Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-de...
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We demonstrate Purcell enhancement of a single T center integrated in a silicon photonic crystal cavity, increasing the fluorescence decay rate by a factor of 6.89 and achieving a photon outcoupling rate of 73.3 kHz. ...
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Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By di...
ISBN:
(纸本)9781713871088
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost.
We generate and reconstruct a maximally-entangled time-bin ququart using quantum state tomography and polarization-projective measurements. We measure a fidelity of 93.7±0.4% to a maximally-entangled ququart with...
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Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-de...
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Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment....
Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.
In supply chains of petroleum industry, maintaining a proper inventory level for multiple petroleum products is a crucial issue. Basically, this chain has several elements like producer bases, consumer bases and termi...
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In supply chains of petroleum industry, maintaining a proper inventory level for multiple petroleum products is a crucial issue. Basically, this chain has several elements like producer bases, consumer bases and terminals that are linked by means of a multi-modal transport network. These elements must cooperate to achieve the global goal of the system with a minimized cost. To achieve this goal, this paper presents a multiagent protocol for simultaneous negotiations based on the Contract-Net and the application of this in the chain aforementioned. As a result, this protocol proved to be very efficient to return a feasible solution in a low processing time.
The recognition of Intellectual Capital as a source of competitive advantage and differentiation element for organizations requires an application of new management strategies that include this feature. Considering th...
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Heavily-doped strained germanium (Ge) can emit light efficiently thanks to its pseudo direct band gap characteristic. This makes Ge a good candidate for on-chip monolithic light sources in silicon (Si) photonics syste...
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In this current digital era, the video game is favorite especially among young people. Besides widely known as a form of entertainment, some messages or lessons can be learned from a video game that may affect the pla...
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