arXiv subjects

Peng Wei

Publications and source records attributed to Peng Wei.

At least 127 records · Page 7Linked to original sources

arXiv2020

Strain controlled superconductivity in few-layer NbSe2

The controlled tunability of superconductivity in low-dimensional materials may enable new quantum devices. Particularly in triplet or topological superconductors, tunneling devices such as Josephson junctions etc. can demonstrate exotic functionalities. The tunnel barrier, an insulating or normal material layer separating two superconductors, is a key component for the junctions. Thin layers of NbSe2 have been shown as a superconductor with strong spin orbit coupling, which can give rise to topological superconductivity if driven by a large magnetic exchange field. Here we demonstrate the superconductor-insulator transitions in epitaxially grown few-layer NbSe2 with wafer-scale uniformity on insulating substrates. We provide the electrical transport, Raman spectroscopy, cross-sectional transmission electron microscopy, and X-ray diffraction characterizations of the insulating phase. We show that the superconductor-insulator transition is driven by strain, which also causes characteristic energy shifts of the Raman modes. Our observation paves the way for high quality hetero-junction tunnel barriers to be seamlessly built into epitaxial NbSe2 itself, thereby enabling highly scalable tunneling devices for superconductor-based quantum electronics.

cond-mat.supr-con↗

arXiv2020

Spatially-resolved Stellar Population Properties of the M 51-NGC 5195 System from Multi-wavelength Photometric Data

Using multi-band photometric images of M 51 and its companion NGC 5195 from ultraviolet to optical and infrared, we investigate spatially resolved stellar population properties of this interacting system with stellar population synthesis models. The observed IRX is used to constrain dust extinction. Stellar mass is also inferred from the model fitting. By fitting observed spectral energy distributions (SEDs) with synthetical ones, we derive two-dimensional distributions of stellar age, metallicity, dust extinction, and stellar mass. In M 51, two grand-designed spiral arms extending from the bulge show young age, rich metallicity, and abundant dust. The inter-arm regions are filled with older, metal-poorer, and less dusty stellar populations. Except for the spiral arm extending from M 51 into NGC 5195, the stellar population properties of NGC 5195 are quite featureless. NGC 5195 is much older than M 51, and its core is very dusty with $A_V$ up to 1.67 mag and dense in stellar mass surface density. The close encounters might drive the dust in the spiral arm of M51 into the center of NGC 5195.

astro-ph.GA↗

arXiv2020

Physical Properties of H II Regions in M51 from Spectroscopic Observations

M51 and NGC 5195 is an interacting system that can be explored in great details with ground-based telescopes. The H II regions in M51 were observed using the 2.16 m telescope of the National Astronomical Observatories of the Chinese Academy of Sciences and the 6.5 m Multiple Mirror Telescope with spatial resolution of less than $\sim100$ pc. We obtain a total of 113 spectra across the galaxy and combine the literature data of Croxall et al. to derive a series of physical properties, including the gas-phase extinction, stellar population age, star formation rate (SFR) surface density, and oxygen abundance. The spatial distributions and radial profiles of these properties are investigated in order to study the characteristics of M51 and the clues to the formation and evolution of this galaxy. M51 presents a mild radial extinction gradient. The lower gas-phase extinction in the north spiral arms compared to the south arms are possibly caused by the past encounters with the companion galaxy of NGC 5195. A number of H II regions have the stellar age between 50 and 500 Myr, consistent with the recent interaction history by simulations in the literatures. The SFR surface density presents a mild radial gradient, which is ubiquitous in spiral galaxies. There is a negative metallicity gradient of $-0.08$ dex $R_{e}^{-1}$ in the disk region, which is also commonly found in many spiral galaxies. It is supported by the "inside-out" scenario of galaxy formation. We find a positive abundance gradient of 0.26 dex $R_{e}^{-1}$ in the inner region. There are possible reasons causing the positive gradient, including the freezing of the chemical enrichment due to the star-forming quenching in the bulge and the gas infall and dilution due to the pseudobulge growth and/or galactic interaction.

astro-ph.GA↗

arXiv2020

Continuous Control for Searching and Planning with a Learned Model

Decision-making agents with planning capabilities have achieved huge success in the challenging domain like Chess, Shogi, and Go. In an effort to generalize the planning ability to the more general tasks where the environment dynamics are not available to the agent, researchers proposed the MuZero algorithm that can learn the dynamical model through the interactions with the environment. In this paper, we provide a way and the necessary theoretical results to extend the MuZero algorithm to more generalized environments with continuous action space. Through numerical results on two relatively low-dimensional MuJoCo environments, we show the proposed algorithm outperforms the soft actor-critic (SAC) algorithm, a state-of-the-art model-free deep reinforcement learning algorithm.

cs.AI↗

arXiv2020

Signature of a pair of Majorana zero modes in superconducting gold surface states

Under certain conditions, a fermion in a superconductor can separate in space into two parts known as Majorana zero modes, which are immune to decoherence from local noise sources and are attractive building blocks for quantum computers. Promising experimental progress has been made to demonstrate Majorana zero modes in materials with strong spin-orbit coupling proximity coupled to superconductors. Here we report signatures of Majorana zero modes in a new material platform utilizing the surface states of gold. Using scanning tunneling microscope to probe EuS islands grown on top of gold nanowires, we observe two well separated zero bias tunneling conductance peaks aligned along the direction of the applied magnetic field, as expected for a pair of Majorana zero modes. This platform has the advantage of having a robust energy scale and the possibility of realizing complex designs using lithographic methods.

cond-mat.supr-con↗

arXiv2020

Site-testing at Muztagh-ata site I: Ground Meteorology and Sky Brightness

Site-testing is crucial for achieving the goal of scientific research and analysis of meteorological and optical observing conditions is one of the basic tasks of it. As one of three potential sites to host 12-meter Large Optical/infrared Telescope (LOT), Muztagh-ata site which is located on the Pamirs Plateau in west China's Xinjiang began its site-testing task in the spring of 2017. In this paper, we firstly start with an introduction to the site and then present a statistical analysis of the ground-level meteorological properties such as air temperature, barometric pressure, relative humidity, wind speed and direction, recorded by automatic weather station with standard meteorological sensors for two-year long. We also show the monitoring results of sky brightness during this period.

astro-ph.IM↗

arXiv2020

Site-testing at Muztagh-ata site II: Seeing statistics

In this article, we present a detailed analysis of the statistical properties of seeing for the Muztagh-ata site which is the candidate site for hosting future Chinese Large Optical/infrared Telescope (LOT) project. The measurement was obtained with Differential Image Motion Monitor (DIMM) from April 2017 to November 2018 at different heights during different periods. The median seeing at 11 meters and 6 meters are very close but different significantly from that on the ground. We mainly analyzed the seeing at 11 meters monthly and hourly, having found that the best season for observing was from late autumn to early winter and seeing tended to improve during the night only in autumn. The analysis of the dependence on temperature inversion, wind speed, direction also was made and the best meteorological conditions for seeing is given.

astro-ph.IM↗

arXiv2020

Prioritized Sequence Experience Replay

Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more efficiently, researchers proposed prioritized experience replay (PER) which samples important transitions more frequently. In this paper, we propose Prioritized Sequence Experience Replay (PSER) a framework for prioritizing sequences of experience in an attempt to both learn more efficiently and to obtain better performance. We compare the performance of PER and PSER sampling techniques in a tabular Q-learning environment and in DQN on the Atari 2600 benchmark. We prove theoretically that PSER is guaranteed to converge faster than PER and empirically show PSER substantially improves upon PER.

cs.LG↗

arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗

arXiv2020

Site testing campaign for the Large Optical/infrared Telescope of China: Overview

The Large Optical/infrared Telescope (LOT) is a ground-based 12m diameter optical/infrared telescope which is proposed to be built in the western part of China in the next decade. Based on satellite remote sensing data, along with geographical, logistical and political considerations, three candidate sites were chosen for ground-based astronomical performance monitoring. These sites include: Ali in Tibet, Daocheng in Sichuan, and Muztagh Ata in Xinjiang. Up until now, all three sites have continuously collected data for two years. In this paper, we will introduce this site testing campaign, and present its monitoring results obtained during the period between March 2017 and March 2019.

astro-ph.IM↗

arXiv2019

On the Convergence of Primal-Dual Proximal Incremental Aggregated Gradient Algorithms

In this paper, we adapt proximal incremental aggregated gradient methods to saddle point problems, which is motivated by decoupling linear transformations in regularized empirical risk minimization models. First, the Primal-Dual Proximal Incremental Aggregated (PD-PIAG) methods with extrapolations were proposed. We proved that the primal-dual gap of the averaged iteration sequence sublinearly converges to 0, and the iteration sequence converges to some saddle point. Under the strong convexity of $f$ and $h^\ast$, we proved that the iteration sequence linearly converges to the saddle point. Then, we propose a PD-PIAG method without extrapolations. The primal-dual gap of the iteration sequence is proved to be sublinearly convergent under strong convexity of $f$.

math.OC↗

arXiv2019

An Efficient Algorithm for Multiple-Pursuer-Multiple-Evader Pursuit/Evasion Game

We present a method for pursuit/evasion that is highly efficient and and scales to large teams of aircraft. The underlying algorithm is an efficient algorithm for solving Markov Decision Processes (MDPs) that supports fully continuous state spaces. We demonstrate the algorithm in a team pursuit/evasion setting in a 3D environment using a pseudo-6DOF model and study performance by varying sizes of team members. We show that as the number of aircraft in the simulation grows, computational performance remains efficient and is suitable for real-time systems. We also define probability-to-win and survivability metrics that describe the teams' performance over multiple trials, and show that the algorithm performs consistently. We provide numerical results showing control inputs for a typical 1v1 encounter and provide videos for 1v1, 2v2, 3v3, 4v4, and 10v10 contests to demonstrate the ability of the algorithm to adapt seamlessly to complex environments.

cs.AI↗

arXiv2019

Stochastic and Simulation-based Models for Setting Flow Rates in Collaborative Trajectory Options Program (CTOP)

As a new tool in the NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines multiple features from its forerunners including Ground Delay Program (GDP), Airspace Flow Program (AFP) and reroutes, and can manage multiple Flow Constrained Areas (FCAs) with a single program. A key research question in CTOP is how to set traffic flow rates under traffic demand and airspace capacity uncertainties. In this paper, we first investigate existing CTOP related stochastic optimization models and point out their roles in CTOP flow rate planning, and their advantages and disadvantages in terms of model flexibility, performance, practicality and Collaborative Decision Making (CDM) software compatibility, etc. CTOP FCA rate planning problem has been split into two steps: traffic flow rate optimization given demand estimation, and flow rate adaptation when flight rerouting is considered. Second, we discuss in detail a class of models called FCA-PCA (Potentially Constrained Area) models, which are extended from GDP models to solve the first step of the problem, and were considered promising as they are designed to be consistent with current CTOP software implementation. We will reveal one inherent shortcoming suffered by FCA-PCA models and show that how this deficiency can be addressed by the PCA model family. We will talk about the problems that prevent stochastic programming being optimal in the second step of the problem. Third, we discuss the applicability of simulation-based optimization, combine it with stochastic programming based heuristics and test the resulting new model on a realistic use case. The results are very encouraging. The models and discussions in this work are not only useful in more effectively implementing and analyzing CTOP programs, but are also valuable for the design of future air traffic flow program.

math.OC↗

arXiv2019

Electroconvection of Thin Liquid Crystals: Model Reduction and Numerical Simulations

We propose a finite element method for the numerical simulation of electroconvection of thin liquid crystals. The liquid is located in between two concentric circular electrodes which are either assumed to be of infinite height or slim. Each configuration results in a different nonlocal electro-magnetic model defined on a two dimensional bounded domain. The numerical method consists in approximating the surface charge density, the liquid velocity and pressure, and the electric potential in the two dimensional liquid region. Finite elements for the space discretization coupled with standard time stepping methods are put forward. Unlike for the infinite electrodes configuration, our numerical simulations indicate that slim electrodes are favorable for electroconvection to occur and are able to sustain the phenomena over long period of time. Furthermore, we provide a numerical study on the influence of the three main parameters of the system: the Rayleigh number, the Prandtl number and the electrodes aspect ratio.

math.NA↗

arXiv2019

Autonomous Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking

Revenue management can enable airline corporations to maximize the revenue generated from each scheduled flight departing in their transportation network by means of finding the optimal policies for differential pricing, seat inventory control and overbooking. As different demand segments in the market have different Willingness-To-Pay (WTP), airlines use differential pricing, booking restrictions, and service amenities to determine different fare classes or products targeted at each of these demand segments. Because seats are limited for each flight, airlines also need to allocate seats for each of these fare classes to prevent lower fare class passengers from displacing higher fare class ones and set overbooking limits in anticipation of cancellations and no-shows such that revenue is maximized. Previous work addresses these problems using optimization techniques or classical Reinforcement Learning methods. This paper focuses on the latter problem - the seat inventory control problem - casting it as a Markov Decision Process to be able to find the optimal policy. Multiple fare classes, concurrent continuous arrival of passengers of different fare classes, overbooking and random cancellations that are independent of class have been considered in the model. We have addressed this problem using Deep Q-Learning with the goal of maximizing the reward for each flight departure. The implementation of this technique allows us to employ large continuous state space but also presents the potential opportunity to test on real time airline data. To generate data and train the agent, a basic air-travel market simulator was developed. The performance of the agent in different simulated market scenarios was compared against theoretically optimal solutions and was found to be nearly close to the expected optimal revenue.

cs.AI↗

arXiv2019

Spin Seebeck effect from antiferromagnetic magnons and critical spin fluctuations in epitaxial FeF2 films

We report a longitudinal spin Seebeck effect (SSE) study in epitaxially grown FeF2(110) antiferromagnetic (AFM) thin films with strong uniaxial anisotropy over the temperature range of 3.8 - 250 K. Both the magnetic field- and temperature-dependent SSE signals below the Néel temperature (TN=70 K) of the FeF2 films are consistent with a theoretical model based on the excitations of AFM magnons without any net induced static magnetic moment. In addition to the characteristic low-temperature SSE peak associated with the AFM magnons, there is another SSE peak at TN which extends well into the paramagnetic phase. All the SSE data taken at different magnetic fields up to 12 T near and above the critical point TN follow the critical scaling law very well with the critical exponents for magnetic susceptibility of 3D Ising systems, which suggests that the AFM spin correlation is responsible for the observed SSE near TN.

cond-mat.mtrl-sci↗

arXiv2019

Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach

Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditional (commercial airliners) and low-altitude (drones and eVTOL aircraft) airspace, an autonomous air traffic control system is needed to accommodate high density air traffic and ensure safe separation between aircraft. We propose a deep multi-agent reinforcement learning framework that is able to identify and resolve conflicts between aircraft in a high-density, stochastic, and dynamic en-route sector with multiple intersections and merging points. The proposed framework utilizes an actor-critic model, A2C that incorporates the loss function from Proximal Policy Optimization (PPO) to help stabilize the learning process. In addition we use a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. We show that our framework is both scalable and efficient for large number of incoming aircraft to achieve extremely high traffic throughput with safety guarantee. We evaluate our model via extensive simulations in the BlueSky environment. Results show that our framework is able to resolve 99.97% and 100% of all conflicts both at intersections and merging points, respectively, in extreme high-density air traffic scenarios.

cs.LG↗

arXiv2019

A Strong Formulation for Stochastic Multiple Constrained Resources Air Traffic Flow Management with Reroutes

This paper addresses the air traffic flow management research problem of determining reroute, ground delay and air delay for flights using stochastic weather forecast information. The overall goal is to minimize system-wide reroute and delay costs. This problem is a primary concern in United States and especially in its northeastern region, and is also the key in enhancing the performance of the new FAA Traffic Management Initiative called Collaborative Trajectory Options Program (CTOP). In this work we present six stochastic integer programming models, including two two-stage models and four multistage models, which are based on a notable strong deterministic flight-by-flight level traffic flow formulation and an aggregate Eulerian traffic flow formulation. Our preliminary numerical results show that completely integer solutions can be achieved from linear relaxation, for all proposed models and for both no-route and reroute cases.

math.OC↗