arXiv subjects

Li Huang

Publications and source records attributed to Li Huang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Auxiliary dynamical mean-field approach for Anderson-Hubbard model with off-diagonal disorder

This work reports a theoretical framework that combines the auxiliary coherent potential approximation (ACPA-DMFT) with dynamical mean-field theory to study strongly correlated and disordered electronic systems with both diagonal and off-diagonal disorders. In this method, by introducing an auxiliary coupling space with extended local degree of freedom,the diagonal and off-diagonal disorders are treated in a unified and self-consistent framework of coherent potential approximation, within which the dynamical mean-field theory is naturally combined to handle the strongly correlated Anderson-Hubbard model. By using this approach, we compute matsubara Green's functions for a simple cubic lattice at finite temperatures and derive impurity spectral functions through the maximum entropy method. Our results reveal the critical influence of off-diagonal disorder on Mott-type metal-insulator transitions. Specifically, a reentrant phenomenon is identified, where the system transitions between insulating and metallic states under varying interaction strengths. The ACPA-DMFT method provides an efficient and robust computational method for exploring the intricate interplay of disorder and strong correlations.

cond-mat.str-el↗

arXiv2025

Filling-dependent intertwined electronic and atomic orders in the flat-band state of 1T-TaS2

The delicate interplay among the complex intra-/inter-layer electron-electron and electron-lattice interactions is the fundamental prerequisite of these exotic quantum states, such as superconductivity, nematic order, and checkerboard charge order. Here we explore the filling-dependent multiple stable intertwined electronic and atomic orders of flat-band state of 1T-TaS2 encompassing hole order, phase orders, coexisting left- and right-chiral orders and mixed phase/chiral orders via scanning tunneling microscopy (STM). Combining first-principles calculations, the emergent electronic/atomic orders can be attributed to the weakening of electron-electron correlations and stacking-dependent interlayer interactions. Moreover, achiral intermediate ring-like clusters and nematic charge density wave (CDW) states are successfully realized in intralayer chiral domain wall and interlayer heterochiral stacking regions through chiral overlap configurations. Our study not only deepens the understanding of filling-dependent electronic/atomic orders in flat-band systems, but also offers perspectives for exploring exotic quantum states in correlated electronic systems.

cond-mat.str-el↗

arXiv2024

SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction

Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements.

eess.IV↗

arXiv2024

Barycentric rational function approximation made simple: A fast analytic continuation method for Matsubara Green's functions

Analytic continuation is a critical step in quantum many-body computations, connecting imaginary-time or Matsubara Green's functions with real-frequency spectral functions, which can be directly compared to experimental results. However, due to the ill-posed nature of the analytic continuation problems, they have not been completely solved so far. In this paper, we suggest a simple, yet highly efficient method for analytic continuations of Matsubara Green's functions. This method takes advantage of barycentric rational functions to directly interpolate Matsubara Green's functions. At first, the nodes and weights of the barycentric rational functions are determined by the adaptive Antoulas-Anderson algorithm, avoiding reliance on the non-convex optimization. Next, the retarded Green's functions and the relatively spectral functions are evaluated by the resulting interpolants. We systematically explore the performance of this method through a series of toy models and realistic examples, comparing its accuracy and efficiency with other popular methods, such as the maximum entropy method. The benchmark results demonstrate that the new method can accurately reproduce not only continuous but also discrete spectral functions, irrespective of their positive definiteness. It works well even in the presence of intermediate noise, and outperforms traditional analytic continuation methods in computational speed. We believe that this method should stand out for its robustness against noise, broad applicability, high precision, and ultra efficiency, offering a promising alternative to the maximum entropy method.

cond-mat.str-el↗

arXiv2024

ACTest: A testing toolkit for analytic continuation methods and codes

ACTest is an open-source toolkit developed in the Julia language. Its central goal is to automatically establish analytic continuation testing datasets, which include a large number of spectral functions and the corresponding Green's functions. These datasets can be used to benchmark various analytic continuation methods and codes. In ACTest, the spectral functions are constructed by a superposition of randomly generated Gaussian, Lorentzian, $δ$-like, rectangular, and Rise-And-Decay peaks. The spectra can be positive definite or non-positive definite. The corresponding energy grids can be linear or non-linear. ACTest supports both fermionic and bosonic Green's functions on either imaginary time or Matsubara frequency axes. Artificial noise can be superimposed on the synthetic Green's functions to simulate realistic Green's functions obtained by quantum Monte Carlo calculations. ACTest includes a standard testing dataset, namely ACT100. This built-in dataset contains 100 testing cases that cover representative analytic continuation scenarios. Now ACTest is fully integrated with the ACFlow toolkit. It can directly invoke the analytic continuation methods as implemented in the ACFlow toolkit for calculations, analyze calculated results, and evaluate computational efficiency and accuracy. ACTest comprises many examples and detailed documentation. The purpose of this paper is to introduce the major features and usages of the ACTest toolkit. The benchmark results on the ACT100 dataset for the maximum entropy method, which is probably the most popular analytic continuation method, are also presented.

physics.comp-ph↗

arXiv2024

Tracing quasiparticle dynamics and hybridization dynamics in PuCoGa5

PuCoGa5 has attracted significant attention due to its record-breaking superconducting transition temperature Tc=18.5 K among known f-electron superconductors. Here we systematically investigated the evolution of correlated electronic states in the plutonium-based unconventional superconductor PuCoGa5 upon temperature using the embedded dynamical mean-field theory merged with density functional theory. The mixed-valence nature of PuCoGa5 leads to intriguing quasiparticle dynamics and hybridization dynamics. Our findings reveal the presence of Dirac fermions and a temperature-driven localized-itinerant crossover of 5f states. As the temperature decreases, the low-energy quasiparticle resonances develop gradually, while the high-energy quasiparticle resonances exhibit quite different behaviors with an initial increase and subsequent decrease. Furthermore, we identified a characteristic temperature of approximately 290 K for the onset of hybridization gaps, which is much lower than the coherence temperature 580 K for 5f electrons. These results provide valuable insight on the the electronic structures, quasiparticle dynamics, and hybridization processes in 5f correlated electron systems.

cond-mat.str-el↗

arXiv2024

$\textit{Ab initio}$ dynamical mean-field theory with natural orbitals renormalization group impurity solver: Formalism and applications

In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package and the $\texttt{Quantum ESPRESSO}$ code to perform density functional theory calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperature. Additionally, this newly developed toolkit incorporates several valuable post-processing tools, such as $\texttt{ACFlow}$, which employs the maximum entropy method and the stochastic pole expansion method for the analytic continuation of Matsubara Green's functions and self-energy functions. To validate the performance of this toolkit, we examine three representative cases: the correlated metal SrVO$_{3}$, the nickel-based unconventional superconductor La$_{3}$Ni$_{2}$O$_{7}$, and the wide-gap Mott insulator MnO. The results obtained demonstrate strong agreement with experimental findings and previously available theoretical results. Notably, we successfully elucidate the quasiparticle peak and band renormalization in SrVO$_{3}$, the dominance of Hund correlation in La$_{3}$Ni$_{2}$O$_{7}$, and the pressure-driven insulator-metal transition as well as the high-spin to low-spin transition in MnO. These findings suggest that $\texttt{Zen}$ is proficient in accurately describing the electronic structures of $d$-electron correlated materials.

cond-mat.str-el↗

arXiv2024

AMSNet: Netlist Dataset for AMS Circuits

Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled significant potential across various fields, suggesting their applicability in streamlining large-scale AMS IC design as well. A bottleneck in employing MLLMs for automatic AMS circuit generation is the absence of a comprehensive dataset delineating the schematic-netlist relationship. We therefore design an automatic technique for converting schematics into netlists, and create dataset AMSNet, encompassing transistor-level schematics and corresponding SPICE format netlists. With a growing size, AMSNet can significantly facilitate exploration of MLLM applications in AMS circuit design. We have made an initial set of netlists public, and will make both our netlist generation tool and the full dataset available upon publishing of this paper.

cs.CV↗

arXiv2024

Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network

Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.

cs.CL↗

arXiv2024

Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data

In the financial risk domain, particularly in credit default prediction and fraud detection, accurate identification of high-risk class instances is paramount, as their occurrence can have significant economic implications. Although machine learning models have gained widespread adoption for risk prediction, their performance is often hindered by the scarcity and diversity of high-quality data. This limitation stems from factors in datasets such as small risk sample sizes, high labeling costs, and severe class imbalance, which impede the models' ability to learn effectively and accurately forecast critical events. This study investigates data pre-processing techniques to enhance existing financial risk datasets by introducing TriEnhance, a straightforward technique that entails: (1) generating synthetic samples specifically tailored to the minority class, (2) filtering using binary feedback to refine samples, and (3) self-learning with pseudo-labels. Our experiments across six benchmark datasets reveal the efficacy of TriEnhance, with a notable focus on improving minority class calibration, a key factor for developing more robust financial risk prediction systems.

cs.LG↗

arXiv2024

Targeted Drug Delivery: Algorithmic Methods for Collecting a Swarm of Particles with Uniform External Forces

We investigate algorithmic approaches for targeted drug delivery in a complex, maze-like environment, such as a vascular system. The basic scenario is given by a large swarm of micro-scale particles (''agents'') and a particular target region (''tumor'') within a system of passageways. Agents are too small to contain on-board power or computation and are instead controlled by a global external force that acts uniformly on all particles, such as an applied fluidic flow or electromagnetic field. The challenge is to deliver all agents to the target region with a minimum number of actuation steps. We provide a number of results for this challenge. We show that the underlying problem is NP-complete, which explains why previous work did not provide provably efficient algorithms. We also develop several algorithmic approaches that greatly improve the worst-case guarantees for the number of required actuation steps. We evaluate our algorithmic approaches by numerous simulations, both for deterministic algorithms and searches supported by deep learning, which show that the performance is practically promising.

cs.CG↗

arXiv2024

Local excitation of kagome spin ice magnetism in HoAgGe seen by scanning tunneling microscopy

The kagome spin ice can host frustrated magnetic excitations by flipping its local spin. Under an inelastic tunneling condition, the tip in a scanning tunneling microscope can flip the local spin, and we apply this technique to kagome metal HoAgGe with a long-range ordered spin ice ground state. Away from defects, we discover a pair of pronounced dips in the local tunneling spectrum at symmetrical bias voltages with negative intensity values, serving as a striking inelastic tunneling signal. This signal disappears above the spin ice formation temperature and has a dependence on the magnetic fields, demonstrating its intimate relation with the spin ice magnetism. We provide a two-level spin-flip model to explain the tunneling dips considering the spin ice magnetism under spin-orbit coupling. Our results uncover a local emergent excitation of spin ice magnetism in a kagome metal, suggesting that local electrical field induced spin flip climbs over a barrier caused by spin-orbital locking.

cond-mat.str-el↗

arXiv2024

Thermal spin-crossover and temperature-dependent zero-field splitting in magnetic nanographene chains

Nanographene-based magnetism at interfaces offers an avenue to designer quantum materials towards novel phases of matter and atomic-scale applications. Key to spintronics applications at the nanoscale is bistable spin-crossover which however remains to be demonstrated in nanographenes. Here we show that antiaromatic 1,4-disubstituted pyrazine-embedded nanographene derivatives, which promote magnetism through oxidation to a non-aromatic radical are prototypical models for the study of carbon-based thermal spin-crossover. Scanning tunneling spectroscopy studies reveal symmetric spin excitation signals which evolve at Tc to a zero-energy peak, and are assigned to the transition of a S = 3/2 high-spin to a S = 1/2 low-spin state by density functional theory. At temperatures below and close to the spin-crossover Tc, the high-spin S= 3/2 excitations evidence pronouncedly different temperature-dependent excitation energies corresponding to a zero-field splitting in the Hubbard-Kanamori Hamiltonian. The discovery of thermal spin crossover and temperature-dependent zero-field splitting in carbon nanomaterials promises to accelerate quantum information, spintronics and thermometry at the atomic scale.

physics.chem-ph↗

arXiv2024

Building spin-1/2 antiferromagnetic Heisenberg chains with diaza-nanographenes

Understanding and engineering the coupling of spins in nanomaterials is of central importance for designing novel devices. Graphene nanostructures with π-magnetism offer a chemically tunable platform to explore quantum magnetic interactions. However, realizing spin chains bearing controlled odd-even effects with suitable nanographene systems is challenging. Here, we demonstrate the successful on-surface synthesis of spin-1/2 antiferromagnetic Heisenberg chains with parity-dependent magnetization based on antiaromatic diaza-hexa-peri-hexabenzocoronene (diaza-HBC) units. Using distinct synthetic strategies, two types of spin chains with different terminals were synthesized, both exhibiting a robust odd-even effect on the spin coupling along the chain. Combined investigations using scanning tunneling microscopy, non-contact atomic force microscopy, density functional theory calculations, and quantum spin models confirmed the structures of the diaza-HBC chains and revealed their magnetic properties, which has an S = 1/2 spin per unit through electron donation from the diaza-HBC core to the Au(111) substrate. Gapped excitations were observed in even-numbered chains, while enhanced Kondo resonance emerged in odd-numbered units of odd-numbered chains due to the redistribution of the unpaired spin along the chain. Our findings provide an effective strategy to construct nanographene spin chains and unveil the odd-even effect in their magnetic properties, offering potential applications in nanoscale spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification

Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification: (1) Existing methods might fail to adopt suitable thresholds since they either use a pre-defined/fixed threshold or an ad-hoc threshold adjusting scheme, resulting in inferior performance and slow convergence. (2) Discarding unlabeled data with confidence below the thresholds results in the loss of discriminating information. To solve these issues, we develop an effective method to make sufficient use of unlabeled data. Specifically, we design a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. Meanwhile, in order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss to mine the discriminative information in low-confidence samples by learning the similarities and differences between sample features. We evaluate our method on several classification benchmarks under partially labeled settings and demonstrate its superiority over the other approaches.

cs.CV↗

arXiv2024

Correlated electrons of the flat band in charge density wave state of 4Hb-TaSexS2-x

Many intriguing quantum states of matter, such as unconventional superconductivity, magnetic phases and fractional quantum Hall physics, emergent from the spatially-correlated localized electrons in the flat band of solid materials. By using scanning tunneling microscopy and spectroscopy (STM/STS), we report the real-space investigation of correlated electrons in the flat band of superlattice 4Hb-TaSexS2-x. In contrast with the pristine 4Hb-TaS2, the selenium (Se) substitutions significantly affect the interfacial transfer of correlated electrons between the CDW states of 1T- and 1H-TaS2 layers, and contribute a real-space fractional electron-filling configurations with the distributed electron-filled and -void SoD clusters of 1T-layer. The site-specific STS spectra directly reveal their respective prominent spectra weight above EF and symmetric Mott-like spectra. In addition, the spatial distributions of these electron-filled SoDs in the 1T-layer of 4Hb-TaSe0.7S1.3 demonstrate different local short-range patterning, clearly indicating the complex neighboring interactions among the localized electrons in the flat band of 1T-layer. Our results not only provide an in-depth insight of correlated electrons in the flat CDW band, and provide a simple platform to manipulate the electron-correlation-related quantum states.

cond-mat.mtrl-sci↗

arXiv2024

Multiscale lubrication simulation based on fourier feature networks with trainable frequency

Rough surface lubrication simulation is crucial for designing and optimizing tribological performance. Despite the growing application of Physical Information Neural Networks (PINNs) in hydrodynamic lubrication analysis, their use has been primarily limited to smooth surfaces. This is due to traditional PINN methods suffer from spectral bias, favoring to learn low-frequency features and thus failing to analyze rough surfaces with high-frequency signals. To date, no PINN methods have been reported for rough surface lubrication. To overcome these limitations, this work introduces a novel multi-scale lubrication neural network architecture that utilizes a trainable Fourier feature network. By incorporating learnable feature embedding frequencies, this architecture automatically adapts to various frequency components, thereby enhancing the analysis of rough surface characteristics. This method has been tested across multiple surface morphologies, and the results have been compared with those obtained using the finite element method (FEM). The comparative analysis demonstrates that this approach achieves a high consistency with FEM results. Furthermore, this novel architecture surpasses traditional Fourier feature networks with fixed feature embedding frequencies in both accuracy and computational efficiency. Consequently, the multi-scale lubrication neural network model offers a more efficient tool for rough surface lubrication analysis.

cs.LG↗

arXiv2024

Execution-free Program Repair

Automatic program repair usually relies heavily on test cases for both bug identification and fix validation. The issue is that writing test cases is tedious, running them takes much time, and validating a fix through tests does not guarantee its correctness. The novel idea in the Proof2Fix methodology and tool presented here is to rely instead on a program prover, without the need to run tests or to run the program at all. Results show that Proof2Fix finds and fixes significant historical bugs.

cs.SE↗