147 research outputs found
Orbital-scale nonlinear response of East Asian summer monsoon to its potential driving forces in the late Quaternary
We conducted a statistical study to characterize the nonlinear response of the East Asian summer monsoon (EASM) to its potential forcing factors over the last 260 ka on orbital timescales. We find that both variation in solar insolation and global ice volume were responsible for the nonlinear forcing of orbital-scale monsoonal variations, accounting for similar to 80% of the total variance. Specifically, EASM records with dominated precession variance exhibit a more sensitive response to changes in solar insolation during intervals of enhanced monsoon strength, but are less sensitive during intervals of reduced monsoon strength. In the case of global ice volume with 100-ka variance, this difference is not one of sensitivity but rather a difference in baseline conditions, such as the relative areas of land and sea which affected the land-sea thermal gradient. We therefore suggest that EASM records with dominated precession variance recorded the signal of a shift in the location of the Inter-tropical Convergence Zone, and the associated changes in the incidence of torrential rainfall; while for proxies with dominated 100-ka variance, it recorded changes in the land-sea thermal gradient via its effects on non-torrential precipitation
Diffusion Mechanism in Residual Neural Network: Theory and Applications
Diffusion, a fundamental internal mechanism emerging in many physical
processes, describes the interaction among different objects. In many learning
tasks with limited training samples, the diffusion connects the labeled and
unlabeled data points and is a critical component for achieving high
classification accuracy. Many existing deep learning approaches directly impose
the fusion loss when training neural networks. In this work, inspired by the
convection-diffusion ordinary differential equations (ODEs), we propose a novel
diffusion residual network (Diff-ResNet), internally introduces diffusion into
the architectures of neural networks. Under the structured data assumption, it
is proved that the proposed diffusion block can increase the distance-diameter
ratio that improves the separability of inter-class points and reduces the
distance among local intra-class points. Moreover, this property can be easily
adopted by the residual networks for constructing the separable hyperplanes.
Extensive experiments of synthetic binary classification, semi-supervised graph
node classification and few-shot image classification in various datasets
validate the effectiveness of the proposed method
An axiomatized PDE model of deep neural networks
Inspired by the relation between deep neural network (DNN) and partial
differential equations (PDEs), we study the general form of the PDE models of
deep neural networks. To achieve this goal, we formulate DNN as an evolution
operator from a simple base model. Based on several reasonable assumptions, we
prove that the evolution operator is actually determined by
convection-diffusion equation. This convection-diffusion equation model gives
mathematical explanation for several effective networks. Moreover, we show that
the convection-diffusion model improves the robustness and reduces the
Rademacher complexity. Based on the convection-diffusion equation, we design a
new training method for ResNets. Experiments validate the performance of the
proposed method
Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
The single-particle cryo-EM field faces the persistent challenge of preferred
orientation, lacking general computational solutions. We introduce cryoPROS, an
AI-based approach designed to address the above issue. By generating the
auxiliary particles with a conditional deep generative model, cryoPROS
addresses the intrinsic bias in orientation estimation for the observed
particles. We effectively employed cryoPROS in the cryo-EM single particle
analysis of the hemagglutinin trimer, showing the ability to restore the
near-atomic resolution structure on non-tilt data. Moreover, the enhanced
version named cryoPROS-MP significantly improves the resolution of the membrane
protein NaX using the no-tilted data that contains the effects of micelles.
Compared to the classical approaches, cryoPROS does not need special
experimental or image acquisition techniques, providing a purely computational
yet effective solution for the preferred orientation problem. Finally, we
conduct extensive experiments that establish the low risk of model bias and the
high robustness of cryoPROS
Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining
Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes
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