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Tianyang Xie

Publications and source records attributed to Tianyang Xie.

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Electronically Inactive Intercalated La$_2$NiO$_4$ Layer in Superconducting La$_5$Ni$_3$O$_{11}$

The recent discovery of superconductivity in La$_5$Ni$_3$O$_{11}$ extends the family of superconducting Ruddlesden--Popper nickelates beyond La$_3$Ni$_2$O$_7$. Unlike conventional members of a single Ruddlesden--Popper series, La$_5$Ni$_3$O$_{11}$ contains an intercalated La$_2$NiO$_4$ layer between La$_3$Ni$_2$O$_7$ blocks, raising the question of whether this additional layer participates in the low-energy electronic structure. Here, we combine density functional theory, Wannier-based tight-binding modeling, and rotationally invariant slave-boson calculations to investigate the electronic role of the intercalated layer. We find that realistic electronic parameters place the La$_2$NiO$_4$ layer in gapped insulating regimes rather than a paramagnetic metallic state. Furthermore, realistic interlayer hybridization fails to generate any appreciable La$_2$NiO$_4$-derived spectral weight at the Fermi level. Our results demonstrate that the low-energy electronic structure of La$_5$Ni$_3$O$_{11}$ is governed primarily by the La$_3$Ni$_2$O$_7$ block, with the intercalated La$_2$NiO$_4$ layer remaining electronically inactive. This establishes a minimal low-energy description of La$_5$Ni$_3$O$_{11}$ and provides a unified framework for understanding superconductivity in intercalated Ruddlesden--Popper nickelates.

cond-mat.supr-con

Itinerant Nature of Spin-Density-Wave Order in Ruddlesden-Popper Nickelates

The nature of magnetism in layered Ruddlesden-Popper nickelates remains a central open question, particularly in light of recent observations of spin-wave-like magnetic excitations in metallic multilayer compounds. Here, we develop a unified itinerant description of spin-density-wave (SDW) order and magnetic excitations in La$_3$Ni$_2$O$_7$ and La$_4$Ni$_3$O$_{10}$. The essential ingredient is the multilayer mirror structure of the NiO$_2$ blocks, which organizes the low-energy electronic states into mirror-even and mirror-odd sectors. We show that dominant interband nesting between mirror-opposite bands drives a mirror-selective itinerant SDW instability, whose collective modes naturally reproduce the experimentally observed spin-wave-like spectra. In La$_4$Ni$_3$O$_{10}$, the SDW further induces a secondary mirror-even charge density wave, yielding intertwined spin and charge textures. Our results demonstrate that magnetism in multilayer nickelates is fundamentally itinerant rather than local-moment in origin, and establish mirror-selective interband SDW order as a unifying organizing principle for magnetic correlations in these systems.

cond-mat.str-el

Spin Dynamics from Niu-Kleinman Adiabatic Approach and Slave Boson Mean Field Theory

Spin-wave excitations provide a central probe of magnetic order and electronic correlations in strongly correlated materials. In this work, we develop an adiabatic theory of spin dynamics by combining the Niu-Kleinman formalism with Kotliar-Ruckenstein slave-boson theory (NK+KRSB). For each frozen spin configuration, the constrained slave-boson saddle point is solved self-consistently, allowing the Berry-curvature matrix and energy Hessian entering the linearized adiabatic equations of motion to be extracted directly. Applied to the half-filled single-orbital Hubbard model, the resulting spin-wave dispersion shows substantially improved agreement with determinant quantum Monte Carlo benchmarks compared with the random phase approximation and closely approaches results from the time-dependent Gutzwiller approximation. We further extend the method to a two-orbital model of $\mathrm{La}_2\mathrm{NiO}_4$, demonstrating its applicability to realistic multi-orbital correlated systems. Because the approach only requires saddle-point solutions near the magnetic ground state, it remains computationally efficient while incorporating strong-correlation effects beyond conventional weak-coupling descriptions, providing a practical framework for studying low-energy spin excitations in correlated quantum materials.

cond-mat.str-el

ExoAtom: A Database of Atomic Spectra in ExoMol Format

We present the ExoAtom database, www.exomol.com/exoatom, an extension of the ExoMol database to provide atomic line lists in the ExoMol format. ExoAtom is designed for detailed astrophysical, planetary, and laboratory applications. ExoAtom currently includes atomic data for 80 neutral atoms and 74 singly charged ions. These data are extracted from both the NIST and Kurucz databases, with 79/71 atoms/ions sourced from NIST and 38/37 atoms/ions sourced from Kurucz. ExoAtom uses the file types .all, .def, .states, .trans and .pf as fundamental components for structuring atomic data in a consistent hierarchy. The .states file contains quantum numbers, uncertainties, lifetimes, etc. The .trans file specifies Einstein A coefficients and their associated wavenumbers. The .pf file provides partition functions over a wide grid of temperatures. Post-processing of the ExoAtom data is provided by the program PyExoCross. Future development of ExoAtom will include additional ionization stages.

physics.atom-ph

Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach

Survival analysis, a vital tool for predicting the time to event, has been used in many domains such as healthcare, criminal justice, and finance. Like classification tasks, survival analysis can exhibit bias against disadvantaged groups, often due to biases inherent in data or algorithms. Several studies in both the IS and CS communities have attempted to address fairness in survival analysis. However, existing methods often overlook the importance of prediction fairness at pre-defined evaluation time points, which is crucial in real-world applications where decision making often hinges on specific time frames. To address this critical research gap, we introduce a new fairness concept: equalized odds (EO) in survival analysis, which emphasizes prediction fairness at pre-defined time points. To achieve the EO fairness in survival analysis, we propose a Conditional Mutual Information Augmentation (CMIA) approach, which features a novel fairness regularization term based on conditional mutual information and an innovative censored data augmentation technique. Our CMIA approach can effectively balance prediction accuracy and fairness, and it is applicable to various survival models. We evaluate the CMIA approach against several state-of-the-art methods within three different application domains, and the results demonstrate that CMIA consistently reduces prediction disparity while maintaining good accuracy and significantly outperforms the other competing methods across multiple datasets and survival models (e.g., linear COX, deep AFT).

cs.LG

Multivariate Pair Trading by Volatility & Model Adaption Trade-off

Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we brought up a simple yet profitable strategy called Volatility & Model Adaption Trade-off (VMAT) to leverage the issues. Experiment studies show its superior profit performance over baselines.

q-fin.PM

Forecasting with Multiple Seasonality

An emerging number of modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. Specifically, time series with multiple seasonality is a difficult task with comparatively fewer discussions. In this paper, we propose a two-stage method for time series with multiple seasonality, which does not require pre-determined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average (ARMA) model in multiple seasonality regime. In the second stage, we utilize an appropriate criterion for lag order selection. Simulation and empirical studies show the excellent predictive performance of our method, especially compared to a recently popular `Facebook Prophet' model for time series.

cs.LG