The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects ...
The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being ...
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Recent advancements in large language models (LLMs) have shown promise in tasks like question answering, text summarization, and code generation. However, their effectiveness within the healthcare sector remains uncer...
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Physics-informed neural networks(PINNs)are known to suffer from optimization *** this work,we reveal the connection between the optimization difficulty of PINNs and activation ***,we show that PINNs exhibit high sensi...
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Physics-informed neural networks(PINNs)are known to suffer from optimization *** this work,we reveal the connection between the optimization difficulty of PINNs and activation ***,we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct *** works usually choose activation functions by inefficient *** avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs,we introduce adaptive activation functions to search for the optimal function when solving different *** compare different adaptive activation functions and discuss their limitations in the context of ***,we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization,which has a higher requirement for the smoothness and diversity on learned *** is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at *** further enhance the search space with adaptive *** proposed adaptive activation function can be used to solve different PDE systems in an interpretable *** effectiveness is demonstrated on a series of *** is available at https://***/LeapLabTHU/AdaAFforPINNs.
Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which lim...
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We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a ...
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We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.
We show that R is regular if ToriR(R+,k)=0forsomei ≥ 1assuming further that R is a N-graded ring of dimension 2 finitely generated over an algebraically closed equicharacteristic zero field k. This answers a question...
We study the problem of learning latent community structure from multiple correlated networks, focusing on edge-correlated stochastic block models with two balanced communities. Recent work of Gaudio, Rácz, and S...
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While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less develop...
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While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine learning algorithms are available for estimating the nuisance parameters. We introduce a new selection criterion aimed at bias reduction in estimating the functional of interest based on a novel definition of pseudo risk inspired by the double robustness property. Intuitively, the proposed criterion selects a pair of learners with the smallest pseudo risk, so that the estimated functional is least sensitive to perturbations of a nuisance parameter. We establish an oracle property for a multi-fold cross-validation version of the new selection criterion that states that our empirical criterion performs nearly as well as an oracle with a priori knowledge of the pseudo risk for each pair of candidate learners. Finally, we apply the approach to model selection of a semiparametric estimator of average treatment effect given an ensemble of candidate machine learners to account for confounding in an observational study that we illustrate in simulations and a data application.
Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible dee...
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