Query: Neural correlates of heart rate variability during emotion

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Heart rate variability and its relation to prefrontal cognitive function: the effects of training and detraining <=SEP=> The aim of the present study was to investigate the relationship between physical fitness, heart rate variability (HRV) and cognitive function in 37 male sailors from the Royal Norwegian Navy. All subjects participated in an 8-week training program, after which the subjects completed the initial cognitive testing (pre-test). The subjects were assigned into a detrained group (DG) and a trained group (TG) based on their application for further duty. The DG withdrew from the training program for 4 weeks after which all subjects then completed the cognitive testing again (post-test). Physical fitness, measured as maximum oxygen consumption (V̇O2max), resting HRV, and cognitive function, measured using a continuous performance task (CPT) and a working memory test (WMT), were recorded during the pre-test and the post-test, and the data presented as the means and standard deviations. The results showed no between-group differences in V̇O2max or HRV at the pre-test. The DG showed a significant decrease in V̇O2max from the pre- to the post-test and a lower resting HRV than the TG on the post-test. Whereas there were no between-group differences on the CPT or WMT at the pre-test, the TG had faster reaction times and more true positive responses on tests of executive function at the post-test compared to the pre-test. The DG showed faster reaction times on non-executive tasks at the post-test compared to the pre-test. The results are discussed within a neurovisceral integration framework linking parasympathetic outflow to the heart to prefrontal neural functions.

Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. <=SEP=> Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.

Vagal influence on working memory and attention. <=SEP=> The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.

A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis <=SEP=> Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

0.9–10GHz low noise amplifier with capacitive cross coupling <=SEP=> This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.

New Direct Torque Control Scheme for BLDC Motor Drives Suitable for EV Applications <=SEP=> This paper proposes a simple and effective scheme for Direct Torque Control (DTC) of Brushless DC Motor. Unlike the traditional DTC where both the torque and flux must to be employed in order to deliver the switching table for the inverter, the proposed DTC scheme utilizes only the torque information. By considering the particular operating principle of the motor, the instantaneous torque can be estimated only by using back EMF ratio in an unified differential equation. The simulation results have shown the good performance of the proposed scheme. It can be seen thus a very suitable technique for EV drives which need fast and precise torque response.

Using the Facebook group as a learning management system: An exploratory study <=SEP=> Facebook is a popular social networking site. It, like many other new technologies, has potential for teaching and learning because of its unique built-in functions that offer pedagogical, social and technological affordances. In this study, the Facebook group was used as a learning management system (LMS) in two courses for putting up announcements, sharing resources, organizing weekly tutorials and conducting online discussions at a teacher education institute in Singapore. This study explores using the Facebook group as an LMS and the students’ perceptions of using it in their courses. Results showed that students were basically satisfied with the affordances of Facebook as the fundamental functions of an LMS could be easily implemented in the Facebook group. However, using the Facebook group as an LMS has certain limitations. It did not support other format files to be uploaded directly, and the discussion was not organized in a threaded structure. Also, the students did not feel safe and comfortable as their privacy might be revealed. Constraints of using the Facebook group as an LMS, implications for practice and limitations of this study are discussed. Practitioner notes What is already known about this topic • Facebook has been popularly used by tertiary students, but many students do not want their teachers to be friends on Facebook • Teacher’s self-disclosure on Facebook can promote classroom atmosphere, teacher’s credibility and student–teacher relationship • Commercial learning management systems (LMSs) have limitations What this paper adds • The Facebook group can be used an LMS as it has certain pedagogical, social and technological affordances • Students are satisfied with the way of using the Facebook group as an LMS British Journal of Educational Technology (2011) doi:10.1111/j.1467-8535.2011.01195.x © 2011 The Authors. British Journal of Educational Technology © 2011 Bera. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. • Younger students are more acceptable with the idea of using the Facebook group as an LMS • Using the Facebook group as an LMS has limitations: it does not support other format files; its discussions are not listed in threads; and it is not perceived as a safe environment Implications for practice and/or policy • The Facebook group can be used an LMS substitute or supplement • Third-party applications are needed to extend the capability of the Facebook group as an LMS • Using Facebook seems to be more appropriate for young learners than adults • Teachers do not have to be students’ friends on Facebook. Introduction Social networking sites (SNSs) are virtual spaces where people of similar interest gather to communicate, share photos and discuss ideas with one another (Boyd & Ellison, 2008; Raacke & Bonds-Raacke, 2008). In recent years, Facebook has become one of the most prominent SNSs. Like any new technology, Facebook seems to offer great potentials for teaching and learning as many students are using Facebook daily. One possible way of using Facebook for teaching and learning is to use its group as an LMS. Research shows that using LMSs possesses numerous benefits for teaching and learning. It enables faculty to shift the focus from content-based learning to process-based learning (Vogel & Klassen, 2001) and helps to “facilitate change from passive to active learning” (Herse & Lee, 2005, p. 51). Using LMSs also has the potential to increase student enrollment (Nunes & McPherson, 2003) and to promote interaction between students and faculty members (Lonn & Teasley, 2009; West, Waddoups & Graham, 2007). Using existing commercial LMSs like Blackboard, however, often has practical constraints (Sanchez-Franco, 2010). For example, LMSs tend to be expensive and that not every school can afford to purchase and maintain them over the long run. Trainee teachers cannot access certain features such as creating a course, enrolling students and setting up student groups as these functions are usually open to instructors or administrators only. The resources in the present LMS are often no longer accessible to trainee teachers after their graduation. Also, the LMS used at school in the future may be different from the one being currently used. They have to shift to a new LMS, and research shows learning a new system is often a painful experience (Black, Beck, Dawson, Jinks & DiPietro, 2007). If the Facebook group can be used as an alternative LMS, it would help to overcome some of the abovementioned constraints. For instance, it would enable a teacher to easily create a new course and enroll students in person if the class size is small. As presented in the following literature review section, many research studies have investigated the usage of Facebook, the effect of teacher’s self-disclosure via Facebook on teacher–student relationship improvement and the academic performance of Facebook users. However, few studies have examined if and how Facebook can be effectively used as an LMS. In this exploratory study, the Facebook group was used as an LMS to put up announcements, share resources, organize weekly tutorial sessions and conduct online discussions. The purpose of this paper is to describe how the Facebook group was used as an LMS in the study and to report students’ perceptions on it. Literature review on Facebook

An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model <=SEP=> Faculty of Economics in Universitas Atma Jaya Yogyakarta (UAJY) has replaced printed textbooks with e-textbooks for its academic activities since 2015. These e-textbooks can be accessed via iPad which is given to each student. Hence, the objective of this study is to test the proposed research model based on integrated UTAUT2 and TTF. Questionnaires were distributed to 326 students in 10 classes. Only junior and sophomore students were eligible to fulfill the questionnaires. The result of the study shows that performance expectancy, effort expectancy, social influence, facilitating condition, and habit have a direct significant relationship on behavioral intention to use mobile e-textbook. The result also shows that both Task technology and technology characteristic positively affect task technology fit. There is also a direct significant relationship between Task technology fit and performance expectancy. We suggest that further studies include experimentation to investigate performance expectancy of mobile e-book. 2) More evaluation should be held in the future in more mature environment. The finding of this study can help policymakers in the Faculty of Economics UAJY to evaluate the policy and formulate better strategy. In addition, these findings can be used by others faculties and universities as mobile e-textbook adoption case reference.

Ethernet-Based Real-Time and Industrial Communications <=SEP=> Despite early attempts to use Ethernet in the industrial context, only recently has it attracted a lot of attention as a support for industrial communication. A number of vendors are offering industrial communication products based on Ethernet and TCP/IP as a means to interconnect field devices to the first level of automation. Others restrict their offer to communication between automation devices such as programmable logic controllers and provide integration means to existing fieldbuses. This paper first details the requirements that an industrial network has to fulfill. It then shows how Ethernet has been enhanced to comply with the real-time requirements in particular in the industrial context. Finally, we show how the requirements that cannot be fulfilled at layer 2 of the OSI model can be addressed in the higher layers adding functionality to existing standard protocols.

Gabor-based kernel PCA with fractional power polynomial models for face recognition <=SEP=> This paper presents a novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images <=SEP=> In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation. Watershed regions that are unlikely to represent nuclei are removed in the postprocessing stage. The proposed algorithm is evaluated on a number of images that are representative of the diversity in pathology in this type of tissue. The method shows good performance in terms of the number of segmented nuclei and segmentation accuracy.

Empathy decline and its reasons: a systematic review of studies with medical students and residents. <=SEP=> PURPOSE
Empathy is a key element of patient-physician communication; it is relevant to and positively influences patients' health. The authors systematically reviewed the literature to investigate changes in trainee empathy and reasons for those changes during medical school and residency.


METHOD
The authors conducted a systematic search of studies concerning trainee empathy published from January 1990 to January 2010, using manual methods and the PubMed, EMBASE, and PsycINFO databases. They independently reviewed and selected quantitative and qualitative studies for inclusion. Intervention studies, those that evaluated psychometric properties of self-assessment tools, and those with a sample size <30 were excluded.


RESULTS
Eighteen studies met the inclusion criteria: 11 on medical students and 7 on residents. Three longitudinal and six cross-sectional studies of medical students demonstrated a significant decrease in empathy during medical school; one cross-sectional study found a tendency toward a decrease, and another suggested stable scores. The five longitudinal and two cross-sectional studies of residents showed a decrease in empathy during residency. The studies pointed to the clinical practice phase of training and the distress produced by aspects of the "hidden," "formal," and "informal" curricula as main reasons for empathy decline.


CONCLUSIONS
The results of the reviewed studies, especially those with longitudinal data, suggest that empathy decline during medical school and residency compromises striving toward professionalism and may threaten health care quality. Theory-based investigations of the factors that contribute to empathy decline among trainees and improvement of the validity of self-assessment methods are necessary for further research.

Multi-Cell Multiuser Massive MIMO Networks: User Capacity Analysis and Pilot Design <=SEP=> We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at the BSs to achieve certain SINR requirements of all users. The numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.

repository copy of Transformational Leadership and Burnout : The Role of Thriving and Followers ' Openness to Experience <=SEP=> eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website.

Adaptive scale based entropy-like estimator for robust fitting <=SEP=> In this paper, we propose a novel robust estimator, called ASEE (Adaptive Scale based Entropy-like Estimator) which minimizes the entropy of inliers. This estimator is based on IKOSE (Iterative Kth Ordered Scale Estimator) and LEL (Least Entropy-Like Estimator). Unlike LEL, ASEE only considers inliers' entropy while excluding outliers, which makes it very robust in parametric model estimation. Compared with other robust estimators, ASEE is simple and computationally efficient. From the experiments on both synthetic and real-image data, ASEE is more robust than several state-of-the-art robust estimators, especially in handling extreme outliers.

Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market <=SEP=> An extensive literature in economics and finance has documented " home bias, " the tendency that transactions are more likely to occur between parties in the same country or state, rather than outside. Can the Internet help overcome this spatial divide, especially in the context of online financial investments? We address this question in the context of a large online crowd funding marketplace. We analyze detailed transaction data over an extended period of time under typical market conditions, as well as those from a natural experiment, a period in which investors were restricted to only one state due to regulations. We further employ a quasi-experimental design by tracking borrowers who moved across state boundaries, and study how investors' behaviors change when those borrowers moved. Home bias appears to be a robust phenomenon under all three scenarios. This finding has important implications not just for the home bias literature, but also more broadly for the growing research and policy interest on Internet-based crowd funding, especially as a new channel for entrepreneurial financing.

Query optimization techniques for partitioned tables <=SEP=> Table partitioning splits a table into smaller parts that can be accessed, stored, and maintained independent of one another. From their traditional use in improving query performance, partitioning strategies have evolved into a powerful mechanism to improve the overall manageability of database systems. Table partitioning simplifies administrative tasks like data loading, removal, backup, statistics maintenance, and storage provisioning. Query language extensions now enable applications and user queries to specify how their results should be partitioned for further use. However, query optimization techniques have not kept pace with the rapid advances in usage and user control of table partitioning. We address this gap by developing new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. Our techniques are designed for easy incorporation into bottom-up query optimizers that are in wide use today. We have prototyped these techniques in the PostgreSQL optimizer. An extensive evaluation shows that our partition-aware optimization techniques, with low optimization overhead, generate plans that can be an order of magnitude better than plans produced by current optimizers.

Balancing minimum spanning trees and shortest-path trees <=SEP=> We give a simple algorithm to find a spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree. The algorithm provides a continuous tradeoff: given the two trees and aγ>0, the algorithm returns a spanning tree in which the distance between any vertex and the root of the shortest-path tree is at most 1+√2γ times the shortest-path distance, and yet the total weight of the tree is at most 1+√2/γ times the weight of a minimum spanning tree. Our algorithm runs in linear time and obtains the best-possible tradeoff. It can be implemented on a CREW PRAM to run a logarithmic time using one processor per vertex.

A Look into 30 Years of Malware Development from a Software Metrics Perspective <=SEP=> During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today’s cyber attacks and has consolidated as a commodity in the underground economy. In this work, we analyze the evolution of malware since the early 1980s to date from a software engineering perspective. We analyze the source code of 151 malware samples and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of regular software. Overall, this supports otherwise confirmed claims about the increasing complexity of malware and its production progressively becoming an industry.

Towards The Deep Model: Understanding Visual Recognition Through Computational Models <=SEP=> Introduction Vision, due to its significance in surviving and socializing, is one of the most important and  extensively studied sensory functions in the human brain. In order to fully understand visual  information processing, or more specifically, visual recognition, David Marr proposed the  Tri-level Hypothesis [29], in that three levels of the system should be studied: the computational  goal of the system, the internal representation or the algorithm the system uses to achieve the  goal, and the neural substrates that implement the system. It is well-known that visual  recognition in the human brain is implemented by the ventral visual pathway [32], which  receives visual information from the retina and goes through a layered structure including V1  (also known as the primary visual cortex), V2, V4, before reaching the inferior temporal cortex  (IT). The topographic mapping between the retina and the human visual cortex follows a  log-polar transformation, in which the Cartesian coordinates of the retina are transformed to  polar coordinates (polar angle and eccentricity) in the human visual cortex. From V1 to V4, each  of the visual areas contains a complete polar angle and eccentricity map, and have a increasing  receptive field (RF) size and respond to increasing complex visual features [12,22]. In higher  level visual areas such as the ventral temporal cortex (VTC), studies using functional magnetic  resonance imaging (fMRI) and diffusion tensor imaging (DTI) have found there exists visual  category-selective regions, such as the Fusiform Face Area (FFA, [26]) for face processing, the  Parahippocampal Place Area (PPA, [13]) for scene perception, and the Lateral Occipital  Complex (LOC, [19]) for general object recognition. Grill-Spector and Malach [20] proposed that  layer-based hierarchical processing, together with the functional specialization, are the two  organizing principles of the human visual cortex. More recent studies have shown that the  central-biased face recognition pathway (FFA) and the peripheral-biased scene recognition  pathway (PPA) are both functionally and anatomically segregated by mid-fusiform sulcus (MFS),  which enables parallel fast processing of visual recognition tasks in the ventral pathway [18, 43,  21, 44].

A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM <=SEP=> This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF's linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.

Investigating Social Entrepreneurship in Developing Countries <=SEP=> Social entrepreneurship has drawn interest from global policy makers and social entrepreneurs to target developing countries. Generally, not-forprofit organizations, funded by government and donor grants have played a significant role in poverty alleviation. We argue that, by applying entrepreneurial concepts, organizations can create social value, hence mitigate poverty. This is a theoretical paper that builds upon a multidimensional model in analysing how three social enterprises from India and Kenya create social value to address social problems. The findings suggest that whilst the social mission is central to all these organizations, they also create social value through innovation and pro-activeness. Additionally, the cultural and political environmental contexts hinder their attempt to create social value. Building networks and partnerships to achieve social value creation is vital for these organizations. Policy makers should devise policies that would assist social enterprises to achieve development goals.

The Anatomic Basis of Midfacial Aging <=SEP=> Facial aging is a multifactorial, three-dimensional (3D) process with anatomic, biochemical, and genetic correlates. Many exogenous and endogenous factors can signifi cantly impact the perceived age of an individual. Solar exposure [ 1– 3 ] , cigarette smoking [ 1, 2, 4, 5 ] , medications [ 1 ] , alcohol use [ 1 ] , body mass index [ 2 ] , and endocrinologic status [ 1, 6, 7 ] have all been implicated as factors that accelerate cutaneous and subcutaneous aging. These factors act in concert to create a variegated spectrum of facial morphologic aging changes, and thus, Mme. Chanel was partially correct in her statement from the last century. Most of the aging changes that occur in the midface, however, occur predictably in the majority of individuals. Stigmata of midfacial aging typically appear by the middle of the fourth decade. Degenerative changes occur in nearly every anatomic component of the midface and include cranial bone remodeling, tissue descent secondary to gravity, fat atrophy, and deterioration in the condition and appearance of the skin. The lower eyelids and adjacent tissues are often the initial areas of patient concern. This chapter reviews the morphologic changes that occur in the aging midface and discusses the pathogenesis of midfacial aging based upon its anatomic components. An integrated theory of facial aging will be presented. A. E. Wulc , MD, FACS ( ) Associate Clinical Professor of Ophthalmology , University of Pennsylvania

Hemihyperplasia-multiple lipomatosis syndrome (HHML): a challenge in spinal care. <=SEP=> A 15-year-old girl developed a progressive paraparesis over a period of six months, secondary to spinal cord compression by a lipomatous mass and anomalies of the vertebral column. Clinically, a right hemihyperplasia affecting the trunk and lower limb was evident, as well as a right convex lumbar scoliosis. CT and MRI demonstrated severe spinal cord compression resulting from intraspinal lipomatosis, overgrowth of right facet joints (T8 to L5), and kyphoscoliosis. Surgical decompression was undertaken. A lumbar scoliosis of 48 degrees was partially corrected by means of dual-rod instrumentation. The neurological deficit improved significantly, and ambulation was progressively restored. The patient carried the diagnosis of Proteus syndrome for several years, but reevaluation of clinical features prompted the diagnosis of Hemihyperplasia Multiple Lipomatosis syndrome (HHML). This rare sporadic disorder is often confused with Proteus syndrome. As in Proteus syndrome, spinal cord compression in patients with HHML can result from lipomatous infiltration and/or significant spinal abnormalities including kyphoscoliosis and overgrowth. HHML and Proteus syndrome are discussed and compared with special emphasis on spinal and orthopaedic pathologies.

DGASensor: Fast Detection for DGA-Based Malwares <=SEP=> DNS protocol has been used by many malwares for command-and-control (C&C). To improve the resiliency of C&C communication, Domain Generation Algorithm (DGA) has been utilized by recent malwares such as Locky, Conficker and Zeus. Many detection systems have been introduced for DGA-based botnets detection. However, such botnets detection approaches suffer from several limitations, for instance, requiring a group of DGA domains, period behaviors, the presence of multiple bots, and so forth. It is very hard for them to detect an individually running DGA-based malware which leave only a few traces. In this paper, we develop DGASensor to detect DGA-based malwares immediately by identifying a single DGA domain using lexical evidence. First, DGASensor automatically analyzes the lexical patterns of the most popular domains listed in Alexa top 100,000, and then extracts two templates, namely distribution template and structure template. Second, the above two templates, pronounceable attributes, and some frequently used properties like entropy and length, are used to extract features from a single domain. Third, we train our classifier using a non-DGA dataset consisting of domains obtained from Alexa rank and a DGA dataset generated by known DGAs. At last, we provide a short word filter to decrease the false positive rate. We implement a prototype system and evaluate it using the above training dataset with 10-fold cross validation. Moreover, a set of real world DNS traffic collected from a recursive DNS server is used to measure real world performance of our system. The results show that DGASensor detects DGA domains with accuracy 93% in our training dataset and is able to identify a variety of malwares in the real world dataset with an extremely high processing capability.

On the effectiveness of address-space randomization <=SEP=> Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a <i>derandomization attack</i> that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original exploit, although it takes a little longer to compromise a target machine: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack.
 We also explore various ways of strengthening address-space randomization and point out weaknesses in each. Surprisingly, increasing the frequency of re-randomizations adds at most 1 bit of security. Furthermore, compile-time randomization appears to be more effective than runtime randomization. We conclude that, on 32-bit architectures, the only benefit of PaX-like address-space randomization is a small slowdown in worm propagation speed. The cost of randomization is extra complexity in system support.

Cool English: a Grammatical Error Correction System Based on Large Learner Corpora <=SEP=> This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the neural sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained on EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.

Uses and Gratification Theory – Why Adolescents Use Facebook ? <=SEP=> Due to a dynamic development of the Web 2.0 and new trends in the social media field that change on a daily basis, contemporary media research is shifting its focus to a greater extent on media users, their motivation and behavior in using social network sites in order to explain the extreme popularity of Facebook, Twitter, WhatsApp and other similar SNSs and mobile chat applications among the young. In this paper we wanted to explore the benefits of Facebook use among adolescents as well as which of their needs are gratified thereat. As the theoretical background we used the uses and gratification theory due to its user oriented approach. Furthermore, we wanted to test whether the uses and gratification concept is adequate for analyzing the motivation and behavior of SNSs users as suggested by some previous research. The survey comprising 431 adolescent Facebook users was conducted from October to December 2013 in the City of Zagreb. The results have shown that most adolescents use Facebook for socializing and communicating with their friends, discussing school activities, setting up meetings and dates with friends as well as