Damping models in the truncated derivative nonlinear Schrödinger equation

Four-dimensional flow in the phase space of three amplitudes of circularly polarized Alfven waves and one relative phase, resulting from a resonant three-wave truncation of the derivative nonlinear Schrödinger equation, has been analyzed; wave 1 is linearly unstable with growth rate , and waves 2 and 3 are stable with damping 2 and 3, respectively. The dependence of gross dynamical features on the damping model as characterized by the relation between damping and wave-vector ratios, 2 /3, k2 /k3, and the polarization of the waves, is discussed; two damping models, Landau k and resistive k2, are studied in depth. Very complex dynamics, such as multiple blue sky catastrophes and chaotic attractors arising from Feigenbaum sequences, and explosive bifurcations involving Intermittency-I chaos, are shown to be associated with the existence and loss of stability of certain fixed point P of the flow. Independently of the damping model, P may only exist as against flow contraction just requiring.In the case of right-hand RH polarization, point P may exist for all models other than Landau damping; for the resistive model, P may exist for RH polarization only if 2+3/2.

Efficient sampling from truncated bivariate Gaussians via Box-Muller transformation

Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. In this work, we introduce a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows us to achieve exact sampling, thus becoming the most efficient approach possible. RESUMEN. Método específico para generar muestras de manera eficiente de Gaussianas bidimensionales truncadas con cualquier zona de truncamiento basado en la transformación de Box-Muller.

Regression analysis of top of descent location for idle-thrust descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. In addition to recording TOD, the cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also identified for use as the independent variables in the regression analysis. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajectory parame- ters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowledge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace.

Regression models with MoPs Bayesian networks

We present a model of Bayesian network for continuous variables, where densities and conditional densities are estimated with B-spline MoPs. We use a novel approach to directly obtain conditional densities estimation using B-spline properties. In particular we implement naive Bayes and wrapper variables selection. Finally we apply our techniques to the problem of predicting neurons morphological variables from electrophysiological ones.

Univariate and bivariate truncated von Mises distributions

In this article we study the univariate and bivariate truncated von Mises distribution, as a generalization of the von Mises distribution (\cite{jupp1989}), (\cite{mardia2000directional}). This implies the addition of two or four new truncation parameters in the univariate and, bivariate cases, respectively. The results include the definition, properties of the distribution and maximum likelihood estimators for the univariate and bivariate cases. Additionally, the analysis of the bivariate case shows how the conditional distribution is a truncated von Mises distribution, whereas the marginal distribution that generalizes the distribution introduced in \cite{repe}. From the viewpoint of applications, we test the distribution with simulated data, as well as with data regarding leaf inclination angles (\cite{safari}) and dihedral angles in protein chains (\cite{prote}). This research aims to assert this probability distribution as a potential option for modelling or simulating any kind of phenomena where circular distributions are applicable.\par

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

Disruption of cellular translational control by a viral truncated eukaryotic translation initiation factor 2α kinase homolog

Phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) is a common cellular mechanism to limit protein synthesis in stress conditions. Baculovirus PK2, which resembles the C-terminal half of a protein kinase domain, was found to inhibit both human and yeast eIF2α kinases. Insect cells infected with wild-type, but not pk2-deleted, baculovirus exhibited reduced eIF2α phosphorylation and increased translational activity. The negative regulatory effect of human protein kinase RNA-regulated (PKR), an eIF2α kinase, on virus production was counteracted by PK2, indicating that baculoviruses have evolved a unique strategy for disrupting a host stress response. PK2 was found in complex with PKR and blocked kinase autophosphorylation in vivo, suggesting a mechanism of kinase inhibition mediated by interaction between truncated and intact kinase domains.

Conditional switching of vascular endothelial growth factor (VEGF) expression in tumors: Induction of endothelial cell shedding and regression of hemangioblastoma-like vessels by VEGF withdrawal

We have recently shown that VEGF functions as a survival factor for newly formed vessels during developmental neovascularization, but is not required for maintenance of mature vessels. Reasoning that expanding tumors contain a significant fraction of newly formed and remodeling vessels, we examined whether abrupt withdrawal of VEGF will result in regression of preformed tumor vessels. Using a tetracycline-regulated VEGF expression system in xenografted C6 glioma cells, we showed that shutting off VEGF production leads to detachment of endothelial cells from the walls of preformed vessels and their subsequent death by apoptosis. Vascular collapse then leads to hemorrhages and extensive tumor necrosis. These results suggest that enforced withdrawal of vascular survival factors can be applied to target preformed tumor vasculature in established tumors. The system was also used to examine phenotypes resulting from over-expression of VEGF. When expression of the transfected VEGF cDNA was continuously “on,” tumors became hyper-vascularized with abnormally large vessels, presumably arising from excessive fusions. Tumors were significantly less necrotic, suggesting that necrosis in these tumors is the result of insufficient angiogenesis.

A substance P (neurokinin-1) receptor mutant carboxyl-terminally truncated to resemble a naturally occurring receptor isoform displays enhanced responsiveness and resistance to desensitization

Two isoforms of the substance P (SP) receptor, differing in the length of the cytoplasmic carboxyl-terminus by ≈8 kDa, have been detected previously in rat salivary glands and other tissues. The binding and functional properties of these two isoforms have been investigated using full-length (407 amino acids) and carboxyl-terminally truncated (324 amino acids) rat SP receptors transfected stably into Chinese hamster ovary cells. Both the full-length and the truncated receptor bound radiolabeled SP with a similar Kd (≈0.1 nM). The average number of high affinity SP binding sites per cell was 1.0 × 105 and 0.3 × 105 for the full-length and the truncated SP receptor, respectively. In both cell lines, SP induced a rapid but transient increase in cytosolic calcium concentration ([Ca2+]i), which consisted of the release of Ca2+ from intracellular stores and the influx of extracellular Ca2+. Both components are dependent on phospholipase C activation. Although the full-length and the truncated receptor utilize the same calcium pathways, they differ in their EC50 values (0.28 nM for the full-length; 0.07 nM for the truncated). These differences in responsiveness may be related to the observed differences in receptor desensitization. The truncated receptor, in contrast to the full-length receptor, does not undergo rapid and long-lasting desensitization. Cells possessing the short isoform of the SP receptor would thus be expected to exhibit a prolonged responsiveness.

Interleukin 12 and B7-1 costimulatory molecule expressed by an adenovirus vector act synergistically to facilitate tumor regression

Stimulation of antitumor immune mechanisms is the primary goal of cancer immunotherapy, and accumulating evidence suggests that effective alteration of the host–tumor relationship involves immunomodulating cytokines and also the presence of costimulatory molecules. To examine the antitumor effect of direct in vivo gene transfer of murine interleukin 12 (IL-12) and B7-1 into tumors, we developed an adenovirus (Ad) vector, AdIL12–B7-1, that encodes the two IL-12 subunits in early region 1 (E1) and the B7-1 gene in E3 under control of the murine cytomegalovirus promoter. This vector expressed high levels of IL-12 and B7-1 in infected murine and human cell lines and in primary murine tumor cells. In mice bearing tumors derived from a transgenic mouse mammary adenocarcinoma, a single intratumoral injection with a low dose (2.5 × 107 pfu/mouse) of AdIL12–B7-1 mediated complete regression in 70% of treated animals. By contrast, administration of a similar dose of recombinant virus encoding IL-12 or B7-1 alone resulted in only a delay in tumor growth. Interestingly, coinjection of two different viruses expressing either IL-12 or B7-1 induced complete tumor regression in only 30% of animals treated at this dose. Significantly, cured animals remained tumor free after rechallenge with fresh tumor cells, suggesting that protective immunity had been induced by treatment with AdIL12–B7-1. These results support the use of Ad vectors as a highly efficient delivery system for synergistically acting molecules and show that the combination of IL-12 and B7-1 within a single Ad vector might be a promising approach for in vivo cancer therapy.

Movements of truncated kinesin fragments with a short or an artificial flexible neck

To investigate the role of the neck domain of kinesin, we used optical trapping nanometry to perform high-resolution measurements of the movements and forces produced by recombinant kinesin fragments in which the neck domains were shortened or replaced by an artificial random coil. Truncated kinesin fragments (K351) that contain a motor domain consisting of ≈340 aa and a short neck domain consisting of ≈11 aa showed fast movement (800 nm/s) and 8-nm steps. Such behavior was similar to that of recombinant fragments containing the full-length neck domain (K411) and to that of native kinesin. Kinesin fragments lacking the short neck domain (K340), however, showed very slow movement (<50 nm/s), as previously reported. Joining an artificial 11-aa sequence that was expected to form a flexible random chain to the motor domain (K340–chain) produced normal fast (≈700 nm/s) and stepwise movement. The results suggest that the neck domain does not act as a rigid lever arm to magnify the structural change at the catalytic domain as has been believed for myosin, but it does act as a flexible joint to guarantee the mobility of the motor domain.

Bootstrap confidence levels for phylogenetic trees

Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783–791] suggested the use of the bootstrap to answer this question. Felsenstein’s method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein’s method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.