Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for ...
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Galactic diffuse gamma ray emission (GDE) is introduced by the galactic cosmic rays (CR) interacting with the interstellar medium (ISM) and radiation fields (ISRF). The GDE is a very important probe of CR propagation ...
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Symbolic execution is still facing the scalability problem caused by path explosion and constraint solving overhead. The recently proposed MuSE framework supports exploring multiple paths by generating partial solutio...
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ISBN:
(数字)9781450367684
ISBN:
(纸本)9781728172811
Symbolic execution is still facing the scalability problem caused by path explosion and constraint solving overhead. The recently proposed MuSE framework supports exploring multiple paths by generating partial solutions in one time of solving. In this work, we improve MuSE from two aspects. Firstly, we use a light-weight check to reduce redundant partial solutions for avoiding the redundant executions having the same results. Secondly, we introduce online learning to devise an adaptive search strategy for the target programs. The preliminary experimental results indicate the promising of the proposed methods.
In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and ma...
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A sub-array of the Large high Altitude Air Shower Observatory (LHAASO), KM2A contains 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). For each shower event that meets the trigger condition...
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The Large high Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA, and WFCTA. As the major array of LHAASO, KM2A has been operating stably in shower mode. To study the near-earth atmospheric ele...
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Heisenberg’s uncertainty principle implies fundamental constraints on what properties of a quantum system we can simultaneously learn. However, it typically assumes that we probe these properties via measurements at ...
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Heisenberg’s uncertainty principle implies fundamental constraints on what properties of a quantum system we can simultaneously learn. However, it typically assumes that we probe these properties via measurements at a single point in time. In contrast, inferring causal dependencies in complex processes often requires interactive experimentation—multiple rounds of interventions where we adaptively probe the process with different inputs to observe how they affect outputs. Here, we demonstrate universal uncertainty principles for general interactive measurements involving arbitrary rounds of interventions. As a case study, we show that they imply an uncertainty trade-off between measurements compatible with different causal dependencies.
On 9 October 2022, the Large high Altitude Air Shower Observatory (LHAASO) reported the observation of the very early TeV afterglow of the brightest-of-all-time gamma-ray burst 221009A, recording the highest photon st...
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On 9 October 2022, the Large high Altitude Air Shower Observatory (LHAASO) reported the observation of the very early TeV afterglow of the brightest-of-all-time gamma-ray burst 221009A, recording the highest photon statistics in the TeV band ever obtained from a gamma-ray burst. We use this unique observation to place stringent constraints on the energy dependence of the speed of light in vacuum, a manifestation of Lorentz invariance violation (LIV) predicted by some quantum gravity (QG) theories. Our results show that the 95% confidence level lower limits on the QG energy scales are EQG,1>10 times the Planck energy EPl for the linear LIV effect, and EQG,2>6×10−8EPl for the quadratic LIV effect. Our limits on the quadratic LIV case improve previous best bounds by factors of 5–7.
Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding...
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ISBN:
(数字)9781728192284
ISBN:
(纸本)9781728185361
Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding and time-consuming. Deep learning (DL) is emerging as a viable means to accelerate CFD simulations by directly predicting the outcomes of multiple simulation iterations. While promising, existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FlowGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training. As FlowGAN does not rely on knowledge of the underlying governing equations, it can quickly adapt to various flow conditions and avoid the need for expensive re-training. We evaluate FlowGAN by applying it to scenarios of simulating both the whole flow field and selected regions of interest (RoI). Compared to the state-of-the-art DL based methods, FlowGAN significantly reduces the prediction errors by 2.27% while exhibiting a better generalization ability.
Searching for ultra-high energy (PeV) cosmic ray accelerating source is the core problem in the study of cosmic ray origin. One of the most direct methods is to look for the ultra-high energy gamma-ray radiation gener...
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