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1. Analytical solution to a time-varying LIP model for quadrupedal walking on a vertically oscillating surface

   https://doi.org/10.1016/j.mechatronics.2023.103073  

   Iqbal, Amir; Veer, Sushant; Gu, Yan (December 2023, Mechatronics)

   Sun, Weichao; Yao, Bin (Ed.)

   This paper introduces an analytically tractable and computationally efficient model for legged robot dynamics during locomotion on a dynamic rigid surface (DRS), along with an approximate analytical solution and a real-time walking pattern generator synthesized based on the model and solution. By relaxing the static-surface assumption, we extend the classical, time-invariant linear inverted pendulum (LIP) model for legged locomotion on a static surface to dynamic-surface locomotion, resulting in a time-varying LIP model termed as “DRS-LIP”. Sufficient and necessary stability conditions of the time-varying DRS-LIP model are obtained based on the Floquet theory. This model is also transformed into Mathieu’s equation to derive an approximate analytical solution that provides reasonable accuracy with a relatively low computational cost. Using the extended model and its solution, a walking pattern generator is developed to efficiently plan physically feasible trajectories for quadrupedal walking on a vertically oscillating surface. Finally, simulations and hardware experiments from a Laikago quadrupedal robot walking on a pitching treadmill (with a maximum vertical acceleration of 1 m/s ) confirm the accuracy and efficiency of the proposed analytical solution, as well as the efficiency, feasibility, and robustness of the pattern generator, under various surface motions and gait parameters. 

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2. Stochastic Simulation of Continuous Time Random Walks: Minimizing Error in Time-Dependent Rate Coefficients for Diffusion-Limited Reactions

   https://doi.org/10.1007/s42519-023-00343-6  

   Grayson, Matthew A; Kangabire, Alain; Aygen, Can; Considine, Kevin (September 2023, Journal of Statistical Theory and Practice)

   A reaction limited by standard diffusion is simulated stochastically to illustrate how the continuous time random walk (CTRW) formalism can be implemented with minimum statistical error. A step-by-step simulation of the diffusive random walk in one dimension reveals the fraction of surviving reactants P(t) as a function of time, and the time-dependent unimolecular reaction rate coefficient K(t). Accuracy is confirmed by comparing the time-dependent simulation to results from the analytical master equation, and the asymptotic solution to that of Fickian diffusion. An early transient feature is shown to arise from higher spatial harmonics in the Fourier distribution of walkers between reaction sites. Statistical ‘shot’ noise in the simulation is quantified along with the offset error due to the discrete time derivative, and an optimal simulation time interval t0 is derived to achieve minimal error in the finite time-difference estimation of the reaction rate. The number of walkers necessary to achieve a given error tolerance is derived, and W = 10^7 walkers is shown to achieve an accuracy of ±0.2% when the survival probability reaches P(t) ∼ 1/3 . The stochastic method presented here serves as an intuitive basis for understanding the CTRW formalism, and can be generalized to model anomalous diffusion-limited reactions to prespecified precision in regimes where the governing wait-time distributions have no analytical solution. 

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3. Data-driven statistical reduced-order modeling and quantification of polycrystal mechanics leading to porosity-based ductile damage

   https://doi.org/10.1016/j.jmps.2023.105386  

   Zhang, Yinling; Chen, Nan; Bronkhorst, Curt A.; Cho, Hansohl; Argus, Robert (October 2023, Journal of the Mechanics and Physics of Solids)

   Predicting the process of porosity-based ductile damage in polycrystalline metallic materials is an essential practical topic. Ductile damage and its precursors are represented by extreme values in stress and material state quantities, the spatial probability density function (PDF) of which are highly non-Gaussian with strong fat tails. Traditional deterministic forecasts utilizing sophisticated continuum-based physical models generally lack in representing the statistics of structural evolution during material deformation. Computational tools which do represent complex structural evolution are typically expensive. The inevitable model error and the lack of uncertainty quantification may also induce significant forecast biases, especially in predicting the extreme events associated with ductile damage. In this paper, a data-driven statistical reduced-order modeling framework is developed to provide a probabilistic forecast of the deformation process of a polycrystal aggregate leading to porosity-based ductile damage with uncertainty quantification. The framework starts with computing the time evolution of the leading few moments of specific state variables from the spatiotemporal solution of full- field polycrystal simulations. Then a sparse model identification algorithm based on causation entropy, including essential physical constraints, is utilized to discover the governing equations of these moments. An approximate solution of the time evolution of the PDF is obtained from the predicted moments exploiting the maximum entropy principle. Numerical experiments based on polycrystal realizations of a representative body-centered cubic (BCC) tantalum illustrate a skillful reduced-order model in characterizing the time evolution of the non-Gaussian PDF of the von Mises stress and quantifying the probability of extreme events. The learning process also reveals that the mean stress is not simply an additive forcing to drive the higher-order moments and extreme events. Instead, it interacts with the latter in a strongly nonlinear and multiplicative fashion. In addition, the calibrated moment equations provide a reasonably accurate forecast when applied to the realizations outside the training data set, indicating the robustness of the model and the skill for extrapolation. Finally, an information-based measurement is employed to quantitatively justify that the leading four moments are sufficient to characterize the crucial highly non-Gaussian features throughout the entire deformation history considered. 

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4. Image-based PDF Malware Detection Using Pre-trained Deep Neural Networks

   https://doi.org/10.1109/ISDFS60797.2024.10527343  

   Nichols, Tyler; Zemlanicky, Jack; Luo, Zhirui; Li, Qingqing; Zheng, Jun (April 2024, 2024 12th International Symposium on Digital Forensics and Security (ISDFS))

   PDF is a popular document file format with a flexible file structure that can embed diverse types of content, including images and JavaScript code. However, these features make it a favored vehicle for malware attackers. In this paper, we propose an image-based PDF malware detection method that utilizes pre-trained deep neural networks (DNNs). Specifically, we convert PDF files into fixed-size grayscale images using an image visualization technique. These images are then fed into pre-trained DNN models to classify them as benign or malicious. We investigated four classical pre-trained DNN models in our study. We evaluated the performance of the proposed method using the publicly available Contagio PDF malware dataset. Our results demonstrate that MobileNetv3 achieves the best detection performance with an accuracy of 0.9969 and exhibits low computational complexity, making it a promising solution for image-based PDF malware detection. 

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5. Phylogeographic model selection using convolutional neural networks

   https://doi.org/10.1111/1755-0998.13427  

   Fonseca, Emanuel M; Colli, Guarino R; Werneck, Fernanda P; Carstens, Bryan C (November 2021, Molecular Ecology Resources)

   Fountain-Jones, Nicholas M; Smith, Megan L; Austerlitz, Frédéric (Ed.)

   Abstract The discipline of phylogeography has evolved rapidly in terms of the analytical toolkit used to analyse large genomic data sets. Despite substantial advances, analytical tools that could potentially address the challenges posed by increased model complexity have not been fully explored. For example, deep learning techniques are underutilized for phylogeographic model selection. In non‐model organisms, the lack of information about their ecology and evolution can lead to uncertainty about which demographic models are appropriate. Here, we assess the utility of convolutional neural networks (CNNs) for assessing demographic models in South American lizards in the genusNorops. Three demographic scenarios (constant, expansion, and bottleneck) were considered for each of four inferred population‐level lineages, and we found that the overall model accuracy was higher than 98% for all lineages. We then evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among the four lineages, identifying a single model with an estimated overall accuracy of 87% when using CNNs. The inferred demography of the lizard system suggests that gene flow between non‐sister populations and changes in effective population sizes through time, probably in response to Pleistocene climatic oscillations, have shaped genetic diversity in this system. Approximate Bayesian computation (ABC) was applied to provide a comparison to the performance of CNNs. ABC was unable to identify a single model among the larger set of 26 models in the subsequent analysis. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer's toolkit. 

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