In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to s...
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We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either ins...
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We investigate modal localization of light in disordered hyperbolic lattices. We examine modal area at the bulk of a disordered hyperbolic lattice, which demonstrates that high degree in the lattice leads to the deloc...
Smartphones as a means of communication are not only used to send and receive messages and make phone calls. Today's smartphones are also intended to store personal data and sensitive information hence it is vulne...
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Active power decoupling (APD) converter is a power dense and reliable solution in dc-ac power conversion systems to balance the fluctuating ac-side power and constant dc-side power. In boost type APD converter, filter...
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Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low stor...
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Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth.
The electric transportation market has experienced rapid growth in recent years, driven by increasing environmental concerns. Resonant converters play a crucial role in this sector due to their ability to provide isol...
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To reduce energy consumption of linear Delta robots, two problems of optimal pick-and-place operation are solved in this work. The first one focuses on the minimization of power consumed by robots in statics as a func...
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Nowadays, the best methods of training specialists who are able to identify new challenges, make original decisions, and explore complex issues are associated with active learning. However, it is appears that the deve...
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Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structur...
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Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structures are considered. But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are often modeled as dynamic objective functions that go beyond the simple static objective structures, which pose significant challenges of using existing bilevel solutions. To tackle this new class of bilevel problems, we introduce the first principled algorithmic framework for solving bilevel RL problems through the lens of penalty formulation. We provide theoretical studies of the problem landscape and its penalty-based (policy) gradient algorithms. We demonstrate the effectiveness of our algorithms via simulations in the Stackelberg game and RLHF. Copyright 2024 by the author(s)
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