arXiv subjects

Hui Shao

Publications and source records attributed to Hui Shao.

At least 19 recordsLinked to original sources

arXiv2026

Extreme-Case Distorted Utility under Moment Ambiguity

Many operations decisions under distributional ambiguity, from pricing and inventory to capacity and contracting, evaluate an action through a tail-sensitive distorted utility of an uncertain payoff and hedge against the least favorable distribution consistent with a few known moments; the resulting worst-case evaluation is the inner problem of a moment-based distributionally robust decision. We study this inner problem, the extreme-case distorted utility under moment constraints, for a locally Lipschitz utility that may be nonsmooth and neither convex nor concave together with a general, possibly atomic, distortion. Recasting the problem in the quantile domain, we develop a unified method that yields exact first-order optimality conditions and closed-form extremal values and distributions for both the worst and best cases, drawing on nonsmooth variational analysis. A central step treats the monotonicity constraint by isotonic projection onto the monotone cone, turning an abstract infinite-dimensional restriction into an inexpensive inner solve that scales linearly in the discretization. The method recovers and extends classical moment bounds through three examples: a range value-at-risk extension of the Scarf bound, GlueVaR distortions with a reward--penalty utility, and a capped incentive contract under conditional value-at-risk. As the inner oracle of a robust min-max decision, the characterization embeds directly in outer robust optimization, illustrated on a real capacity-provisioning problem for generative artificial intelligence inference where accounting for moment ambiguity lowers required capacity while preserving service compliance.

math.OC↗

arXiv2026

Space-filling foldover designs for order-of-addition experiments under Kendall tau distance criteria

Order-of-addition experiments arise when the response depends on the order in which a set of components is added. Since the number of possible orders increases factorially with the number of components, full permutation designs are rarely feasible except for small problems. This paper studies space-filling fractional designs for order-of-addition experiments based on the Kendall tau distance, a natural metric for comparing permutations through pairwise ordering disagreements. We consider the maximin Kendall tau distance criterion and related dispersion criteria, and establish their connections with statistical optimality under the pairwise ordering model and a Gaussian process model with the Mallows kernel. To construct such designs, we propose an efficient foldover simulated annealing algorithm, denoted by FSA-KD, based on swap moves in the permutation space, together with foldover and incremental updating strategies. Numerical studies show that the resulting FSA-KD designs have large minimum pairwise Kendall tau distances, denoted by k_min(D), and stable pairwise distance distributions, and perform well in surrogate modeling and permutation-based optimization tasks.

stat.ME↗

arXiv2026

Deterministic Loop Stochastic Series Expansion Algorithm for Quantum Spin Models in Magnetic Fields

The stochastic series expansion (SSE) algorithm is one of the most powerful quantum Monte Carlo methods and has been extensively applied to the study of quantum many body systems. Its efficiency is particularly enhanced with a deterministic loop update scheme in the study of the S=1/2 quantum spin systems that preserve SU(2) spin rotational symmetry. Once the symmetry is broken, such as by an external field, a directed loop method is typically required, resulting in a significant reduction in efficiency. Inspired by the SSE approach developed for the quantum Ising model, we introduce a deterministic loop SSE method that is particularly suited for antiferromagnetic systems under a staggered magnetic field. This method enables separate investigations of longitudinal and transverse modes in magnetically ordered phases arising from spontaneous symmetry breaking. We benchmark the performance of our algorithm against the standard directed loop approach applied to the antiferromagnetic Heisenberg chain and demonstrate that our method substantially reduces CPU time per Monte Carlo step, thereby can outperform the directed loop algorithm in efficiency.

cond-mat.str-el↗

arXiv2025

Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes

Integrating novel medical concepts and relationships into existing ontologies can significantly enhance their coverage and utility for both biomedical research and clinical applications. Clinical notes, as unstructured documents rich with detailed patient observations, offer valuable context-specific insights and represent a promising yet underutilized source for ontology extension. Despite this potential, directly leveraging clinical notes for ontology extension remains largely unexplored. To address this gap, we propose CLOZE, a novel framework that uses large language models (LLMs) to automatically extract medical entities from clinical notes and integrate them into hierarchical medical ontologies. By capitalizing on the strong language understanding and extensive biomedical knowledge of pre-trained LLMs, CLOZE effectively identifies disease-related concepts and captures complex hierarchical relationships. The zero-shot framework requires no additional training or labeled data, making it a cost-efficient solution. Furthermore, CLOZE ensures patient privacy through automated removal of protected health information (PHI). Experimental results demonstrate that CLOZE provides an accurate, scalable, and privacy-preserving ontology extension framework, with strong potential to support a wide range of downstream applications in biomedical research and clinical informatics.

cs.AI↗

arXiv2025

RiskPO: Risk-based Policy Optimization via Verifiable Reward for LLM Post-Training

Reinforcement learning with verifiable reward has recently emerged as a central paradigm for post-training large language models (LLMs); however, prevailing mean-based methods, such as Group Relative Policy Optimization (GRPO), suffer from entropy collapse and limited reasoning gains. We argue that these issues stem from overemphasizing high-probability output sequences while neglecting rare but informative reasoning paths. To address these challenges, we propose Risk-based Policy Optimization (RiskPO), which substitutes classical mean-based objectives with principled risk measures. Specifically, we introduce a Mixed Value-at-Risk objective that integrates weighted attention over multiple regions of the reward distribution, thereby amplifying gradient signals on challenging instances and preventing overconfident convergence. We further design a bundling scheme that aggregates multiple questions into bundles, thus enriching the feedback signal and yielding more stable and informative training dynamics. Theoretically, we prove that the risk-averse update alleviates entropy collapse and promotes exploration. Numerically, RiskPO achieves consistent and significant improvements in mathematical reasoning, multi-modal reasoning, and code generation benchmarks, surpassing GRPO and its variants on both Pass@1 and Pass@k metrics. Our results demonstrate that risk-based optimization provides a rigorous and effective paradigm for enhancing LLM reasoning capabilities.

cs.LG↗

arXiv2024

Ground state of the S = 1/2 Heisenberg spin chain with random ferro- and antiferromagnetic couplings

We study the Heisenberg $S=1/2$ chain with random ferro- and antiferromagnetic couplings using quantum Monte Carlo simulations at ultra-low temperatures, converging to the ground state. Finite-size scaling of correlation functions and excitation gaps demonstrate an exotic critical state in qualitative agreement with previous strong-disorder renormalization group calculations but with scaling exponents depending on the coupling distribution. We find dual scaling regimes of the transverse correlations versus the distance, with an $L$ independent form $C(r)=r^{-\mu}$ for $r \ll L$ and $C(r,L)=L^{-\eta}f(r/L)$ for $r/L > 0$, where $\mu > \eta$ and the scaling function is delivered by our analysis. These results are at variance with previous spin-wave and density-matrix renormalization group calculations, thus highlighting the power of unbiased quantum Monte Carlo simulations.

cond-mat.str-el↗

arXiv2024

Multiscale Excitations in the Diluted Two-dimensional S = 1/2 Heisenberg Antiferromagnet

We study the excitation spectrum of the $S=1/2$ Heisenberg model on the randomly diluted square lattice by analytic continuation of QMC data. At dilution fractions $p=1/16$ and $p=1/8$, the dynamic structure factor $S({\bf q},\omega)$ exhibits a damped magnon peak with anomalous dispersion near ${\bf q}=(0,0)$ and $(\pi,\pi)$, a non-dispersive low-energy localization peak, and a second peak between these two features. A magnon with anomalous dispersion, close to our result, was predicted in spin wave and $T$-matrix theory [A. Chernyshev et al., PRB {\bf 65}, 104407 (2002)], above the localization energy. However, no intermediate mode was predicted. Analyzing spectral functions in real space for individual vacancy realizations by energy tomography, we find that these excitations are concentrated on a small subset of the spins adjacent to vacancies. We argue that the low-energy excitations are those of a sparse random network of effective moments at a fraction of the vacancies. There is a shift in magnon spectral weight distribution, from the spins away from vacancies at high energy to those adjacent to vacancies at lower energy. We also analyze the Anderson quantum rotor excitation at $\omega \propto N^{-1}$ (with $N=L^2$ the system size), which in the clean system is visible in $S({\bf q},\omega)$ only at ${\bf q}=(\pi,\pi)$ but spreads through the Brillouin zone when $p>0$. Weight close to ${\bf q}=(0,0)$ and $(\pi,\pi)$ is explained by local sublattice imbalance within a dimer-monomer model but there is also structure arising from correlated singlet fluctuations, which we demonstrate by enhancing said fluctuations with four-spin couplings. All spectral features found here should be observable by elastic neutron scattering experiments on layered quantum antiferromagnets doped with nonmagnetic impurities.

cond-mat.str-el↗

arXiv2024

SO(5) multicriticality in two-dimensional quantum magnets

We resolve the nature of the quantum phase transition between a N\'eel antiferromagnet and a valence-bond solid in two-dimensional spin-1/2 magnets. We study a class of $J$-$Q$ models, in which Heisenberg exchange $J$ competes with interactions $Q_n$ formed by products of $n$ singlet projectors on adjacent parallel lattice links. QMC simulations provide unambiguous evidence for first-order transitions, with the discontinuities increasing with $n$. For $n=2$ and $n=3$ models, the first-order signatures are very weak. On intermediate length scales, we extract well-defined scaling dimensions (critical exponents) that are common to the models with small $n$, indicating proximity to a quantum critical point. By combining two $Q$ terms, the transition can be tuned from weak to more strongly first-order. The two coexisting orders on the first-order line scale with a large exponent $\beta \approx 0.85$. This exponent and others are close to bounds for an SO($5$) symmetric CFT with a relevant SO($5$) singlet. We characterize the emergent SO($5$) symmetry by the scaling dimensions of its leading irrelevant perturbations. The large $\beta$ value and a large correlation length exponent, $\nu \approx 1.4$, partially explain why the transition remains near-critical even quite far away from the critical point and in many different models without fine-tuning. In addition, we find that few-spin lattice operators are dominated by the SO($5$) violating field (the traceless symmetric tensor), and interactions involving many spins are required to observe strong effects of the relevant SO($5$) singlet. The exponent that had previously been identified with the divergent correlation length when crossing between the two phases does not have a corresponding CFT operator. We explain this emergent pseudocritical scale by a mechanism relying on a dangerously irrelevant SO($5$) perturbation.

cond-mat.str-el↗

arXiv2024

Extremal cases of distortion risk measures with partial information

This paper investigates the impact of distributional uncertainty on key risk measures under the partial knowledge of underlying distributions characterized by their first two moments and shape information (specifically symmetry and/or unimodality). We first employ probability inequalities to establish the theoretical best- and worst-case bounds on Value-at-Risk, reflecting the most extreme tail risk achievable within the moment and shape constraints, and then we extend this worst-case/best-case analysis to a broad class of distortion risk measures by the modified Schwarz inequality, deriving their corresponding robust bounds under the same partial information setting concerning moments and distribution shapes of the underlying distributions. In addition, we give a clear characterization of the distributions that attain the best- and worst-case scenarios. The proposed approach provides a unified framework for extremal problems of distortion risk measures.

q-fin.RM↗

arXiv2024

LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction

Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses, labs, prescriptions) into natural language narratives. We evaluate the zero-shot and few-shot performance of LLMs using various EHR-prediction-oriented prompting strategies. Furthermore, we propose a novel approach that utilizes LLM agents with different roles: a predictor agent that makes predictions and generates reasoning processes and a critic agent that analyzes incorrect predictions and provides guidance for improving the reasoning of the predictor agent. Our results demonstrate that with the proposed approach, LLMs can achieve decent few-shot performance compared to traditional supervised learning methods in EHR-based disease predictions, suggesting its potential for health-oriented applications.

cs.CL↗

arXiv2023

Magnon, doublon and quarton excitations in 2D S=1/2 trimerized Heisenberg models

We investigate the magnetic excitations of the trimerized Heisenberg models with intra-trimer interaction $J_1$ and inter-trimer interaction $J_2$ on four different two-dimensional lattices using a combination of stochastic series expansion quantum Monte Carlo (SSE QMC) and stochastic analytic continuation methods (SAC), complemented by cluster perturbation theory (CPT). These models exhibit quasi-particle-like excitations when $g=J_2/J_1$ is small, characterized by low-energy magnons, intermediate-energy doublons, and high-energy quartons. The low-energy magnons are associated with the magnetic ground states. They can be described by the linear spin wave theory (LSWT) of the effective block spin model and the original spin model. Doublons and quartons emerge from the corresponding internal excitations of the trimers with distinct energy levels, which can be effectively analyzed using perturbation theory when the ratio of exchange interactions $g$ is small. In this small $g$ regime, we observe a clear separation between the magnon and higher-energy spectra. However, as $g$ increases, these three spectra gradually merge into the magnon modes or continua. Nevertheless, the LSWT fails to provide quantitative descriptions of the higher-energy excitation bands due to significant quantum fluctuations. Notably, in the Collinear II and trimerized hexagon lattice, a broad continuum emerges above the single-magnon spectrum, originating from the quasi-1D physics due to the dilute connections between chains. Our numerical analysis of these 2D trimers yields valuable theoretical predictions and explanations for the inelastic neutron scattering (INS) spectra of 2D magnetic materials featuring trimerized lattices.

cond-mat.str-el↗

arXiv2023

Primary and Secondary Order Parameters in the Fully Frustrated Transverse Field Ising Model on the Square Lattice

Using quantum Monte Carlo simulations and field-theory arguments, we study the fully frustrated (Villain) transverse-field Ising model on the square lattice. We consider a "primary" spin order parameter and a "secondary" dimer order parameter, which both lead to the same phase diagram but detect $Z_8$ and $Z_4$ symmetry, respectively. The spin order scales with conventional exponents, both in the finite temperature critical phase and at the $T = 0$ quantum critical point. The scaling of the dimer order requires more detailed investigations of the applicable low-energy theories; the height model at $T > 0$ and the $O(2)$ model in 2+1 dimensions at $T = 0$. Relating the order parameters to operators in these effective models, we predict the secondary critical exponents and confirm them numerically. The relationships between the primary and secondary order parameters have not been previously discussed in this context and provide insight more broadly for Ising models whose low-energy physics involves dimer degrees of freedom.

cond-mat.str-el↗

arXiv2023

Range Resolution Enhanced Method with Spectral Properties for Hyperspectral Lidar

Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the "Pseudo-monopulse" waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the "Pseudo-monopulse" presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in "Pseudo-monopulse" waveform decomposition.

eess.SP↗

arXiv2022

Progress on stochastic analytic continuation of quantum Monte Carlo data

We report multipronged progress on the stochastic averaging approach to numerical analytic continuation of quantum Monte Carlo data. With the sampled spectrum parametrized with delta-functions in continuous frequency space, a calculation of the configurational entropy lends support to a simple goodness-of-fit criterion for the optimal sampling temperature. To further investigate entropic effects, we compare spectra sampled in continuous frequency with results of amplitudes sampled on a fixed frequency grid. We demonstrate equivalences between sampling and optimizing spectral functions with the maximum-entropy approach with different forms of the entropy. These insights revise prevailing notions of the maximum-entropy method and its relationship to stochastic analytic continuation. We further explore various adjustable (optimized) constraints that allow sharp spectral features to be resolved, in particular at the lower frequency edge. The constraints, e.g., the location of the edge or the spectral weight of a quasi-particle peak, are optimized using a statistical criterion. We show that this method can correctly reproduce both narrow and broad quasi-particle peaks. We next introduce a parametrization for more intricate spectral functions with sharp edges, e.g., power-law singularities. Tests with synthetic data as well as with real simulation data for the spin-1/2 Heisenberg chain demonstrate that constrained sampling methods can reproduce spectral functions with sharp edge features at unprecedented fidelity. We present new results for S=1/2 Heisenberg 2-leg and 3-leg ladders to illustrate the ability of the methods to resolve spectral features arising from both elementary and composite excitations. Finally, we also propose how the methods developed here could be used as "pre processors" for analytic continuation by machine learning.

cond-mat.str-el↗

arXiv2020

Unconventional U(1) to $\mathbf{Z_q}$ cross-over in quantum and classical ${\bf q}$-state clock models

We consider two-dimensional $q$-state quantum clock models with quantum fluctuations connecting states with clock transitions with different choices for matrix elements. We study the quantum phase transitions in these models using quantum Monte Carlo simulations, with the aim of characterizing the cross-over from emergent U(1) symmetry at the transition (for $q \ge 4$) to $Z_q$ symmetry of the ordered state. We also study classical three-dimensional clock models with spatial anisotropy corresponding to the space-time anisotropy of the quantum systems. The U(1) to ${Z_q}$ symmetry cross-over in all these systems is governed by a dangerously irrelevant operator. We specifically study $q=5$ and $q=6$ models with different forms of the quantum fluctuations and different anisotropies in the classical models. We find the expected classical XY critical exponents and scaling dimensions $y_q$ of the clock fields. However, the initial weak violation of the U(1) symmetry in the ordered phase, characterized by a $Z_q$ symmetric order parameter $\phi_q$, scales in an unexpected way. As a function of the system size $L$, close to the critical temperature $\phi_q \propto L^p$, where the known value of the exponent is $p=2$ in the classical isotropic clock model. In contrast, for strongly anisotropic classical models and the quantum models we find $p=3$. For weakly anisotropic classical models we observe a cross-over from $p=2$ to $p=3$ scaling. The exponent $p$ directly impacts the exponent $\nu'$ governing the divergence of the U(1) to $Z_q$ cross-over length scale $\xi'$ in the thermodynamic limit, according to the relationship $\nu'=\nu(1+|y_q|/p)$, where $\nu$ is the conventional correlation length exponent. We present a phenomenological argument based on an anomalous renormalization of the clock field in the presence of anisotropy, possibly as a consequence of topological (vortex) line defects.

cond-mat.str-el↗

arXiv2020

Monte Carlo Renormalization Flows in the Space of Relevant and Irrelevant Operators: Application to Three-Dimensional Clock Models

We present a way to visualize and quantify renormalization group flows in a space of observables computed using Monte Carlo simulations. We apply the method to classical three-dimensional clock models, i.e., the planar (XY) spin model perturbed by a $Z_q$ symmetric anisotropy field. The method performs significantly better than standard techniques for determining the scaling dimension $y_q$ of the $Z_q$ field at the critical point if it is irrelevant ($q\ge4$). Furthermore, we analyze all stages of the complex renormalization flow, including the cross-over from the U(1) Nambu-Goldstone fixed point to the ultimate $Z_q$ symmetry-breaking fixed point due to the relevance of the $Z_q$ field inside the ordered phase. We expect our method to be particularly useful in the context of quantum-critical points with inherent dangerously irrelevant operators that cannot be tuned away microscopically but whose renormalization flows can be analyzed exactly as we do here for the clock models.

cond-mat.str-el↗

arXiv2019

A general Lewis acidic etching route for preparing MXenes with enhanced electrochemical performance in non-aqueous electrolyte

Two-dimensional carbides and nitrides of transition metals, known as MXenes, are a fast-growing family of 2D materials that draw attention as energy storage materials. So far, MXenes are mainly prepared from Al-containing MAX phases (where A = Al) by Al dissolution in F-containing solution, but most other MAX phases have not been explored. Here, a redox-controlled A-site-etching of MAX phases in Lewis acidic melts is proposed and validated by the synthesis of various MXenes from unconventional MAX phase precursors with A elements Si, Zn, and Ga. A negative electrode of Ti3C2 MXene material obtained through this molten salt synthesis method delivers a Li+ storage capacity up to 738 C g-1 (205 mAh g-1) with high-rate performance and pseudocapacitive-like electrochemical signature in 1M LiPF6 carbonate-based electrolyte. MXene prepared from this molten salt synthesis route offer opportunities as high-rate negative electrode material for electrochemical energy storage applications.

physics.app-ph↗

arXiv2018

Random-Singlet Phase in Disordered Two-Dimensional Quantum Magnets

We study effects of disorder (randomness) in a 2D square-lattice $S=1/2$ quantum spin system, the $J$-$Q$ model with a 6-spin interaction $Q$ supplementing the Heisenberg exchange $J$. In the absence of disorder the system hosts antiferromagnetic (AFM) and columnar valence-bond-solid (VBS) ground states. The VBS breaks $Z_4$ symmetry, and in the presence of arbitrarily weak disorder it forms domains. Using QMC simulations, we demonstrate two kinds of such disordered VBS states. Upon dilution, a removed site leaves a localized spin in the opposite sublattice. These spins form AFM order. For random interactions, we find a different state, with no order but algebraically decaying mean correlations. We identify localized spinons at the nexus of domain walls between different VBS patterns. These spinons form correlated groups with the same number of spinons and antispinons. Within such a group, there is a strong tendency to singlet formation, because of spinon-spinon interactions mediated by the domain walls. Thus, no long-range AFM order forms. We propose that this state is a 2D analog of the well-known 1D random singlet (RS) state, though the dynamic exponent $z$ in 2D is finite. By studying the T-dependent magnetic susceptibility, we find that $z$ varies, from $z=2$ at the AFM--RS phase boundary and larger in the RS phase The RS state discovered here in a system without geometric frustration should correspond to the same fixed point as the RS state recently proposed for frustrated systems, and the ability to study it without Monte Carlo sign problems opens up opportunities for further detailed characterization of its static and dynamic properties. We also discuss experimental evidence of the RS phase in the quasi-two-dimensional square-lattice random-exchange quantum magnets Sr$_2$CuTe$_{1-x}$W$_x$O$_6$.

cond-mat.str-el↗