We introduce the Mori-Zwanzig (MZ) Modal Decomposition (MZMD), a novel technique for performing modal analysis of large scale spatio-temporal structures in complex dynamical systems, and show that it represents an eff...
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Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog...
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Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on single-step Feynman-Kitaev verification of an analog quantum simulation, in which the verifier need only run an O(λ2)-time classical computation, and the prover need only prepare O(1) samples of a history state and perform O(λ2) single-qubit measurements, for a security parameter λ. We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations.
Insights on the salient features of malicious software spreading over large-scale wireless sensor networks (WSNs) in low-power Internet of Things (IoT) are not only essential to project, but also mitigate the persiste...
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This paper considers the problem of image segmentation for medical images, in particular, cutaneous lesions. Given a digital image of a skin lesion, our goal is to compute the border curve separating the lesion from t...
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Background: MicroRNAs act as post-transcriptional regulators that repress translation or degrade mRNA *** microRNA has many mRNA targets and each mRNA may be targeted by several microRNAs. Skeletal muscles express a p...
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Background: MicroRNAs act as post-transcriptional regulators that repress translation or degrade mRNA *** microRNA has many mRNA targets and each mRNA may be targeted by several microRNAs. Skeletal muscles express a plethora of microRNA genes that regulate muscle development and function by controlling the expression of protein-coding target genes. To expand our understanding of the role of microRNA, specifically btamiR-365-3 p, in muscle biology, we investigated its functions in regulating primary bovine myoblast proliferation and ***: Firstly, we found that bta-miR-365-3 p was predominantly expressed in skeletal muscle and heart tissue in Chinese Qinchuan beef cattle. Quantitative PCR and western blotting results showed that overexpression of btamiR-365-3 p significantly reduced the expression levels of cyclin D1(CCND1), cyclin dependent kinase 2(CDK2) and proliferating cell nuclear antigen(PCNA) but stimulated the expression levels of muscle differentiation markers, i.e.,MYOD1, MYOG at both mRNA and protein level. Moreover, downregulation of bta-miR-365-3 p increased the expression of CCND1, CDK2 and PCNA but decreased the expression of MYOD1 and MYOG at both mRNA and protein levels. Furthermore, flow cytometry, EdU proliferation assays and immunostaining results showed that increased levels of bta-miR-365-3 p suppressed cell proliferation but promoted myotube formation, whereas decreased levels of bta-miR-365-3 p resulted in the opposite consequences. Finally, we identified that activin A receptor type I(ACVR1) could be a direct target of bta-miR-365-3 p. It was demonstrated that bta-miR-365-3 p can bind to the 3'UTR of ACVR1 gene to regulate its expression based on dual luciferase gene reporter ***, knock-down of ACVR1 was associated with decreased expressions of CDK2, CCND1 and PCNA but increased expression of MYOG and MYOD1 both at mRNA and protein ***: Collectively, these data suggested that bta-m
Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covar...
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Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.
In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionalit...
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Understanding the dynamics of financial transactions among people is critically important for various applications such as fraud detection. One important aspect of financial transaction networks is temporality. The or...
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This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Departmen...
This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.
Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situ...
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