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Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of the mechanisms underlying brain development or disorders associated with abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. Results: In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle, and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development. Availability: The code of STW-MD is available at https://github.com/tsnm1/STW-MD., Comment: 11 pages, 6 figures

We study the two-dimensional motion of a magnetic skyrmion driven by a ratchetlike polarized electric current that is periodic in both space and time. Some general cases are considered, in each of which,in the low temperature and adiabatic limit, regardless of the details of the driving current, the time and statistical average velocity along any direction is topologically quantized as a Chern number, multiplied by a basic unit. We make two approaches, one based on identifying the drift direction, and the other based on the nonhermitian adiabatic perturbation theory developed for the Fokker-Planck operator. Both approach applies in the case of periodicity along the direction of the driving current and homogeneity in the transverse direction, for which the analytical result is confirmed by our numerical simulation on the constituent spins,and a convenient experiment is proposed., Comment: 32 pages, preprint format

The behavior of accelerating Unruh-DeWitt detectors coupled with a spinor field in (3+1)-dimensional spacetime is investigated. For a single point-like detector with Gaussian switching function, the transition probability increases with the acceleration and thus the antiUnruh effect effect cannot occur. Due to the spinor structure of the Dirac field, UV divergences are encountered in the calculation of the entanglement between the detectors. After introducing some UV cutoff $\Lambda$, the logarithmic negativity of detectors is shown to behave nonmonotonically with respect to the acceleration. Besides, the logarithmic negativity increases with the cutoff $\Lambda$ and decreases with the distance between the detectors. The mutual information between the two detectors is also discussed., Comment: 30 pages

We consider two accelerating Unruh-DeWitt detectors coupled linearly or quadratically with a scalar field. We show that entanglement can be created by acceleration, and is divergent only when the two detectors coincide. For linear coupling, entanglment decreases monotonically with the increase of acceleration. For quadratic coupling, entanglement behaves non-monotonically., Comment: 25 pages

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat.

Developing appropriate analytic-function-based constitutive models for new materials with nonlinear mechanical behavior is demanding. For such kinds of materials, it is more challenging to realize the integrated design from the collection of the material experiment under the classical topology optimization framework based on constitutive models. The present work proposes a mechanistic-based data-driven topology optimization (DDTO) framework for three-dimensional continuum structures under finite deformation. In the DDTO framework, with the help of neural networks and explicit topology optimization method, the optimal design of the three-dimensional continuum structures under finite deformation is implemented only using the uniaxial and equi-biaxial experimental data. Numerical examples illustrate the effectiveness of the data-driven topology optimization approach, which paves the way for the optimal design of continuum structures composed of novel materials without available constitutive relations.

In this article, an efficient sequential linear programming algorithm (SLP) for uncertainty analysis-based data-driven computational mechanics (UA-DDCM) is presented. By assuming that the uncertain constitutive relationship embedded behind the prescribed data set can be characterized through a convex combination of the local data points, the upper and lower bounds of structural responses pertaining to the given data set, which are more valuable for making decisions in engineering design, can be found by solving a sequential of linear programming problems very efficiently. Numerical examples demonstrate the effectiveness of the proposed approach on sparse data set and its robustness with respect to the existence of noise and outliers in the data set.

In this paper, a mechanistic data-driven approach is proposed to accelerate structural topology optimization, employing an in-house developed finite element convolutional neural network (FE-CNN). Our approach can be divided into two stages: offline training, and online optimization. During offline training, a mapping function is built between high and low resolution representations of a given design domain. The mapping is expressed by a FE-CNN, which targets a common objective function value (e.g., structural compliance) across design domains of differing resolutions. During online optimization, an arbitrary design domain of high resolution is reduced to low resolution through the trained mapping function. The original high-resolution domain is thus designed by computations performed on only the low-resolution version, followed by an inverse mapping back to the high-resolution domain. Numerical examples demonstrate that this approach can accelerate optimization by up to an order of magnitude in computational time. Our proposed approach therefore shows great potential to overcome the curse-of-dimensionality incurred by density-based structural topology optimization. The limitation of our present approach is also discussed.

HepLib is a C++ Library for computations in High Energy Physics, it works on top of GiNaC, a well-established C++ library used to perform symbolic computations. HepLib combines serval well-known packages to get high efficiency, including Qgraf to generate Feynman aptitudes, FORM to perform Dirac/Color matrix related computations, and FIRE or KIRA for integration-by-parts (IBP) reduction. Another core feature of HepLib lies in the numerical evaluation of master integrals using sector decomposition, which is a general method widely used in high-order numerical computation and has been implemented in many public packages in many different languages, and we present another implementation in the language of C++ with many new features. We use GiNaC to handle the symbolic operations, and export the corresponding integrand into an optimized C++ code, that will be compiled internally and linked dynamically, a customizable numerical integrator is selected to perform the numerical integration, while the integrand can be evaluated in different float precisions, including the arbitrary precision supported by MPFR., Comment: 26 pages, 1 figure