Rotational Bose-Einstein condensates can exhibit quantized vortices as topological *** this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the...
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Rotational Bose-Einstein condensates can exhibit quantized vortices as topological *** this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy *** excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics *** excitation mechanisms are identified:vortex addition,rearrangement,merging,and *** demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states.
This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the re...
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Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different pref...
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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.
Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly imp...
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We consider decentralized stochastic optimization problems, where a network of n nodes cooperates to find a minimizer of the globally-averaged cost. A widely studied decentralized algorithm for this problem is the dec...
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We consider decentralized stochastic optimization problems, where a network of n nodes cooperates to find a minimizer of the globally-averaged cost. A widely studied decentralized algorithm for this problem is the decentralized SGD (D-SGD), in which each node averages only with its neighbors. D-SGD is efficient in single-iteration communication, but it is very sensitive to the network topology. For smooth objective functions, the transient stage (which measures the number of iterations the algorithm has to experience before achieving the linear speedup stage) of D-SGD is on the order of O(n/(1 - β)2) and O(n3/(1 - β)4) for strongly and generally convex cost functions, respectively, where 1 - β ∈ (0, 1) is a topology-dependent quantity that approaches 0 for a large and sparse network. Hence, D-SGD suffers from slow convergence for large and sparse *** this work, we revisit the convergence property of the D2/Exact-Diffusion algorithm. By eliminating the influence of data heterogeneity between nodes, D2/Exact-diffusion is shown to have an enhanced transient stage that is on the order of Õ(n/(1 - β)) and O(n3/(1-β)2) for strongly and generally convex cost functions (where Õ (·) hides all logarithm factors), respectively. Moreover, when D2/Exact-Diffusion is implemented with both gradient accumulation and multi-round gossip communications, its transient stage can be further improved to Õ (1/(1-β)1/2) and Õ(n/(1-β)) for strongly and generally convex cost functions, respectively. To our knowledge, these established results for D2/Exact-Diffusion have the best (i.e., weakest) dependence on network topology compared to existing decentralized algorithms. Numerical simulations are conducted to validate our theories.
Comprehending how humans process visual information in dynamic settings is crucial for psychology and designing user-centered interactions. While mobile eye-tracking systems combining egocentric video and gaze signals...
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In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machinelearning with graphs ...
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In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machinelearning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.
We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained o...
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Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat mess...
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