The geometry and environment of repeating FRBs

Kavli Affiliate: Renxin Xu | First 5 Authors: Shuang Du, Weihua Wang, Xuhao Wu, Renxin Xu, | Summary: We propose a geometrical explanation for periodically and nonperiodically repeating fast radio bursts (FRBs) under neutron star (NS)-companion systems. We suggest a constant critical binary separation, $r_{rm c}$, within which the interaction between the NS and companion […]


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Radiative Transfer modeling of EC 53: An Episodically Accreting Class I Young Stellar Object

Kavli Affiliate: Gregory Herczeg | First 5 Authors: Giseon Baek, Benjamin A. MacFarlane, Jeong-Eun Lee, Dimitris Stamatellos, Gregory Herczeg | Summary: In the episodic accretion scenario, a large fraction of the protostellar mass accretes during repeated and large bursts of accretion. Since outbursts on protostars are typically identified at specific wavelengths, interpreting these outbursts requires […]


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Dendrite Net: A White-Box Module for Classification, Regression, and System Identification

Kavli Affiliate: Jing Wang | First 5 Authors: Gang Liu, Jing Wang, , , | Summary: The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD’s […]


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On the Optimal Feedback Law in Stochastic Optimal Nonlinear Control

Kavli Affiliate: Ran Wang | First 5 Authors: Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal, Ran Wang, | Summary: We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman’s infamous "curse of dimensionality". We present a result that shows that repeatedly solving an open-loop […]


Continue.. On the Optimal Feedback Law in Stochastic Optimal Nonlinear Control

On the Feedback Law in Stochastic Optimal Nonlinear Control

Kavli Affiliate: Ran Wang | First 5 Authors: Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal, Ran Wang, | Summary: We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman’s infamous "curse of dimensionality". We present a result that shows that repeatedly solving an open-loop […]


Continue.. On the Feedback Law in Stochastic Optimal Nonlinear Control

On the Feedback Law in Stochastic Optimal Nonlinear Control

Kavli Affiliate: Ran Wang | First 5 Authors: Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal, Ran Wang, | Summary: We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman’s “curse of dimensionality". We present a result that shows that repeatedly solving an open-loop deterministic […]


Continue.. On the Feedback Law in Stochastic Optimal Nonlinear Control

On the Feedback Law in Stochastic Optimal Nonlinear Control

Kavli Affiliate: Ran Wang | First 5 Authors: Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal, Ran Wang, | Summary: We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman’s "curse of dimensionality". We present a result that shows that repeatedly solving an open-loop deterministic […]


Continue.. On the Feedback Law in Stochastic Optimal Nonlinear Control

Bounds for exit times of Brownian motion and the first Dirichlet eigenvalue for the Laplacian

Kavli Affiliate: Jing Wang | First 5 Authors: Rodrigo Banuelos, Phanuel Mariano, Jing Wang, , | Summary: For domains in $mathbb{R}^d$, $dgeq 2$, we prove universal upper and lower bounds on the product of the bottom of the spectrum for the Laplacian to the power $p>0$ and the supremum over all starting points of the […]


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On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Kavli Affiliate: Li Xin Li | First 5 Authors: Xiangrui Li, Xin Li, Deng Pan, Dongxiao Zhu, | Summary: Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax […]


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