arXiv subjects

Yi Xia

Publications and source records attributed to Yi Xia.

At least 37 records · Page 2Linked to original sources

arXiv2024

Many-insurer robust games of reinsurance and investment under model uncertainty in incomplete markets

This paper studies the robust reinsurance and investment games for competitive insurers. Model uncertainty is characterized by a class of equivalent probability measures. Each insurer is concerned with relative performance under the worst-case scenario. Insurers' surplus processes are approximated by drifted Brownian motion with common and idiosyncratic insurance risks. The insurers can purchase proportional reinsurance to divide the insurance risk with the reinsurance premium calculated by the variance principle. We consider an incomplete market driven by the 4/2 stochastic volatility mode. This paper formulates the robust mean-field game for a non-linear system originating from the variance principle and the 4/2 model. For the case of an exponential utility function, we derive closed-form solutions for the $n$-insurer game and the corresponding mean-field game. We show that relative concerns lead to new hedging terms in the investment and reinsurance strategies. Model uncertainty can significantly change the insurers' hedging demands. The hedging demands in the investment-reinsurance strategies exhibit highly non-linear dependence with the insurers' competitive coefficients, risk aversion and ambiguity aversion coefficients. Finally, numerical results demonstrate the herd effect of competition.

q-fin.MF↗

arXiv2024

Robust mean-variance stochastic differential reinsurance and investment games under volatility risk and model uncertainty

This paper investigates robust stochastic differential games among insurers under model uncertainty and stochastic volatility. The surplus processes of ambiguity-averse insurers (AAIs) are characterized by drifted Brownian motion with both common and idiosyncratic insurance risks. To mitigate these risks, AAIs can purchase proportional reinsurance. Besides, AAIs allocate their wealth in a financial market consisting of cash, and a stock characterized by the 4/2 stochastic volatility model. AAIs compete with each other based on relative performance with the mean-variance criterion under the worst-case scenario. This paper formulates a robust time-consistent mean-field game in a non-linear system. The AAIs seek robust, time-consistent response strategies to achieve Nash equilibrium strategies in the game. We introduce $n$-dimensional extended Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations and corresponding verification theorems under compatible conditions. Semi-closed forms of the robust $n$-insurer equilibrium and mean-field equilibrium are derived, relying on coupled Riccati equations. Suitable conditions are presented to ensure the existence and uniqueness of the coupled Riccati equation as well as the integrability in the verification theorem. As the number of AAIs increases, the results in the $n$-insurer game converge to those in the mean-field game. Numerical examples are provided to illustrate economic behaviors in the games, highlighting the herd effect of competition on the AAIs.

q-fin.MF↗

arXiv2024

Realizing Intrinsically Glass-like Thermal Transport via Weakening the Ag-Ag Bonds in Ag$_{6}$ Octahedra

Crystals exhibiting glass-like and low lattice thermal conductivity ($κ_{\rm L}$) are not only scientifically intriguing but also practically valuable in various applications, including thermal barrier coatings, thermoelectric energy conversion, and thermal management. However, such unusual $κ_{\rm L}$ are typically observed only in compounds containing heavy elements, with large unit cells, or at high temperatures, primarily due to significant anharmonicity. In this study, we utilize chemical bonding principles to weaken the Ag-Ag bonds within the Ag$_6$ octahedron by introducing a ligand in the bridge position. Additionally, the weak Ag-chalcogen bonds, arising from fully filled $p$-$d$ antibonding orbitals, provide an avenue to further enhance lattice anharmonicity. We propose the incorporation of a chalcogen anion as a bridge ligand to promote phonon rattling in Ag$_6$-octahedron-based compounds. Guided by this design strategy, we theoretically identified five Ag$_6$ octahedron-based compounds, $A$Ag$_3X_2$ ($A$ = Li, Na, and K; $X$ = S and Se), which are characterized by low average atomic masses and exhibit exceptionally strong four-phonon scattering. Consequently, these compounds demonstrate ultralow thermal conductivities (0.3 $\sim$ 0.6 Wm$^{-1}$K$^{-1}$) with minimal temperature dependence (T$^{-0.1}$) across a wide temperature range. Experimental validation confirmed that the $κ_{\rm L}$ of NaAg$_3$S$_2$ is 0.45 Wm$^{-1}$K$^{-1}$ within the temperature range of 200 to 550 K. Our results clearly demonstrate that weak chemical bonding plays a crucial role in designing compounds with glass-like $κ_{\rm L}$, highlighting the effectiveness of chemical bonding engineering in achieving desired thermal transport properties.

cond-mat.mtrl-sci↗

arXiv2024

A Two-layer Stochastic Game Approach to Reinsurance Contracting and Competition

We propose a two-layer stochastic game model to study reinsurance contracting and competition in a market with one insurer and two competing reinsurers. The insurer negotiates with both reinsurers simultaneously for proportional reinsurance contracts that are priced using the variance premium principle. The reinsurance contracting between the insurer and each reinsurer is modeled as a Stackelberg game. The two reinsurers compete for business from the insurer and optimize the so-called relative performance, instead of their own surplus, and their competition is settled by a noncooperative Nash game. We obtain a sufficient and necessary condition, related to the competition degrees of the two reinsurers, for the existence of an equilibrium. We show that the equilibrium, if exists, is unique, and the equilibrium strategy of each player is constant, fully characterized in semiclosed form. Furthermore, we obtain interesting sensitivity results for the equilibrium strategies through both analytical and numerical studies.

q-fin.MF↗

arXiv2024

Motional sideband asymmetry of a solid-state mechanical resonator at room temperature

The motional sideband asymmetry of a mechanical oscillator interacting with a laser field can be observed when approaching the quantum ground state, where the zero-point energy of the mechanical oscillator becomes a sizable contribution to its motion. In the context of quantum optomechanics, it allows, in principle, calibration-free inference of the thermal equilibrium of a macroscopic mechanical resonator with its optical bath. At room temperature, this phenomenon has been observed in pioneering experiments using levitated nanoparticles. Measuring this effect with solid-state mechanical resonators has been compounded by thermal intermodulation noise, mirror frequency noise and low quantum cooperativity. Here, we sideband-cool a membrane-in-the-middle system close to the quantum ground state from room temperature, and observe motional sideband asymmetry in a dual-homodyne measurement. Sideband thermometry yields a minimum phonon occupancy of $\bar{n}_{eff}=9.5$. Our work provides insights into nonlinear optomechanical dynamics at room temperature and facilitates accessible optomechanical quantum technologies without the need for complex feedback control and cryogenic cooling.

quant-ph↗

arXiv2024

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials

Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials. We train a state-of-the-art machine learning interatomic potential, based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2,738 crystal structures with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density functional theory (DFT) calculations. Our approach significantly reduces the number of required supercells for phonon calculations while maintaining high accuracy in predicting harmonic phonon properties across diverse materials. The trained model is validated against phonon calculations for a held-out subset of 384 materials, achieving a mean absolute error (MAE) of 0.18 THz for vibrational frequencies from full phonon dispersions, 2.19 meV/atom for Helmholtz vibrational free energies at 300K, as well as a classification accuracy of 86.2% for dynamical stability of materials. A thermodynamic analysis of polymorphic stability in 126 systems demonstrates good agreement with DFT results at 300 K and 1000 K. In addition, the diverse and extensive high-quality DFT dataset curated in this study serves as a valuable resource for researchers to train and improve other machine learning interatomic potential models.

cond-mat.mtrl-sci↗

arXiv2024

Hierarchy of Exchange-Correlation Functionals in Computing Lattice Thermal Conductivities of Rocksalt and Zincblende Semiconductors

Lattice thermal conductivity ($κ_{\rm L}$) is a crucial characteristic of crystalline solids with significant implications for practical applications. While the higher order of anharmonicity of phonon gas model is commonly used for explaining extraordinary heat transfer behaviors in crystals, the impact of exchange-correlation (XC) functionals in DFT on describing anharmonicity has been largely overlooked. Most XC functionals in solids focus on ground state properties that mainly involve the harmonic approximation, neglecting temperature effects, and their reliability in studying anharmonic properties remains insufficiently explored. In this study, we systematically investigate the room-temperature $κ_{\rm L}$ of 16 binary compounds with rocksalt and zincblende structures using 8 XC functionals such as LDA, PBE, PBEsol, optB86b, revTPSS, SCAN, rSCAN, r$^2$SCAN in combination with three perturbation orders, including phonon within harmonic approximation (HA) plus three-phonon scattering (HA+3ph), phonon calculated using self-consistent phonon theory (SCPH) plus three-phonon scattering (SCPH+3ph), and SCPH phonon plus three- and four-phonon scattering (SCPH+3,4ph). Our results show that the XC functional exhibits strong entanglement with perturbation order and the mean relative absolute error (MRAE) of the computed $κ_{\rm L}$ is strongly influenced by both the XC functional and perturbation order, leading to error cancellation or amplification. The minimal (maximal) MRAE is achieved with revTPSS (rSCAN) at the HA+3ph level, SCAN (r$^2$SCAN) at the SCPH+3ph level, and PBEsol (rSCAN) at the SCPH+3,4ph level. Among these functionals, PBEsol exhibits the highest accuracy at the highest perturbation order. The SCAN-related functionals demonstrate moderate accuracy but are suffer from numerical instability and high computational costs.

cond-mat.mtrl-sci↗

arXiv2024

ChatGPT4PCG 2 Competition: Prompt Engineering for Science Birds Level Generation

This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the first edition, but make several improvements and changes. We introduce a new evaluation metric along with allowing a more flexible format for participants' submissions and making several improvements to the evaluation pipeline. Continuing from the first edition, we aim to foster and explore the realm of prompt engineering (PE) for procedural content generation (PCG). While the first competition saw success, it was hindered by various limitations; we aim to mitigate these limitations in this edition. We introduce diversity as a new metric to discourage submissions aimed at producing repetitive structures. Furthermore, we allow submission of a Python program instead of a prompt text file for greater flexibility in implementing advanced PE approaches, which may require control flow, including conditions and iterations. We also make several improvements to the evaluation pipeline with a better classifier for similarity evaluation and better-performing function signatures. We thoroughly evaluate the effectiveness of the new metric and the improved classifier. Additionally, we perform an ablation study to select a function signature to instruct ChatGPT for level generation. Finally, we provide implementation examples of various PE techniques in Python and evaluate their preliminary performance. We hope this competition serves as a resource and platform for learning about PE and PCG in general.

cs.AI↗

arXiv2024

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids

Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force-displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science.

cond-mat.mtrl-sci↗

arXiv2024

Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation

Domain Generalization (DG) aims to reduce domain shifts between domains to achieve promising performance on the unseen target domain, which has been widely practiced in medical image segmentation. Single-source domain generalization (SDG) is the most challenging setting that trains on only one source domain. Although existing methods have made considerable progress on SDG of medical image segmentation, the performances are still far from the applicable standards when faced with a relatively large domain shift. In this paper, we leverage the Segment Anything Model (SAM) to SDG to greatly improve the ability of generalization. Specifically, we introduce a parallel framework, the source images are sent into the SAM module and normal segmentation module respectively. To reduce the calculation resources, we apply a merging strategy before sending images to the SAM module. We extract the bounding boxes from the segmentation module and send the refined version as prompts to the SAM module. We evaluate our model on a classic DG dataset and achieve competitive results compared to other state-of-the-art DG methods. Furthermore, We conducted a series of ablation experiments to prove the effectiveness of the proposed method. The code is publicly available at https://github.com/SARIHUST/SAMMed.

cs.CV↗

arXiv2023

Phase Stability of Lead Phosphate Apatite Pb$_{10-x}$Cu$_{x}$(PO$_{4}$)$_{6}$O, Pb$_{10-x}$Cu$_{x}$(PO$_{4}$)$_{6}$(OH)$_{2}$, and Pb$_{8}$Cu$_{2}$(PO$_{4}$)$_{6}$

Recently, Cu-substituted lead apatite LK-99 was reported to have room-temperature ambient-pressure superconductivity. Here we utilize density functional theory (DFT) total energy and harmonic phonon calculations to investigate the thermodynamic and dynamic stability of two lead phosphate apatites in their pure and Cu-substituted structures. Though Pb$_{10}$(PO$_4$)$_6$O and Pb$_{10}$(PO$_4$)$_6$(OH)$_2$ are found to be thermodynamically stable (i.e., on the T=0K ground state convex hull), their Cu-substituted counterparts are above the convex hull. Harmonic phonon calculations reveal dynamic instabilities in all four of these structures. Oxygen vacancy formation energies demonstrate that the addition of Cu dopant substituting for Pb increases the likelihood of the formation of oxygen vacancies on the anion site. We propose a new possible phase in this system, Pb$_8$Cu$_2$(PO$_4$)$_6$, where two monovalent Cu atoms are substituted for two Pb(1) atoms and the anion oxygen is removed. We also propose several reaction pathways for Pb$_9$Cu(PO$_4$)$_6$O and Pb$_8$Cu$_2$(PO$_4$)$_6$, and found that both of these two structures are likely to be synthesized under a 1:1 ratio of reactants Pb$_2$SO$_5$ and Cu$_3$P. Our work provides a thorough foundation for the thermodynamic and dynamic stabilities of LK-99 related compounds and we propose several possible novel synthesis reaction pathways and a new predicted structure for future studies.

cond-mat.mtrl-sci↗

arXiv2023

Entanglement-Based Quantum Information Technology

Entanglement is a quintessential quantum mechanical phenomenon with no classical equivalent. First discussed by Einstein, Podolsky, and Rosen and formally introduced by Schrödinger in 1935, entanglement has grown from a scientific debate to a radically new resource that sparks a technological revolution. This review focuses on the fundamentals and recent advances in entanglement-based quantum information technology (QIT), specifically in photonic systems. Photons are unique quantum information carriers with several advantages, such as their ability to operate at room temperature, their compatibility with existing communication and sensing infrastructures, and the availability of readily accessible optical components. Photons also interface well with other solid-state quantum platforms. We will first provide an overview on entanglement, starting with an introduction to its development from a historical perspective followed by the theory for entanglement generation and the associated representative experiments. We will then dive into the applications of entanglement-based QIT for sensing, imaging, spectroscopy, data processing, and communication. Before closing, we will present an outlook for the architecture of the next-generation entanglement-based QIT and its prospective applications.

quant-ph↗

arXiv2023

A Unified Understanding of Minimum Lattice Thermal Conductivity

We propose a first-principles model of minimum lattice thermal conductivity ($κ_{\rm L}^{\rm min}$) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and discover a universal behavior of $κ_{\rm L}^{\rm min}$ in crystals in the high-temperature limit: the isotropically averaged $κ_{\rm L}^{\rm min}$ is independent of structural complexity and bounded within a range from $\sim$0.1 to $\sim$2.6 W/[m$\cdot$K], in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound $κ_{\rm L}^{\rm min}$ is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated $κ_{\rm L}^{\rm min}$ in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill-Watson-Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow $κ_{\rm L}$. Our work offers a unified understanding of $κ_{\rm L}^{\rm min}$, which can guide the rational engineering of materials to achieve $κ_{\rm L}^{\rm min}$.

cond-mat.mtrl-sci↗

arXiv2023

The role of high-order anharmonicity and off-diagonal terms in thermal conductivity: a case study of multi-phase CsPbBr3

We investigate the influence of three- and four-phonon scattering, perturbative anharmonic phonon renormalization, and off-diagonal terms of coherent phonons on the thermal conductivity of CsPbBr3 phase change perovskite, by using advanced implementations and first-principles simulations. Our study spans a wide temperature range covering the entire structural spectrum. Notably, we demonstrate that the interactions between acoustic and optical phonons result in contrasting trends of phonon frequency shifts for the high-lying optical phonons in orthorhombic and cubic CsPbBr3 as temperature varies. Our findings highlight the significance of wave-like tunneling of coherent phonons in ultralow and glass-like thermal conductivity in halide perovskites.

cond-mat.mtrl-sci↗

arXiv2023

Optimal management of DB pension fund under both underfunded and overfunded cases

This paper investigates the optimal management of an aggregated defined benefit pension plan in a stochastic environment. The interest rate follows the Ornstein-Uhlenbeck model, the benefits follow the geometric Brownian motion while the contribution rate is determined by the spread method of fund amortization. The pension manager invests in the financial market with three assets: cash, bond and stock. Regardless of the initial status of the plan, we suppose that the pension fund may become underfunded or overfunded in the planning horizon. The optimization goal of the manager is to maximize the expected utility in the overfunded region minus the weighted solvency risk in the underfunded region. By introducing an auxiliary process and related equivalent optimization problems and using the martingale method, the optimal wealth process, optimal portfolio and efficient frontier are obtained under four cases (high tolerance towards solvency risk, low tolerance towards solvency risk, a specific lower bound, and high lower bound). Moreover, we also obtain the probabilities that the optimal terminal wealth falls in the overfunded and underfunded regions. At last, we present numerical analyses to illustrate the manager's economic behaviors.

q-fin.PM↗

arXiv2022

Entanglement-enhanced optomechanical sensor array for dark matter searches

The nature of dark matter is one of the most important open questions in modern physics. The search for dark matter is challenging since, besides gravitational interaction, it feebly interacts with ordinary matter. Mechanical sensors are one of the leading candidates for dark matter searches in the low frequency region. Here, we propose entanglement-enhanced optomechanical sensing systems to assist the search for DM with mechanical sensing devices. To assess the performance of our setup, we adopt the integrated sensitivity, which is particularly suitable for broadband sensing as it precisely quantifies the bandwidth-sensitivity tradeoff of the system. We then show that, by coherently operating the optomechanical sensor array and utilizing continuous-variable multi-partite entanglement between the optical fields, the array of sensors has a scaling advantage over independent sensors (i.e., $\sqrt{M}\rightarrow M$, where $M$ is the number of sensors) as well as a performance boost due to entanglement. Such an advantage is robust to imhomogeneities of the mechanical sensors and is achievable with off-the-shelf experimental components.

quant-ph↗

arXiv2022

Entanglement-Enhanced Optomechanical Sensing

Optomechanical systems have been exploited in ultrasensitive measurements of force, acceleration, and magnetic fields. The fundamental limits for optomechanical sensing have been extensively studied and now well understood -- the intrinsic uncertainties of the bosonic optical and mechanical modes, together with the backaction noise arising from the interactions between the two, dictate the Standard Quantum Limit (SQL). Advanced techniques based on nonclassical probes, in-situ pondermotive squeezed light, and backaction-evading measurements have been developed to overcome the SQL for individual optomechanical sensors. An alternative, conceptually simpler approach to enhance optomechanical sensing rests upon joint measurements taken by multiple sensors. In this configuration, a pathway toward overcoming the fundamental limits in joint measurements has not been explored. Here, we demonstrate that joint force measurements taken with entangled probes on multiple optomechanical sensors can improve the bandwidth in the thermal-noise-dominant regime or the sensitivity in shot-noise-dominant regime. Moreover, we quantify the overall performance of entangled probes with the sensitivity-bandwidth product and observe a 25% increase compared to that of the classical probes. The demonstrated entanglement-enhanced optomechanical sensing could enable new capabilities for inertial navigation, acoustic imaging, and searches for new physics.

quant-ph↗

arXiv2021

Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity

Intrinsically low lattice thermal conductivity ($κ_l$) is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transition metals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stability of $\sim$ 1 million compounds using the ML model iteratively and performing density functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their $κ_l$ using phonon-Boltzmann transport equation, which reveals ultralow-$κ_l$ ($<$ 2 Wm$^{-1}$K$^{-1}$ at room-temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high-efficiency of scale-invariant ML model in predicting novel compounds and presents experimental research opportunities with these new compounds.

cond-mat.mtrl-sci↗