The first historical account of Vietnam mathematics research on arXiv

Ho, Manh-Toan

doi:10.31219/osf.io/sqygn

Cited by 89 publications

(168 citation statements)

References 1 publication

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“…from Lyman-, Prescott et al 2015) or indirectly in absorption (e.g. from galaxy-quasar pairs, as studied in Zabl et al 2019;Ho & Martin 2019), all suggesting filamentary accretion from the cosmic web.…”

Section: Introductionmentioning

confidence: 96%

Rivers of gas – I. Unveiling the properties of high redshift filaments

Ramsøy

Slyz

Devriendt

et al. 2021

Monthly Notices of the Royal Astronomical Society

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At high redshift, the cosmic web is widely expected to have a significant impact on the morphologies, dynamics, and star formation rates of the galaxies embedded within it, underscoring the need for a comprehensive study of the properties of such a filamentary network. With this goal in mind, we perform an analysis of high-z gas and dark matter (DM) filaments around a Milky Way-like progenitor simulated with the ramses adaptive mesh refinement (AMR) code from cosmic scales (∼1 Mpc) down to the virial radius of its DM halo host (∼20 kpc at z = 4). Radial density profiles of both gas and DM filaments are found to have the same functional form, namely a plummer-like profile modified to take into account the wall within which these filaments are embedded. Measurements of the typical filament core radius r0 from the simulation are consistent with that of isothermal cylinders in hydrostatic equilibrium. Such an analytic model also predicts a redshift evolution for the core radius of filaments in fair agreement with the measured value for DM [r0∝ (1 + z)−3.18 ± 0.28]. Gas filament cores grow as [r0∝ (1 + z)−2.72 ± 0.26]. In both gas and DM, temperature and vorticity sharply drop at the edge of filaments, providing an excellent way to constrain the outer filament radius. When feedback is included, the gas temperature and vorticity fields are strongly perturbed, hindering such a measurement in the vicinity of the galaxy. However, the core radius of the filaments as measured from the gas density field is largely unaffected by feedback; and the median central density is only reduced by about 20 per cent.

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Section: Introductionmentioning

confidence: 96%

Rivers of gas – I. Unveiling the properties of high redshift filaments

Ramsøy

Slyz

Devriendt

et al. 2021

Monthly Notices of the Royal Astronomical Society

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“…However, the thermal entropy arising from the intrinsic heating (3,4), non-adiabaticity (5,6), and inefficient thermalization (7) hinders the exploration of exotic phases of matter (8) and the ability to scale up entangled states (9,10). Numerous methods have been applied to cooling of quantum gases (11)(12)(13)(14)(15)(16); some theoretical schemes suggest immersing lattice-trapped atoms into superfluid reservoirs that could eventually carry away the thermal entropy (17,18). With the help of a microscope, cooling of an atomic sample with a removable surrounding reservoir was demonstrated, leading to the realization of a Fermi-Hubbard antiferromagnet (5,19).…”

mentioning

confidence: 99%

Cooling and entangling ultracold atoms in optical lattices

Yang

Sun

Huang

et al. 2020

Science

116

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Scalable, coherent many-body systems can enable the realization of previously unexplored quantum phases and have a potential to exponentially speed up information processing. Here we report the cooling of a quantum simulator with ten thousand atoms and mass production of high-fidelity entangled pairs. In a two-dimensional plane, we cool Mott-insulator samples by immersing them into removable superfluid reservoirs, achieving an entropy per particle of 1.9−0.4+1.7×10−3kB. The atoms are then rearranged into a two-dimensional lattice free of defects. We further demonstrate a two-qubit gate with a fidelity of 0.993(1) for entangling 1250 atom pairs. Our results offer a setting for exploring low-energy many-body phases and could enable the creation of large-scale entanglement.

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“…Methods of behavioral cloning and IRL learn strategies from demonstrations, but can not interact with the expert to further optimize the policy [91]. Therefore, the method of generative adversarial imitation learning is proposed to solve the problem based on adversarial networks [92].…”

Section: Inverse Reinforcement Learningmentioning

confidence: 99%

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

Jiang

Zeng

et al. 2021

Sensors

139

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Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

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The first historical account of Vietnam mathematics research on arXiv

Abstract: To celebrate Vietnam Teachers' Day, the VIASM SciMath Database Project team members have completed the first working draft, depicting the 80-year development history of Vietnam mathematical research.

Cited by 89 publications

References 1 publication

Rivers of gas – I. Unveiling the properties of high redshift filaments

Rivers of gas – I. Unveiling the properties of high redshift filaments

Cooling and entangling ultracold atoms in optical lattices

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

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