arXiv subjects

Hai Yang

Publications and source records attributed to Hai Yang.

At least 55 records · Page 3Linked to original sources

arXiv2023

Topological Phases, Local Magnetic Moments, and Spin Polarization Triggered by C558-Line Defects in Graphene

We study the electronic properties of a novel topological defect structure for graphene interspersed with C558-line defects along the Armchair boundary. This system has the topological property of being topologically three-periodic and the type-II Dirac-fermionic character of the embedded topological phase. At the same time, we show computationally that the topological properties of the system are overly dependent on the coupling of this line defect. Using strain engineering to regulate the magnitude of hopping at the defect, the position of the energy level can be easily changed to achieve a topological phase transition. We also discuss the local magnetic moment and the ferromagnetic ground state in the context of line defects, which is the conclusion after considering additional Coulomb interactions. This leads to spin polarization of the whole system. Finally, by modulating the local magnetic moment at the position of the line defect, we achieve a tunable spin quantum conductance in a one-dimensional nanoribbon. Near the Fermi energy level, it also has the property of complete spin polarization. Consequently, spin filtering can be achieved by varying the incident energy of the electrons.

cond-mat.mes-hall↗

arXiv2023

A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. Evaluated on real-world data based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.

cs.AI↗

arXiv2023

The properties of wind and jet from a super-Eddington accretion flow around a supermassive black hole

Wind and jet are important medium of AGN feedback thus it is crucial to obtain their properties for the feedback study. In this paper we investigate the properties of wind and jet launched from a magnetized super-Eddington accretion flow around a supermassive black hole. For this aim, we have performed radiation magnetohydrodynamical simulation of a magnetically arrested super-Eddington accretion flows. We then have analyzed the simulation data by the ``virtual particle trajectory'' approach and obtained the mass flux, poloidal and toroidal velocities, and mass-flux-weighted momentum and energy fluxes of wind and jet. The mass flux is found to be 2-6 times higher than that obtained based on the time-averaged streamline method widely used in literature. The momentum flux of wind is found to be larger than that of jet, while the total energy flux of jet is at most 3 times larger than that of wind. These results are similar to the case of hot accretion flows and imply that winds likely play a more important role than jet in AGN feedback. The acceleration mechanism of wind and jet is analyzed and found to be dominated by Lorentz force rather than radiation force.

astro-ph.HE↗

arXiv2023

A ring-like accretion structure in M87 connecting its black hole and jet

The nearby radio galaxy M87 is a prime target for studying black hole accretion and jet formation^{1,2}. Event Horizon Telescope observations of M87 in 2017, at a wavelength of 1.3 mm, revealed a ring-like structure, which was interpreted as gravitationally lensed emission around a central black hole^3. Here we report images of M87 obtained in 2018, at a wavelength of 3.5 mm, showing that the compact radio core is spatially resolved. High-resolution imaging shows a ring-like structure of 8.4_{-1.1}^{+0.5} Schwarzschild radii in diameter, approximately 50% larger than that seen at 1.3 mm. The outer edge at 3.5 mm is also larger than that at 1.3 mm. This larger and thicker ring indicates a substantial contribution from the accretion flow with absorption effects in addition to the gravitationally lensed ring-like emission. The images show that the edge-brightened jet connects to the accretion flow of the black hole. Close to the black hole, the emission profile of the jet-launching region is wider than the expected profile of a black-hole-driven jet, suggesting the possible presence of a wind associated with the accretion flow.

astro-ph.HE↗

arXiv2022

Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data

Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve accurate subtyping results. We compared Subtype-Former with the other state-of-the-art subtyping methods across the TCGA 10 cancer types. We found that Subtype-Former can perform better on the benchmark datasets of more than 5000 tumors based on the survival analysis. In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level. Finally, we applied Subtype-Former to the TCGA 10 types of cancers. We identified 50 essential biomarkers, which can be used to study targeted cancer drugs and promote the development of cancer treatments in the era of precision medicine.

cs.LG↗

arXiv2022

Formation of episodic jets and associated flares from black hole accretion systems

Episodic ejections of blobs (episodic jets) are widely observed in black hole sources and usually associated with flares. In this paper, by performing and analyzing three dimensional general relativity magnetohydrodynamical numerical simulations of accretion flows, we investigate their physical mechanisms. We find that magnetic reconnection occurs in the accretion flow, likely due to the turbulent motion and differential rotation of the accretion flow, resulting in flares and formation of flux ropes. Flux ropes formed inside of 10-15 gravitational radii are found to mainly stay within the accretion flow, while flux ropes formed beyond this radius are ejected outward by magnetic forces and form the episodic jets. These results confirm the basic scenario proposed in Yuan et al.(2009). Moreover, our simulations find that the predicted velocity of the ejected blobs is in good consistency with observations of Sgr A*, M81, and M87. The whole processes are found to occur quasi-periodically, with the period being the orbital time at the radius where the flux rope is formed. The predicted period of flares and ejections is consistent with those found from the light curves or image of Sgr A*, M87, and PKS 1510-089. The possible applications to protostellar accretion systems are discussed.

astro-ph.HE↗

arXiv2022

Improved Multi-step FCS-MPCC with Disturbance Compensation for PMSM Drives -- Methods and Experimental Validation

In this paper, an improved multi-step finite control set model predictive current control (FCS-MPCC) strategy with speed loop disturbance compensation is proposed for permanent magnet synchronous machine (PMSM) drives system. A multi-step prediction mechanism is beneficial to significantly improve the steady-state performance of the motor system. While the conventional multi-step prediction has the defect of heavy computational burden, an improved multi-step finite control set model predictive current control (IM MPCC) strategy is proposed by developing a new multi-step prediction mechanism. Furthermore, in order to improve the dynamic response of the system, a disturbance compensation (DC) mechanism based on an extended state observer (ESO) is proposed to estimate and compensate the total disturbance in the speed loop of the PMSM system. Both simulation and experimental results validate the effectiveness of the proposed control strategy.

eess.SY↗

arXiv2022

The Accretion flow in M87 is really MAD

The supermassive black holes in most galaxies in the universe are powered by hot accretion flows. Both theoretical analysis and numerical simulations have indicated that, depending on the degree of magnetization, black hole hot accretion flow is divided into two modes, namely SANE (standard and normal evolution) and MAD (magnetically arrested disk). It has been an important question which mode the hot accretion flows in individual sources should belong to in reality, SANE or MAD. This issue has been investigated in some previous works but they all suffer from various uncertainties. By using the measured rotation measure values in the prototype low-luminosity active galactic nuclei in {M87} at 2, 5, and 8 GHz along the jet at various distances from the black hole, combined with three dimensional general relativity magnetohydrodynamical numerical simulations of SANE and MAD, we show in this paper that the predicted rotation measure values by MAD are well consistent with observations, while the SANE model overestimates the rotation measure by over two orders of magnitude thus is ruled out.

astro-ph.HE↗

arXiv2021

Spatial Pricing in Ride-Sourcing Markets under a Congestion Charge

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform.

math.OC↗

arXiv2021

Numerical simulation of hot accretion flows (IV): effects of black hole spin and magnetic field strength on the wind and the comparison between wind and jet properties

This is the fourth paper of our series of works studying winds from hot accretion flows around black holes. In the first two papers, we have shown the existence of strong winds in hot accretion flows using hydrodynamical and magnetohydrodynamical (MHD) simulations. In the third paper, by using three dimensional general relativity MHD numerical simulation data of hot accretion flows and adopting a "virtual particle trajectory" data analysis approach, we have calculated the properties of wind, such as its mass flux and velocity. However, that paper focuses only on a non-spinning black hole and SANE (standard and normal accretion). In the present paper, we extend the third paper by including cases of a rapidly rotating black hole and MAD (magnetically arrested disk). We focus on investigating the effect of spin and magnetic field on the properties of wind and jet. It is found that a larger spin and stronger magnetic field usually enhance the wind and jet. The formulae describing the mass flux, poloidal velocity, and fluxes of momentum, kinetic energy, and total energy of wind and jet are presented. One interesting finding, among others, is that even in the case of very rapidly spinning black hole where the jet is supposed to be the strongest, the momentum flux of jet is smaller than that of wind, while the total energy flux of jet is larger than that of wind by at most a factor of 10. This result suggests that wind potentially plays a more important role than jet at least for some problems in active galactic nuclei feedback.

astro-ph.HE↗

arXiv2020

Flux ropes in SANE disks

Three-dimensional numerical simulations of a hot accretion flow around a supermassive black hole are performed using the general relativity magneto-hydrodynamic (GRMHD) code Athena++. We focus on the case of SANE, with the initial magnetic field consisting of multiple loops with oppositely directed poloidal magnetic field in the torus. Using the simulation data, we investigate the formation of flux ropes, follow the forming of flux ropes atop the disk, and their release into corona.

astro-ph.HE↗

arXiv2020

Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach

Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for different service modes are correlated, and the prediction of demand for one service mode can benefit from historical observations of demands for other service modes. Moreover, an accurate joint prediction of demands for multiple service modes can help the platforms better allocate and dispatch vehicle resources. Although there is a large stream of literature on ride-hailing demand predictions for one specific service mode, little efforts have been paid towards joint predictions of ride-hailing demands for multiple service modes. To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks. More specifically, two multi-task learning structures are established. The first one is the regularized cross-task learning, which builds cross-task connections among the inputs and outputs of multiple MGC networks. The second one is the multi-linear relationship learning, which imposes a prior tensor normal distribution on the weights of various MGC networks. Although there are no concrete bridges between different MGC networks, the weights of these networks are constrained by each other and subject to a common prior distribution. Evaluated with the for-hire-vehicle datasets in Manhattan, we show that our propose approach outperforms the benchmark algorithms in prediction accuracy for different ride-hailing modes.

cs.LG↗

arXiv2020

Competitive ride-sourcing market with a third-party integrator

Recently, some transportation service providers attempt to integrate the ride services offered by multiple independent ride-sourcing platforms, and passengers are able to request ride through such third-party integrators or connectors and receive service from any one of the platforms. This novel business model, termed as third-party platform-integration in this paper, has potentials to alleviate the cost of market fragmentation due to the demand splitting among multiple platforms. While most existing studies focus on the operation strategies for one single monopolist platform, much less is known about the competition and platform-integration as well as the implications on operation strategy and system efficiency. In this paper, we propose mathematical models to describe the ride-sourcing market with multiple competing platforms and compare system performance metrics between two market scenarios, i.e., with and without platform-integration, at Nash equilibrium as well as social optimum. We find that platform-integration can increase total realized demand and social welfare at both Nash equilibrium and social optimum, but may not necessarily generate a greater profit when vehicle supply is sufficiently large or/and market is too fragmented. We show that the market with platform-integration generally achieves greater social welfare. On one hand, the integrator in platform-integration is able to generate a thicker market and reduce matching frictions; on the other hand, multiple platforms are still competing by independently setting their prices, which help to mitigate monopoly mark-up as in the monopoly market.

econ.GN↗

arXiv2020

Off-Street Parking for TNC Vehicles to Reduce Cruising Traffic

This paper considers off-street parking for the cruising vehicles of transportation network companies (TNCs) to reduce the traffic congestion. We propose a novel business that integrates the shared parking service into the TNC platform. In the proposed model, the platform (a) provides interfaces that connect passengers, drivers and garage operators (commercial or private garages); (b) determines the ride fare, driver payment, and parking rates; (c) matches passengers to TNC vehicles for ride-hailing services; and (d) matches vacant TNC vehicles to unoccupied parking garages to reduce the cruising cost. A queuing-theoretic model is proposed to capture the matching process of passengers, drivers, and parking garages. A market-equilibrium model is developed to capture the incentives of the passengers, drivers, and garage operators. An optimization-based model is formulated to capture the optimal pricing of the TNC platform. Through a realistic case study, we show that the proposed business model will offer a Pareto improvement that benefits all stakeholders, which leads to higher passenger surplus, higher drivers surplus, higher garage operator surplus, higher platform profit, and reduced traffic congestion.

math.OC↗

arXiv2020

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: a pedestrian dynamics-based microscopic simulation approach

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demand. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open-hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs.

physics.soc-ph↗

arXiv2019

Go left or right? Explore the side preference behavior with circle antipode experiments

Side preference is a critical behavior in conflict handling, and here the behavior is investigated with pedestrian trajectories in circle antipode experiments that own both conflicting and symmetrical participant situations. In the series of experiments, more participants(around 70\%) prefer to walk on the right side, and the statistical analyses reveal that factors such as handedness, gender, and height have no significant impacts. Further investigations show that most pedestrians actually make the side choices at the very beginning, and empirical results suggest that selecting the dominate side preference (right side in our experiments) can benefit the individual movement efficiency. To reflect the realistic side preference characteristics in simulations, a Voronoi diagram based model is introduced as well as a side preference parameter with normal distribution is formulated and calibrated. Further simulations prove that the modified model is able to reproduce realistic side preference behaviors in circle antipode experiments and other common situations.

physics.soc-ph↗

arXiv2019

Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand prediction is one of the most fundamental components in supply-demand management systems of ride-sourcing platforms. With accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations and ride-sharing vehicle routing, etc. Compared to zone-based demand prediction that has been examined by many previous studies, OD-based demand prediction is more challenging. This is mainly due to the complicated spatial and temporal dependencies among demand of different OD pairs. To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs. Firstly, the model constructs OD graphs, which utilize adjacent matrices to characterize the non-Euclidean pair-wise geographical and semantic correlations among different OD pairs. Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand. Through extensive experiments on the for-hire-vehicles datasets in Manhattan, New York City, we show that our proposed deep learning framework outperforms the state-of-arts by a significant margin.

eess.SP↗

arXiv2019

Optimizing Online Matching for Ride-Sourcing Services with Multi-Agent Deep Reinforcement Learning

Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and waiting passengers is one of the most key components in a ride-sourcing system. The average pickup distance or time is an important measurement of system efficiency since it affects both passengers' waiting time and drivers' utilization rate. It is naturally expected that a more effective bipartite matching (with smaller average pickup time) can be implemented if the platform accumulates more idle drivers and waiting passengers in the matching pool. A specific passenger request can also benefit from a delayed matching since he/she may be matched with closer idle drivers after waiting for a few seconds. Motivated by the potential benefits of delayed matching, this paper establishes a two-stage framework which incorporates a combinatorial optimization and multi-agent deep reinforcement learning methods. The multi-agent reinforcement learning methods are used to dynamically determine the delayed time for each passenger request (or the time at which each request enters the matching pool), while the combinatorial optimization conducts an optimal bipartite matching between idle drivers and waiting passengers in the matching pool. Two reinforcement learning methods, spatio-temporal multi-agent deep Q learning (ST-M-DQN) and spatio-temporal multi-agent actor-critic (ST-M-A2C) are developed. Through extensive empirical experiments with a well-designed simulator, we show that the proposed framework is able to remarkably improve system performances.

cs.MA↗