arXiv subjects

Ran Wang

Publications and source records attributed to Ran Wang.

At least 73 records · Page 4Linked to original sources

arXiv2024

Superconductivity in Two-Dimensional Systems with Unconventional Rashba Bands

In two-dimensional system with Rashba spin-orbit coupling, it is well-known that superconductivity can have mixed spin-singlet and -triplet parity, and the $\boldsymbol{d}$-vector of spin-triplet pairing is parallel to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling. Here, we propose a model to describe a two-dimensional system with unconventional Rashba bands and study its superconductivity. We show that the $\boldsymbol{d}$-vector of spin-triplet pairing can be either parallel or perpendicular to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling depending on the different pairing interaction. We also propose a junction to generate tunneling current depending on the direction of $\boldsymbol{d}$-vector. It provides a detectable evidence to distinguish these two different but very similar pairing channels. Furthermore, we find this model can give arise to a subleading spin-singlet chiral $p$-wave topological superconducting state. More significantly, we find that such unconventional Rashba bands and unconventional superconudcting pairings can be realized on surface of some superconducting topological materials, such as trigonal layered PtBi$_{2}$.

cond-mat.supr-con↗

arXiv2024

On the Feedback Law in Stochastic Optimal Nonlinear Control

We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman's "curse of dimensionality". We present a result that shows that repeatedly solving an open-loop deterministic problem from the current state with progressively shorter horizons, similar to Model Predictive Control (MPC), results in a feedback policy that is $O(ε^4)$ near to the true global stochastic optimal policy, where $ε$ is a perturbation parameter modulating the noise. We also show that the optimal deterministic feedback problem has a perturbation structure such that higher-order terms of the feedback law do not affect lower-order terms and that this structure is lost in the optimal stochastic feedback problem. Consequently, solving the Stochastic Dynamic Programming problem is highly susceptible to noise, even in low dimensional problems, and in practice, the MPC-type feedback law offers superior performance even for high noise levels.

eess.SY↗

arXiv2024

Constraining Quasar Feedback from Analysis of the Hydrostatic Equilibrium of the Molecular Gas in Their Host Galaxies

We investigate the kinematics and dynamics of the molecular and ionized gas in the host galaxies of three Palomar-Green quasars at low redshifts, benefiting from the archival millimeter-wave interferometric and optical integral field unit data. We study the kinematics of both cold molecular and hot ionized gas by analyzing the CO and H$α$ data cubes, and construct the mass distributions of our sample through gas dynamics, utilizing a priori knowledge regarding the galaxy light distribution. We find no systematic offset between the stellar mass derived from our dynamical method and that from the broad-band photometry and mass-to-light ratio, suggesting the consistency of both methods. We then study the kinetic pressure and the weight of the interstellar medium using our dynamical mass model. By studying the relationship between kinetic pressure and gravitational pressure of the quasar host galaxies, we find an equivalence in the hydrostatic equilibrium states of ISM in the quasar host galaxies, similar to the result of gas equilibrium in normal star-forming galaxies, suggesting minimal quasar feedback. Regarding non-circular motion as indicative of quasar-driven outflows, we observe an exceptionally low coupling efficiency between molecular gas outflow and AGN bolometric luminosities. These results demonstrate the marginal influence of the central engine on the properties of cold molecular gas in quasar host galaxies.

astro-ph.GA↗

arXiv2024

Subgoal-based Hierarchical Reinforcement Learning for Multi-Agent Collaboration

Recent advancements in reinforcement learning have made significant impacts across various domains, yet they often struggle in complex multi-agent environments due to issues like algorithm instability, low sampling efficiency, and the challenges of exploration and dimensionality explosion. Hierarchical reinforcement learning (HRL) offers a structured approach to decompose complex tasks into simpler sub-tasks, which is promising for multi-agent settings. This paper advances the field by introducing a hierarchical architecture that autonomously generates effective subgoals without explicit constraints, enhancing both flexibility and stability in training. We propose a dynamic goal generation strategy that adapts based on environmental changes. This method significantly improves the adaptability and sample efficiency of the learning process. Furthermore, we address the critical issue of credit assignment in multi-agent systems by synergizing our hierarchical architecture with a modified QMIX network, thus improving overall strategy coordination and efficiency. Comparative experiments with mainstream reinforcement learning algorithms demonstrate the superior convergence speed and performance of our approach in both single-agent and multi-agent environments, confirming its effectiveness and flexibility in complex scenarios. Our code is open-sourced at: \url{https://github.com/SICC-Group/GMAH}.

cs.MA↗

arXiv2024

The RAdio Galaxy Environment Reference Survey (RAGERS): Evidence of an anisotropic distribution of submillimeter galaxies in the 4C 23.56 protocluster at z=2.48

High-redshift radio(-loud) galaxies (H$z$RGs) are massive galaxies with powerful radio-loud active galactic nuclei (AGNs) and serve as beacons for protocluster identification. However, the interplay between H$z$RGs and the large-scale environment remains unclear. To understand the connection between H$z$RGs and the surrounding obscured star formation, we investigated the overdensity and spatial distribution of submillimeter-bright galaxies (SMGs) in the field of 4C\,23.56, a well-known H$z$RG at $z=2.48$. We used SCUBA-2 data ($σ\,{\sim}\,0.6$\,mJy) to estimate the $850\,{\rm μm}$ source number counts and examine the radial and azimuthal overdensities of the $850\,{\rm μm}$ sources in the vicinity of the H$z$RG. The angular distribution of SMGs is inhomogeneous around the H$z$RG 4C\,23.56, with fewer sources oriented along the radio jet. We also find a significant overdensity of bright SMGs (${\rm S}_{850\rm\,μm}\geq5\,$mJy). Faint and bright SMGs exhibit different spatial distributions. The former are concentrated in the core region, while the latter prefer the outskirts of the H$z$RG field. High-resolution observations show that the seven brightest SMGs in our sample are intrinsically bright, suggesting that the overdensity of bright SMGs is less likely due to the source multiplicity.

astro-ph.GA↗

arXiv2024

JobViz: Skill-driven Visual Exploration of Job Advertisements

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users' swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.

cs.HC↗

arXiv2024

Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem.

cs.AI↗

arXiv2024

Dynamic Deep Factor Graph for Multi-Agent Reinforcement Learning

This work introduces a novel value decomposition algorithm, termed \textit{Dynamic Deep Factor Graphs} (DDFG). Unlike traditional coordination graphs, DDFG leverages factor graphs to articulate the decomposition of value functions, offering enhanced flexibility and adaptability to complex value function structures. Central to DDFG is a graph structure generation policy that innovatively generates factor graph structures on-the-fly, effectively addressing the dynamic collaboration requirements among agents. DDFG strikes an optimal balance between the computational overhead associated with aggregating value functions and the performance degradation inherent in their complete decomposition. Through the application of the max-sum algorithm, DDFG efficiently identifies optimal policies. We empirically validate DDFG's efficacy in complex scenarios, including higher-order predator-prey tasks and the StarCraft II Multi-agent Challenge (SMAC), thus underscoring its capability to surmount the limitations faced by existing value decomposition algorithms. DDFG emerges as a robust solution for MARL challenges that demand nuanced understanding and facilitation of dynamic agent collaboration. The implementation of DDFG is made publicly accessible, with the source code available at \url{https://github.com/SICC-Group/DDFG}.

cs.RO↗

arXiv2024

Lower classes and Chung's LILs of the fractional integrated generalized fractional Brownian motion

Let $\{X(t)\}_{t\geqslant0}$ be the generalized fractional Brownian motion introduced by Pang and Taqqu (2019): \begin{align*} \{X(t)\}_{t\ge0}\overset{d}{=}&\left\{ \int_{\mathbb R} \left((t-u)_+^α-(-u)_+^α \right) |u|^{-γ/2} B(du) \right\}_{t\ge0}, \end{align*} where $ γ\in [0,1), \ \ α\in \left(-\frac12+\fracγ{2}, \ \frac12+\fracγ{2} \right)$ are constants. For any $θ>0$, let \begin{align*} Y(t)=\frac{1}{Γ(θ)}\int_0^t (t-u)^{θ-1} X(u)du, \quad t\ge 0. \end{align*} Building upon the arguments of Talagrand (1996), we give integral criteria for the lower classes of $Y$ at $t=0$ and at infinity, respectively. As a consequence, we derive its Chung-type laws of the iterated logarithm. In the proofs, the small ball probability estimates play important roles.

math.PR↗

arXiv2024

EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization

Deep Neural Network (DNN)-based video analytics significantly improves recognition accuracy in computer vision applications. Deploying DNN models at edge nodes, closer to end users, reduces inference delay and minimizes bandwidth costs. However, these resource-constrained edge nodes may experience substantial delays under heavy workloads, leading to imbalanced workload distribution. While previous efforts focused on optimizing hierarchical device-edge-cloud architectures or centralized clusters for video analytics, we propose addressing these challenges through collaborative distributed and autonomous edge nodes. Despite the intricate control involved, we introduce EdgeVision, a Multiagent Reinforcement Learning (MARL)- based framework for collaborative video analytics on distributed edges. EdgeVision enables edge nodes to autonomously learn policies for video preprocessing, model selection, and request dispatching. Our approach utilizes an actor-critic-based MARL algorithm enhanced with an attention mechanism to learn optimal policies. To validate EdgeVision, we construct a multi-edge testbed and conduct experiments with real-world datasets. Results demonstrate a performance enhancement of 33.6% to 86.4% compared to baseline methods.

cs.DC↗

arXiv2024

A Spatially Resolved [CII] Survey of 31 $z\sim7$ Massive Galaxies Hosting Luminous Quasars

The [CII] 158 $μ$m emission line and the underlying far-infrared (FIR) dust continuum are important tracers for studying star formation and kinematic properties of early galaxies. We present a survey of the [CII] emission lines and FIR continua of 31 luminous quasars at $z>6.5$ using the Atacama Large Millimeter Array (ALMA) and the NOrthern Extended Millimeter Array (NOEMA) at sub-arcsec resolution. This survey more than doubles the number of quasars with [CII] and FIR observations at these redshifts and enables statistical studies of quasar host galaxies deep into the epoch of reionization. We detect [CII] emission in 27 quasar hosts with a luminosity range of $L_{\rm [CII]}=(0.3-5.5)\times10^9~L_\odot$ and detect the FIR continuum of 28 quasar hosts with a luminosity range of $L_{\rm FIR}=(0.5-13.0)\times10^{12}~L_\odot$. Both $L_{\rm [CII]}$ and $L_{\rm FIR}$ are correlated ($ρ\simeq0.4$) with the quasar bolometric luminosity, albeit with substantial scatter. The quasar hosts detected by ALMA are clearly resolved with a median diameter of $\sim$5 kpc. About 40% of the quasar host galaxies show a velocity gradient in [CII] emission, while the rest show either dispersion-dominated or disturbed kinematics. Basic estimates of the dynamical masses of the rotation-dominated host galaxies yield $M_{\rm dyn}=(0.1-7.5)\times10^{11}~M_\odot$. Considering our findings alongside those of literature studies, we found that the ratio between $M_{\rm BH}$ and $M_{\rm dyn}$ is about ten times higher than that of local $M_{\rm BH}-M_{\rm dyn}$ relation on average but with substantial scatter (the ratio difference ranging from $\sim$0.6 to 60) and large uncertainties.

astro-ph.GA↗

arXiv2024

FEASTS Combined with Interferometry (I): Overall Properties of Diffuse HI and Implications for Gas Accretion in Nearby Galaxies

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion.

astro-ph.GA↗

arXiv2024

Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by the diffusion Monte Carlo simulations.

physics.chem-ph↗

arXiv2024

An Information-State Based Approach to Linear Time Varying System Identification and Control

This paper considers the problem of system identification for linear time varying systems. We propose a new system realization approach that uses an "information-state" as the state vector, where the "information-state" is composed of a finite number of past inputs and outputs. The system identification algorithm uses input-output data to fit an autoregressive moving average model (ARMA) to represent the current output in terms of finite past inputs and outputs. This information-state-based approach allows us to directly realize a state-space model using the estimated time varying ARMA paramters linear time varying (LTV) systems. The paper develops the theoretical foundation for using ARMA parameters-based system representation using only the concept of linear observability, details the reasoning for exact output modeling using only the finite history, and shows that there is no need to separate the free and the forced response for identification. The paper also discusses the implications of using the information-state system for optimal output feedback control and shows that the solution obtained using a suitably posed information state problem is optimal for the original problem. The proposed approach is tested on various different systems, and the performance is compared with state-of-the-art LTV system identification techniques.

eess.SY↗

arXiv2024

Atomic-scale effect of 2D π-conjugated metal-organic frameworks as electrocatalysts for CO2 reduction reaction towards highly selective products

Electrocatalytic CO2 reduction technology is key to mitigating greenhouse gas emissions and the energy crisis. However, controlling the selectivity of CO2RR products at low overpotential remains a challenge. In this paper, we predicted five high-performance CO2RR electrocatalysts with different product-specific selectivities at the theoretical level based on the advantages of the compositional structure and the tunable pore size of 2D π-conjugated MOFs. In addition, through the reaction mechanism and electronic structure analysis, we found that the synergistic interaction between metal atoms and organic linkers of 2D MOFs can effectively regulate the electronic structure of the active center. Their pore size as well as the diversity of carbon materials can regulate the spin magnetic moments of the metal atoms, thus affecting the improvement of their catalytic performance. Meanwhile, the oxygen or carbon affinity of the catalyst surface determines the differences in the formation of key intermediates, which ultimately determines the reaction path and product selectivity. These insights we present will be useful for the development and design of highly active CO2RR electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2024

Diverse molecular gas excitations in quasar host galaxies at z \sim 6

We present observations using the NOrthern Extended Millimetre Array (NOEMA) of CO and $\rm H_{2}O$ emission lines, and the underlying dust continuum in two quasars at $z \sim 6$, i.e., P215-16 at $z$ = 5.78 and J1429+5447 at $z$ = 6.18. Notably, among all published CO SLEDs of quasars at $z \sim 6$, the two systems reveal the highest and the lowest CO level of excitation, respectively. Our radiative transfer modeling of the CO SLED of P215-16 suggests that the molecular gas heated by AGN could be a plausible origin for the high CO excitation. For J1429+5447, we obtain the first well-sampled CO SLED (from transitions from 2-1 to 10-9) of a radio-loud quasar at $z\gtrsim 6$. Analysis of the CO SLED suggests that a single photo-dissociation region (PDR) component could explain the CO excitation in the radio-loud quasar J1429+5447. This work highlights the utility of the CO SLED in uncovering the ISM properties in these young quasar-starburst systems at the highest redshift. The diversity of the CO SLEDs reveals the complexities in gas conditions and excitation mechanisms at their early evolutionary stage.

astro-ph.GA↗

arXiv2024

Optically Helicity-Dependent Orbital and Spin Dynamics in Two-Dimensional Ferromagnets

Disentangling orbital (OAM) and spin (SAM) angular momenta in the ultrafast spin dynamics of two-dimensional (2D) ferromagnets on subfemtoseconds is a challenge in the field of ultrafast magnetism. Herein, we employed non-collinear spin version of real-time time-dependent density functional theory to investigate the orbital and spin dynamics of 2D ferromagnets Fe3GeTe2 (FGT) induced by circularly polarized light. Our results show the demagnetization of Fe sublattice in FGT is accompanied by helicity-dependent precession of OAM and SAM excited by circularly polarized lasers. We further identify that precession of OAM and SAM in FGT is faster than the demagnetization within a few femtoseconds. Remarkably, circularly polarized lasers can significantly induce a periodically transverse response of OAM and SAM on very ultrafast timescales of ~250 attoseconds. Our finding suggests a powerful new route for attosecond regimes of the angular momentum manipulation to coherently control helicity-dependent orbital and spin dynamics in 2D limits.

cond-mat.mtrl-sci↗

arXiv2023

Analysis of the gradient for the stochastic fractional heat equation with spatially-colored noise in $\mathbb R^d$

Consider the stochastic partial differential equation $$ \frac{\partial }{\partial t}u_t(\mathbf{x})= -(-Δ)^{\fracα{2}}u_t(\mathbf{x}) +b\left(u_t(\mathbf{x})\right)+σ\left(u_t(\mathbf{x})\right) \dot F(t, \mathbf{x}), \ \ \ t\ge0, \mathbf{x}\in \mathbb R^d, $$ where $-(-Δ)^{\fracα{2}}$ denotes the fractional Laplacian with the power $α/2\in (1/2,1]$, and the driving noise $\dot F$ is a centered Gaussian field which is white in time and with a spatial homogeneous covariance given by the Riesz kernel. We study the detailed behavior of the approximation spatial gradient $u_t(\mathbf{x})-u_t(\mathbf{x}-\varepsilon \mathbf e)$ at any fixed time $t>0$, as $\varepsilon\downarrow 0$, where $\mathbf e$ is the unit vector in $\mathbb R^d$. As applications, we deduce the law of iterated logarithm and the behavior of the $q$-variations of the solution in space.

math.PR↗