title: Joint face alignment and 3D face reconstruction

abstract: We present an approach to simultaneously solve the two problems of face alignment and 3D face reconstruction from an input 2D face image of arbitrary poses and expressions. The proposed method iteratively and alternately applies two sets of cascaded regressors, one for updating 2D landmarks and the other for updating reconstructed pose-expression-normalized (PEN) 3D face shape. The 3D face shape and the landmarks are correlated via a 3D-to-2D mapping matrix. In each iteration, adjustment to the landmarks is firstly estimated via a landmark regressor, and this landmark adjustment is also used to estimate 3D face shape adjustment via a shape regressor. The 3D-to-2D mapping is then computed based on the adjusted 3D face shape and 2D landmarks, and it further refines the 2D landmarks. An effective algorithm is devised to learn these regressors based on a training dataset of pairing annotated 3D face shapes and 2D face images. Compared with existing methods, the proposed method can fully automatically generate PEN 3D face shapes in real time from a single 2D face image and locate both visible and invisible 2D landmarks. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face shapes.


title: Antimicrobial activity of varying concentrations of sodium hypochlorite on the endodontic microorganisms Actinomyces israelii, A. naeslundii, Candida albicans and Enterococcus faecalis

abstract: AIM ::: To determine the resistance of microorganisms associated with refractory endodontic infections to sodium hypochlorite used as a root canal irrigant. ::: ::: ::: METHODOLOGY ::: Two strains each of Actinomyces naeslundii, Candida albicans and Enterococcus faecalis were tested as late logarithmic phase inocula, against sodium hypochlorite adjusted to 0.5, 1.0, 2.5 and 5.25% w/v. Contact times used were 0, 10, 20, 30, 60 and 120 s. In the case of E. faecalis, additional experiments used contact times of 1.0, 2.0, 5.0, 10.0 and 30.0 min. Anti-microbial action was halted by sodium thiosulphate addition. Survivors were measured primarily using viable counts on drop plates. Additionally, pour plates were used to count low colony-forming units (cfu) and dilutions to 10(-6) were used to count high cfu. ::: ::: ::: RESULTS ::: All concentrations of NaOCl lowered cfu below the limit of detection after 10 s in the case of A. naeslundii and C. albicans. However, E. faecalis proved to be more resistant to NaOCl. Using 0.5% NaOCl for 30 min reduced cfu to zero for both strains tested. This compares with 10 min for 1.0%, 5 min for 2.5% and 2 min for 5.25% (P < 0.001). Regression analysis for the dependent variable log(e)(count + 1) with log(e)(time + 1) and concentration as explanatory variables gave rise to a significant interaction between time and concentration (P < 0.001). ::: ::: ::: CONCLUSION ::: The published association of E. faecalis with refractory endodontic infection may result, at least partially, from high resistance of this species to NaOCl. This does not appear to be the case with A. naeslundii or C. albicans.


Organizational training within digital transformation: The ToOW model


title: Integrated Buck-Flyback Converter as a High-Power-Factor Off-Line Power Supply

abstract: This paper investigates the integrated buck-flyback converter (IBFC) as a good solution for implementing low-cost high-power-factor ac-dc converters with fast output regulation. It will be shown that, when both buck and flyback semistages are operated in discontinuous conduction mode, the voltage across the bulk capacitor, which is used to store energy at low frequency, is independent of the output power. This makes it possible to maintain the bulk capacitor voltage at a low value within the whole line voltage range. The off-line operating modes of the IBFC are also investigated to demonstrate that the control switch of the proposed converter handles lower root-mean-square currents than those in similar integrated converters. The off-line operation of the IBFC is analyzed to obtain the design characteristics of the bulk capacitor voltage. Finally, the design and experimental results of a universal input 48 V-output 100 W ac-dc converter operating at 100 kHz is presented. Experiments show that the IEC-61000-3-2 input current harmonic limits are well satisfied and efficiency can be as high as 82%.


title: Improvements in horizontality performance as a function of type of training.

abstract: 90 undergraduates with low spatial perceptual ability were assigned to one of three groups of control, learner-guided instructions, and tutor-guided instructions. The groups differed in amount and type of spatial training administered in a laboratory setting. Participants completed horizontality and vertically spatial perceptual tasks immediately after pretesting and again one month later. Both groups given training showed more accurate performance at posttest than at pretest. In addition, the learner-guided instruction group outperformed the tutor-guided instruction group and control group at posttest and on novel but related tasks. Gender differences in posttest performance favoring males (n = 35) were obtained in the tutor-guided performance group and control group but not in the learner-guided instruction group. The number of hints the latter needed during training was predictive of posttest performance. Implications for assessment and remediation of undergraduates' spatial perceptual abilities are discussed. Language: en


Lightly supervised recognition for automatic alignment of large coherent speech recordings


title: Self-Efficacy, Motivation, and Performance

abstract: Abstract This article discusses the relation of self-efficacy to motivation and performance in cognitive and sport domains. Self-efficacy refers to one's beliefs about accomplishing a task and can influence choice of activities. effort, persistence. and achievement. People enter activities with varying levels of self-efficacy derived from prior experience, personal qualities, and social support. As they work on tasks they acquire information about how well they are doing. This information influences their self-efficacy for continued learning and performance. Research is described in which interventions involving models, goal setting, and feedback, were employed to affect self-efficacy. Regardless of domain, research shows that self-efficacy helps to predict motivation and performance. and studies testing causal models highlight the important role played by self-efficacy. Suggestions for future research are given, along with implications of theory and research for education and training.


title: Evaluation of face recognition techniques using PCA, wavelets and SVM

abstract: In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy according to increasing dimension of training set, chosen feature extractor-classifier pairs and chosen kernel function for SVM classifier. As test set we used ORL face database which is known as a standard face database for face recognition applications including 400 images of 40 people. At the end of the overall separation task, we obtained the classification accuracy 98.1% with Wavelet-SVM approach for 240 image training set.


title: Towards resolution invariant face recognition in uncontrolled scenarios

abstract: Face images captured by surveillance cameras usually have poor quality, particularly low resolution (LR), which affects the performance of face recognition seriously. In this paper, we develop a novel approach to address the problem of matching a LR face image against a gallery of relatively high resolution (HR) face images. Existing methods deal with such cross-resolution face recognition problem either by importing the information of HR images to help synthesize HR images from LR images or by applying the discrimination of HR images to help search for a unified feature space. Instead, we treat the discrimination information of HR and LR face images equally to boost the performance. The proposed approach learns resolution invariant features aiming to: (1) classify the identity of both LR and HR face images accurately, and (2) preserve the discriminative information among subjects across different resolutions. We conduct experiments on databases of uncontrolled scenarios, i.e., SCface and COX, and results show that the proposed approach significantly outperforms state-of-the-art methods.


title: A New 6-DOF Quadrotor Manipulation System: Design, Kinematics, Dynamics, and Control

abstract: The research on aerial manipulation has increased rapidly in recent years. In the previous work, a manipulator or a gripper is attached to the bottom of a quadrotor to facilitate the interaction with the environment. However, the previously introduced systems suffer from either limited end-effector degrees of freedom (DOF) or small payload capacity. In this paper, a quadrotor with a 2-DOF manipulator that has a unique topology to enable the end-effector to track a desired 6-DOF trajectory with minimum possible actuators is investigated. The proposed system is designed and modeled. However, such a system produces complexity in its inverse kinematics and control. A novel solution to the inverse kinematics problem is presented, which requires a solution of complicated algebraic/differential equations. Its accuracy is verified via numerical results. In order to solve the control problem, the system nonholonomic constraints are utilized with a model-free and low computation cost robust control technique. Moreover, the system stability proof under the proposed controller is carried out. A prototype of the proposed system is built and its design is verified via a flight test. In addition, the system feasibility and efficiency are enlightened.


Failure of urethane to influence early steps of pyrimidine biosynthesis in man


title: Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

abstract: Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.


abstract: In asymmetric key cryptography, also called Public Key cryptography, two different keys (which forms a key pair) are used. One key is used for encryption & only the other corresponding key must be used for decryption. No other key can decrypt the message, not even the original (i.e. the first) key used for encryption. The beauty of this scheme is that every communicating party needs just a key pair for communicating with any number of other communicating parties. Once some one obtains a key pair, he /she can communicate with any one else. RSA is a well known public-key cryptography algorithm. It is the first algorithm known to be suitable for signing as well as encryption, and was one of the first great advances in public key cryptography. The security of the RSA cryptosystem is based on two mathematical problems: the problem of factoring large numbers know mathematical attack and the problem of trying all possible private keys know brute force attack. So to improve the security, this scheme presents a new cryptography algorithm based on additive homomorphic properties called Modified RSA Encryption Algorithm (MREA). MREA is secure as compared to RSA as it is based on the factoring problem as well as decisional composite residuosity assumptions which is the intractability hypothesis. The scheme is an additive homomorphic cryptosystem, this means that, given only the public-key and the encryption of m1 and m2, one can compute the encryption of m1 + m2. This scheme also presents comparison between RSA and MREA cryptosystems in terms of security and performance.


title: Chizpurfle: A Gray-Box Android Fuzzer for Vendor Service Customizations

abstract: Android has become the most popular mobile OS, as it enables device manufacturers to introduce customizations to compete with value-added services. However, customizations make the OS less dependable and secure, since they can introduce software flaws. Such flaws can be found by using fuzzing, a popular testing technique among security researchers.This paper presents Chizpurfle, a novel "gray-box" fuzzing tool for vendor-specific Android services. Testing these services is challenging for existing tools, since vendors do not provide source code and the services cannot be run on a device emulator. Chizpurfle has been designed to run on an unmodified Android OS on an actual device. The tool automatically discovers, fuzzes, and profiles proprietary services. This work evaluates the applicability and performance of Chizpurfle on the Samsung Galaxy S6 Edge, and discusses software bugs found in privileged vendor services.


title: Social network classifier for churn prediction in telecom data

abstract: Telecom Social Network Analysis (TSNA) is a set of research procedures for identifying structural and behavioral pattern in systems based on the relations among customers. Grounded in graph and system theories, this approach has proven to be a powerful tool for studying networks of social world. This paper addresses the Social position of each customer in a network and Equivalence approaches to classify the telecom customers. Social position can be evaluated by finding the centrality of a node identified through a number of connections among network members. Such measures are used to characterize degrees of influence, prominence and importance of certain members. Regular equivalence analysis seeks to identify customers as churners and non-churners based on regularities in the patterns of network ties.


title: The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience

abstract: Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.


title: Predicting occupancy trends in Barcelona's bicycle service stations using open data

abstract: In 2008, the CEO of the company that manages and maintains the public bicycle service in Barcelona recognized that one may not expect to always find a place to leave the rented bike nearby their destination, similarly to the case when, driving a car, people may not find a parking lot. In this work, we make predictions about the statuses of the stations of the public bicycle service in Barcelona. We show that it is feasible to correctly predict nearly half of the times when the stations are either completely full of bikes or completely empty, up to 2 days before they actually happen. That is, users might avoid stations at times when they could not return a bicycle that they have rented before, or when they would not find a bike to rent. To achieve that, we apply the Random Forest algorithm to classify the status of the stations and improve the lifetime of the models using publicly available data, such as information about the weather forecast. Finally, we expect that the results of the predictions can be used to improve the quality of the service and make it more reliable for the users.


title: A modular compliant actuator for emerging high performance and fall-resilient humanoids

abstract: The application of humanoids in real world environments necessarily requires robots that can demonstrate physical resilience against strong physical interactions with the environment and impacts, that may occur during falling incidents, that are unavoidable. This paper introduces a modular high performance actuation unit designed to be robust against impacts and strong physical perturbations. The protection against impacts is achieved with the use of elastic transmission combined with soft cover elements on the link side. The paper introduce the details of the actuator design and implementation and discuss the effects of the soft cover and series elastic transmission on the reduction of the impact forces which reach the reduction drive of the actuator during impacts. The model of prototype joint, including the actuator unit, its elastic transmission and the driving link soft cover, is introduced and simulations were performed to study the effect of the elastic properties of the transmission and the soft cover on the reduction of the impact forces transmitted to the reduction drive. The results from the simulations are confirmed by experimental measurements on the real system under induced experimental impact trials, demonstrating the significant effect of the soft cover in the further reduction of impact forces. The performance of the proposed actuator unit in terms of physical robustness makes it ideal for the development of emerging humanoids robots that will be capable of surviving falls and recovers from them.


title: Mutual Coupling Reduction and Pattern Error Correction in a 5G Beamforming Linear Array Using CSRR

abstract: A four-element printed antenna array operating at 25-GHz frequency with complementary split ring resonator (CSRR) has been proposed for beamforming applications. The CSRR elements have been used to suppress the mutual coupling in the proposed array. The existence of the CSRR configuration in antenna array controls the unnecessary surface current flow between the array elements, and thus the mutual coupling between array elements has been significantly reduced up to −55 dB. The effect of mutual coupling on the array radiation patterns has been studied in the presence and absence of CSRRs. The effectiveness of the CSRR has been studied by steering the main beam as well as the nulls in different angles. By implementing the CSRR elements in array antenna, the distorted array patterns have been recovered and are presented. The proposed antenna array with the CSRR has the advantage of easy and low-cost fabrication and it offers excellent coupling suppression without changing the antenna profile. The commercially available simulation tools such as MATLAB and Ansys HFSS have been used for array weights calculation and antenna design respectively. Finally, the fabricated prototype has been experimentally verified, and it shows that the analytical and computed results agree well with the measured results.


title: ARTICLE NO. 0019 Curve Fitting with Bézier Cubics

abstract: Abstract In this paper, a new curve-fitting algorithm is presented. This algorithm can automatically fit a set of data points with piecewise geometrically continuous ( G 1 ) cubic Bezier curves. The algorithm consists of two steps. During the first step, significant points are identified from the given data set and are further classified as either corners or joints. Curve fitting is done in the second step. A weighted least-squares technique is used to find an optimal solution for the construction of piecewise Bezier curves. The resulting Bezier curve segments will be smoothly connected at all joint points. This algorithm has sbeen applied to many digital images with good results.


title: Information Theory And An Extension Of The Maximum Likelihood Principle By Hirotogu Akaike

abstract: Accelerated Failure-Time Regression Models With a Regression Model of Surviving Fraction: An Application to the Analysis of Permanent Employment in Japan KAZUO YAMAGUCHI* Accelerated failure-time regression models with an additional regression model for the surviving fraction are proposed for the analysis of events that may never occur, regardless of censoring, for some people in the population risk set. The models attempt to estimate simultaneously the effects of covariates on the acceleration/deceleration of the timing of a given event and the surviving fraction; that is, the proportion of the population for which the event never occurs. The extended family of the generalized Gamma distribution is used for the accelerated failure-time regression model; the logistic function is used for the regression model of the surviving fraction. The models are applied to the data of interfirm job mobility in Japan to assess variability in permanent employment among white collar and blue collar employees in firms of different sizes, independent from their variability in the timing of interfirm job separations. K E Y WORDS: Accelerated failure time; Generalized gamma model; Job mobility; Permanent employment; Surviving fraction. Hazard rate models can reflect two distinct factors asso- ciated with high/low hazard rates: (1) acceleration/deceler- ation in the timing of the event and (2) high/low limiting survival probability. The limiting survival probability implies the limiting value of the survivor function when time goes to infinity. A nonzero limiting survival probability often is called a surviving fraction (Miller 1981). Although we never can observe subjects up to time infinity, the assumption of a nonzero limiting survival probability seems consistent with the characteristics of empirical data for many life events. Some specific parametric functions with a nonzero limiting survival probability have been shown to fit the data of mar- riage, divorce, or occupational mobility better than repre- sentative parametric functions with a zero limiting survival probability (Diekmann and Mitter 1983, 1984; Diekmann 1989; Hemes 1972; Wu 1990). Most parametric hazard rate models implicitly assume a zero limiting survival probability. On the other hand, pro- portional hazards models based on Cox's partial likelihood method are compatible with nonzero limiting survival prob- abilities, because the unspecified baseline hazard function can take a value of 0 for any time t at which the event does not occur in the sample. In analyses based on the propor- tional hazards and related hazard rate models, however, the interpretations of high/low hazard rates often are ambiguous or arbitrary regarding the distinction between timing and limiting survival probability of the event's occurrence. For example, for an event experienced by most people in the population risk set, such as marriage, a higher hazard rate generally is interpreted as an advancement or acceler- ation of timing with respect to age; a lower hazard rate is interpreted as a postponement or deceleration. On the other hand, for events not experienced by a fairly large proportion * Kazuo Yamaguchi is Professor, Department of Sociology, University of Chicago, Chicago, IL 60637. This article was written when the author was affiliated with the Department of Sociology, U C L A . The research is partially supported by National Science Foundation Grant SES-9008163 and a U C L A Academic Senate Grant. The author thanks James S. Coleman, an associate editor, and all referees for their comments on earlier drafts of the article. In addition, he thanks Linda Ferguson for her research and ed- itorial assistance. of people in the population risk set, such as divorce or the birth of a second child, a higher hazard rate usually is inter- preted as a higher lifetime probability for the occurrence of the event rather than an acceleration in the timing of the event. However, alternative interpretations of high/low hazard rates can be given for both situations. For example, blacks have lower rates of marriage than whites not because they marry later, but rather because they are less likely to marry (Bennett et al, 1989; Schoen and Kluegel 1988; Spanier 1983). Similarly, an early age offirstchildbirth predicts not only a higher lifetime probability of having a second child (Millman and Hendershot 1980), but also a shorter interval between the first and second births (Marini and Hodsdon Proportional hazards models do not permit an useful parametric distinction between the effects of covariates on the timing of the event and the effects of covariates on the limiting survival probability of the event. Unlike proportional hazards models, accelerated failure-time models assume that high/low hazard rates result solely from acceleration/decel- eration in the timing of the event. They also typically assume a zero limiting survival probability. Although the assumption of a zero limiting survival probability is unrealistic for many life events, the fact that their parameters only govern accel- eration/deceleration and do not affect the limiting survival probability makes it possible to modify and extend these models to include another set of parameters governing the limiting survival probability. The modification is made by (1) introducing a surviving fraction into the accelerated fail- ure-time models and (2) applying the logistic regression model for the surviving fraction. An important point here is that although both the accel- erated failure-time model and the proportional hazard model can include a surviving fraction, the surviving fraction be- comes independent of other parameters only for the former model. For the accelerated failure time model that satisfies © 1992 American Statistical Association Journal of the American Statistical Association June 1992, Vol. 87, No. 418, Applications and Case Studies


title: CRONO: A Benchmark Suite for Multithreaded Graph Algorithms Executing on Futuristic Multicores

abstract: Algorithms operating on a graph setting are known to be highly irregular and unstructured. This leads to workload imbalance and data locality challenge when these algorithms are parallelized and executed on the evolving multicore processors. Previous parallel benchmark suites for shared memory multicores have focused on various workload domains, such as scientific, graphics, vision, financial and media processing. However, these suites lack graph applications that must be evaluated in the context of architectural design space exploration for futuristic multicores. This paper presents CRONO, a benchmark suite composed of multi-threaded graph algorithms for shared memory multicore processors. We analyze and characterize these benchmarks using a multicore simulator, as well as a real multicore machine setup. CRONO uses both synthetic and real world graphs. Our characterization shows that graph benchmarks are diverse and challenging in the context of scaling efficiency. They exhibit low locality due to unstructured memory access patterns, and incur fine-grain communication between threads. Energy overheads also occur due to nondeterministic memory and synchronization patterns on network connections. Our characterization reveals that these challenges remain in state-of-the-art graph algorithms, and in this context CRONO can be used to identify, analyze and develop novel architectural methods to mitigate their efficiency bottlenecks in futuristic multicore processors.


title: Anatomic study of the blood supply of perioral region

abstract: The use of flaps to reconstruct lip defects requires detailed knowledge of the local vasculature. New flaps for surgery around the mouth can be devised if the surgeon knows the distribution of the perioral arterial branches. Examination of the anatomy of perioral branches of the facial artery (FA) confirmed the consistent presence of septal and alar branches in the upper lip and a labiomental branch in the lower lip. Mucosal flaps from the upper lip based on the deep septal branch or the alar branch of the FA can be used to restore lower lip defects. A composite flap from the lower lip supplied by the labiomental branch of the FA can be used to restore combined defects of the upper lip and nose or partial defects of the lower lip. We studied the vascular anatomy of the perioral region in 25 cadaver dissections. Fixation was by 10% formaldehyde solution. Red latex was injected into the common carotid arteries before dissection. In the 50 specimens, the primary supplying vessels were identified and the size and distribution of the vessels were investigated. The FA was symmetrical in 17 (68%) of 25 heads. It terminated as an angular facial vessel in 11 (22%), as a nasal facial vessel in 30 (60%), as an alar vessel in six (12%), and as a superior labial vessel in two (4%) facial halves. It terminated as a hypoplastic type of FA in one (2%) facial half. The average external diameter of the superior labial artery (SLA) was 1.6 mm (min–max: 0.6–2.8 mm) at its origin. The origin of the SLA was superior to the angle of the mouth in 34 of 47 specimens (72.3%), and at the angle of the mouth in 13 of 47 specimens (27.7%). In two of the remaining three specimens, the SLA was the continuation of the FA and the other was of the hypoplastic type. The SLA supplied the columellar branches in all specimens except for the hypoplastic type (49 specimens). Columellar branches were classified according to their number and their type. In five specimens (10%) the inferior labial artery (ILA) was not found. In the other specimens, the site of origin of the ILA varied between the lower margin of the mandible and the corner of the mouth. Its external diameter measured min–max: 0.5–1.5 mm. The ILA arose from the FA above the angle of mouth in 4 specimens (8%), inferior to the angle of mouth in 11 specimens (22%), and at angle of mouth in 30 specimens (60%). We observed that the labiomental arteries, which formed anastomoses between the FA, ILA, and submental artery, showed variations in their course in the labiomental region. We suggest that knowledge of the location of arteries with respect to easily identifiable landmarks will help to avoid complications at surgery. Clin. Anat. 18:330–339, 2005. © 2005 Wiley-Liss, Inc.


title: The stress system in the human brain in depression and neurodegeneration

abstract: Corticotropin-releasing hormone (CRH) plays a central role in the regulation of the hypothalamic-pituitary-adrenal (HPA)-axis, i.e., the final common pathway in the stress response. The action of CRH on ACTH release is strongly potentiated by vasopressin, that is co-produced in increasing amounts when the hypothalamic paraventricular neurons are chronically activated. Whereas vasopressin stimulates ACTH release in humans, oxytocin inhibits it. ACTH release results in the release of corticosteroids from the adrenal that, subsequently, through mineralocorticoid and glucocorticoid receptors, exert negative feedback on, among other things, the hippocampus, the pituitary and the hypothalamus. The most important glucocorticoid in humans is cortisol, present in higher levels in women than in men. During aging, the activation of the CRH neurons is modest compared to the extra activation observed in Alzheimer's disease (AD) and the even stronger increase in major depression. The HPA-axis is hyperactive in depression, due to genetic factors or due to aversive stimuli that may occur during early development or adult life. At least five interacting hypothalamic peptidergic systems are involved in the symptoms of major depression. Increased production of vasopressin in depression does not only occur in neurons that colocalize CRH, but also in neurons of the supraoptic nucleus (SON), which may lead to increased plasma levels of vasopressin, that have been related to an enhanced suicide risk. The increased activity of oxytocin neurons in the paraventricular nucleus (PVN) may be related to the eating disorders in depression. The suprachiasmatic nucleus (SCN), i.e., the biological clock of the brain, shows lower vasopressin production and a smaller circadian amplitude in depression, which may explain the sleeping problems in this disorder and may contribute to the strong CRH activation. The hypothalamo-pituitary thyroid (HPT)-axis is inhibited in depression. These hypothalamic peptidergic systems, i.e., the HPA-axis, the SCN, the SON and the HPT-axis, have many interactions with aminergic systems that are also implicated in depression. CRH neurons are strongly activated in depressed patients, and so is their HPA-axis, at all levels, but the individual variability is large. It is hypothesized that particularly a subgroup of CRH neurons that projects into the brain is activated in depression and induces the symptoms of this disorder. On the other hand, there is also a lot of evidence for a direct involvement of glucocorticoids in the etiology and symptoms of depression. Although there is a close association between cerebrospinal fluid (CSF) levels of CRH and alterations in the HPA-axis in depression, much of the CRH in CSF is likely to be derived from sources other than the PVN. Furthermore, a close interaction between the HPA-axis and the hypothalamic-pituitary-gonadal (HPG)-axis exists. Organizing effects during fetal life as well as activating effects of sex hormones on the HPA-axis have been reported. Such mechanisms may be a basis for the higher prevalence of mood disorders in women as compared to men. In addition, the stress system is affected by changing levels of sex hormones, as found, e.g., in the premenstrual period, ante- and postpartum, during the transition phase to the menopause and during the use of oral contraceptives. In depressed women, plasma levels of estrogen are usually lower and plasma levels of androgens are increased, while testosterone levels are decreased in depressed men. This is explained by the fact that both in depressed males and females the HPA-axis is increased in activity, parallel to a diminished HPG-axis, while the major source of androgens in women is the adrenal, whereas in men it is the testes. It is speculated, however, that in the etiology of depression the relative levels of sex hormones play a more important role than their absolute levels. Sex hormone replacement therapy indeed seems to improve mood in elderly people and AD patients. Studies of rats have shown that high levels of cumulative corticosteroid exposure and rather extreme chronic stress induce neuronal damage that selectively affects hippocampal structure. Studies performed under less extreme circumstances have so far provided conflicting data. The corticosteroid neurotoxicity hypothesis that evolved as a result of these initial observations is, however, not supported by clinical and experimental observations. In a few recent postmortem studies in patients treated with corticosteroids and patients who had been seriously and chronically depressed no indications for AD neuropathology, massive cell loss, or loss of plasticity could be found, while the incidence of apoptosis was extremely rare and only seen outside regions expected to be at risk for steroid overexposure. In addition, various recent experimental studies using good stereological methods failed to find massive cell loss in the hippocampus following exposure to stress or steroids, but rather showed adaptive and reversible changes in structural parameters after stress. Thus, the HPA-axis in AD is only moderately activated, possibly due to the initial (primary) hippocampal degeneration in this condition. There are no convincing arguments to presume a causal, primary role for cortisol in the pathogenesis of AD. Although cortisol and CRH may well be causally involved in the signs and symptoms of depression, there is so far no evidence for any major irreversible damage in the human hippocampus in this disorder.


title: 1700 Pixels Per Inch (PPI) Passive-Matrix Micro-LED Display Powered by ASIC

abstract: We report the first 1700 pixels per inch (PPI) passive-matrix blue light-emitting diodes on silicon (LEDoS) micro-displays. By flip-chip bonding a micro-LED array onto an ASIC display driver, we successfully fabricated a 0.19- inch display with a resolution of 256 x 192, the highest ever reported in LED-based micro-display. In addition, the LEDoS micro-display can deliver brightness as high as 1300 mcd/m 2 and render images in 6-bit grayscale. The remarkable performance suggests the tremendous potential of LEDoS micro-displays for portable display applications which require high performance, small size and low power consumption.


title: One-pot synthesis of coumarins

abstract: The synthesis of coumarins (2H-benzopyran-2-ones) via Knoevenagel condensation over a recoverable, inexpensive solid base catalyst, calcined Mg–Al hydrotalcite, is reported for the first time. This method produces substituted coumarins in very high yields (>85%) and selectivities (ca. 100%) when aromatic hydroxy aldehydes are reacted with 2-substituted ethyl acetate.


title: NS4: A P4-driven Network Simulator

abstract: We present NS4, a P4-driven network simulator, which is, to the best of our knowledge, the first research effort in applying P4 to network simulation. Key features of NS4 include (1) elimination of laborious and redundant work for developing internal models of the simulator; (2) direct migration from simulation code to real-world P4 devices; (3) simulation of P4-enabled devices and network systems; (4) seamless compatibility with ns-3; (5) better scalability over other P4 behavioral model validation tools. We proposed and prototyped NS4 by integrating a P4 behavioral model in ns-3, and evaluated its effectiveness by a user case study. Source codes and examples of NS4 are publicly available at https://ns-4.github.io/


title: Probabilistic Reasoning For Entity And Relation Recognition

abstract: This paper develops a method for recognizing relations and entities in sentences, while taking mutual dependencies among them into account. e.g., the kill (Johns, Oswald) relation in: "J. V. Oswald was murdered at JFK after his assassin, K. F. Johns. . ." depends on identifying Oswald and Johns as people, JFK being identified as a location, and the kill relation between Oswald and Johns; this, in turn, enforces that Oswald and Johns are people.In our framework, classifiers that identify entities and relations among them are first learned from local information in the sentence; this information, along with constraints induced among entity types and relations, is used to perform global inference that accounts for the mutual dependencies among the entities.Our preliminary experimental results are promising and show that our global inference approach improves over learning relations and entities separately.


title: PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning

abstract: Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure. Second, ISAAC attempts to increase system throughput with a very deep pipeline. It is only beneficial when a large number of consecutive images can be fed into the architecture. In training, the notion of batch (e.g. 64) limits the number of images can be processed consecutively, because the images in the next batch need to be processed based on the updated weights. Third, the deep pipeline in ISAAC is vulnerable to pipeline bubbles and execution stall. In this paper, we present PipeLayer, a ReRAM-based PIM accelerator for CNNs that support both training and testing. We analyze data dependency and weight update in training algorithms and propose efficient pipeline to exploit inter-layer parallelism. To exploit intra-layer parallelism, we propose highly parallel design based on the notion of parallelism granularity and weight replication. With these design choices, PipeLayer enables the highly pipelined execution of both training and testing, without introducing the potential stalls in previous work. The experiment results show that, PipeLayer achieves the speedups of 42.45x compared with GPU platform on average. The average energy saving of PipeLayer compared with GPU implementation is 7.17x.


title: Matrix eQTL: Ultra fast eQTL analysis via large matrix operations

abstract: Motivation: Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. For modern datasets, eQTL analysis is a computationally intensive task as it involves testing for association of billions of transcript-SNP (single-nucleotide polymorphism) pair. The heavy computational burden makes eQTL analysis less popular and sometimes forces analysts to restrict their attention to just a small subset of transcript-SNP pairs. As more transcripts and SNPs get interrogated over a growing number of samples, the demand for faster tools for eQTL analysis grows stronger. ::: ::: Results: We have developed a new software for computationally efficient eQTL analysis called Matrix eQTL. In tests on large datasets, it was 2–3 orders of magnitude faster than existing popular tools for QTL/eQTL analysis, while finding the same eQTLs. The fast performance is achieved by special preprocessing and expressing the most computationally intensive part of the algorithm in terms of large matrix operations. Matrix eQTL supports additive linear and ANOVA models with covariates, including models with correlated and heteroskedastic errors. The issue of multiple testing is addressed by calculating false discovery rate; this can be done separately for cis-and trans-eQTLs. ::: ::: Availability: Matlab and R implementations are available for free at http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL ::: ::: Contact: shabalin@email.unc.edu


title: Globally Optimal Crowdsourcing Quality Management

abstract: We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying Expectation-Maximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management.


title: State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

abstract: This paper proposes a novel real-time model-based state of charge (SOC) estimation method for lithium-ion batteries. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) a fast upper-triangular and D-diagonal recursive least square (FUDRLS)-based online parameter identification algorithm for the electrical battery model, and 3) an iterated smooth variable structure filter (ISVSF) for SOC estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Due to its low complexity, the proposed method is suitable for the real-time embedded battery management system (BMS) application. Simulation and experiment are performed to validate the proposed method for a polymer lithium-ion cell.


title: FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

abstract: Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations.Theresult is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.


title: A novel modular voltage balancing topology for active battery management system

abstract: This paper proposes a novel modular voltage balancing topology for an active battery management system (BMS). The proposed topology consists of two power stages. The first stage is composed by a switch network, which is responsible for selecting the battery that will provide energy and the battery that will receive energy. In the switch network, for each battery, are used two cells, which allow the operation with bidirectional current flow and bipolar voltage. The second stage is composed by a capacitor used as energy storage element, which consists in the component used to link both batteries. The switch network control and its interaction with the energy storage element is performed by a digital controller. The paper presents the main computer simulations, as well as the main experimental results obtained to validate the proposed voltage balancing topology for an active BMS.


title: A network in a laptop: rapid prototyping for software-defined networks

abstract: Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop. The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes. Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow. We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN). Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon.


title: Feature learning for detection and prediction of freezing of gait in parkinson's disease

abstract: Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impact the patient's quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field - Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. ::: ::: We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.


title: Isolated WDDL: A Hiding Countermeasure for Differential Power Analysis on FPGAs

abstract: Security protocols are frequently accelerated by implementing the underlying cryptographic functions in reconfigurable hardware. However, unprotected hardware implementations are susceptible to side-channel attacks, and Differential Power Analysis (DPA) has been shown to be especially powerful. In this work, we evaluate and compare the effectiveness of common hiding countermeasures against DPA in FPGA-based designs, using the Whirlpool hash function as a case study. In particular, we develop a new design flow called Isolated WDDL (IWDDL). In contrast with previous works, IWDDL isolates the direct and complementary circuit paths, and also provides DPA resistance in the Hamming distance power model. The analysis is supported using actual implementation results.


Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses


title: A Multiscale Snake Algorithm Based on Improved External Force

abstract: The magnitude-only external force is modified by integrating direction information explicitly and a new snake model is derived. The new snake model can greatly eliminate the disturbance of false strong edges. Based on dynamic programming approach to snake model, a wavelet-based multiscale algorithm is proposed. Combined with Mallat's wavelet-based edge detection theory, the proposed multiresolution algorithm can greatly decrease the amount of computation, become more flexible in selecting filter than continuation method, and has less initial position sensitivity and parameter sensitivity, which enlarges the target capturing region and makes the setting of parameters simplified.


title: A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network

abstract: Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages of the powerful feature representation capability of SAE and superior predicting capacity of BPNN. To further improve the non-linearity simulation capability, we first classify all the data into several categories by the K-means clustering. Then, multiple SAE-BP modules are adopted to simulate their corresponding categories of data. The proposed approach is respectively compared with the support-vector-machine (SVM) model, the BP neural network model, the RBF neural network model and extreme learning machine (ELM) model. The experimental results show that the SAE-BP integrated algorithm performs much better than other benchmarks.


title: Exploiting web search to generate synonyms for entities

abstract: Tasks recognizing named entities such as products, people names, or locations from documents have recently received significant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table. Prior approaches have relied on string-based similarity which only compare a candidate string and an entity it matches with. In this paper, we exploit web search engines in order to define new similarity functions. We then develop efficient techniques to facilitate approximate matching in the context of our proposed similarity functions. In an extensive experimental evaluation, we demonstrate the accuracy and efficiency of our techniques.


title: SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources.

abstract: Rapidly increasing volumes of news feeds from diverse data sources, such as online newspapers, Twitter and online blogs are proving to be extremely valuable resources in helping anticipate, detect, and forecast outbreaks of rare diseases. This paper presents SourceSeer, a novel algorithmic framework that combines spatio-temporal topic models with sourcebased anomaly detection techniques to effectively forecast the emergence and progression of infectious rare diseases. SourceSeer is capable of discovering the location focus of each source allowing sources to be used as experts with varying degrees of authoritativeness. To fuse the individual source predictions into a final outbreak prediction we employ a multiplicative weights algorithm taking into account the accuracy of each source. We evaluate the performance of SourceSeer using incidence data for hantavirus syndromes in multiple countries of Latin America provided by HealthMap over a timespan of fifteen months. We demonstrate that SourceSeer makes predictions of increased accuracy compared to several baselines and is capable of forecasting disease outbreaks in a timely manner even when no outbreaks were previously reported.


Depth From Defocus in Presence of Partial Self Occlusion


title: Using ASP.NET and AJAX to Build Late-model Community Digital Management System

abstract: Community digital management is a key link to enhance the level of community information management. The paper introduces the features and advantages of ASP.NET and AJAX technology, analyzes the overall structure and main function modules of community digital management system, and realizes the community digital management system using of ASP.NET and AJAX technology. The practice shows that, the digital community management system based on ASP.NET and AJAX better realizes the digital management of community, and optimizes the performance of the system, which can certainly enhance the informatization management of community to a new level.


title: In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

abstract: In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised image-to-image translation problem to multiple input setting. Given a set of paired images from multiple modalities, a transformation is learned to translate the input into a specified domain. For this purpose, we introduce a Generative Adversarial Network (GAN) based framework along with a multi-modal generator structure and a new loss term, latent consistency loss. Through various experiments we show that leveraging multiple inputs generally improves the visual quality of the translated images. Moreover, we show that the proposed method outperforms current state-of-the-art unsupervised image-to-image translation methods.


title: Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

abstract: Mental stress reduces performances, on the work place and in daily life, and is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. This study systematically reviewed existing literature investigating, in healthy subjects, the associations between acute mental stress and short term Heart Rate Variability (HRV) measures in time, frequency and non-linear domain. The goal of this study was to provide reliable information about the trends and the pivot values of HRV measures during mental stress. A systematic review and meta-analysis of the evidence was conducted, performing an exhaustive research of electronic repositories and linear researching references of papers responding to the inclusion criteria. After removing duplicates and not pertinent papers, journal papers describing well-designed studies that analyzed rigorously HRV were included if analyzed the same population of healthy subjects at rest and during mental stress. 12 papers were shortlisted, enrolling overall 758 volunteers and investigating 22 different HRV measures, 9 of which reported by at least 2 studies and therefore meta-analyzed in this review. Four measures in time and non-linear domains, associated with a normal degree of HRV variations resulted significantly depressed during stress. The power of HRV fluctuations at high frequencies was significantly depressed during stress, while the ratio between low and high frequency resulted significantly increased, suggesting a sympathetic activation and a parasympathetic withdrawal during acute mental stress. Finally, among the 15 non-linear measures extracted, only 2 were reported by at least 2 studies, therefore pooled, and only one resulted significantly depressed, suggesting a reduced chaotic behaviour during mental stress. HRV resulted significantly depressed during mental stress, showing a reduced variability and less chaotic behaviour. The pooled frequency domain measures demonstrated a significant autonomic balance shift during acute mental stress towards the sympathetic activation and the parasympathetic withdrawal. Pivot values for the pooled mean differences of HRV measures are provided. Further studies investigating HRV non-linear measures during mental stress are still required. However, the method proposed to transform and then meta-analyze the HRV measures can be applied to other fields where HRV proved to be clinically significant.


title: A routing protocol for urban vehicular ad hoc networks to support non-safety applications

abstract: Abstract Urban Vehicular Ad Hoc Network (UVANET) provides non-safety applications like media sharing, Internet service, file transfer, gaming, and so on. To provide better services to the users in UVANET, routing plays an important role. In this paper, a novel routing protocol is proposed for UVANET to support the non-safety applications. We have considered a non-safety application where the drivers and passengers of different parking lots can play multi-player games with each other. To play the games smoothly, the game data should reach the destination in a minimum time. Simulation results show that, when the density of the vehicles in the city area is high, then the proposed protocol fulfills the end-to-end delay tolerance of 100 ms. At last, an experimental work is performed to validate the proposed routing protocol by running a simple puzzle game in a UVANET prototype designed in an indoor laboratory environment and outdoor environment.


title: As-awareness in Tor path selection

abstract: Tor is an anonymous communications network with thousands of router nodes worldwide. An intuition reflected in much of the literature on anonymous communications is that, as an anonymity network grows, it becomes more secure against a given observer because the observer will see less of the network. In particular, as the Tor network grows from volunteers operating relays all over the world, it becomes less and less likely for a single autonomous system (AS) to be able to observe both ends of an anonymous connection. Yet, as the network continues to grow significantly, no analysis has been done to determine if this intuition is correct. Further, modifications to Tor's path selection algorithm to help clients avoid an AS-level observer have not been proposed and analyzed. Five years ago a previous study examined the AS-level threat against client and destination addresses chosen a priori to be likely or interesting to examine. Using an AS-level path inference algorithm with improved accuracy, more extensive Internet routing data, and, most importantly, a model of typical Tor client AS-level sources and destinations based on data gathered from the live network, we demonstrate that the threat of a single AS observing both ends of an anonymous Tor connection is greater than previously thought. We look at the growth of the Tor network over the past five years and show that its explosive growth has had only a small impact on the network's robustness against an AS-level attacker. Finally, we propose and evaluate the effectiveness of some simple, AS-aware path selection algorithms that avoid the computational overhead imposed by full AS-level path inference algorithms. Our results indicate that a novel heuristic we propose is more effective against an AS-level observer than other commonly proposed heuristics for improving location diversity in path selection.


title: AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

abstract: We present AMIGOS-- A dataset for Multimodal research of affect, personality traits and mood on Individuals and GrOupS. Different to other databases, we elicited affect using both short and long videos in two social contexts, one with individual viewers and one with groups of viewers. The database allows the multimodal study of people's affective responses, by means of neuro-physiological signals, and their relation with personality, mood, social context and stimuli duration. The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos. In the second one, they watched 4 long videos, some of them alone and the rest in groups. The participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response (GSR), were recorded using wearable sensors. Participants' frontal HD video and both RGB and depth full body videos were also recorded. Participants emotions have been annotated with both self-assessment of affective levels (valence, arousal, control, familiarity, liking and basic emotions) and external-assessment of valence and arousal. We present a detailed correlation analysis of the different dimensions as well as baseline methods and results for single-trial classification of valence, arousal, personality traits, mood and social context. The database is publicly available.


title: A Low-Profile Wideband Surface-Mountable Substrate-Integrated Waveguide Horn Antenna

abstract: A low-profile surface-mountable substrate-integrated waveguide (SIW) horn antenna with wideband performance is proposed in this letter. In order to reduce the serious impedance mismatch at the SIW horn aperture resulting from the thin substrate (0.12 λ0), a semi-open SIW horn is designed by gradually detaching upper broadwall while maintaining another one. Furthermore, in order to realize wide bandwidth for the SIW horn antenna, air-vias are drilled into the semi-open horn to realize smooth transition from semi-open horn to free space. Finally, a fabricated prototype is measured, and results indicate that a 35.7% impedance bandwidth ranging from 19.1 to 27.4 GHz and a 31.8% 3 dB gain bandwidth ranging from 19.3 to 26.6 GHz are obtained, respectively. Stable end fire radiation patterns are observed across the operating band.


title: Why users keep answering questions in online question answering communities: A theoretical and empirical investigation

abstract: Abstract This study theorized and validated a model of knowledge sharing continuance in a special type of online community, the online question answering (Q&A) community, in which knowledge exchange is reflected mainly by asking and answering specific questions. We created a model that integrated knowledge sharing factors and knowledge self-efficacy into the expectation confirmation theory. The hypotheses derived from this model were empirically validated using an online survey conducted among users of a famous online Q&A community in China, “Yahoo! Answers China”. The results suggested that users’ intention to continue sharing knowledge (i.e., answering questions) was directly influenced by users’ ex-post feelings as consisting of two dimensions: satisfaction, and knowledge self-efficacy. Based on the obtained results, we also found that knowledge self-efficacy and confirmation mediated the relationship between benefits and satisfaction.


title: Action Recognition From Depth Maps Using Deep Convolutional Neural Networks

abstract: This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the 3-D points of the captured depth maps. This not only synthesizes more data, but also makes the trained ConvNets view-tolerant. Second, WHDMMs at several temporal scales are constructed to encode the spatiotemporal motion patterns of actions into 2-D spatial structures. The 2-D spatial structures are further enhanced for recognition by converting the WHDMMs into pseudocolor images. Finally, the three ConvNets are initialized with the models obtained from ImageNet and fine-tuned independently on the color-coded WHDMMs constructed in three orthogonal planes. The proposed algorithm was evaluated on the MSRAction3D, MSRAction3DExt, UTKinect-Action, and MSRDailyActivity3D datasets using cross-subject protocols. In addition, the method was evaluated on the large dataset constructed from the above datasets. The proposed method achieved 2–9% better results on most of the individual datasets. Furthermore, the proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions.


title: Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving

abstract: With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered, and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 54 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-survey-datasets. Specifically, we categorize the datasets according to the use cases and highlight the datasets that capture the complicated and hazardous driving conditions, which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by the existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.


title: A capacitorless 1T-DRAM technology using gate-induced drain-leakage (GIDL) current for low-power and high-speed embedded memory

abstract: A capacitorless one-transistor (1T)-dynamic random-access memory (DRAM) cell using gate-induced drain-leakage (GIDL) current for write operation was demonstrated. Compared with the conventional write operation with impact-ionization (II) current, the write operation with GIDL current achieves power consumption that is lower by four orders of magnitude and a write speed within several nanoseconds. The capacitorless 1T DRAM is the most promising technology for high-performance embedded-DRAM large-scale integration.


title: Complex activity recognition using context-driven activity theory and activity signatures

abstract: In pervasive and ubiquitous computing systems, human activity recognition has immense potential in a large number of application domains. Current activity recognition techniques (i) do not handle variations in sequence, concurrency and interleaving of complex activities; (ii) do not incorporate context; and (iii) require large amounts of training data. There is a lack of a unifying theoretical framework which exploits both domain knowledge and data-driven observations to infer complex activities. In this article, we propose, develop and validate a novel Context-Driven Activity Theory (CDAT) for recognizing complex activities. We develop a mechanism using probabilistic and Markov chain analysis to discover complex activity signatures and generate complex activity definitions. We also develop a Complex Activity Recognition (CAR) algorithm. It achieves an overall accuracy of 95.73p using extensive experimentation with real-life test data. CDAT utilizes context and links complex activities to situations, which reduces inference time by 32.5p and also reduces training data by 66p.


title: Improved Face Recognition Rate Using HOG Features and SVM Classifier

abstract: A novel face recognition algorithm is presented in this paper. Histogram of Oriented Gradient features are extracted both for the test image and also for the training images and given to the Support Vector Machine classifier. The detailed steps of HOG feature extraction and the classification using SVM is presented. The algorithm is compared with the Eigen feature based face recognition algorithm. The proposed algorithm and PCA are verified using 8 different datasets. Results show that in all the face datasets the proposed algorithm shows higher face recognition rate when compared with the traditional Eigen feature based face recognition algorithm. There is an improvement of 8.75% face recognition rate when compared with PCA based face recognition algorithm. The experiment is conducted on ORL database with 2 face images for testing and 8 face images for training for each person. Three performance curves namely CMC, EPC and ROC are considered. The curves show that the proposed algorithm outperforms when compared with PCA algorithm. IndexTerms: Facial features, Histogram of Oriented Gradients, Support Vector Machine, Principle Component Analysis.


title: Phylogeographical patterns shed light on evolutionary process in South America

abstract: The South American continent is composed of several biogeographical regions harbouring the highest biodiversity on the globe, encompassing five of the world's biodiversity 'hot spots'. Nonetheless, the patterns and processes responsible for shaping its astonishing species diversity are largely unknown. Here, we present a review of current South American phylogeographical knowledge based on published articles on this topic. An appraisal of the literature reveals emerging phylogeographical patterns in the biota of South America. The striking phylogeographical divergence observed among organism lineages in South American studies is suggestive of high levels of undocumented species diversity. The interplay between Pleistocene climatic oscillations and Pliocene/Miocene orogenic events has contributed to shaping the current diversity and distribution of modern lineages in both the tropical and temperate regions of South America. Although older divergence times were observed for a range of species, most herpetofauna underwent an intraspecific lineage split much earlier than other organisms. The geographical ranges of species associated with forest habitats were reduced mainly during glacial cycles, whereas species associated with open vegetation domains have shown variable responses to climatic oscillations. The results suggest a highly complex mosaic of phylogeographical patterns in South America. We suggest future research directions to promote a better understanding of the origin and maintenance of the South American biota.


title: EEG manifestations of nondual experiences in meditators

abstract: The holistic experiential benefits of meditation among a widely ranging population have been well established within the empirical literature. What remain less clear are the underlying mechanisms of the meditative process. A large impediment to this clarity is attributable to the lack of a unified and comprehensive taxonomy, as well as to the absence of clear differentiation within the literature between method of practice and resulting state. The present study discusses and then attempts to identify within our sample a theoretically universal culminating meditative state known as Nondual Awareness, which is differentiated from the method or practice state. Participants completed an in-lab meditation, during which neurological patterns were analyzed using electroencephalography (EEG). Analyses indicated significantly higher EEG power among slower wave frequencies (delta, theta, alpha) during the reported nondual events. These events appear neurologically distinct from meditation sessions as a whole, which interestingly demonstrated significant elevation within the gamma range.


Vascular Normalization: Study Examines How Antiangiogenesis Therapies Work


title: Video Games: Perspective, Point-of-view, and Immersion

abstract: A automatic focus detecting apparatus in which a focal point position corresponding to a change in position of an object after the elapse of a predetermined time is foreseen and calculated on the basis of the result of a past focal point adjustment. In accordance with the aperture value and lens characteristic at the time of photographing, a selection is made with respect to whether the calculation is executed in a normal mode to calculate the focal point position on the basis of only the result of the detection of the focal point at the present time independently of the result of the past focal point adjustment, or the calculation is executed in the foreseeing mode. Upon photographing, if there is no need to execute the foreseeing calculation, the focal point position is calculated in the normal mode.


title: A new cuffless optical sensor for blood pressure measuring with self-adaptive signal processing

abstract: A new cuffless, wireless optical blood pressure (BP) sensor is successfully developed by this study. This device employs the technique of photoplethysmograph (PPG) to read out in real time the intravascular blood volume change and then to calculate BP. The system utilizes a red-light LED with a wavelengths of 660 nm. The readout circuit includes a pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), an Arduino unit for calculation and a Bluetooth module for wireless communication. A notebook or a mobile phone is also used to display continuous BPs and conducts statistical analysis/results. The passband is from 0.3 to 3.4 Hz. The range of adjustable gain are from 60 to 80 dB. Measured PPGs of different subjects can be auto-adjusted with varied gain by a PGA with a pre-designed digital circuit. In results, the signal-to-noise ratio (SNR) is improved for accurate estimates on BPs. 8 subjects participated in the experimental validation, in which the obtained BPs are compared with the results from a commercial blood pressure monitor by OMRON. The maximum error of experimental results is ± 6 mmHg, which is less than 8 mmHg conforming to the requirement by the Advancement of Medical Instrumentation (AAMI). This study pioneers to report stable, accurate estimates on BPs by a cuffless optical hand-held device.


title: Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces

abstract: We investigate a technique to adapt unsupervised word embeddings to specific applications, when only small and noisy labeled datasets are available. Current methods use pre-trained embeddings to initialize model parameters, and then use the labeled data to tailor them for the intended task. However, this approach is prone to overfitting when the training is performed with scarce and noisy data. To overcome this issue, we use the supervised data to find an embedding subspace that fits the task complexity. All the word representations are adapted through a projection into this task-specific subspace, even if they do not occur on the labeled dataset. This approach was recently used in the SemEval 2015 Twitter sentiment analysis challenge, attaining state-of-the-art results. Here we show results improving those of the challenge, as well as additional experiments in a Twitter Part-Of-Speech tagging task.


title: Compact Asymmetric-Slit Microstrip Antennas for Circular Polarization

abstract: Four compact asymmetric-slit microstrip antennas are proposed and studied for circular polarization. By cutting asymmetrical slits in diagonal directions onto the square microstrip patches, the single coaxial-feed microstrip patch antennas are realized for circularly polarized radiation with compact antenna size. The performances of the proposed antennas with several asymmetric-slit shapes onto the patch radiators are compared. The measured 10-dB return loss and 3-dB axial-ratio bandwidths of the antenna prototype are around 2.5% and 0.5%, respectively. The proposed asymmetric-slit configurations are useful for compact circularly polarized microstrip patch antennas and array design.


title: Quadrature FM gyroscope

abstract: A dual mass vibratory gyroscope sensor demonstrates the quadrature frequency modulated (QFM) operating mode, where the frequency of the circular orbit of a proof mass is measured to detect angular rate. In comparison to the mode-matched open loop rate mode, the QFM mode receives the same benefit of improved SNR but without the penalties of unreliable scale factor and decreased bandwidth. A matched pair of gyroscopes, integrated onto the same die, is used for temperature compensation, resulting in 6 ppb relative frequency tracking error, or an Allan deviation of 370 deg/hr with a 70 kHz resonant frequency. The integrated CMOS electronics achieve a capacitance resolution of 0.1 zF/rt-Hz with nominal 6 fF sense electrodes.


title: Everyday functioning of people with Parkinson’s disease and impairments in executive function: a qualitative investigation

abstract: AbstractPurpose: Executive function is the key area of cognitive impairment in Parkinson’s disease. This study investigated how cognitive difficulties impact on everyday life of people with Parkinson’s disease and their carers, and whether they explicitly mention executive-type difficulties.Methods: Semistructured interviews with 11 people with Parkinson’s disease and six carers were analyzed thematically. People with Parkinson’s disease performed within the normal range on cognitive screening tests, but all had abnormal scores on tests of executive function.Results: Despite relatively mild executive deficits and no global cognitive impairment, participants described executive-type difficulties as well as a range of problems in other cognitive domains, such as memory, processing speed and apathy. Cognitive difficulties had a far-reaching impact on everyday life and their significance depended on personal circumstances, such as the level of responsibilities of the person with Parkinson’s disease and the ex...


title: Software engineering research for computer games: A systematic review

abstract: Context: Currently, computer game development is one of the fastest growing industries in the worldwide economy. In addition to that, computer games are rapidly evolving in the sense that newer game versions arrive in a very short interval. Thus, software engineering techniques are needed for game development in order to achieve greater flexibility and maintainability, less cost and effort, better design, etc. In addition, games present several characteristics that differentiate their development from classical software development. Objective: This study aims to assess the state of the art on research concerning software engineering for computer games and discuss possible important areas for future research. Method: We employed a standard methodology for systematic literature reviews using four well known digital libraries. Results: Software engineering for computer games is a research domain that has doubled its research activity during the last 5years. The dominant research topic has proven to be requirements engineering, while topics such as software verification and maintenance have been neglected up to now. Conclusion: The results of the study suggest that software engineering for computer games is a field that embraces many techniques and methods from conventional software engineering and adapts them so as to fit the specific requirements of game development. In addition to that, the study proposes the employment of more elaborate empirical methods, i.e. controlled experiments and case studies, in game software engineering research, which, have not been extensively used up to now.


title: User Experience Design and Agile Development: From Theory to Practice

abstract: We used existing studies on the integration of user experience design and agile methods as a basis to develop a framework for integrating UX and Agile. We performed a field study in an ongoing project of a medium-sized company in order to check if the proposed framework fits in the real world, and how some aspects of the integration of UX and Agile work in a real project. This led us to some conclusions situating contributions from practice to theory and back again. The framework is briefly described in this paper and consists of a set of practices, artifacts, and techniques derived from the literature. By combining theory and practice we were able to confirm some thoughts and identify some gaps—both in the company process and in our proposed framework—and drive our attention to new issues that need to be addressed. We believe that the most important issues in our case study are: UX designers cannot collaborate closely with developers because UX designers are working on multiple projects and that UX designers cannot work up front because they are too busy with too many projects at the same time.


title: An Empirical Study on E-learning Participation Affected by Flow Experience and Internet Addiction

abstract: Abstract This paper reports the findings of research into the effect of flow experience and internet addiction on e-learning participation. The total number of access, playing time of contents and lag time (time between content upload and first access) were investigated as e-learning participation by analyzing web log data. The paper also examines the relationship between academic achievement and e-learning participation. The results suggest that the flow experience significantly correlates with all variables related with e-learning participation and internet addiction significantly correlates with lag time. It is found that female students significantly access more, longer and have shorter lag time than their male counterparts. It also suggest total number of access, lag time and gender difference have significant effect on academic achievement. Key Words : E-learning Participation, Flow Experience, Internet Addiction, Web Log Analysis, Academic Achievement * 세종대학교 산업대학원(eMA) 호텔관광경영학과 석사과정


title: Portfolio and . . .Investment Opportunities and Habit Formation in Preferences

abstract: This paper investigates the dynamic consumption and portfolio choice of an investor with habit formation in preferences and access to a complete financial market with time-varying investment opportunities. An exact and simple characterization of the optimal behavior under general, possibly non-Markov, dynamics of market prices is derived. Relative to the benchmark case of time-additive power utility, habit formation affects the hedge component of the optimal portfolio differently than the speculative component. The quantitative effects of habit formation are studied in three concrete settings. Firstly, a closed-form solution of the optimal consumption and portfolio choice with mean-reverting stock returns is derived. Secondly, with Cox-Ingersoll-Ross interest rate dynamics the optimal strategies are expressed in terms of the solution to a partial differential equation, which has an explicit solution for time-additive utility, but not with habit formation. Thirdly, a new model with both mean-reverting stock returns and stochastic interest rates is studied. Overall, the numerical examples show that, while hedging demands for various assets are affected differently by habit persistence, the main effect on relative asset allocations stems from the fact that some assets (bonds and cash) are better investment objects than others (stocks) when it comes to ensuring that future consumption will not fall below the habit level.


title: From recession to renewal:the impact of the financial crisis on public services and local government

abstract: Book review of From Recession to Renewal: The Impact of the Financial Crisis on Public Services and Local Government, Joanna Richardson (Ed). Bristol, Policy Press, 2010, ISBN 9781847426994


title: A miniature UWB planar monopole antenna with 5-GHz band-rejection filter and the time-domain characteristics

abstract: A planar monopole antenna with a staircase shape and small volume (25times26times1 mm3) is proposed in this paper. With the use of a half-bowtie radiating element, the staircase-shape, and a modified ground plane structure, the proposed antenna has a very wide impedance bandwidth measured at about 11.6 GHz (2.9-14.5 GHz, bandwidth ratio about 1:5) below VSWR 2 including the WLAN band notched in the vicinity of 5 GHz. An omnidirectional radiation pattern is obtained. The group delay which is an indication of linearity between two proposed antennas is less than 1 ns. The electrical characteristics in terms of frequency and time domains and physical ones of the proposed antenna make it attractive for use in ultrawideband (UWB) systems


title: Reward-Motivated Learning: Mesolimbic Activation Precedes Memory Formation

abstract: We examined anticipatory mechanisms of reward-motivated memory formation using event-related FMRI. In a monetary incentive encoding task, cues signaled high- or low-value reward for memorizing an upcoming scene. When tested 24 hr postscan, subjects were significantly more likely to remember scenes that followed cues for high-value rather than low-value reward. A monetary incentive delay task independently localized regions responsive to reward anticipation. In the encoding task, high-reward cues preceding remembered but not forgotten scenes activated the ventral tegmental area, nucleus accumbens, and hippocampus. Across subjects, greater activation in these regions predicted superior memory performance. Within subject, increased correlation between the hippocampus and ventral tegmental area was associated with enhanced long-term memory for the subsequent scene. These findings demonstrate that brain activation preceding stimulus encoding can predict declarative memory formation. The findings are consistent with the hypothesis that reward motivation promotes memory formation via dopamine release in the hippocampus prior to learning.


title: Nonlinear control law with application to high angle-of-attack flight

abstract: This paper proposes a general structure for aircraft control at whose core are several blocks of dynamic inversion of the controlled system. This control law is then applied to a nonlinear model of the high angle-of-attack research vehicle (HARV). The control law itself uses the complete nonlinear aerodata base and has no restrictions on the manner in which the control inputs enter the dynamic equations. An algorithm that handles surface limiting while ensuring a solution to the moment equation inverse is incorporated into the control law. The performance of the control law is demonstrated via simulation of a selected supermaneuver.


title: Design and optimization of a dielectric-gas-based single-phase electrostatic motor

abstract: Electrostatic motor can work as a supplement to the electromagnetic motor due to its simple structure, low cost, light weight, and high efficiency. Increasing the applied voltage under ultra-high vacuum (UHV) condition and using dielectric liquid with high relative permittivity are two approaches to increase its torque and make electrostatic motors competitive with the electromagnetic ones. This paper presents a dielectric-gas-based single-phase electrostatic motor with simplified construction. 3-D finite element analysis (FEA) simulation and optimization process of the electrostatic motor is performed and an electrostatic motor with a torque of 0.25 Nm and power of 50 W is designed. 3-D printing is used to build a prototype machine to eliminate the process of mould manufacture and achieve a weight reduction. Predicted capacitance of machine is compared with measured result and good agreement is achieved.


title: Governance and Trust in Family Firms: An Introduction

abstract: We provide an overview of the articles and commentaries devoted to theories of family enterprise in this special issue and link them to the concept of trust. Trust is a governance mechanism and theoretical construct of particular relevance for family firms, encapsulating some of their advantages and disadvantages. Trust is also linked to theoretical frameworks such as agency theory, stewardship theory, social capital theory, and transaction cost economics that are often used in family business studies, including those found in this special issue. Consequently, we advance trust as a bridging concept to reconcile and enhance our understanding of family firms as a unique organizational form.


title: The antibiotic resistance crisis: part 2: management strategies and new agents.

abstract: Controlling antibiotic-resistant bacteria calls for widespread adoption of antibiotic stewardship programs; better diagnosis, tracking and prescribing practices; optimized therapeutic regimens; prevention of infection transmission-and new drugs.


title: Diaphragm breathing movement measurement using ultrasound and radiographic imaging: a concurrent validity.

abstract: Recent ultrasound imaging evidence asserts that the diaphragm is an important multifunctional muscle to control breathing as well as stabilize the core and posture in humans. However, the validity and accuracy of ultrasound for the measurement of dynamic diaphragm movements during breathing and functional core activities have not been determined. The specific aim of this study was to validate the accuracy of ultrasound imaging measurements of diaphragm movements by concurrently comparing these measurements to the gold standard of radiographic imaging measurements. A total of 14 asymptomatic adults (9 males, 5 females; mean age =28.4 ± 3.0 years) were recruited to participate in the study. Ultrasound and radiographic images were used concurrently to determine diaphragm movement (inspiration, expiration, and excursion) during tidal breathing. Pearson correlation analysis showed strong correlations, ranging from r=0.78 to r=0.83, between ultrasound and radiographic imaging measurements of the diaphragm during inhalation, exhalation, and excursion. These findings suggest that ultrasound imaging measurement is useful to accurately evaluate diaphragm movements during tidal breathing. Clinically, ultrasound imaging measurements can be used to diagnose and treat diaphragm movement impairments in individuals with neuromuscular disorders including spinal cord injuries, stroke, and multiple sclerosis.


title: Higher-order Graph Convolutional Networks

abstract: Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique for graph-structured data. A recently-proposed method called Graph Convolutional Networks has been able to achieve state-of-the-art results in the task of node classification. However, since the proposed method relies on localized first-order approximations of spectral graph convolutions, it is unable to capture higher-order interactions between nodes in the graph. In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks (MCNs), which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods. A novel attention mechanism is used to allow each individual node to select the most relevant neighborhood to apply its filter. Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.


title: of Social Science Counter Negative Stereotypes and Assumptions

abstract: What do we currently know about atheists and secular people? In what ways are atheism and secularity correlated with positive societal outcomes? This article offers a thorough presentation and discussion of the latest social scientific research concerning the identities, values, and behaviors of people who don’t believe in God or are non-religious, and addresses the ways in which atheism and secularity are positively correlated with societal well-being.


title: Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics

abstract: Frequent subgraph mining has been extensively studied on certain graph data. However, uncertainties are inherently accompanied with graph data in practice, and there is very few work on mining uncertain graph data. This paper investigates frequent subgraph mining on uncertain graphs under probabilistic semantics. Specifically, a measure called φ-frequent probability is introduced to evaluate the degree of recurrence of subgraphs. Given a set of uncertain graphs and two numbers 0


title: FLICA: A Framework for Leader Identification in Coordinated Activity.

abstract: Leadership is an important aspect of social organization that affects the processes of group formation, coordination, and decision-making in human societies, as well as in the social system of many other animal species. The ability to identify leaders based on their behavior and the subsequent reactions of others opens opportunities to explore how group decisions are made. Understanding who exerts influence provides key insights into the structure of social organizations. In this paper, we propose a simple yet powerful leadership inference framework extracting group coordination periods and determining leadership based on the activity of individuals within a group. We are able to not only identify a leader or leaders but also classify the type of leadership model that is consistent with observed patterns of group decision-making. The framework performs well in differentiating a variety of leadership models (e.g. dictatorship, linear hierarchy, or local influence). We propose five simple features that can be used to categorize characteristics of each leadership model, and thus make model classification possible. The proposed approach automatically (1) identifies periods of coordinated group activity, (2) determines the identities of leaders, and (3) classifies the likely mechanism by which the group coordination occurred. We demonstrate our framework on both simulated and real-world data: GPS tracks of a baboon troop and video-tracking of fish schools, as well as stock market closing price data of the NASDAQ index. The results of our leadership model are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our approach is easily generalizable to any coordinated activity data from interacting entities.


title: Compensatory mechanisms contributing to keep the sagittal balance of the spine

abstract: Introduction Aging spine is characterized by facet joints arthritis, degenerative disc disease, bone remodeling and atrophy of extensor muscles resulting in a progressive kyphosis of the lumbar spine. Objective The aim of this paper is to describe the different compensatory mechanisms for patients with severe degenerative lumbar spine. Material and methods According to the severity of the imbalance, three stages are observed: balanced, balanced with compensatory mechanisms and imbalanced. For the two last stages, the compensatory mechanisms permit to limit the consequences of loss of lumbar lordosis on global sagittal alignment and therefore contribute to keep the sagittal balance of the spine. Results The basic concept is to extend adjacent segments of the kyphotic spine allowing for compensation of the sagittal unbalance but potentially inducing adverse effects. Conclusion Finally, we propose a three-step algorithm to analyze the global balance status and take into consideration the presence of the compensatory mechanisms in the spinal, pelvic and lower limb areas.