arXiv subjects

Xiaoqing Liang

Publications and source records attributed to Xiaoqing Liang.

13 recordsLinked to original sources

arXiv2026

Searching for Spider-Like Pulsars from TESS Ellipsoidal Lightcurves with X-ray counterparts

We present a search for new spider pulsar candidates through multi-wavelength cross-matching, including $γ$-ray, X-ray, and optical data. A search for sinusoidal-like optical modulations in TESS data of 183 eROSITA X-ray sources coincident with unassociated Fermi-LAT gamma-ray sources led to the identification of four promising spider pulsar candidates. We found optical variability periods ranging from 5 to 13 hours. All candidates display smooth sinusoidal-like phase light curves, similar to what can be expected from ellipsoidal variation; one shows double-peaked profiles indicative of harmonics. The absence of sharp minima, which are often found in black widow systems due to irradiation, together with their optical magnitudes of about G~14, suggests these sources are more likely redback-type binaries. One of the Fermi-LAT counterparts is included in a machine-learning catalog of unassociated gamma-ray sources, with relatively high pulsar probabilities. We also identify potential Gaia counterparts for several sources and estimate their distances and luminosities where parallax measurements are available. Future observations, including further spectroscopic and multi-wavelength studies, are needed to fully characterize these systems.

astro-ph.HE↗

arXiv2026

Equilibrium Strategies for the N-agent Mean-Variance Investment Problem over a Random Horizon

We study equilibrium feedback strategies for a family of dynamic mean-variance problems with competition among a large group of agents. We assume that the time horizon is random and each agent's risk aversion depends dynamically on the current wealth. We consider both the finite population game and the corresponding mean-field one. Each agent can invest in a risk-free asset and a specific individual stock, which is correlated with other stocks by a common noise. By applying stochastic control theory, we derive the extended Hamilton-Jacobi-Bellman (HJB) system of equations for both $n$-agent and mean-field games. Under an exponentially distributed random horizon, in each case, we explicitly obtain the equilibrium feedback strategies and the value function. Our results show that the agent's equilibrium feedback strategy depends not only on his/her current wealth but also on the wealth of other competitors. Moreover, when the risk aversion is state-independent and the risk-free interest rate is zero, the equilibrium strategies degenerate to constants, which is identical to the unique equilibrium obtained in \citet{lacker2019mean} with exponential risk preferences; when the competition parameter goes to zero and the risk aversion equals some specific value, the equilibrium strategies coincide with the ones derived in \citet{landriault2018equilibrium}.

math.OC↗

arXiv2024

Two-dimensional quantum droplets in binary quadrupolar condensates

We study the stability and characteristics of two-dimensional (2D) quasi-isotropic quantum droplets (QDs) of fundamental and vortex types, formed by binary Bose-Einstein condensate with magnetic quadrupole-quadrupole interactions (MQQIs). The magnetic quadrupoles are built as pairs of dipoles and antidipoles polarized along the x-axis. The MQQIs are induced by applying an external magnetic field that varies along the x-axis. The system is modeled by the Gross-Pitaevskii equations including the MQQIs and Lee-Huang-Yang correction to the mean-field approximation. Stable 2D fundamental QDs and quasi-isotropic vortex QDs with topological charges S<4 are produced by means of the imaginary-time-integration method for configurations with the quadrupoles polarized parallel to the systems two-dimensional plane. Effects of the norm and MQQI strength on the QDs are studied in detail. Some results, including an accurate prediction of the effective area, chemical potential, and peak density of QDs, are obtained in an analytical form by means of the Thomas-Fermi approximation. Collisions between moving QDs are studied by means of systematic simulations.

cond-mat.quant-gas↗

arXiv2023

Optimal Monotone Mean-Variance Problem in a Catastrophe Insurance Model

This paper explores an optimal investment and reinsurance problem involving both ordinary and catastrophe insurance businesses. The catastrophic events are modeled as following a compound Poisson process, impacting the ordinary insurance business. The claim intensity for the ordinary insurance business is described using a Cox process with a shot-noise intensity, the jump of which is proportional to the size of the catastrophe event. This intensity increases when a catastrophe occurs and then decays over time. The insurer's objective is to maximize their terminal wealth under the Monotone Mean-Variance (MMV) criterion. In contrast to the classical Mean-Variance (MV) criterion, the MMV criterion is monotonic across its entire domain, aligning better with fundamental economic principles. We first formulate the original MMV optimization problem as an auxiliary zero-sum game. Through solving the Hamilton-Jacobi-Bellman-Isaacs (HJBI) equation, explicit forms of the value function and optimal strategies are obtained. Additionally, we provides the efficient frontier within the MMV criterion. Several numerical examples are presented to demonstrate the practical implications of the results.

math.OC↗

arXiv2022

Optimal Insurance to Maximize RDEU Under a Distortion-Deviation Premium Principle

In this paper, we study an optimal insurance problem for a risk-averse individual who seeks to maximize the rank-dependent expected utility (RDEU) of her terminal wealth, and insurance is priced via a general distortion-deviation premium principle. We prove necessary and sufficient conditions satisfied by the optimal solution and consider three ambiguity orders to further determine the optimal indemnity. Finally, we analyze examples under three distortion-deviation premium principles to explore the specific conditions under which no insurance or deductible insurance is optimal.

q-fin.RM↗

arXiv2022

Optimal Reinsurance to Minimize the Probability of Drawdown under the Mean-Variance Premium Principle: Asymptotic Analysis

In this paper, we consider an optimal reinsurance problem to minimize the probability of drawdown for the scaled Cramér-Lundberg risk model when the reinsurance premium is computed according to the mean-variance premium principle. We extend the work of Liang et al. [16] to the case of minimizing the probability of drawdown. By using the comparison method and the tool of adjustment coefficients, we show that the minimum probability of drawdown for the scaled classical risk model converges to the minimum probability for its diffusion approximation, and the rate of convergence is of order $O(n^{-1/2})$. We further show that using the optimal strategy from the diffusion approximation in the scaled classical risk model is $O(n^{-1/2})$-optimal.

math.OC↗

arXiv2021

Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving

Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes in them. Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images. To generate these rasterized images, we project map elements onto images in the camera view, yielding meaningful map representations that can be consumed by a DNN accordingly. As we formulate the change detection task as an object detection problem, we leverage the anchor-based structure that predicts bounding boxes with different change status categories. To the best of our knowledge, the proposed method is the first end-to-end network that tackles the high-definition map change detection task, yielding a single stage solution. Furthermore, rather than relying on single frame input, we introduce a spatio-temporal fusion module that fuses features from history frames into the current, thus improving the overall performance. Finally, we comprehensively validate our method's effectiveness using freshly collected datasets. Results demonstrate that our Diff-Net achieves better performance than the baseline methods and is ready to be integrated into a map production pipeline maintaining an up-to-date HD map.

cs.CV↗

arXiv2021

Minimizing the Probability of Lifetime Exponential Parisian Ruin

We find the optimal investment strategy in a Black-Scholes market to minimize the probability of so-called {\it lifetime exponential Parisian ruin}, that is, the probability that wealth exhibits an excursion below zero of an exponentially distributed time before the individual dies. We find that leveraging the risky asset is worse for negative wealth when minimizing the probability of lifetime exponential Parisian ruin than when minimizing the probability of lifetime ruin. Moreover, when wealth is negative, the optimal amount invested in the risky asset increases as the hazard rate of the exponential ``excursion clock'' increases. In view of the heavy leveraging when wealth is negative, we also compute the minimum probability of lifetime exponential Parisian ruin under a constraint on investment. Finally, we derive an asymptotic expansion of the minimum probability of lifetime exponential Parisian ruin for small values of the hazard rate of the excursion clock. It is interesting to find that, for small values of this hazard rate, the minimum probability of lifetime exponential Parisian ruin is proportional to the minimum occupation time studied in Bayraktar and Young, and the proportion equals the hazard rate. To the best of our knowledge, our work is the first to {\it control} the probability of Parisian ruin.

math.OC↗

arXiv2020

Minimizing the Discounted Probability of Exponential Parisian Ruin via Reinsurance

We study the problem of minimizing the discounted probability of exponential Parisian ruin, that is, the discounted probability that an insurer's surplus exhibits an excursion below zero in excess of an exponentially distributed clock. The insurer controls its surplus via reinsurance priced according to the mean-variance premium principle, as in Liang, Liang, and Young (2019). We consider the classical risk model and apply stochastic Perron's method, as introduced by Bayraktar and Sirbu (2012,2013,2014), to show that the minimum discounted probability of exponential Parisian ruin is the unique viscosity solution of its Hamilton-Jacobi-Bellman equation with boundary conditions at $\pm \infty$. A major difficulty in proving the comparison principle arises from the discontinuity of the Hamiltonian.

math.OC↗

arXiv2020

Optimal Reinsurance under the Mean-Variance Premium Principle to Minimize the Probability of Ruin

We consider the problem of minimizing the probability of ruin by purchasing reinsurance whose premium is computed according to the mean-variance premium principle, a combination of the expected-value and variance premium principles. We derive closed-form expressions of the optimal reinsurance strategy and the corresponding minimum probability of ruin under the diffusion approximation of the classical Cramér-Lundberg risk process perturbed by a diffusion. We find an explicit expression for the reinsurance strategy that maximizes the adjustment coefficient for the classical risk process perturbed by a diffusion. Also, for this risk process, we use stochastic Perron's method to prove that the minimum probability of ruin is the unique viscosity solution of its Hamilton-Jacobi-Bellman equation with appropriate boundary conditions. Finally, we prove that, under an appropriate scaling of the classical risk process, the minimum probability of ruin converges to the minimum probability of ruin under the diffusion approximation.

math.OC↗

arXiv2018

Medium-sized Sin- (n=14-20) clusters: a combined study of photoelectron spectroscopy and DFT calculations

Size-selected anionic silicon clusters, Sin- (n=14-20), have been investigated by photoelectron spectroscopy and density functional theory (DFT) calculations. Low-energy structures of the clusters are globally searched for by using a genetic algorithm based on DFT calculations. The electronic density of states and VDEs have been simulated by using ten DFT functionals and compared to the experimental results. We systematically evaluated the DFT functionals for the calculation of the energetics of silicon clusters. CCSD(T) single-point energies based on MP2 optimized geometries for selected isomers of Sin- are also used as benchmark for the energy sequence. The HSE06 functional with aug-cc-pVDZ basis set is found to show the best performance. Our global minimum search corroborates that most of the lowest-energy structures of Sin- (n=14-20) clusters can be derived from assembling tricapped trigonal prisms (TTP) in various ways. For most sizes previous structures are confirmed, whereas for Si20- a new structure has been found.

physics.atm-clus↗

arXiv2017

Record High Magnetic Anisotropy in Chemically Engineered Iridium Dimer

Exploring giant magnetic anisotropy in small magnetic nanostructures is of both fundamental interest and technological merit for information storage. To prevent spin flipping at room temperature due to thermal fluctuation, large magnetic anisotropy energy (MAE) over 50 meV in magnetic nanostructure is desired for practical applications. We chose one of the smallest magnetic nanostructures-Ir2 dimer, to investigate its magnetic properties and explore possible approach to engineer the magnetic anisotropy. Through systematic first-principles calculations, we found that the Ir2 dimer already possesses giant MAE of 77 meV. We proposed an effective way to enhance the MAE of the Ir2 dimer to 223~294 meV by simply attaching a halogen atom at one end of the Ir-Ir bond. The underlying mechanism for the record high MAE is attributed to the modification of the energy diagram of the Ir2 dimer by the additional halogen-Ir bonding, which alters the spin-orbit coupling Hamiltonian and hence the magnetic anisotropy. Our strategy can be generalized to design other magnetic molecules or clusters with giant magnetic anisotropy.

physics.atm-clus↗

arXiv2017

A note on optimal expected utility of dividend payments with proportional reinsurance

In this paper, we consider the problem of maximizing the expected discounted utility of dividend payments for an insurance company that controls risk exposure by purchasing proportional reinsurance. We assume the preference of the insurer is of CRRA form. By solving the corresponding Hamilton-Jacobi-Bellman equation, we identify the value function and the corresponding optimal strategy. We also analyze the asymptotic behavior of the value function for large initial reserves. Finally, we provide some numerical examples to illustrate the results and analyze the sensitivity of the parameters.

q-fin.PM↗