Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known ...
Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as “glitches,” which greatly impact the search and identification of GWs. Recent research has achieved remarkable results in data denoising, often using effective modeling methods to remove glitches. However, for glitches from uncertain or unknown sources, current methods cannot completely eliminate them from the GW signal. In this work, we leverage the inherent robustness of machine learning to obtain reliable posterior parameter distributions directly from GW data contaminated by glitches. Our network model provides reasonable and rapid parameter inference even in the presence of glitches, without needing to remove them. We also investigate various factors affecting the rationality of parameter inference in our normalizing flow network, including glitch and GW parameters. The results demonstrate that the normalizing flow can reasonably infer the source parameters of GWs even with unknown contamination. We find that the nature of the glitch itself is the only factor that can affect the rationality of the inferred results. With improvements to our model, we anticipate accelerating the localization of electromagnetic counterparts and providing priors for more accurate deglitching, thereby speeding up subsequent data processing procedures.
Ultralight vectors can extract energy and angular momentum from a Kerr black hole (BH) due to superradiant instability, resulting in the formation of a BH-condensate system. In this work, we carefully investigate the ...
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Ultralight vectors can extract energy and angular momentum from a Kerr black hole (BH) due to superradiant instability, resulting in the formation of a BH-condensate system. In this work, we carefully investigate the evolution of this system numerically with multiple superradiant modes. Simple formulas are obtained to estimate important timescales, maximum masses of different modes, as well as the BH mass and spin at various times. Due to the coexistence of modes with small frequency differences, the BH-condensate system emits gravitational waves with a unique beat signature, which could be directly observed by current and projected interferometers. Besides, the current BH spin-mass data from the binary BH merger events already exclude the vector mass in the range 5×10−15 eV<μ<9×10−12 eV.
Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent *** far as we know...
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Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent *** far as we know,the studies of natural polyphenols in aging and aging-related diseases have never been extensively *** the present paper,we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural *** results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***,structural modifications,and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related *** conclusion,natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.
X-ray imaging technology is vital in national public security. However, compressing 16-bit high dynamic range X-ray images into 8-bit displayable images often leads to loss of crucial details, hampering effective secu...
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ISBN:
(数字)9798331506056
ISBN:
(纸本)9798331506063
X-ray imaging technology is vital in national public security. However, compressing 16-bit high dynamic range X-ray images into 8-bit displayable images often leads to loss of crucial details, hampering effective security inspection. To address this, we propose XTM-GAN, a dual-energy X-ray tone mapping generative adversarial network, to enhance X-ray imaging quality. By leveraging the properties of dual-energy X-rays, we construct a dedicated dataset called DEXray. We design a dual-branch generation network and a discrimination network that combines global and local image information. Through end-to-end generative adversarial training using pairs of dual-energy HDR and LDR X-ray images, which can generate high-quality X-ray security images. We train and compare our algorithm using the DEXray dataset against other state-of-the-art tone mapping algorithms. The results demonstrate the superior performance of our approach. The proposed method has significant practical applications and holds research significance in the fields of security and transportation.
Semiconductor resistance sensors often face problems such as low response and long response and recovery time when detecting ammonia at room temperature (25℃). This is because traditional semiconductor oxides are not...
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Identifying traversable areas is a critical task for unmanned vehicles exploring safely through unstructured environments. In practice, the ambiguity in perceiving terrain traversability usually brings great challenge...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Identifying traversable areas is a critical task for unmanned vehicles exploring safely through unstructured environments. In practice, the ambiguity in perceiving terrain traversability usually brings great challenges for autonomous exploration in unknown and uneven terrain, which often leads to conservative strategies or potential risk of vehicle damage, resulting in many unexplored areas in the environment. To that end, this paper proposes a plane-assisted autonomous robot exploration framework (PARE) to achieve maximum volume and safe autonomous exploration. The process is carried out by a three-step dual-layer framework: constructing a local tree using Plane-Assisted RRT* (PA-RRT*), calculating exploration gain based on terrain information, and maintaining a global search graph. Firstly, the planar feature metrics (flatness, sparsity, elevation variation, slope and slope variation) are introduced to determine the terrain traversability. Secondly, to completely explore the rugged environment, we propose a dual-layer exploration framework comprising local and global strategies. A local planner based on PA-RRT* is proposed to find the best path by evaluating the planar information and the volumetric gain within the local exploration tree. Meanwhile, a global planner constructed by graph is proposed to record unexplored nodes with high exploration gain from the local tree to ensure a high level of exploration volume. Extensive simulation and real-world experiments demonstrate that our method significantly outperforms existing frameworks, with an average improvement of more than 12% in exploration volume.
This paper is concerned with the problem of safety control for switched systems, where different safe sets are allowed for different subsystems and safety is not necessarily possessed for subsystems. A necessary and s...
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Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray...
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X-ray prohibited item detection is an essential component of security check and categories of prohibited item are continuously increasing in accordance with the latest laws. Previous works all focus on close-set scena...
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Both particle physics experiments and cosmological observations have been used to explore neutrino properties. Cosmological researches of neutrinos often rely on the early-universe cosmic microwave background observat...
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