Results 1 to 10 of about 210,130 (70)

Shadow enhanced self-charging power system for wave and solar energy harvesting from the ocean

open access: yesNature Communications, 2021
Hybrid energy-harvesting systems that capture both wave and solar energy from the oceans using triboelectric nanogenerators and photovoltaic cells are promising renewable energy solutions.
Qian Zhang   +2 more
exaly   +2 more sources

Gravitational Test beyond the First Post-Newtonian Order with the Shadow of the M87 Black Hole [PDF]

open access: yesPhysical Review Letters, 2020
The 2017 Event Horizon Telescope (EHT) observations of the central source in M87 have led to the first measurement of the size of a black-hole shadow. This observation offers a new and clean gravitational test of the black-hole metric in the strong-field
Dimitrios Psaltis   +2 more
exaly   +2 more sources

Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models [PDF]

open access: yesarXiv.org, 2023
Warning: This paper contains examples of harmful language, and reader discretion is recommended. The increasing open release of powerful large language models (LLMs) has facilitated the development of downstream applications by reducing the essential ...
Xianjun Yang   +6 more
semanticscholar   +1 more source

ShadowFormer: Global Context Helps Image Shadow Removal [PDF]

open access: yesAAAI Conference on Artificial Intelligence, 2023
Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as ...
Lanqing Guo   +4 more
semanticscholar   +1 more source

ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal [PDF]

open access: yesComputer Vision and Pattern Recognition, 2022
Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling ...
Lanqing Guo   +6 more
semanticscholar   +1 more source

Comparison of arrogance in Shahnameh and Bahmannameh based on the ancient story of Bahmannameh [PDF]

open access: yes, 2021
One of the most important aspects of the struggle of warriors and warriors in epic works is the arrogance that is called by the comrades before the start of the hand-to-hand battle. It is accompanied by pride in ancestry, race, and ridicule.
Amiri, Zohreh   +2 more
core   +2 more sources

Scalable and Flexible Classical Shadow Tomography with Tensor Networks [PDF]

open access: yesQuantum, 2022
Classical shadow tomography is a powerful randomized measurement protocol for predicting many properties of a quantum state with few measurements. Two classical shadow protocols have been extensively studied in the literature: the single-qubit (local ...
A. Akhtar, Hong-ye Hu, Yi-Zhuang You
semanticscholar   +1 more source

Optimizing Shadow Tomography with Generalized Measurements. [PDF]

open access: yesPhysical Review Letters, 2022
Advances in quantum technology require scalable techniques to efficiently extract information from a quantum system. Traditional tomography is limited to a handful of qubits, and shadow tomography has been suggested as a scalable replacement for larger ...
H. C. Nguyen   +3 more
semanticscholar   +1 more source

Auto-Exposure Fusion for Single-Image Shadow Removal [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
Shadow removal is still a challenging task due to its inherent background-dependent1 and spatial-variant properties, leading to unknown and diverse shadow patterns.
Lan Fu   +7 more
semanticscholar   +1 more source

DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network [PDF]

open access: yesIEEE International Conference on Computer Vision, 2021
Shadow removal from a single image is generally still an open problem. Most existing learning-based methods use supervised learning and require a large number of paired images (shadow and corresponding non-shadow images) for training.
Yeying Jin, Aashish Sharma, R. Tan
semanticscholar   +1 more source

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