques proposed in IDS literature to minimize false alarms, many of which are widely used in practice in commercial Security Information and Event Management (SIEM) tools. In this paper, we review existing false alarm minimization techniques in signature-based Network Intrusion Detection System (NIDS). We give a taxonomy of false alarm minimization techniques in signature-based IDS and present the pros and cons of each class. We also study few of the prominent commercial SIEM tools which have implemented these techniques along with their performance. Finally, we conclude with some directions to the future research.


title: Hair breakage in normal and weathered hair: focus on the Black patient.

abstract: Hair breakage and fragility are a large problem for many patients as well as a treatment challenge to the dermatologist. Understanding the factors that lead to acquired hair shaft fragility and breakage is paramount to recommending appropriate treatment to affected patients. African or Black hair is known to be more affected by breakage with easily observed fragility in vivo. To date there are no known structural or chemical differences in Black hair as compared to Caucasian or Asian hair that explains this observed fragility. This review explores the impact of hair care practices on the development of hair breakage with a focus on patients of color. The examination and recommended ancillary testing for the process are discussed, and advances in the measurement of mechanical fracture of human hair are reviewed.


title: Gigabit wireless LANs: an overview of IEEE 802.11ac and 802.11ad

abstract: This paper gives an overview of the upcoming IEEE Gigabit Wireless LAN amendments, i.e. IEEE 802.11ac and 802.11ad. Both standard amendments advance wireless networking throughput beyond gigabit rates. 802.11ac adds multi-user access techniques in the form of downlink multi-user (DL MU) multiple input multiple output (MIMO)and 80 and 160 MHz channels in the 5 GHz band for applications such as multiple simultaneous video streams throughout the home. 802.11ad takes advantage of the large swath of available spectrum in the 60 GHz band and defines protocols to enable throughput intensive applications such as wireless I/O or uncompressed video. New waveforms for 60 GHz include single carrier and orthogonal frequency division multiplex (OFDM). Enhancements beyond the new 60 GHz PHY include Personal Basic Service Set (PBSS) operation, directional medium access, and beamforming. We describe 802.11ac channelization, PHY design, MAC modifications, and DL MU MIMO. For 802.11ad, the new PHY layer, MAC enhancements, and beamforming are presented.


title: Open data kit: tools to build information services for developing regions

abstract: This paper presents Open Data Kit (ODK), an extensible, open-source suite of tools designed to build information services for developing regions. ODK currently provides four tools to this end: Collect, Aggregate, Voice, and Build. Collect is a mobile platform that renders application logic and supports the manipulation of data. Aggregate provides a "click-to-deploy" server that supports data storage and transfer in the "cloud" or on local servers. Voice renders application logic using phone prompts that users respond to with keypad presses. Finally, Build is a application designer that generates the logic used by the tools. Designed to be used together or independently, ODK core tools build on existing open standards and are supported by an open-source community that has contributed additional tools. We describe four deployments that demonstrate how the decisions made in the system architecture of ODK enable services that can both push and pull information in developing regions.


title: Diagnosis of Motor Faults Using Sound Signature Analysis

abstract: The objective of this paper is to present recent developments in the field of Induction motor fault signature analysis with particular regard to Sound signature analysis of induction motor of fan. The different types of fan faults that can be identified from the sound signature analysis (1) are, for example, rotor faults, bearing faults, unbalances wings etc. Corresponding to the above-mentioned faults, many types of machine fault signature analysis techniques (2) have been proposed for motor faults detection and diagnosis. These techniques include vibration monitoring, motor current signature analysis (MCSA) (3-6), electromagnetic field monitoring (7), chemical analysis, temperature measurability (8, 9), infrared measurement, acoustic noise analysis (10), and partial discharge measurement (11, 12). Among these methods, vibration analysis, current analysis and Sound signature analysis are the most popular due to their easy measurability, high accuracy, and reliability.


title: Deep Recurrent Q-Learning for Partially Observable MDPs

abstract: Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting \textit{Deep Recurrent Q-Network} (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.


title: Short circuit current analysis of DFIG wind turbines with crowbar protection

abstract: For DFIG (Doubly Fed Induction Generator)- based wind turbines, crowbar is a commonly used protection method against surge current caused by sudden drop of the grid voltage during the period of LVRT (Low Voltage Ride-Through). Based on detailed analysis of the transient flux characteristics of DFIG with the crowbar fired, a simplified model is proposed to analyze and approximate the short circuit current accurately. Meanwhile the maximum short circuit current and the LVRT capability are evaluated. After that the dc-link voltage clamp effect is pointed out and a crowbar design method based on the dc-link voltage is proposed accordingly. A typical 1.5 MW DFIG wind turbine is used for case study. The analysis and simulation results show that, when the crowbar resistance is small or the fault is not severe, the dclink clamp effect does not occur so the simplified model can give a satisfactory approximation of the short circuit current; otherwise, the dc-link clamp effect must be taken into account in determining the short circuit current. In this case, it is found that excessive high crowbar resistance will not improve the LVRT capability substantially, but put more stress on the fly-wheel diodes of the rotor side converter.


title: Inverse kinematics and gesture pattern recognition using Hidden Markov Model on BeatMe! project: Traditional dance digitalization

abstract: Indonesian traditional dance preservation efforts that nowadays increasingly eroded by foreign culture needs to be improved and adapted to technological improvement. To answer that, BeatMe! Project developed for fulfil the needs of entertainment and traditional dance learning media by integrate 3D motion capture, processing data and visualization. This paper will explain the processing data detail implementation which include big data summarizing as in summarizing XYZ coordinate of joint to be angle between two joints. In addition, this paper also explain detail implementation of HMM learning system for gesture pattern learning and recognizing. Environment used is Kinect, Visual Studio and MATLAB. Result show summarized data of discrete value between joints angle, learning curve as the learning process output that tends to rise and converge on a value within limits of 700.000 symbols, new gesture pattern recognition show a good performance in one degree joint because its position nearest the main torso, and not to good performance in two degree joint because of randomized value that happen when the body position isn't aligned with Kinect.


title: Gm/ID based noise analysis for CMOS analog circuits

abstract: This paper proposes a technique to obtain thermal noise coefficient (γ) and corner frequency (f co ) from simulation. Once determined, γ and f co can be integrated into the g m /I D design flow to simplify CMOS analog circuit design. A folded cascode operational transconductance amplifier (OTA) is designed to provide a point of comparison. A 10% error in input noise and a 25% error in output noise show that γ and f co can indeed be integrated into the g m /I D design flow.


title: ToCAI: A framework for indexing and retrieval of multimedia documents

abstract: This paper presents the ToCAI (table of content-analytical index) description scheme (DS) for content description of audio-visual documents. The original idea comes from the structure used for technical books. One may easily understand a book's sequential organization by looking at its table of contents while quickly retrieving elements of interest by means of the analytical index. This description scheme provides therefore a hierarchical description of the time sequential structure of a multimedia document (thanks to the ToC), suitable for browsing, together with an "analytical index" (AI) of audio-visual objects of the document, suitable for effective retrieval. Besides, two sub-description schemes for information about description generation and about the metadata associated with the document are also enclosed in the general DS. The detailed structure of the DS is also presented by means of UML (unified modelling language) notation and an application example is shown. Finally, some considerations concerning the adopted visual interface are made.


title: Hybrid krill herd algorithm with differential evolution for global numerical optimization

abstract: In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.


title: Blue screen matting

abstract: A classical problem of imaging—the matting problem—is separation of a non-rectangular foreground image from a (usually) rectangular background image—for example, in a film frame, extraction of an actor from a background scene to allow substitution of a different background. Of the several attacks on this difficult and persistent problem, we discuss here only the special case of separating a desired foreground image from a background of a constant, or almost constant, backing color. This backing color has often been blue, so the problem, and its solution, have been called blue screen matting. However, other backing colors, such as yellow or (increasingly) green, have also been used, so we often generalize to constant color matting. The mathematics of constant color matting is presented and proven to be unsolvable as generally practiced. This, of course, flies in the face of the fact that the technique is commonly used in film and video, so we demonstrate constraints on the general problem that lead to solutions, or at least significantly prune the search space of solutions. We shall also demonstrate that an algorithmic solution is possible by allowing the foreground object to be shot against two constant backing colors—in fact, against two completely arbitrary backings so long as they differ everywhere.


title: Most likely heteroscedastic Gaussian process regression

abstract: This paper presents a novel Gaussian process (GP) approach to regression with input-dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value. In contrast to Goldberg et al., however, we do not use a Markov chain Monte Carlo method to approximate the posterior noise variance but a most likely noise approach. The resulting model is easy to implement and can directly be used in combination with various existing extensions of the standard GPs such as sparse approximations. Extensive experiments on both synthetic and real-world data, including a challenging perception problem in robotics, show the effectiveness of most likely heteroscedastic GP regression.


title: Genome-wide DNA methylation profiling reveals novel epigenetically regulated genes and non-coding RNAs in human testicular cancer

abstract: Background:Testicular germ cell tumour (TGCT) is the most common malignant tumour in young males. Although aberrant DNA methylation is implicated in the pathophysiology of many cancers, only a limited number of genes are known to be epigenetically changed in TGCT. This report documents the genome-wide analysis of differential methylation in an in vitro model culture system. Interesting genes were validated in TGCT patient samples.Methods:In this study, we used methylated DNA immunoprecipitation (MeDIP) and whole-genome tiling arrays to identify differentially methylated regions (DMRs).Results:We identified 35 208 DMRs. However, only a small number of DMRs mapped to promoters. A genome-wide analysis of gene expression revealed a group of differentially expressed genes that were regulated by DNA methylation. We identified several candidate genes, including APOLD1, PCDH10 and RGAG1, which were dysregulated in TGCT patient samples. Surprisingly, APOLD1 had previously been mapped to the TGCT susceptibility locus at 12p13.1, suggesting that it may be important in TGCT pathogenesis. We also observed aberrant methylation in the loci of some non-coding RNAs (ncRNAs). One of the ncRNAs, hsa-mir-199a, was downregulated in TGCT patient samples, and also in our in vitro model culture system.Conclusion:This report is the first application of MeDIP-chip for identifying epigenetically regulated genes and ncRNAs in TGCT. We also demonstrated the function of intergenic and intronic DMRs in the regulation of ncRNAs.


title: DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

abstract: We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$).


title: Sleep and Risk-Taking Behavior in Adolescents

abstract: The primary purpose of this study was to examine the relationship between adolescents' sleep-wake patterns and risk-taking behavior. A second goal was to replicate the results obtained by Wolfson and Carskadon (1998) regarding adolescents' sleep habits. Three hundred eighty-eight adolescents (217 males, 171 females) completed the Sleep Habits Survey and the Youth Risk Behavior Survey. The results indicated that adolescents who reported longer weekend delay and higher levels of sleep problems also reported significantly higher levels of risk-taking behaviors, and students' weekend delay was also related to their academic performance in this sample. As in the sample studied by Wolfson and Carskadon (1998), the adolescents in this study exhibited changes in both weekday and weekend sleep habits across grade/age. However in the present study, only school-night total sleep time and weekend delay were related to adolescents' daytime functioning, with no significant relationships being found between weekend oversleep and daytime functioning. This provides partial support for the findings of Wolfson and Carskadon (1998). Overall, sleep-wake patterns were found to relate to risk-taking behavior during adolescence in this study.


title: Current treatment of dermatophytoses.

abstract: There is a wide variety of highly effective topical antifungal agents available for the treatment of dermatophytosis. In more widespread infections or those involving hair or nails oral therapy with griseofulvin or ketoconazole can be used. With both forms of therapy certain types of dermatophyte infection are clinically resistant to treatment. These include onychomycosis, infections of the sole caused by T.rubrum and certain forms of tinea capitis (e.g. favus) or tinea corporis. In future consideration needs to be given to find the optimum duration and frequency of treatment and better methods of allowing penetration of drugs into nails and heavely keratinised sites.


title: Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

abstract: Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.


title: ENTREPRENEURIAL ORIENTATION AND NEW VENTURE PERFORMANCE: THE MEDIATING EFFECT OF NETWORK STRATEGIES.

abstract: While scholars have examined the direct effects of entrepreneurial orientation (EO) on new venture performance, we propose that ventures? network strategies intervene in the EO performance relation...


title: New azonitrile derivatives as corrosion inhibitors for copper in nitric acid solution

abstract: AbstractThe inhibitive effect of new synthesized azonitriles, namely 3-phenyl azo-2-amino-1,1,3-tricyano propene, 3-p-tolyl azo-2-amino-1,1,3-tricyano propene, 3-p-anisyl azo-2-amino-1,1,3-tricyano propene, 3-p-nitro phenyl azo-2 amino-1,1,3-tricyano propene, and 3-p-phenyl carboxylic azo-2-amino-1,1,3-tricyano propene on the corrosion of copper in nitric acid solution was investigated using chemical and electrochemical methods. The inhibition efficiency was found to increase with inhibitor concentration and decreased with rise in solution temperature. Various thermodynamic parameters were calculated and discussed in order to elaborate the mechanism of corrosion inhibition. Polarization measurements revealed that the studied azonitriles acted as mixed inhibitors. The results revealed that these compounds were adsorbed onto the copper surface and the adsorption obeyed the Temkin adsorption isotherm. The quantum chemical calculations were in good agreement with the experimental results.


title: Involuntary paretic wrist/finger flexion forces and EMG increase with shoulder abduction load in individuals with chronic stroke

abstract: Objective ::: Clinical observations of the flexion synergy in individuals with chronic hemiparetic stroke describe coupling of shoulder, elbow, wrist, and finger joints. Yet, experimental quantification of the synergy within a shoulder abduction (SABD) loading paradigm has focused only on shoulder and elbow joints. The paretic wrist and fingers have typically been studied in isolation. Therefore, this study quantified involuntary behavior of paretic wrist and fingers during concurrent activation of shoulder and elbow.


title: Overlapping community detection in labeled graphs

abstract: We present a new approach for the problem of finding overlapping communities in graphs and social networks. Our approach consists of a novel problem definition and three accompanying algorithms. We are particularly interested in graphs that have labels on their vertices, although our methods are also applicable to graphs with no labels. Our goal is to find k communities so that the total edge density over all k communities is maximized. In the case of labeled graphs, we require that each community is succinctly described by a set of labels. This requirement provides a better understanding for the discovered communities. The proposed problem formulation leads to the discovery of vertex-overlapping and dense communities that cover as many graph edges as possible. We capture these properties with a simple objective function, which we solve by adapting efficient approximation algorithms for the generalized maximum-coverage problem and the densest-subgraph problem. Our proposed algorithm is a generic greedy scheme. We experiment with three variants of the scheme, obtained by varying the greedy step of finding a dense subgraph. We validate our algorithms by comparing with other state-of-the-art community-detection methods on a variety of performance measures. Our experiments confirm that our algorithms achieve results of high quality in terms of the reported measures, and are practical in terms of performance.


title: Satellite Tracking Control System Using Fuzzy PID Controller

abstract: This paper presents the results of study, design and manufacture of a searching and satellite auto-tracking system used for mobile receiver. This system applies both the traditional proportional integral derivative (PID) control and the fuzzy PID control methods. Firstly, the PID controller was designed by Ziegler-Nichols tuning method to obtain control parameters. Then, a fuzzy controller was applied to tuning online parameters of the PID controller. In order to apply for mobile satellite receiver, the tracking control system has been designed, fabricated on a PCB board. Simulated and experimental results indicate that the system performances obtain from applying the fuzzy PID controller was better than traditional PID controller. Keywords: Satellite tracking system, step-tracking algorithm, PID controller, Fuzzy controller, satellite communication.


title: Security Issues in NoSQL Databases

abstract: applications has created the need to store large amount of data in distributed databases that provide high availability and scalability. In recent years, a growing number of companies have adopted various types of non-relational databases, commonly referred to as NoSQL databases, and as the applications they serve emerge, they gain extensive market interest. These new database systems are not relational by definition and therefore they do not support full SQL functionality. Moreover, as opposed to relational databases they trade consistency and security for performance and scalability. As increasingly sensitive data is being stored in NoSQL databases, security issues become growing concerns. This paper reviews two of the most popular NoSQL databases (Cassandra and MongoDB) and outlines their main security features and problems.


title: Depth Super-Resolution on RGB-D Video Sequences With Large Displacement 3D Motion

abstract: To enhance the resolution and accuracy of depth data, some video-based depth super-resolution methods have been proposed, which utilizes its neighboring depth images in the temporal domain. They often consist of two main stages: motion compensation of temporally neighboring depth images and fusion of compensated depth images. However, large displacement 3D motion often leads to compensation error, and the compensation error is further introduced into the fusion. A video-based depth super-resolution method with novel motion compensation and fusion approaches is proposed in this paper. We claim that 3D nearest neighboring field (NNF) is a better choice than using positions with true motion displacement for depth enhancements. To handle large displacement 3D motion, the compensation stage utilized 3D NNF instead of true motion used in the previous methods. Next, the fusion approach is modeled as a regression problem to predict the super-resolution result efficiently for each depth image by using its compensated depth images. A new deep convolutional neural network architecture is designed for fusion, which is able to employ a large amount of video data for learning the complicated regression function. We comprehensively evaluate our method on various RGB-D video sequences to show its superior performance.


title: A Study of Long-Tail Latency in n-Tier Systems: RPC vs. Asynchronous Invocations

abstract: Long-tail latency of web-facing applications continues to be a serious problem. Most of the previously published research addresses two classes of long latency problems: uneven workloads such as web search, and resource saturation in single nodes. We describe an experimental study of a third class of long tail latency problemsthat are specific to distributed systems: Cross-Tier Queue Overflow (CTQO) due to a combination of millibottlenecks (with sub-second duration) and tightly-coupled servers in n-tier systems (e.g., Apache, Tomcat, and MySQL) using RPC-style request-response communications. Our experiments show that the appearance of millibottlenecks (e.g., created by short workload bursts) in one server often causes another server (which has no saturated resources) in the synchronous invocation chain to fill up its queues (CTQO) and drop packets, creating very long response time queries. CTQO can be reduced or avoided by replacing the server dropping packets with an asynchronous server. In synchronous n-tier system experiments, long tail latency due to CTQO can be reproduced consistently atutilization as low as 43%. In contrast, when all n-tier servers are replaced by asynchronous versions, CTQO and consequent dropped packets remain absent at utilization levels as high as 83%, despite the same millibottlenecks.


title: Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction

abstract: Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. Therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.


title: High-resolution radar imaging of moving humans using doppler processing and compressed sensing

abstract: Frontal radar imaging of human activities may be useful in certain applications, such as through-wall surveillance, where cameras and x-ray sensors cannot be deployed. High-resolution radar images are currently obtained using electrically large antenna apertures operating at high frequencies. However, high frequencies are heavily attenuated by most walls. Also, the implementation of a radar with lots of array elements and associated data acquisition channels is costly and complex. In this paper, we propose methods to generate high-resolution Doppler-enhanced radar images of moving humans at low carrier frequencies with limited number of antenna elements. When a human moves, different body parts give rise to distinct Doppler returns. The key feature of our method is to dynamically resolve multiple body parts of the human across three dimensions: Doppler, azimuth, and elevation. The additional Doppler dimension allows us to relax the resolution requirements in terms of the carrier frequency and number of array elements across the other dimensions. We further reduce the number of array elements below the Nyquist limits by incorporating compressed sensing principles into two-dimensional beamforming because compressed sensing is particularly suited for solving certain types of under-resolved problems.We test our technique on simulated electromagnetic radar scattered data from a moving human for different radar configurations.We also study the robustness of the proposed technique to noise.


title: MultiUN: A Multilingual Corpus from United Nation Documents.

abstract: This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.


Developing Smart Car Parking System Using Wireless Sensor Networks


title: Poisoning Attacks against Support Vector Machines

abstract: We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM's decision function due to malicious input and use this ability to construct malicious data. ::: ::: The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM's optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier's test error.


title: A DC-10GHz linear-in-dB attenuator in 0.13 /spl mu/m CMOS technology

abstract: A CMOS attenuator has been designed and fabricated in a commercial 0.13 /spl mu/m CMOS process. Two /spl Pi/ stages are cascaded to achieve more than 35 dB of maximum attenuation over a frequency range of DC to 10 GHz. Minimum insertion loss varies from 0.8 dB at DC to 3 dB at 10 GHz and remains less than 2 dB in the frequency band of DC-4 GHz. Attenuation is achieved with a control voltage that varies from 0 V to 1.2 V and it is linear-in-dB with respect to the control voltage. A DC feedback loop is implemented to achieve input and output impedance matching over the frequency of interest.


title: Adapting CRISP-DM Process for Social Network Analytics: Application to Healthcare

abstract: One of the key limitations about research involving big data is the lack of a sound methodological process that drives the conceptual and analytical questions posed to the data. In this study, we adapt the popular CRISP$DM process to analyze large volumes of unstructured data to generate analytical insights. We add specificity to the CRISP$DM methodology. Specifically, we propose “Cross Industry Standard Process for Electronic Social Network Platforms (CRISP$eSNeP)”, as an extension to the CRISP$DM methodology. Our methodology focuses on efficient pre$processing of large and unstructured electronic social network data. We illustrate our arguments by applying this methodology to understand the relationship between user influence and information characteristics as depicted on the Twitter microblogging platform.


title: Greedy algorithm for the general multidimensional knapsack problem

abstract: In this paper, we propose a new greedy-like heuristic method, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1 MDKP. Our heuristic differs from the existing greedy-like heuristics in two aspects. First, existing heuristics rely on each item’s aggregate consumption of resources to make item selection decisions, whereas our heuristic uses the effective capacity, defined as the maximum number of copies of an item that can be accepted if the entire knapsack were to be used for that item alone, as the criterion to make item selection decisions. Second, other methods increment the value of each decision variable only by one unit, whereas our heuristic adds decision variables to the solution in batches and consequently improves computational efficiency significantly for large-scale problems. We demonstrate that the new heuristic significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions. The new heuristic proves especially efficient for high dimensional knapsack problems with small-to-moderate numbers of decision variables, usually considered as “hard” MDKP and no computationally efficient heuristic is available to treat such problems.


title: Rumors detection in Chinese via crowd responses

abstract: In recent years, microblogging platforms have become good places to spread various spams, making the problem of gauging information credibility on social networks receive considerable attention especially under an emergency situation. Unlike previous studies on detecting rumors using tweets' inherent attributes generally, in this work, we shift the premise and focus on identifying event rumors on Weibo by extracting features from crowd responses that are texts of retweets (reposting tweets) and comments under a certain social event. Firstly the paper proposes a method of collecting theme data, including a sample set of tweets which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Weibo. Secondly clustering analysis of tweets are made to examine the text features extracted from retweets and comments, and a classifier is trained based on observed feature distribution to automatically judge rumors from a mixed set of valid news and false information. The experiments show that the new features we propose are indeed effective in the classification, and especially some stop words and punctuations which are treated as noises in previous works can play an important role in rumor detection. To the best of our knowledge, this work is the first to detect rumors in Chinese via crowd responses under an emergency situation.


title: Implementing digital game-based learning in schools: augmented learning environment of ‘Europe 2045’

abstract: It is widely agreed that the traditional process of schooling can benefit from the usage of computers as supportive tools. Of various approaches using computers in education over the last decade, e-learning and edutainment have become the most prominent. Recently, a number of authors have criticised these approaches arguing that they conserve traditional `drill and practice' behaviouristic methods of teaching instead of enhancing and augmenting them. It has been proposed that a `paradigm shift' is needed and that this shift may come through utilizing all the advantages of full-fledged video games, so-called digital game-based learning (DGBL). However, several case studies reported serious problems with the DGBL. Among the most notable issues are the lack of acceptance of games as an educational tool, problems with integration of games into formal schooling environments, and the so-called transfer problem, which is the problem of the inherent tension between game play and learning objectives, the tension that mitigates the ability of students to transfer knowledge gained in the video game to the real-world context. Here, we present a framework for an augmented learning environment (ALE), which verbalises one way of how these problems can be challenged. The ALE framework has been constructed based on our experience with the educational game, Europe 2045, which we developed and which has been implemented in a number of secondary schools in the Czech Republic during 2008. The key feature of this game is that it combines principles of on-line multi-player computer games with social, role-playing games. The evaluation which we present in this paper indicates the successful integration of the game and its acceptance by teachers and students. The ALE framework isolates key principles of the game contributing to this success, abstracts them into theoretical entities we call action-based spaces and causal and grounding links, and condenses them in a coherent methodological structure, which paves the way for further exploitation of the DGBL by educational game researchers and designers.


title: A high-performance brain–computer interface

abstract: The cover shows Matt Nagle, first participant in the BrainGate pilot clinical trial. He is unable to move his arms or legs following cervical spinal cord injury. Researchers at the Department of Neuroscience at Brown University, working with biotech company Cyberkinetics and 3 other institutions, demonstrate that movement-related signals can be relayed from the brain via an implanted BrainGate chip, allowing the patient to drive a computer screen cursor and activate simple robotic devices. Such neuromotor prostheses could pave the way towards systems to replace or restore lost motor function in paralysed humans. Prior to this advance, this type of work has been performed chiefly in monkeys. The latest example of such research has achieved a large increase in speed over current devices, enhancing the prospects for clinically viable brain-machine interfaces. A new type of brain–machine interface device enables recording of neural activity in the monkey cortex and extraction of intended target signals, leaving the monkey able to place a cursor on a location, just by thinking about it. Recent studies have demonstrated that monkeys1,2,3,4 and humans5,6,7,8,9 can use signals from the brain to guide computer cursors. Brain–computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques1,2,3,4,5,7,8 or electroencephalogram recordings using less-6 or non-invasive9 techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported9,10. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or ∼15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.


title: Cost-Benefit Analysis of Moving-Target Defense in Power Grids

abstract: We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selected reactance perturbations, but these perturbations cannot guarantee effective attack detection. To address the issue, we present formal design criteria to select MTD reactance perturbations that are truly effective. However, based on a key optimal power flow (OPF) formulation, we find that the effective MTD may incur a non-trivial operational cost that has not hitherto received attention. Accordingly, we characterize important tradeoffs between the MTD's detection capability and its associated required cost. Extensive simulations, using the MATPOWER simulator and benchmark IEEE bus systems, verify and illustrate the proposed design approach that for the first time addresses both key aspects of cost and effectiveness of the MTD.


title: Comparisons of 6.5kV 25A Si IGBT and 10-kV SiC MOSFET in Solid-State Transformer application

abstract: A 6.5kV 25A dual IGBT module is customized and packaged specially for high voltage low current application like solid state transformer and its characteristics and losses have been tested under the low current operation and compared with 10kV SiC MOSFET. Based on the test results, the switching losses under different frequencies in a 20kVA Solid-State Transformer (SST) has been calculated for both devices. The result shows 10kV SiC MOSFET has 7–10 times higher switching frequency capability than 6.5kV Si IGBT in the SST application.


title: On Bistatic Inverse Synthetic Aperture Radar

abstract: The use of multiple radar configurations can overcome some of the geometrical limitations that exist when obtaining radar images of a target using inverse synthetic aperture radar (ISAR) techniques. It is shown here how a particular bistatic configuration can produce three view angles and three ISAR images simultaneously. A new ISAR signal model is proposed and the applicability of employing existing monostatic ISAR techniques to bistatic configurations is analytically demonstrated. An analysis of the distortion introduced by the bistatic geometry to the ISAR image point spread function (PSF) is then carried out and the limits of the applicability of ISAR techniques (without the introduction of additional signal processing) are found and discussed. Simulations and proof of concept experimental data are also provided that support the theory.


title: Sparse depth map upsampling with RGB image and anisotropic diffusion tensor

abstract: This paper proposes a novel algorithm for up-sampling 2D sparse depth map projected by laser scanner (i.e. Velodyne HDL-64E) using its synchronised RGB image and Anisotropic Diffusion Tensor. We assume each depth-unknown pixel's depth value derives from all depth-known pixels and their affinity can be measured in a geodesic manner. Specifically, for each depth-unknown point, we compute its geodesic distance to all depth-known points, the cost of each geodesic path is calculated by spatial distance, color aberrance and tensor discrepancy, which compacts with the assumption that color homogenous region corresponds to close or smooth depth value, while depth discontinuity often happens in image edges. To mitigate computation complexity, we further introduced a flexible approximation algorithm in which the complexity is linear to image's size and can be further reduced w.r.t. different accuracy requirement. Finally, we evaluate our algorithm on both KITTI visual benchmark suite and Middlebury dataset. Experiment shows that, while generating smooth and dense upsampling result, our algorithm retains sharp depth discontinuity even in edges of objects lies very far and few laser scanner points cover it. Besides, our algorithm is parameter-nonsensitive, which frees us from laboriously finding appropriate parameters to get fine result. We hope our work would motivate more research on laser point upsampling and their combination with RGB image, especially in outdoor scenes for autonomous driving.


title: Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses

abstract: Detecting objects in cluttered scenes and estimating articulated human body parts from 2D images are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g., playing tennis), where the relevant objects tend to be small or only partially visible and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other-recognizing one facilitates the recognition of the other. In this paper, we propose a mutual context model to jointly model objects and human poses in human-object interaction activities. In our approach, object detection provides a strong prior for better human pose estimation, while human pose estimation improves the accuracy of detecting the objects that interact with the human. On a six-class sports data set and a 24-class people interacting with musical instruments data set, we show that our mutual context model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human-object interaction activities.


title: Bio-inspired mechanisms for inclined locomotion in a legged insect-scale robot

abstract: Legged locomotion is an open problem in robotics, particularly for non-level surfaces. With decreasing robot size, different issues for climbing mechanisms and their attachment and detachment appear due to the physics of scaling. This paper describes micro-scale phenomena for different adhesion methods that can be employed in microrobots. These adhesion methods are applied to a sub-2 gram legged robot, the Harvard Ambulatory MicroRobot (HAMR), by leveraging recent advances in milli- and micrometer-scale manufacturing. The presented designs utilize different passively oriented adhesives on the legs of the robot to improve inclined locomotion performance. A 3DoF ankle joint is designed and implemented and the effects of a passive tail are studied. As a result, HAMR's climbing capability is increased from 3° inclines to 22° inclines and 45° declines. Finally, an analytical model of leg and foot force generation is presented and compared with experimental force data from the attachment mechanism on a single-leg experimental setup.


title: Mobile Social Big Data: WeChat Moments Dataset, Network Applications, and Opportunities

abstract: In parallel to the increase of various mobile technologies, the mobile social network (MSN) service has brought us into an era of mobile social big data, where people are creating new social data every second and everywhere. It is of vital importance for businesses, government, and institutes to understand how peoples' behaviors in the online cyberspace can affect the underlying computer network, or their offline behaviors at large. To study this problem, we collect a dataset from WeChat Moments, called WeChatNet, which involves 25,133,330 WeChat users with 246,369,415 records of link reposting on their pages. We revisit three network applications based on the data analytics over WeChatNet, i.e., the information dissemination in mobile cellular networks, the network traffic prediction in backbone networks, and the mobile population distribution projection. Meanwhile, we discuss the potential research opportunities for developing new applications using the released dataset.


title: Extraction of ERP Selection Criteria using Critical Decisions Analysis

abstract: Companies use ERP systems to automate business processes in order to increase productivity, reduce costs, and meet customer requirements. ERP selection for an enterprise is a decision-making project that is both risky and expensive, a wrong selection of this system or project partners can lead to the failure of the ERP implementation project. In this paper, we combined the theoretical findings of ERP selection issue with expert’s practical recommendations to determine the critical decisions that need to be made in the pre-implementation phase. Then we presented a methodology to determine ERP selection criteria based on the critical decisions analysis. A part of this work was performed within a company that has just launched an ERP implementation project.


Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge


title: Cholesterol sensor ORP1L contacts the ER protein VAP to control Rab7–RILP–p150Glued and late endosome positioning

abstract: Late endosomes (LEs) have characteristic intracellular distributions determined by their interactions with various motor proteins. Motor proteins associated to the dynactin subunit p150Glued bind to LEs via the Rab7 effector Rab7-interacting lysosomal protein (RILP) in association with the oxysterol-binding protein ORP1L. We found that cholesterol levels in LEs are sensed by ORP1L and are lower in peripheral vesicles. Under low cholesterol conditions, ORP1L conformation induces the formation of endoplasmic reticulum (ER)–LE membrane contact sites. At these sites, the ER protein VAP (VAMP [vesicle-associated membrane protein]-associated ER protein) can interact in trans with the Rab7–RILP complex to remove p150Glued and associated motors. LEs then move to the microtubule plus end. Under high cholesterol conditions, as in Niemann-Pick type C disease, this process is prevented, and LEs accumulate at the microtubule minus end as the result of dynein motor activity. These data explain how the ER and cholesterol control the association of LEs with motor proteins and their positioning in cells.


title: Cell discovery in 5G HetNets using location-based cell selection

abstract: Future heterogeneous networks (HetNets) are going to consist of cellular macro cell base stations (macro-BSs) and various types of small cell base stations (SC-BS), each operating in microwave and/or millimeter bands. Cell discovery of mm-wave SC-BSs is a challenging issue because of the limited communication range. In this paper, a location-based cell selection scheme that takes into account the effects of inaccuracy of the location estimation is introduced. The proposed scheme selects the SC-BSs according to the probability that they can reach the UE. This probability is dynamically calculated and updated during the search process to assure that at any step the SC-BS with highest detection probability is selected and assigned. The simulation results show a 30% reduction in the latency of the proposed cell discovery compared to the conventional scheme. Furthermore, even more than 70% reduction in latency is possible if the proposed algorithm is extended to simultaneously select three SC-BSs with maximum joint probability of detection.


THORACOLUMBAR BURST FRACTURE: LOAD SHARING CLASSIFICATION AND POSTERIOR INSTRUMENTATION FAILURE


Introduction Cervical cancer : screening , diagnosis and staging


title: Mindfulness-Based Cognitive Therapy and the Adult ADHD Brain: A Neuropsychotherapeutic Perspective

abstract: Attention-deficit/hyperactivity disorder (ADHD) is a recognized serious mental disorder that often persists into adulthood. The symptoms and impairments associated with ADHD often cause significant mental suffering in affected individuals. ADHD has been associated with abnormal neuronal activity in various neuronal circuits, such as the dorsofrontostriatal, orbitofrontostriatal, and frontocerebellar circuits. Psychopharmacological treatment with methylphenidate hydrochloride is recommended as the first-line treatment for ADHD. It is assumed that medication ameliorates ADHD symptoms by improving the functioning of the brain areas affected in the condition. However, side effects, contraindications, or non-response can limit the effectiveness of a psychopharmacological treatment for ADHD. It is therefore necessary to develop non-pharmacological interventions that target neuronal mechanisms associated with the condition in the same way as pharmacological treatment. We think that mindfulness meditation employed as a neuropsychotherapeutic intervention could help patients with ADHD to regulate impaired brain functioning and thereby reduce ADHD symptoms. In this paper, we highlight the mechanisms of such mindfulness meditation, and thus provide a rationale for further research and treatment development from a neuropsychotherapeutic perspective. We conclude that mindfulness meditation employed as a neuropsychotherapeutic intervention in therapy is a promising treatment approach in ADHD.


FORMATION AND ANALYSIS OF TARGET FUNCTION FOR OPTIMIZATION OF THE PROCESS OF ELECTRICITY DISTRIBUTION AT THE TRANSFORMER SUBSTATION LEVEL


title: Ghost-in-ZigBee: Energy Depletion Attack on ZigBee-Based Wireless Networks

abstract: ZigBee has been widely recognized as an important enabling technique for Internet of Things (IoT). However, the ZigBee nodes are normally resource-limited, making the network susceptible to a variety of security threats. This paper closely investigates a severe attack on ZigBee networks termed as ghost , which leverages the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete the energy of the nodes. We show that the impact of ghost is very large and that it can facilitate a variety of threats including denial of service and replay attacks. We highlight that merely deploying a standard suite of advanced security techniques does not necessarily guarantee improved security, but instead might be leveraged by adversaries to cause severe disruption in the network. We propose several recommendations on how to localize and withstand the ghost and other related attacks in ZigBee networks. Extensive simulations are provided to show the impact of the ghost and the performance of the proposed recommendations. Moreover, physical experiments also have been conducted and the observations confirm the severity of the impact by the ghost attack. We believe that the presented work will aid the researchers to improve the security of ZigBee further.


title: Implementation of a redundant binary input acceptable multiplier

abstract: A multiplier which can accept redundant binary number inputs is proposed using available unsigned multiplier (array multiplier, carry-save-addition tree multiplier, or multiplier using redundant binary representations). The proposed multiplier is applicable in redundant binary ALU unit or redundant binary processor.


title: End-To-End Face Detection and Recognition

abstract: Plenty of face detection and recognition methods have been proposed and got delightful results in decades. Common face recognition pipeline consists of: 1) face detection, 2) face alignment, 3) feature extraction, 4) similarity calculation, which are separated and independent from each other. The separated face analyzing stages lead the model redundant calculation and are hard for end-to-end training. In this paper, we proposed a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix was directly learned to align the faces, instead of predicting the facial landmarks. In training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE \cite{Yang2016} dataset and CASIA-WebFace \cite{Yi2014} dataset. Tested on Face Detection Dataset and Benchmark (FDDB) \cite{Jain2010} dataset and Labeled Face in the Wild (LFW) \cite{Huang2007} dataset, we have achieved 89.24\% recall for face detection task and 98.63\% verification accuracy for face recognition task simultaneously, which are comparable to state-of-the-art results.


title: Social Media and Fake News in the 2016 Election

abstract: Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.


title: Quantitative Evaluation of 2 Scatter-Correction Techniques for 18F-FDG Brain PET/MRI in Regard to MR-Based Attenuation Correction.

abstract: In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (μ-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 μ-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo-based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based μ-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and μ-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the μ-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the μ-map used, with a difference of 2% on average and 4% at maximum when a μ-map without bone was used. Conclusion: The effect of different MR-based μ-maps on the performance of scatter correction was minimal in non-time-of-flight 18F-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging.


title: "WHO IS KEY...?" - CHARACTERIZING VALUE ADDING USERS IN ENTERPRISE SOCIAL NETWORKS

abstract: Whereas the use of Enterprise Social Networks (ESN) is a pervasive topic in research and practice, both parties are still struggling to come to a better understanding of the role and impact of ESN in and on knowledge-intensive corporate work. As a part of this phenomenon, employees who communicate their knowledge in ESN helping other users to do their daily work play a decisive role. We need to come to a better understanding of the role and behaviour of such value adding users. This is a prerequisite, for example, for understanding knowledge support hubs or for enabling more effective internal information and knowledge sharing. Against this background, we investigate the structural characteristics of value adding users in ESN using qualitative text analysis and Social Network Analysis. Based on a large scale dataset of a global consulting company using the ESN Yammer.com we analyse the social relationships of value adding users. We confirm their significant position and draw conclusions for research and practice.


E-Commerce Sales Prediction Using Listing Keywords


title: Agnostic Active Learning Without Constraints

abstract: We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with maintaining version spaces, yet still allows for substantial improvements over supervised learning for classification.


title: Combinatorial optimization on a Boltzmann machine

abstract: We discuss the problem of solving (approximately) combinatorial optimization problems on a Boltzmann machine. It is shown for a number of combinatorial optimization problems how they can be mapped directly onto a Boltzmann machine by choosing appropriate connection patterns and connection strengths. In this way maximizing the consensus in the Boltzmann machine is equivalent to finding an optimal solution of the corresponding optimization problem. The approach is illustrated by numerical results obtained by applying the model of Boltzmann machines to randomly generated instances of the independent set, the max cut, and the graph coloring problem. For these instances the Boltzmann machine finds near-optimal solutions whose quality is comparable to that obtained with sequential simulated annealing algorithms. The advantage of the Boltzmann machine is the potential for carrying out operations in parallel. For the problems we have been investigating, this results in a considerable speedup over the sequential simulated annealing algorithms.


title: Survey of classification algorithms for formulating yield prediction accuracy in precision agriculture

abstract: Precision agriculture is the implementation of the recent technology in agriculture. Huge amount of data is collected in agriculture and various techniques of data mining are used to make efficient use of it. In this paper, we have discussed various algorithms related to classification techniques of data mining. These algorithms are implemented on a data set that has been collected over the years for the yield prediction of soybean crop. Further, a comparative analysis is done to show which classification algorithm is best suited for predicting the yield with respect to classification techniques.


title: Scheduling of plug-in electric vehicle battery charging with price prediction

abstract: This paper proposes a reinforcement learning algorithm that solves the problem of scheduling the charging of a plug-in electric vehicle's (PEV) battery. The algorithm is employed in the demand side management of smart grids. The goal of the algorithm is to minimize the charging cost of the consumer over long term time horizon. The PEV battery charging problem is modeled as a Markov decision process (MDP) with unknown transition probabilities. A Sarsa reinforcement learning method with eligibility traces is proposed for learning the pricing patterns and solving the charging problem. The model uses true day-ahead prices for the current day and predicted prices for the next day. Simulation results using true pricing data demonstrate the cost savings to the consumer.


title: Evaluating Color Descriptors for Object and Scene Recognition

abstract: Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.


title: Discriminative Learning of Local Image Descriptors

abstract: In this paper, we explore methods for learning local image descriptors from training data. We describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both linear and nonlinear transforms with dimensionality reduction, and make use of discriminant learning techniques such as Linear Discriminant Analysis (LDA) and Powell minimization to solve for the parameters. Using these techniques, we obtain descriptors that exceed state-of-the-art performance with low dimensionality. In addition to new experiments and recommendations for descriptor learning, we are also making available a new and realistic ground truth data set based on multiview stereo data.


title: A near linear time constant factor approximation for Euclidean bichromatic matching (cost)

abstract: We give an NlogO(1) N-time randomized O(1)-approximation algorithm for computing the cost of minimum bichromatic matching between two planar point-sets of size N.


title: www.elsevier.nl/locate/patrec Lane detection using spline model

abstract: Abstract In this paper, a Catmull–Rom spline-based lane model which describes the perspective effect of parallel lines has been proposed for generic lane boundary. Since Catmull–Rom spline can form arbitrary shapes by different sets of control points, it can describe a wider range of lane structures compared with other lane models, i.e. straight and parabolic models. Moreover, the lane detection problem has been formulated here as the problem of determining the set of control points of lane model. The proposed algorithm first detects the vanishing point (line) by using a Hough-like technique and then solves the lane detection problem by suggesting a maximum likelihood approach. Also, we have employed a multi-resolution strategy for rapidly achieving an accurate solution. This coarse-to-fine matching offers us an acceptable solution at an affordable computational cost, and thus speeds up the process of lane detection. As a result, the proposed method is robust to noise, shadows, and illumination variations in the captured road images, and is also applicable to both the marked and the unmarked roads.


title: Spatio-Temporal Querying of Video Content Using SQL for Quantizable Video Databases

abstract: Multimedia database modeling and representation play an important role for efficient storage and retrieval of multimedia. Modeling of semantic video content that enables spatiotemporal queries is one of the challenging tasks. A video is called as "quantizable" if the instants of a video are enough for a person to imagine the missing scenes properly. A semantic query for quantizable videos can be defined in a more flexible way using spatio-temporal instants. In this paper, we provide a semantic modeling and retrieval system, termed as G-SMART. Firstly, the videos are quantized according to semantic events. Then semantic instants and events of the video that include objects, events, and locations are provided as a grammar-based string. This linear string representation enables both the spatial and temporal retrieval of the video using Structured Query Language (SQL). The redundancy in this linear representation is reduced by using data reduction properties such as removal of implied information. Various types of queries such as event-object-location, event-location, object-location, event- object, current-next event, projection and semantic event are supported by G-SMART. A graphical user interface is designed to build queries and view the query results. G- SMART enables multimodal presentation by displaying the query results in the form images and videos. We show our results on a tennis video database. Index Terms-Video databases, semantic modeling, multimodal information retrieval, spatiotemporal queries


title: Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.

abstract: This research was supported by grants provided to the first author by the Social and Behavioral Sciences Research Institute, University of Arizona, and the Aspen Institute Nonprofit Sector Research Fund and by grants to the second author by the College of Business and Public Administration, University of Arizona. We have benefited from productive exchanges with numerous audiences to whom portions of this paper have been presented: a session at the 1994 Academy of Management meetings, the Social Organization workshop at the University of Arizona, the Work, Organizations, and Markets workshop at the Harvard Sociology Department, the 1994 SCOR Winter Conference at Stanford University, and colloquia at the business schools at the University of Alberta, UC-Berkeley, Duke, and Emory, and the JFK School at Harvard. For detailed comments on an earlier draft, we are extremely grateful to Victoria Alexander, Ashish Arora, Maryellen Kelley, Peter Marsden, Charles Kadushin, Dick Nelson, Christine Oliver, Lori Rosenkopf, Michael Sobel, Bill Starbuck, Art Stinchcombe, and anonymous reviewers at ASQ. We thank Dina Okamoto for research assistance and Linda Pike for editorial guidance. Address correspondence to Walter W. Powell, Department of Sociology, University of Arizona, Tucson, AZ 85721. We argue in this paper that when the knowledge base of an industry is both complex and expanding and the sources of expertise are widely dispersed, the locus of innovation will be found in networks of learning, rather than in individual firms. The large-scale reliance on interorganizational collaborations in the biotechnology industry reflects a fundamental and pervasive concern with access to knowledge. We develop a network approach to organizational learning and derive firm-level, longitudinal hypotheses that link research and development alliances, experience with managing interfirm relationships, network position, rates of growth, and portfolios of collaborative activities. We test these hypotheses on a sample of dedicated biotechnology firms in the years 1990-1994. Results from pooled, within-firm, time series analyses support a learning view and have broad implications for future theoretical and empirical research on organizational networks and strategic alliances.*


title: Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

abstract: Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1.


Debunking the pyramidal mind: A plea for synergy between reason and emotion


title: Monocular 3D Human Pose Estimation by Predicting Depth on Joints

abstract: This paper aims at estimating full-body 3D human poses from monocular images of which the biggest challenge is the inherent ambiguity introduced by lifting the 2D pose into 3D space. We propose a novel framework focusing on reducing this ambiguity by predicting the depth of human joints based on 2D human joint locations and body part images. Our approach is built on a two-level hierarchy of Long Short-Term Memory (LSTM) Networks which can be trained end-to-end. The first level consists of two components: 1) a skeleton-LSTM which learns the depth information from global human skeleton features; 2) a patch-LSTM which utilizes the local image evidence around joint locations. The both networks have tree structure defined on the kinematic relation of human skeleton, thus the information at different joints is broadcast through the whole skeleton in a top-down fashion. The two networks are first pre-trained separately on different data sources and then aggregated in the second layer for final depth prediction. The empirical e-valuation on Human3.6M and HHOI dataset demonstrates the advantage of combining global 2D skeleton and local image patches for depth prediction, and our superior quantitative and qualitative performance relative to state-of-the-art methods.


title: A Tool for Enterprise Architecture Analysis

abstract: The discipline of enterprise architecture advocates the use of models to support decision-making on enterprise-wide information system issues. In order to provide such support, enterprise architecture models should be amenable to analyses of various properties, as e.g. the availability, performance, interoperability, modifiability, and information security of the modeled enterprise information systems. This paper presents a software tool for such analyses. The tool guides the user in the generation of enterprise architecture models and subjects these models to analyses resulting in quantitative measures of the chosen quality attribute. The paper describes and exemplifies both the architecture and the usage of the tool.


title: Relationship between electron-phonon interaction and low-frequency Raman anisotropy in high-mobility organic semiconductors

abstract: Recent theoretical studies have shown that charge transport in high-mobility organic semiconductors is limited by low-frequency vibrations because of strong non-local electron–phonon interaction. Here we investigate two high-electron-mobility organic semiconductors with similar molecular structures but considerably different crystal packings, TCNQ and F2-TCNQ, and reveal the relationship between the experimental low-frequency Raman spectra and the calculated contributions of various vibrational modes to the electron–phonon interaction. We suggest that the combination of Raman spectroscopy with solid-state DFT is a powerful tool for probing electron–phonon interaction and focused search for high-mobility organic semiconductors.


title: Minimum Margin Loss for Deep Face Recognition

abstract: Abstract Face recognition has achieved great success owing to the fast development of deep neural networks in the past few years. Different loss functions can be used in a deep neural network resulting in different performance. Most recently some loss functions have been proposed, which have advanced the state of the art. However, they cannot solve the problem of margin bias which is present in class imbalanced datasets, having the so-called long-tailed distributions. In this paper, we propose to solve the margin bias problem by setting a minimum margin for all pairs of classes. We present a new loss function, Minimum Margin Loss (MML), which is aimed at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features. MML, together with Softmax Loss and Centre Loss, supervises the training process to balance the margins of all classes irrespective of their class distributions. We implemented MML in Inception-ResNet-v1 and conducted extensive experiments on seven face recognition benchmark datasets, MegaFace, FaceScrub, LFW, SLLFW, YTF, IJB-B and IJB-C. Experimental results show that the proposed MML loss function has led to new state of the art in face recognition, reducing the negative effect of margin bias.


Deaf-Mute Communication Interpreter


title: City Forensics: Using Visual Elements to Predict Non-Visual City Attributes

abstract: We present a method for automatically identifying and validating predictive relationships between the visual appearance of a city and its non-visual attributes (e.g. crime statistics, housing prices, population density etc.). Given a set of street-level images and (location, city-attribute-value) pairs of measurements, we first identify visual elements in the images that are discriminative of the attribute. We then train a predictor by learning a set of weights over these elements using non-linear Support Vector Regression. To perform these operations efficiently, we implement a scalable distributed processing framework that speeds up the main computational bottleneck (extracting visual elements) by an order of magnitude. This speedup allows us to investigate a variety of city attributes across 6 different American cities. We find that indeed there is a predictive relationship between visual elements and a number of city attributes including violent crime rates, theft rates, housing prices, population density, tree presence, graffiti presence, and the perception of danger. We also test human performance for predicting theft based on street-level images and show that our predictor outperforms this baseline with 33% higher accuracy on average. Finally, we present three prototype applications that use our system to (1) define the visual boundary of city neighborhoods, (2) generate walking directions that avoid or seek out exposure to city attributes, and (3) validate user-specified visual elements for prediction.


title: Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue

abstract: Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious preparation, which stains the tissue to visualize different histological features under a microscope. Here, we demonstrate a label-free approach to create a virtually-stained microscopic image using a single wide-field auto-fluorescence image of an unlabeled tissue sample, bypassing the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neural network trained using a generative adversarial network model to transform an auto-fluorescence image of an unlabeled tissue section into an image that is equivalent to the bright-field image of the stained-version of the same sample. We validated this method by successfully creating virtually-stained microscopic images of human tissue samples, including sections of salivary gland, thyroid, kidney, liver and lung tissue, also covering three different stains. This label-free virtual-staining method eliminates cumbersome and costly histochemical staining procedures, and would significantly simplify tissue preparation in pathology and histology fields.


title: When do older adults turn to the internet for health information? Findings from the wisconsin longitudinal study

abstract: AbstractBACKGROUND: Understanding how and when patients use nonphysician sources of health information is important to facilitate shared decision making within provider outpatient visits. However, little is known about which older adults seek health information on the internet or when. ::: OBJECTIVE: To determine how patient characteristics are related to seeking health information online and to the timing of these searches in relation to doctor visits. ::: PARTICIPANTS: Six thousand two hundred and seventy-nine respondents (aged 63 to 66 years) who completed the 2004 round of phone and mail surveys (70% response) as part of the Wisconsin Longitudinal Study Graduate Sample. ::: MEASUREMENTS: Self-reported use of the internet to search for health information and timing of use. ::: RESULTS: One-third of respondents had searched online for information about their own health or health care. Half of these searched for health information unrelated to their last doctor visit, while 1/3 searched after a visit, and 1/6 searched before. Among respondents with internet access at home or work, years of education (odds ratio [OR]=1.09, confidence interval [CI]=1.06 to 1.13) and openness-to-experience (OR=1.26, CI=1.16 to 1.36) were positively associated with searching online for health information irrespective of timing in relation to doctor visits. Compared with those who had never sought health information online, sicker individuals (especially those with cancer, OR=1.51, CI=1.14 to 1.99) were more likely to seek information online after a doctor visit. Attitudinal and personality factors were related to seeking health information online before or unrelated to a visit. ::: CONCLUSIONS: There are important differences in the timing of online health information searches by psychological and health characteristics among older adults with internet access.