arXiv subjects

Hong Zheng

Publications and source records attributed to Hong Zheng.

At least 19 recordsLinked to original sources

arXiv2026

Layer Architecture Shapes Electronic, Magnetic, and Lattice Interactions in Ruddlesden-Popper Nickelates

The discovery of superconductivity in Ruddlesden-Popper nickelates has raised a central question: how does layer architecture shape the electronic, magnetic, and lattice interactions relevant to pairing? Here, we report a detailed comparative study of the two polymorphs of La3Ni2O7--the alternating monolayer-trilayer (LNO-1313) and bilayer (LNO-2222) structures--and the related trilayer compound La4Ni3O10, using both Ni L3- and O K-edge RIXS. We find that LNO-1313 and La4Ni3O10 share strikingly similar electronic, magnetic, and lattice excitations, whereas bilayer LNO-2222 exhibits distinct features. Compared to LNO-2222, LNO-1313 and La4Ni3O10 have weaker orbital polarization, enhanced 3d8L character, a reduced out-of-plane magnetic-exchange scale, and stronger EPC. Within an effective local-moment framework, an entangled-dimer scenario provides a natural description of the spin excitations generated by strong antiferromagnetic interlayer coupling. Its advantage over conventional spin-wave theory is clearest in bilayer LNO-2222, where the interlayer coupling dominates the intralayer interactions. These findings provide critical experimental constraints for future theoretical models for the low-energy physics relevant to superconductivity in these layered nickelates.

cond-mat.str-el↗

arXiv2026

Phonons reflect dynamic spin-state order in LaCoO$_3$

We investigate lattice dynamics in LaCoO$_3$ using inelastic neutron and x-ray scattering over $T = 2\mbox{-}650\,\mathrm{K}$, spanning the spin-state crossover at $T_{1} \approx 100\,\mathrm{K}$ and the insulator--metal transition at $T_{2} \approx 550\,\mathrm{K}$. Comparison with quasi-harmonic $ab-initio$ lattice-dynamical calculations helps reveal anomalous softening of a $\approx 10\,\mathrm{meV}$ oxygen phonon, confined to the temperature interval $T_{1} \leq T \leq T_{2}$ and localized in momentum space at $\boldsymbol{q}_{\mathrm{SSO}} = \left( \frac{1}{2},\frac{1}{2},\frac{1}{2} \right)_{c}$. This wave vector corresponds to the spin-state ordering originally proposed by Goodenough [J. Phys. Chem. Solids 6, 287-297 (1958)]. Our results therefore provide momentum-resolved evidence for dynamic correlations of high-spin and low-spin Co$^{3+}$ states in LaCoO$_{3}$, linking spin-state fluctuations to anomalous phonon renormalization.

cond-mat.str-el↗

arXiv2026

Distribution Shift Is Key to Learning Invariant Prediction

An interesting phenomenon arises: Empirical Risk Minimization (ERM) sometimes outperforms methods specifically designed for out-of-distribution tasks. This motivates an investigation into the reasons behind such behavior beyond algorithmic design. In this study, we find that one such reason lies in the distribution shift across training domains. A large degree of distribution shift can lead to better performance even under ERM. Specifically, we derive several theoretical and empirical findings demonstrating that distribution shift plays a crucial role in model learning and benefits learning invariant prediction. Firstly, the proposed upper bounds indicate that the degree of distribution shift directly affects the prediction ability of the learned models. If it is large, the models' ability can increase, approximating invariant prediction models that make stable predictions under arbitrary known or unseen domains; and vice versa. We also prove that, under certain data conditions, ERM solutions can achieve performance comparable to that of invariant prediction models. Secondly, the empirical validation results demonstrated that the predictions of learned models approximate those of Oracle or Optimal models, provided that the degree of distribution shift in the training data increases.

cs.LG↗

arXiv2025

A novel approach to profile global circulation pathway of SARS-CoV-2 variants by site-based mutation dynamics

The genetic evolution of SARS-CoV-2 has caused recurring epidemic waves, understanding its global dispersal patterns is critical for effective surveillance. We developed the Site-based mutation dynamics - Equal Power Sampling (S-EPS) framework, a phylogenetic-free, bias-correcting framework for profiling viral source-sink dynamics. Applying S-EPS to 6.6 million SARS-CoV-2 genomes (March 2020 - June 2024) from 13 regions worldwide, we identified Africa and the Indian subcontinent as the predominant sources of key mutations. Southeast Asia serves as an early transmission hub, while Russia and South America mainly acted as sinks. Key mutations took longer to establish fitness in source regions than externally. Once an amino acid substitution on the receptor-binding domain reached 1% prevalence in major sources, there is an 80% probability it would spread elsewhere, with a 2-month median lead time (IQR: 1-4). Our findings underscore the importance of genetic surveillance, with S-EPS offering enhanced capability for monitoring emerging viral threats.

q-bio.PE↗

arXiv2025

Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation

Noise reduction constitutes a crucial operation within Digital Signal Processing. Regrettably, it frequently remains neglected when dealing with the processing of convolutional features in segmentation networks. This oversight could trigger the butterfly effect, impairing the subsequent outcomes within the entire feature system. To complete this void, we consider convolutional features following Gaussian distributions as feature signal matrices and then present a simple and effective feature filter in this study. The proposed filter is fundamentally a low-amplitude pass filter primarily aimed at minimizing noise in feature signal inputs and is named Convolutional Feature Filter (CFF). We conducted experiments on two established 2D segmentation networks and two public cardiac MR image datasets to validate the effectiveness of the CFF, and the experimental findings demonstrated a decrease in noise within the feature signal matrices. To enable a numerical observation and analysis of this reduction, we developed a binarization equation to calculate the information entropy of feature signals.

cs.CV↗

arXiv2025

Lattice-charge coupling in a trilayer nickelate with intertwined density wave order

Intertwined charge and spin correlations are ubiquitous in a wide range of transition metal oxides and are often perceived as intimately related to unconventional superconductivity. Theoretically envisioned as driven by strong electronic correlations, the intertwined order is usually found to be strongly coupled to the lattice as signaled by pronounced phonon softening. Recently, both charge/spin density waves (CDW/SDW) and superconductivity have been discovered in several Ruddlesden-Popper (RP) nickelates, in particular trilayer nickelates RE4Ni3O10 (RE=Pr, La). The nature of the intertwined order and the role of lattice-charge coupling are at the heart of the debate about these materials. Using inelastic X-ray scattering, we mapped the phonon dispersions in RE4Ni3O10 and found no evidence of phonon softening near the CDW wavevector over a wide temperature range. Calculations of the electronic susceptibility revealed a peak at the observed SDW ordering vector but not at the CDW wavevector. The absence of phonon softening is in sharp contrast to that in canonical oxide materials, notably cuprates. Our experimental and theoretical findings highlight the crucial role of the spin degree of freedom and establish a foundation for understanding the interplay between superconductivity and density-wave transitions in RP nickelate superconductors and beyond.

cond-mat.str-el↗

arXiv2025

Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases.

eess.IV↗

arXiv2025

LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection

Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF representations, leaving the field without a unified learning framework. To bridge this gap, we present LFX, the first unified framework for LF perception. LFX establishes a representation-invariant feature modulation space, enabling it to adapt to heterogeneous LF representations and diverse perception tasks. Specifically, we propose Field-of-Parallax Angular Subspace Modeling (FoP-ASM), which assigns an independent angular marker to each auxiliary view, enabling view-wise independent modeling. Meanwhile, shared manifold subspace constraints and regularization losses enforce globally consistent semantic modulation across views. Extensive evaluations across three LF benchmarks show that LFX achieves state-of-the-art results across distinct LF representations, outperforming representation-specific methods by up to 12% and 20% with 0.029/0.027 MAE for salient object detection, and achieving 84.37 mIoU for semantic segmentation. The source code will be made publicly available at https://github.com/FeiT-FeiTeng/LFX.

cs.CV↗

arXiv2025

Disentangled Modeling of Preferences and Social Influence for Group Recommendation

The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. However, existing methods either neglect the social influence of individual members or bundle preferences and social influence together as a unified representation. As a result, these models emphasize the preferences of the majority within the group rather than the actual interaction items, which we refer to as the preference bias issue in GR. Moreover, the self-supervised learning (SSL) strategies they designed to address the issue of group data sparsity fail to account for users' contextual social weights when regulating group representations, leading to suboptimal results. To tackle these issues, we propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec). Concretely, we first design a user-level disentangling network to disentangle the preferences and social influence of group members with separate embedding propagation schemes based on (hyper)graph convolution networks. We then introduce a socialbased contrastive learning strategy, selectively excluding user nodes based on their social importance to enhance group representations and alleviate the group-level data sparsity issue. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two realworld datasets.

cs.IR↗

arXiv2024

Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features. Materials and Methods: 89,714 radiomic features were extracted from five cancer datasets: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). Features were categorized by computational complexity into morphological, intensity, texture, linear filters, and nonlinear filters. Models were trained and evaluated on each complexity level using the area under the curve (AUC). The most informative features were identified, and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was developed to facilitate radiomic studies. Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning the Hounsfield unit range improved results for CECT-based renal cell carcinoma (AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the optimal radiomics complexity level for specific medical outcomes, potentially simplifying the use of radiomics in predictive modeling across various contexts.

eess.IV↗

arXiv2023

Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement

Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input. However, the traditional method of providing large language models with all demonstration input-output pairs at once may not effectively guide large language models to learn the specific input-output mapping relationship. In this paper, inspired by the regulatory and supportive role of metacognition in students' learning, we propose a novel metacognition-enhanced few-shot prompting, which guides large language models to reflect on their thought processes to comprehensively learn the given demonstration examples. Furthermore, considering that positive reinforcement can improve students' learning motivation, we introduce positive reinforcement into our metacognition-enhanced few-shot prompting to promote the few-shot learning of large language models by providing response-based positive feedback. The experimental results on two real-world datasets show that our metacognition-enhanced few-shot prompting with positive reinforcement surpasses traditional few-shot prompting in classification accuracy and macro F1.

cs.CL↗

arXiv2023

Frustration-Mediated Crossover from Long-Range to Short-Range Magnetic Ordering in $\mathbf{Y_{1-x}Lu_xBaCo_4O_7} $

We present a comprehensive magnetic and structural phase diagram for $\mathrm{Y_{1-x}Lu_xBaCo_4O_7}$, established through neutron diffraction and magnetization measurements. Our results outline the evolution of various nuclear structures, encompassing both orthorhombic and monoclinic symmetries, in response to changes in composition and temperature. The phase transition temperature of the orthorhombic phase, $T_{s1}$, decreases with increasing Lu content from 310 K for x = 0.0 to 110 K for x = 1.0. In Lu-rich compositions (0.7 \le x \le 1.0), first-order structural transitions are observed with coexisting and competing orthorhombic Pbn$2_1$ and metastable monoclinic Cc phases. Composition- and temperature-dependent refinements of the magnetic structure reveal an antiferromagnetic arrangement of Co spin pairs, below the magnetic transition temperatures of the Y-rich compositions, in the ab plane within both the triangular and Kagom\'e layers. A gradual suppression of long-range magnetic order is observed with increasing the Lu content, accompanied by the development of short-range magnetic correlations present in all the samples.

cond-mat.str-el↗

arXiv2023

All-Dielectric Structural Coloration Empowered by Bound States in the Continuum

The technological requirements of low-power and high-fidelity color displays have been instrumental in driving research into advanced coloration technologies. At the forefront of these developments is the implementation of dye-free coloration techniques, which overcome previous constraints related to insufficient resolution and color fading. In this context, resonant dielectric nanostructures have emerged as a promising paradigm, showing great potential for high efficiency, remarkably high color saturation, wide gamut palette, and realistic image reproduction. However, they still face limitations related to color accuracy, purity, and simultaneous brightness tunability. Here, we demonstrate an all-dielectric metasurface empowered by photonic bound states in the continuum (BICs), which supports sharp resonances throughout the visible spectral range, ideally suited for producing a wide range of structural colors. The metasurface design consists of titanium dioxide (TiO2) ellipses with carefully controlled sizes and geometrical asymmetry, allowing versatile and on-demand variation of the brightness and hue of the output colors, respectively.

physics.optics↗

arXiv2023

Is ChatGPT a Good Personality Recognizer? A Preliminary Study

In recent years, personality has been regarded as a valuable personal factor being incorporated into numerous tasks such as sentiment analysis and product recommendation. This has led to widespread attention to text-based personality recognition task, which aims to identify an individual's personality based on given text. Considering that ChatGPT has recently exhibited remarkable abilities on various natural language processing tasks, we provide a preliminary evaluation of ChatGPT on text-based personality recognition task for generating effective personality data. Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level. The experimental results on two representative real-world datasets reveal that ChatGPT with zero-shot chain-of-thought prompting exhibits impressive personality recognition ability and is capable to provide natural language explanations through text-based logical reasoning. Furthermore, by employing the level-oriented prompting strategy to optimize zero-shot chain-of-thought prompting, the performance gap between ChatGPT and corresponding state-of-the-art model has been narrowed even more. However, we observe that ChatGPT shows unfairness towards certain sensitive demographic attributes such as gender and age. Additionally, we discover that eliciting the personality recognition ability of ChatGPT helps improve its performance on personality-related downstream tasks such as sentiment classification and stress prediction.

cs.CL↗

arXiv2023

Low temperature dynamic polaron liquid in a manganite exhibiting colossal magnetoresistance

Polarons - fermionic charge carriers bearing a strong companion lattice deformation - exhibit a natural tendency for self-localization due to the recursive interaction between electrons and the lattice. While polarons are ubiquitous in insulators, how they evolve in transitions to metallic and superconducting states in quantum materials remains an open question. Here, we use resonant inelastic x-ray scattering (RIXS) to track the electron-lattice coupling in the colossal magneto-resistive bi-layer manganite La$_{1.2}$Sr$_{1.8}$Mn$_2$O$_7$ across its metal-to-insulator transition. The response in the insulating high-temperature state features harmonic emissions of a dispersionless oxygen phonon at small energy transfer. Upon cooling into the metallic state, we observe a drastic redistribution of spectral weight from the region of these harmonic emissions to a broad high energy continuum. In concert with theoretical calculations, we show that this evolution implies a shift in electron-lattice coupling from static to dynamic lattice distortions that leads to a distinct polaronic ground state in the low temperature metallic phase - a dynamic polaron liquid.

cond-mat.str-el↗

arXiv2022

Electronic structure and correlations in planar trilayer nickelate Pr4Ni3O8

The recent discovery of superconductivity in hole-doped planar nickelates R1-xSrNiO2 (R=Pr,Nd) raises the foundational question of how the electronic structure and electronic correlations of these Ni1+ compounds compare to those of the Cu2+ cuprate superconductors. Here, we present an Angle-Resolved Photoemission Spectroscopy (ARPES) study of the trilayer nickelate Pr4Ni3O8, revealing an electronic structure and Fermi surface very similar to that of the hole-doped cuprates but with a few critical differences. Specifically, the main portions of the Fermi surface are extremely similar to that of the bilayer cuprates, with an additional piece that can accommodate additional hole doping. We find that the electronic correlations are about twice as strong in the nickelates and are almost k-independent, indicating that they originate from a local effect-likely the Mott interaction, whereas the cuprate interactions are somewhat less local. Nevertheless, the nickelates still demonstrate an approximately linear in energy and linear in temperature scattering rate. Understanding the similarities and differences between these two related families of strongly-correlated novel superconductors is an important challenge.

cond-mat.supr-con↗

arXiv2022

Tunable superconductivity at the oxide-insulator/KTaO$_3$ interface and its origin

Superconductivity forms out of the condensation of Cooper pairs of electrons. The mechanism by which Cooper pairs are created in non-conventional superconductors is often elusive because experimental signatures that connect a specific pairing mechanism to the properties of superconducting state are rare. The recently discovered superconducting oxide-insulator/KTaO$_3$ interface may offer clues about its origins. Here we observe distinct dependences of the superconducting transition temperature Tc on carrier density n$_{2D}$ for electron gases formed at KTaO$_3$ (111), (001) and (110) interfaces. For the KTaO$_3$ (111) interface, a remarkable linear dependence of Tc on n$_{2D}$ is observed over a range of nearly one order of magnitude. Further, our study of the dependence of superconductivity on gate electric fields reveals the role of the interface in mediating superconductivity, which also allows for a reversible electric switching of superconductivity at T = 2 K. We found that the extreme sensitivity of superconductivity to crystallographic orientation can be explained by Cooper pairing via inter-orbital interactions induced by the inversion-breaking transverse optical (TO1) phonons and quantum confinement. This mechanism is also consistent with the dependence of Tc on n$_{2D}$ at the KTaO$_3$ (111) interface. Our study may shed light on the pairing mechanism in other superconducting quantum-paraelectrics.

cond-mat.supr-con↗

arXiv2022

Magnetic terahertz resonances above the Néel temperature in the frustrated kagome antiferromagnet averievite

Time-domain magneto-THz spectroscopy is utilized to study the frustrated magnet averievite Cu$_{5-x}$Zn$_x$V$_2$O$_{10}$(CsCl). Pronounced THz resonances are observed in unsubstituted samples ($x=0$) when cooling below the onset of short-range magnetic correlations. The influence of external magnetic effects confirms the magnetic origin of these resonances. Increasing Zn substitution suppresses the resonances, as frustration effects dominate, reflecting the non-magnetic phases for $x> 0.25$ compounds. The temperature evolution of the THz spectra is complemented with electron spin resonance spectroscopy. This comparison allows a direct probe of the different contributions from magnetic order, frustration, and structural properties in the phase diagram of averievite. Our results illustrate the effect of magnetic interactions in THz spectra of frustrated magnets.

cond-mat.str-el↗