Generative unfolding with distribution mapping

Raman mapping and numerical simulation of calcium carbonates distribution in experimentally carbonated Portland-cement cores

Analytical performance evaluation of association of active and passive tracks for airborne sensors

A model of a population dynamics is solved numerically taking into account a discrete set of offsprings and the nonlinear (directed) diffusion. The model consists of a system of integro-partial differential equations subject to conditions of integral type. A spread of initially lokalized population is studied. Some numerical results are discussed.

In this work, we present a two-dimensional finite element analysis (FEA) model that describes fundamental intracellular signals of cyclic adenosine monophosphate (cAMP) in a general fashion. The model was subsequently solved numerically and the results were displayed in forms of time-course plots of cAMP concentration at a cellular location or color-filled contour maps of cAMP signal distribution within the cell at specific time points. Basic intracellular cAMP signaling was described in this model so it can be numerically validated by verifying its numerical results against available analytical solutions and against results obtained from other numerical techniques reported in the literature. This is the first important step before the model can be expanded in future work. Model simulations demonstrate that under certain conditions, sustained cAMP concentrations can be formed within endothelial cells (ECs), similar to those observed in rat pulmonary microvascular ECs. Spatial and temporal cAMP dynamic simulations indicated that the proposed FEA model is an effective tool for the study of the kinetics and spatial spread of second messenger signaling and can be expanded to simulate second messenger signals in the pulmonary vasculature.

We provide two complementary approaches to the treatment of disorder in a fundamental nonequilibrium model, the asymmetric simple exclusion process. First, a mean-field steady-state mapping is generalized to the disordered case, where it provides a mapping of probability distributions and demonstrates how disorder results in a new flat regime in the steady-state current-density plot for periodic boundary conditions. This effect was earlier observed by Phys. Rev. E 58, 1911 (1998)] but we provide a treatment for more general distributions of disorder, including both numerical results and analytic expressions for the width 2 Delta(C) of the flat section. We then apply an argument based on moving shock fronts [Europhys. Lett. 48, 257 (1999)]] to show how this leads to an increase in the high-current region of the phase diagram for open boundary conditions. Second, we show how equivalent results can be obtained easily by taking the continuum limit of the problem and then using a disordered version of the well-known Cole-Hopf mapping to linearize the equation. Within this approach we show that adding disorder induces a localization transformation (verified by numerical scaling), and Delta(C) maps to an inverse localization length, helping to give a physical interpretation to the problem.

Generative and contrastive graph representation learning with message passing.

Methods of time-controlled optimal flight-trajectory generation are developed that include the effects of risk from a threat environment. Lateral and vertical algorithms are developed for military jet aircraft with the intent of near real-time application. Simple analytic functions are used for threat models, as the focus of this paper is on general problem formulation and not detailed solutions for specific threats. A constant altitude, lateral flight-trajectory generation method is developed that optimizes with respect to time, fuel, final position, and risk exposure. Existing vertical plane trajectory generation methods that use standard direct operating costs of time and fuel are modified to include the effect of risks. Singular perturbation methods are used to obtain reduced-order airplane models that allow static rather than dynamic optimization. Pontryagin's Minimum Principle is used with a Fibonacci search method to minimize the cost functional. Formulation and numerical results are presented for both the horizontal and vertical plane problems. N the 1970's, rising fuel prices and improved micropro- cessor capability made consideration of onboard flight- path optimization appealing as a way to reduce operating costs. More recently, increased air traffic and the associated control problems have shifted research emphasis toward four- dimensional trajectory optimization. The four-dimensional problem typically includes a cost of time that allows trajecto- ries to meet required times of destination arrival. The majority of research in this area has been directed toward commercial operations and involves using the energy state approximation for generating vertical flight paths. It has been shown1^ that singular perturbation theory (SPT) may be used successfully to reduce the order of this problem. A reduced-order, often static, optimization technique can then be employed that greatly reduces computational burden. In this paper, methods of trajectory generation are developed that expand the basic SPT techniques to address the needs of military operations. Trajectory generation will be a fundamental requirement for future military aircraft flight management systems. These systems will be required to take advantage of all available information to perform integrated task processing and reduce pilot workload. In addition, the systems should be able to provide updates at any time throughout a mission or at regular time intervals sufficient for threat avoidance. Much of the previous work in trajectory generation for military air- craft57 has concentrated on feasible directions algorithms that use dynamic programming. These methods tend to be computationally intense and, therefore, are not well suited for onboard applications in dynamic threat environments. An ideal flight trajectory for military operations meets the mission requirements within the constraints of the aircraft limitations while minimizing exposure to threats. Several lev- els of information are used in the selection of such a strategic flight path and velocity profile. The trajectory will be a function of mission requirements (time of arrival, point of arrival, etc.), aircraft performance limitations (for example, fuel quantity and thrust limits), and the threat environment. The threat models used in this study do not refer to any specific military scenario. They are simple analytic functions

The primary threat to online social network (OSN) users is account compromisation. The challenge in detecting a compromised account is due to the trusted relationship established between the account owners, their friends, and the service providers. The available research which focuses on using machine learning has limitations with human experts involved in feature selection and a standardized dataset. The paper discusses users` various behaviors of users of OSN and the up-to-date approaches in detecting a compromised OSN account with emphasis on the limitations and challenges. Furthermore, we propose an enhanced machine learning approach Word Embedding and KNN (WE-KNN), which addresses the limitations faced by the previous techniques used. We detailed our proposed WE-KNN for feature extraction, selection of behavior of OSN users, and classification. Our proposed model is evaluated using the standard benchmark datasets, namely KDD Cup ‘99 and NSL-KDD and implemented it in WEKA. Besides, we used state-of-the-art evaluation metrics to assess the performance of our model. The results obtained depicts that the proposed approach in compromise account detection performs better.

The Monte Carlo method is applied to solve Cauchy problems for a system of linear and nonlinear ordinary differential equations. The Monte Carlo method is relevant for the solution of large systems of equations and in the case of small smoothness of initial functions. In this case, the system is reduced to an equivalent system of integral equations of the Volterra type. For linear systems, this transformation allows removing constraints connected with a convergence of a majorizing process. Examples of estimates of solution functionals are provided, and a behavior of their variances are discussed. In the general case, a solution interval is divided into finite subintervals, on which the nonlinear function is approximated by a polynomial. The obtained integral equation is solved by using branched Markov chains with absorption. Algorithm parallelization problems arising in this case are discussed in this paper. A one-dimensional cubic equation is considered as an example. A choice of transition densities of branching is discussed. A method of generations is described in detail. Numerical results are compared with a solution obtained by the Runge–Kutta method.

We describe a new algorithm, the reflection method, to generate off-lattice random walks of specified, though arbitrarily large, thickness in and prove that our method is ergodic on the space of thick walks. The data resulting from our implementation of this method is consistent with the scaling of the squared radius of gyration of random walks, with no thickness constraint. Based on this, we use the data to describe the complex relationship between the presence and nature of knotting and size, thickness and shape of the random walk. We extend the current understanding of excluded volume by expanding the range of analysis of how the squared radius of gyration scales with length and thickness. We also examine the profound effect of thickness on knotting in open chains. We will quantify how thickness effects the size of thick open chains, calculating the growth exponent for squared radius of gyration as a function of thickness. We will also show that for radius , increasing thickness by 0.1 decreases the probability of knot formation by 50% or more.

Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. To this extent, an effective algorithm needs to identify a suitable subset of features, as small as possible, for the classification. In this work we present ReGEC_L1, a classifier with embedded feature selection based on the Regularized Generalized Eigenvalue Classifier (ReGEC) and equipped with a L1-norm regularization term. We detail the mathematical formulation and the numerical algorithm. Numerical results, obtained on some de facto standard benchmark data sets, show that the approach we propose produces a remarkable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM_L1 method. A MATLAB implementation of ReGEC_L1 is available at http://www.na.icar.cnr.it/~mariog/regec_l1.html .

The generation of average probability-of-detection curves for threedimensional, or pencil-beam, radars, with compensation for random target position included statistically, is discussed. A fixed-array radar with step-scan operation is assumed and, with 3-dB beam crossover points, this is effectively the case of one pulse per beamwidth or one hit per scan. Numerical results are presented showing detection dependence on beam overlap, detection threshold, and amplitude scintillation. The fact that the average energy received from a target, assuming a uniform distribution of target position within a beam, does not lead to the correct average probability of detection at other than 0.5 P D is discussed and shown graphically.

In this paper, the received wave at the observation point is calculated when observation point moves. It can be shown that the frequency of the received wave is shifted. To verify the over set gird generation method, numerical results are compared with the body fitted grid generation method and the theoretical results. Good agreements are obtained in these three results. The over set grid generation technique is easier to use for the moving boundary problems. We believe that these numerical techniques will have great promises for the analysis of the moving boundary problems in electromagnetic field.

Visual scanpaths of multiple humans on an image represent the process by which they capture the information in it. State-of-the-art models to predict visual scanpaths on images learn directly from recorded human visual scanpaths. However, the generation of multiple visual scanpaths on an image having diversity like human visual scanpaths has not been explicitly considered. In this paper, we propose a deep network for predicting multiple diverse visual scanpaths on an image. Image-specific hidden Markov model based generative data augmentation is performed in the beginning to increase the number of image-visual scanpath training pairs. Considering a similarity between our generative data augmentation process and the use of long short-term memory (LSTM) for prediction, we propose an LSTM based visual scanpath predictor. A network to predict a single visual scanpath on an image is designed first. The network is then modified to predict multiple diverse visual scanpaths representing different viewer varieties by using a parameter indicating the uniqueness of a viewer. A random vector is also employed for subtle variations within scanpaths of the same viewer variety. Our models are evaluated on three standard datasets using multiple performance measures, which demonstrate the superiority of the proposed approach over the state-of-the-art. Empirical studies are also given indicating the significance of our generative data augmentation method and our multiple scanpath prediction strategy producing diverse visual scanpaths. Code link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ashishverma03.github.io/Diverse-Visual-Scanpath</uri>

This paper presents a control algorithm for a nonlinear mathematical model of a quad-copter. Under the proposed control law, the machine follows polynomial trajectories with zero stationary error. An original method for the autonomous generation of trajectories is presented along with a stability result concerning the swarm formed by agents following this algorithm. The same algorithm is then slightly modified to provide an elegant solution to the obstacle navigation problem. Simulations and numerical results are presented.