We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the location of middle-aged (62.4 kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM...
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The Water Cherenkov Detector Array (WCDA) is one of the components of Large high Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98% d...
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In this paper, we report the detection of the very-high-energy (VHE, 100 GeV 100 TeV) γ-ray emissions from the direction of the young star-forming region W43, observed by the Large high Altitude Air Shower Observatio...
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The first source catalog of Large high Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temp...
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The attenuation length of the muon content in extensive air showers provides important information regarding the generation and development of air showers. This information can be used not only to improve the descript...
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Experimental investigations of ultrafast electro-optical properties in magnetic materials manifest their great potential for emerging spintronic optoelectronic devices. Here, using time-resolved terahertz emission spe...
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Experimental investigations of ultrafast electro-optical properties in magnetic materials manifest their great potential for emerging spintronic optoelectronic devices. Here, using time-resolved terahertz emission spectroscopy, we construct a spintronic terahertz emitter consisting of an IrMn3/Ni−Fe heterojunction. A femtosecond spin current pulse is generated in the thin film of the Ni−Fe layer when it absorbs a femtosecond laser pulse, and then the spin current is converted into a transient charge current by the metallic IrMn3 layer on picosecond timescales. We timely record the terahertz emission associated with this ultrafast conversion process by means of electro-optic sampling. Besides, the spin-to-charge conversion efficiency of the IrMn3/Ni−Fe heterojunction is determined via quantitative analysis of the spin torque ferromagnetic resonance results. We have both optically verified and electrically studied the spin-to-charge conversion of the IrMn3/Ni−Fe heterojunction. Our results enlarge the material choice range of spintronic terahertz emitters, which may promote further investigations of ultrafast spin-to-charge conversion in different heterojunction materials.
In this paper,rank factorizations and factor left prime factorizations are *** authors prove that any polynomial matrix with full row rank has factor left prime *** for a class of polynomial matrices,the authors give ...
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In this paper,rank factorizations and factor left prime factorizations are *** authors prove that any polynomial matrix with full row rank has factor left prime *** for a class of polynomial matrices,the authors give an algorithm to decide whether they have rank factorizations or factor left prime factorizations and compute these factorizations if they exist.
Model-free reinforcement learning is capable of learning high-dimensional robotic tasks, but the requirement of large-scale training data makes it hard to reach better performance in limited time. On the contrary, mod...
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ISBN:
(数字)9781728146539
ISBN:
(纸本)9781728146546
Model-free reinforcement learning is capable of learning high-dimensional robotic tasks, but the requirement of large-scale training data makes it hard to reach better performance in limited time. On the contrary, model-based methods are capable of learning low-dimensional tasks efficiently, but lack of extensibility for complex robotic tasks. It is an instinct that, combining the advantage of both, transferring knowledge to higher dimension may benefit in sample efficiency and model accuracy. In the thesis, we present a hybrid framework that transfer low-dimensional action features to high-dimensional deep reinforcement learning model through imitation learning, in order to decrease the training data needed to reach practical performance. In this work, the hybrid framework is experimented on the simulated locomotion tasks, showing that our framework can improve model-free learning process. Our hybrid algorithm outperforms the pure model-free method, utilizing the low-dimensional action features efficiently and being competent in model accuracy.
Deep reinforcement learning is making advances in robotics with the platforms of realistic environment simulation. However, as shown in this paper, the realistic simulation introduces vast time cost which is the bottl...
Deep reinforcement learning is making advances in robotics with the platforms of realistic environment simulation. However, as shown in this paper, the realistic simulation introduces vast time cost which is the bottleneck of the learning procedure. To solve this problem generally, we propose a parallel reinforcement learning platform which follows the master-slave principle and integrates learning programs with multiple distributedrobot simulators. The platform is intrinsically scalable and requires no modification to existing serially designed learning environments or algorithms. Experimental results demonstrate that our platform significantly accelerates the learning progress of robots, in direct proportion to the parallel scale. The parallelism also brings richer exploration and sampling, enhancing the performance of deep reinforcement learning algorithms compared with existing serial platforms.
A well-protected and characterised source in a quantum key distribution system is needed for its security. Unfortunately, the source is vulnerable to light-injection attacks, such as Trojan-horse, laser-seeding, and l...
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