NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

For better translational outcomes researchers and clinicians alike demand novel tools to distil complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction, and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper—designed specifically for neuroimaging data—that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic “ongoing” cognition. Looking forward, we hope our framework could help researchers push the boundaries of psychiatric neuroimaging towards generating insights at the single-participant level while scaling across consortium-size datasets.

An alternative look at the linear regression model

An alternative look at the linear regression model is taken by proposing an original treatment of a full column rank model (design) matrix. In such a situation, the Moore–Penrose inverse of the matrix can be obtained by utilizing a particular formula which is applicable solely when a matrix to be inverted can be columnwise partitioned into two matrices of disjoint ranges. It turns out that this approach, besides simplifying derivations, provides a novel insight into some of the notions involved in the model and reduces computational costs needed to obtain sought estimators. The paper contains also a numerical example based on astronomical observations of the localization of Polaris, demonstrating usefulness of the proposed approach.

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

A universal deep learning-based framework towards fully ab initio simulation of atmospheric aerosol nucleation

Atmospheric aerosol nucleation contributes to around half of cloud condensation nuclei globally. Despite the importance for climate, detailed nucleation mechanisms are still poorly understood. Understanding aerosol nucleation dynamics is hindered by non-reactivity of force fields and high computational costs due to rare event nature of aerosol nucleation. Developing reactive force fields for nucleation systems are even more challenging than covalently bonded materials because of wide size range and high dimensional characteristics of non-covalent hydrogen bonding bridging clusters. Here we proposes a system transferable framework to train an accurate reactive force field (FF) based on deep neural network (DNN) and further bridges the DNN-FF based molecular dynamics (MD) with cluster kinetics model based on Poisson distributions of reactive events to overcome high computational costs from direct MD. We found that previously reported acid-base formation rates tend to be underestimated several times, emphasizing acid-base nucleation observed in multiple environments should be revisited.

Boundary element methods for the wave equation based on hierarchical matrices and adaptive cross approximation

Time-domain Boundary Element Methods (BEM) have been successfully used in acoustics, optics and elastodynamics to solve transient problems numerically. However, the storage requirements are immense, since the fully populated system matrices have to be computed for a large number of time steps or frequencies. In this article, we propose a new approximation scheme for the Convolution Quadrature Method powered BEM, which we apply to scattering problems governed by the wave equation. We use $${\mathscr {H}}^2$$ H 2 -matrix compression in the spatial domain and employ an adaptive cross approximation algorithm in the frequency domain. In this way, the storage and computational costs are reduced significantly, while the accuracy of the method is preserved.

Artificial Intelligence-Based Aircraft Accident Threat Parrying Method

An anti-aircraft accident method is proposed, implemented in the decision support module, which is the main element of the flight safety control system and is a dynamic expert system. On the basis of the proposed method, recommendations are formed to the threat countering crew accidents using the information about its psychophysical state, the technical state an aircraft, external influencing factors, as well as a forecast of changes in flight conditions. The advantage of the proposed method is the ability to identify the immediate threat of an accident, as well as the development of management decisions to reduce the impact of the cause of the accident on flight safety. The peculiarity of the method of parrying the threat of an aircraft accident is the classification of management decisions depending on the flight conditions of the aircraft, which will reduce the computational costs for generating a threat parrying signal. Numerical modeling of the work using the assessment of a set of decision support rules made it possible to confirm its performance. The results can be used in systems development for safety an aircraft’s flight, the mathematical support of decision support systems.

LIFE ASSESSMENT OF RAILS BY CRITERION OF OCCURRENCE OF FATIGUE CRACKS

The paper presents modern concepts of the contact fatigue crack occurrence in the railhead. Numerical simulation of the contact interaction between rails and rolling stock wheels by finite elements method are presented. During the simulation, the problem was solved in elastic and elastic-plastic formulations. We considered R65 rail profile and standard railway wheels. The conditions for the rail–wheel interaction corresponded to train movement on the straight section of the track without slipping. The modern life assessment methodology involves the calculation of the material damage increment at each point of the element as the load varies over time, and subsequent summation of this damage. Upon reaching the ultimate value of the total damage, the structural element is believed to lose its load-carrying capacity, i.e. a crack is formed in it. Despite the substantial simplification of the real problem, the computational costs for the implementation of such methods for predicting the durability will be unnecessarily high. In this regard, we propose the simplified method of the durability calculation.

An Improved Phase-Derived Range Method Based on High-Order Multi-Frame Track-Before-Detect for Warhead Detection

It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be separated by its micro-Doppler features introduced by the precession and nutation. As is well known, the accuracy of the phase-derived range method, to extract micro-Doppler curves, can reach sub-wavelength. However, it suffers from an inefficiency of energy integration and high computational costs. In this paper, a novel phase-derived range method, using high-order multi-frame track-before-detect is proposed for micro-Doppler curve extraction under a low signal-to-noise ratio (SNR). First, the sinusoidal micro-Doppler range sequence is treated as the state, and the dynamic model is described as a Markov chain to obtain the envelopes and then the ambiguous phases. Instead of processing the whole frames, the proposed method only processes the latest frame at an arbitrary given time, which reduces the computational costs. Then, the correlation of all pairs of adjacent pulses is calculated along the slow time dimension to find the number of cells that the point scatterer crosses, which can be further used in phase unwrapping. Finally, the phase-derived range method is employed to get the micro-Doppler curves. Simulation results show that the proposed method is capable of extracting the micro-Doppler curves with sub-wavelength accuracy, even if SNR = −15 dB, with a lower computational cost.

Reverse time migration of phase encoded all-order multiples

Multiples follow long paths and carry more information on the subsurface than primary reflections, making them particularly useful for imaging. However, seismic migration using multiples can generate crosstalk artifacts in the resulting images because multiples of different orders interfere with each others, and crosstalk artifacts greatly degrade the quality of an image. We propose to form a supergather by applying phase-encoding functions to image multiples and stacking several encoded controlled-order multiples. The multiples are separated into different orders using multiple decomposition strategies. The method is referred to as the phase-encoded migration of all-order multiples (PEM). The new migration can be performed by applying only two finite-difference solutions to the wave equation. The solutions include backward-extrapolating the blended virtual receiver data and forward-propagating the summed virtual source data. The proposed approach can significantly attenuate crosstalk artifacts and also significantly reduce computational costs. Numerical examples demonstrate that the PEM can remove relatively strong crosstalk artifacts generated by multiples and is a promising approach for imaging subsurface targets.

Flow Balancing in Modeling of Multi-Phase Flow using Non-Conformal Finite Element Meshes

The method of balancing numerical finite element flows in modeling a process of multiphase flow using non-conformal hexagonal meshes is considered. Studies have been carried out for a simple reservoir configuration and on a more complex model of a real field of high-viscosity oil in the Tatarstan. The research results showed that the balancing method allows one to obtain a conservative solution when using non-conformal finite element meshes with sufficiently large cells. At the same time, this method is completely free of problems associated with grid orientation, even for complex models containing zones with highly variable permeability. The proposed algorithm for the adaptive choice of parameters allows to do the factorization of the SLAE matrix at sufficiently small number of time steps; therefore, the computational costs of the flow balancing procedure are an order of magnitude less than the costs associated with calculating the pressure field and phase transfer. The used non-conformal finite element meshes with an arbitrary number of docked hexagons can significantly reduce the number of degrees of freedom when modeling multiphase flows in reservoirs with much small local heterogeneity and in the presence of several perforated zones. As a result, computational costs are reduced by almost an order of magnitude, and, at the same time, the required approximation accuracy is maintained. With an increase in the scale of the model and the number of operating wells, this advantage increases even more.