arXiv subjects

Jing Liang

Publications and source records attributed to Jing Liang.

104 records · Page 6Linked to original sources

arXiv2020

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.

cs.AI↗

arXiv2020

Thermodynamics with pressure and volume of 4D Gauss-Bonnet AdS Black Holes under the scalar field

By using the scattering of a scalar field, we discuss the thermodynamics and overcharging problem in a 4D Gauss-Bonnet AdS black hole in both the normal phase space and extended phase space. In the normal phase space, where the cosmological constant and Gauss-Bonnet parameter are fixed, the first law and the second law of thermodynamics are valid. In addiction, the black hole cannot be overcharged and the weak cosmic censorship conjecture is valid. In the extended phase space, where the cosmological constant and Gauss-Bonnet parameter are treated as the thermodynamic variables, the first law is still valid. However, the second law is indefinite. Moreover, after the scattering of the scalar field, the extremal black hole cannot be overcharged and the near-extremal black hole can be overcharged.

gr-qc↗

arXiv2020

Autonomous Social Distancing in Urban Environments using a Quadruped Robot

COVID-19 pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19. Against this backdrop, we propose that the surveillance robots can not only monitor but also promote social distancing. Robots can be flexibly deployed and they can take precautionary actions to remind people of practicing social distancing. In this paper, we introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd-aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to over-crowded pedestrians. We demonstrate and validate that our robot can be operated autonomously by conducting several experiments in various urban scenarios.

cs.RO↗

arXiv2020

Thermodynamics and overcharging problem in the extended phase spaces of charged AdS black holes with cloud of strings and quintessence under charged particle absorption

The thermodynamics and overcharging problem in the RN-AdS black hole with the cloud of strings and quintessence are investigated by absorption of scalar particle and fermion in the extend phase space. The cosmological constant is treated as the pressure with a conjugate volume. Besides, the parameters related to quintessence and cloud of strings are treated as thermodynamic variables. Finally we find the first law of thermodynamics is satisfied and the second law of thermodynamics is indefinite. Furthermore, the near-extremal and extremal black holes can not be overcharged.

gr-qc↗

arXiv2020

Remarks on the weak cosmic censorship conjecture of RN-AdS black holes with cloud of strings and quintessence under the scalar field

In this paper, we investigate the thermodynamics and weak cosmic censorship conjecture in a RN-AdS black hole with the cloud of strings and quintessence by the scattering of a scalar field. The variations of the thermodynamic variables are calculated in the normal and extended phase spaces. In the normal phase space, where the cosmological constant is considered as a constant, the first and second laws of thermodynamics are satisfied. In the extended phase space, where the cosmological constant and the parameters related to the cloud of strings and quintessence are treated as variables, the first law of thermodynamics is still satisfied and the second law of thermodynamics is indefinite. Moreover, we find that the weak cosmic censorship conjecture is valid for the extremal and near-extremal black holes in both phase spaces.

gr-qc↗

arXiv2020

Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning

We present a novel learning-based collision avoidance algorithm, CrowdSteer, for mobile robots operating in dense and crowded environments. Our approach is end-to-end and uses multiple perception sensors such as a 2-D lidar along with a depth camera to sense surrounding dynamic agents and compute collision-free velocities. Our training approach is based on the sim-to-real paradigm and uses high fidelity 3-D simulations of pedestrians and the environment to train a policy using Proximal Policy Optimization (PPO). We show that our learned navigation model is directly transferable to previously unseen virtual and dense real-world environments. We have integrated our algorithm with differential drive robots and evaluated its performance in narrow scenarios such as dense crowds, narrow corridors, T-junctions, L-junctions, etc. In practice, our approach can perform real-time collision avoidance and generate smooth trajectories in such complex scenarios. We also compare the performance with prior methods based on metrics such as trajectory length, mean time to goal, success rate, and smoothness and observe considerable improvement.

cs.RO↗

arXiv2020

DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors

We present DenseCAvoid, a novel navigation algorithm for navigating a robot through dense crowds and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses visual sensors and a pedestrian trajectory prediction algorithm to track pedestrians in a set of input frames and provide bounding boxes that extrapolate the pedestrian positions in a future time. Our hybrid approach combines this trajectory prediction with a Deep Reinforcement Learning-based collision avoidance method to train a policy to generate smoother, safer, and more robust trajectories during run-time. We train our policy in realistic 3-D simulations of static and dynamic scenarios with multiple pedestrians. In practice, our hybrid approach generalizes well to unseen, real-world scenarios and can navigate a robot through dense crowds (~1-2 humans per square meter) in indoor scenarios, including narrow corridors and lobbies. As compared to cases where prediction was not used, we observe that our method reduces the occurrence of the robot freezing in a crowd by up to 48%, and performs comparably with respect to trajectory lengths and mean arrival times to goal.

cs.RO↗

arXiv2019

Expediting hydrogen evolution through topological surface states on Bi2Te3

Recently, the development of efficient and non-noble metal electrocatalysts with excellent durability for the hydrogen evolution reaction (HER) has attracted increasing attention. The exotic and robust metallic surface states of topological insulators (TIs) are theoretically predicted to enhance surface catalytic activity of overlaying catalysts, but no experimental evidence for TIs directly used as electrocatalysts has ever been reported. In this work, we fabricated the TI thin films of Bi2Te3 with different thicknesses using the molecular beam epitaxy method, and found that these thin films exhibit high electrocatalytic activity in HER. The 48 nm Bi2Te3 thin film has the best performance, which is attributed to its largest active area arising from the spiral growth mode of triangular domains as revealed by atomic force microscopy imaging. Importantly, our theoretical calculations reveal that while pure Bi2Te3 is not a good electrocatalyst, the Bi2Te3 thin films with partially oxidized surfaces or Te vacancies have high HER activity. The existence of the corresponding surface oxides on the Bi2Te3 thin films is supported by our X-ray photoelectron spectroscopy data. Particularly, we demonstrate that the topological surface states play a key role in enhancing the HER performance. Our study offers a new direction to design cost-effective electrocatalysts.

physics.chem-ph↗

arXiv2019

Studies on the origin of the interfacial superconductivity of Sb2Te3/Fe1+yTe heterostructures

The recent discovery of the interfacial superconductivity (SC) of the Bi2Te3/Fe1+yTe heterostructure has attracted extensive studies due to its potential as a novel platform for trapping and controlling Majorana fermions. Here we present studies of another topological insulator (TI)/Fe1+yTe heterostructure, Sb2Te3/Fe1+yTe, which also enjoys an interfacial two-dimensional SC. The results of transport measurements support that the reduction of excess Fe concentration of the Fe1+yTe layer not only increases the fluctuation of its antiferromagnetic (AFM) order but also enhances the quality of the SC of this heterostructure system. On the other hand, the interfacial SC of this heterostructure was found to have a wider-ranging TI-layer thickness dependence than that of the Bi2Te3/Fe1+yTe heterostructure, which is believed to be attributed to the much higher bulk conductivity of Sb2Te3 that enhances indirect coupling between its top and bottom topological surface states (TSSs). Our results provide the evidence of the interplay among the AFM order, itinerant carries from the TSSs and the induced interfacial SC of the TI/Fe1+yTe heterostructure system.

cond-mat.supr-con↗

arXiv2018

Hierarchical Macro Strategy Model for MOBA Game AI

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.

cs.MA↗

arXiv2018

Cascade-Net: a New Deep Learning Architecture for OFDM Detection

In this paper, we consider using deep neural network for OFDM symbol detection and demonstrate its performance advantages in combating large Doppler Shift. In particular, a new architecture named Cascade-Net is proposed for detection, where deep neural network is cascading with a zero-forcing preprocessor to prevent the network stucking in a saddle point or a local minimum point. In addition, we propose a sliding detection approach in order to detect OFDM symbols with large number of subcarriers. We evaluate this new architecture, as well as the sliding algorithm, using the Rayleigh channel with large Doppler spread, which could degrade detection performance in an OFDM system and is especially severe for high frequency band and mmWave communications. The numerical results of OFDM detection in SISO scenario show that cascade-net can achieve better performance than zero-forcing method while providing robustness against ill conditioned channels. We also show the better performance of the sliding cascade network (SCN) compared to sliding zero-forcing detector through numerical simulation.

eess.SP↗

arXiv2018

Towards the growth of single-crystal boron nitride monolayer on Cu

Atom-layered hexagonal boron nitride (hBN), with excellent stability, flat surface and large bandgap, has been reported to be the best 2D insulator to open up the great possibilities for exciting potential applications in electronics, optoelectronics and photovoltaics. The ability to grow high-quality large single crystals of hBN is at the heart for those applications, but the size of single-crystal 2D BN is less than a millimetre till now. Here, we report the first epitaxial growth of a 10*10 cm2 single-crystal hBN monolayer on a low symmetry Cu(110) "vicinal surface". The growth kinetics, unidirectional alignment and seamless stitching of hBN domains are unambiguously illustrated using centimetre- to the atomic-scale characterization techniques. The findings in this work are expected to significantly boost the massive applications of 2D materials-based devices, and also pave the way for the epitaxial growth of broad non-centrosymmetric 2D materials.

cond-mat.mtrl-sci↗

arXiv2017

Large-area epitaxial growth of MoSe2 via an incandescent molybdenum source

We have developed an incandescent Mo source to fabricate large-area single-crystalline MoSe2 thin films. The as-grown MoSe2 thin films were characterized using transmission electron microscopy, energy dispersive X-ray spectroscopy, atomic force microscopy, Raman spectroscopy, photoluminescence, reflection high energy electron diffraction (RHEED) and angular resolved photoemission spectroscopy (ARPES). A new Raman characteristic peak at 1591 cm-1 was identified. Results from Raman spectroscopy, photoluminescence, RHEED and ARPES studies consistently reveal that large-area single crystalline mono-layer of MoSe2 could be achieved by this technique. This technique enjoys several advantages over conventional approaches and could be extended to the growth of other two-dimensional layered materials containing a low-vapor-pressure element.

cond-mat.mtrl-sci↗

arXiv2017

Reinforcement of interfacial superconductivity in a Bi2Te3/Fe1+yTe heterostructure under hydrostatic pressure

We investigate the hydrostatic pressure dependence of interfacial superconductivity occurring at the atomically sharp interface between two non-superconducting materials: the topological insulator (TI) Bi2Te3 and the parent compound Fe1+yTe of the chalcogenide iron based superconductors. Under pressure, a significant increase in the superconducting transition temperature Tc is observed. We trace the pressure dependence of a superconducting twin gap structure by Andreev reflection point contact spectroscopy (PCARS), which shows that a large superconducting gap associated with the interfacial superconductivity increases along with Tc. A second smaller gap, which is attributed to proximity-induced superconductivity in the TI layer, increases first, but then reaches a maximum and appears to be gradually suppressed at higher pressure. We interpret our data in the context of a pressure-induced doping effect of the interface, in which charge is transferred from the TI layer to the interface and the interfacial superconductivity is enhanced. This demonstrates the important role of the TI in the interfacial superconductivity mechanism.

cond-mat.supr-con↗