arXiv subjects

Zhenhua Chen

Publications and source records attributed to Zhenhua Chen.

16 recordsLinked to original sources

arXiv2026

Termination-Dependent Surface States and Magnetic Fingerprints of Chiral Helimagnet Cr1/3TaS2

Chiral helimagnets based on intercalated transition-metal dichalcogenides, characterized by nano-scale spin ordering, provide a powerful route to engineer chiral spin textures (e.g. the topologically protected magnetic solitons) and emergent electronic functionality at reduced dimensions, where surface and interface states often dominate device operation. However, despite growing interest, direct experimental studies of termination-dependent surface electronic structures and their temperature-driven magnetic evolution remain largely unexplored, hindering a microscopic understanding of the electronic states that is crucial for the development of low-dimensional spintronic devices. Here, for the first time, taking Cr1/3TaS2 as a representative example, we systematically investigate the termination-dependent surface electronic states of the chiral helimagnets and uncover their distinct temperature evolution across the magnetic transition (TC~142K) by combining high-resolution ARPES with a micro-focused beam and surface-state-resolved first-principles calculations. The TaS2-terminated surface hosts folded monolayer-like TaS2 bands under the $\sqrt3\times\sqrt3$ superlattice potential and a shallow triangular electron pocket at the superlattice $\bar K$ point arising from Cr-Ta orbital hybridization. In contrast, the Cr-terminated surface exhibits reconstructed hole pockets with pronounced magnetic band splitting. This splitting disappears above TC and closely follows the chiral helimagnetic order parameter, providing a direct spectroscopic fingerprint of chiral helimagnetic order. In addition, multiple ultranarrow Cr-d-derived surface flat bands are resolved. These findings establish Cr1/3TaS2 as a model system in which surface electronic states are strongly coupled to chiral magnetism, opening new opportunities for chiral spintronic and valleytronic micro/nanodevices.

cond-mat.mtrl-sci↗

arXiv2026

Engineering excitonic metal-insulator transitions in ultra-thin doped copper sulfides

Delicate engineering of the bands remains challenging due to complex electronic, structural, and compositional interplay. We demonstrate the formation of distinct metallic and insulating ground states in ultra-thin copper sulfide films by effectively tuning the band structure via changing the composition of Cu and S in the system. Using angle-resolved photoemission spectroscopy (ARPES), we observed a continuous band renormalization and opening of a full gap at low temperatures over a wide range of doping. The electronic origin of this metal-insulator transition is supported by scanning tunneling microscopy (STM) and low energy electron diffraction (LEED) measurements, which show no indication of superlattice modulation and lattice symmetry breaking. The evidence of excitonic insulating phase is further provided by carrier density dependent transitions, a combined effect of electron screening and Coulomb interaction strength. Our findings demonstrate tunability of the band structure of copper sulfides, allowing for new opportunities to study exotic quantum phases.

cond-mat.str-el↗

arXiv2025

Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation

Recent advances in diffusion-based and autoregressive video generation models have achieved remarkable visual realism. However, these models typically lack accurate physical alignment, failing to replicate real-world dynamics in object motion. This limitation arises primarily from their reliance on learned statistical correlations rather than capturing mechanisms adhering to physical laws. To address this issue, we introduce a novel framework that integrates symbolic regression (SR) and trajectory-guided image-to-video (I2V) models for physics-grounded video forecasting. Our approach extracts motion trajectories from input videos, uses a retrieval-based pre-training mechanism to enhance symbolic regression, and discovers equations of motion to forecast physically accurate future trajectories. These trajectories then guide video generation without requiring fine-tuning of existing models. Evaluated on scenarios in Classical Mechanics, including spring-mass, pendulums, and projectile motions, our method successfully recovers ground-truth analytical equations and improves the physical alignment of generated videos over baseline methods.

cs.CV↗

arXiv2025

Observation of Orbital-Selective Dual Modulations in an Anisotropic Antiferromagnetic Kagome Metal TbTi3Bi4

Orbital selectivity is pivotal in dictating the phase diagrams of multiorbital systems, with prominent examples including the orbital-selective Mott phase and superconductivity, etc. The intercalation of anisotropic layers represents an effective method for enhancing orbital selectivity and, thereby shaping the low-energy physics of multiorbital systems. Despite its potential, related experimental studies remain limited. In this work, we systematically examine the interplay between orbital selectivity and magnetism in the newly discovered anisotropic kagome TbTi3Bi4 single crystal, and report a unidirectional, orbital-selective band reconstruction within the antiferromagnetic (AFM) state. By combining soft X-ray and vacuum ultraviolet angle-resolved photoemission spectroscopy (ARPES) measurements with orbital-resolved density functional theory (DFT) calculations, we identify that the band reconstruction is a manifestation of the AFM order, driven by a 1/3 nesting instability of the intercalated Tb 5dxz orbitals. Such an orbital-selective modulation leads the unusual momentum-dependent band folding and the emergence of symmetry-protected Dirac cones only at the M1 point. More importantly, the discovery of orbital-selective 3 x 1 AFM order offers crucial insights into the underlying mechanism of the fractional magnetization plateau in this Kagome AFM metal. Our findings not only underscore the essential role of both conducting and localized electrons in determining the magnetic orders of LnTi3Bi4 (Ln = Lanthanide) kagome metals but also offer a pathway for manipulating magnetism through selective control of anisotropic electronic structures.

cond-mat.str-el↗

arXiv2025

Orbital textures and evolution of correlated insulating state in monolayer 1T phase transition metal dichalcogenides

Strong electron-electron interaction can induce Mott insulating state, which is believed to host unusual correlated phenomena such as quantum spin liquid when quantum fluctuation dominates and unconventional superconductivity through doping. Transition metal compounds as correlated materials provide a versatile platform to engineer the Mott insulating state. Previous studies mostly focused on the controlling of the repulsive interaction and bandwidth of the electrons by gating or doping. Here, we performed angle-resolved photoemission spectroscopy (ARPES) on monolayer 1T phase NbSe2, TaSe2, and TaS2 and directly observed their band structures with characteristic lower Hubbard bands. By systematically investigating the orbital textures and temperature dependence of the energy gap of the materials in this family, we discovered that hybridization of the chalcogen p states with lower Hubbard band stabilizes the Mott phase via tuning of the bandwidth, as shown by a significant increase of the transition temperature (TC) at a stronger hybridization strength. Our findings reveal a mechanism for realizing a robust Mott insulating phase and establish monolayer 1T phase transition metal dichalcogenide family as a promising platform for exploring correlated electron problems.

cond-mat.str-el↗

arXiv2025

Spectroscopic evidence for possible quantum spin liquid behavior in a two-dimensional Mott insulator

Mott insulators with localized magnetic moments will exhibit a quantum spin liquid (QSL) state when the quantum fluctuations are strong enough to suppress the ordering of the spins. Such an entangled state will give rise to collective excitations, in which spin and charge information are carried separately. Our angle-resolved photoemission spectroscopy (ARPES) measurements on single-layer 1T-TaS2 show a flat band around the zone center and a gap opening of about 200 meV in the low temperature, indicating 2D Mott insulating nature in the system. This flat band is dispersionless in momentum space but shows anomalously broad width around the zone center and the spectral weight decays rapidly as momentum increases. The observation is described as a spectral continuum from electron fractionalization, corroborated by a low energy effective model.The intensity of the flat band is reduced by surface doping with magnetic adatoms and the gap is closing, a result from the interaction between spin impurities coupled with spinons and the chargons, which gives rise to a charge redistribution. Doping with nonmagnetic impurities behaves differently as the chemical potential shift dominates. These findings provide insight into the QSL states of strongly correlated electrons on 2D triangular lattices.

cond-mat.str-el↗

arXiv2023

Coexistence of near-EF flat band and van Hove singularity in a two-phase superconductor

In quantum many-body systems, particularly, the ones with large near-EF density states, like flat bands or van Hove singularity (VHS), electron correlations often give rise to rich phase diagrams with multiple coexisting/competing orders occurring at similar energy scales. The recently discovered locally noncentrosymmetric heavy fermion superconductor CeRh2As2 has stimulated extensive attention due to its unusual H-T phase diagram, consisting of two-phase superconductivity, antiferromagnetic order, and possible quadrupole-density wave orders. However, despite its great importance, the near-EF electronic structure remains experimentally elusive. Here, we provide this key information by combining soft X-ray and vacuum ultraviolet (VUV) angle-resolved photoemission spectroscopy measurements and atom-resolved DFT+U calculations. With bulk-sensitive soft X-rays, we reveal quasi-2D hole- and 3D electron- pockets with a pronounced nesting feature. Most importantly, we observe a symmetry-protected fourfold VHS coexisting with the Ce 4f flat bands near the EF, which, to the best of our knowledge, has never been reported before. Such a rare coexistence is expected to lead to a large density of states at the zone edge, enhancement in electron correlations, and a large upper critical field of the odd-parity superconducting phase. Uniquely, it will also result in a new type of f-VHS hybridization that alters the order and fine electronic structure of the symmetry-protected VHS and flat bands. These peculiarities offer important dimensions for understanding the reported rich phase diagram and are discussed as an origin of superconductivity with two phases. Our findings not only provide key insights into the nature of multiple phases in CeRh$_2$As$_2$, but also open up new prospects for exploring the novelties of many-body systems with f-VHS hybridization.

cond-mat.str-el↗

arXiv2023

A Tensor-based Convolutional Neural Network for Small Dataset Classification

Inspired by the ConvNets with structured hidden representations, we propose a Tensor-based Neural Network, TCNN. Different from ConvNets, TCNNs are composed of structured neurons rather than scalar neurons, and the basic operation is neuron tensor transformation. Unlike other structured ConvNets, where the part-whole relationships are modeled explicitly, the relationships are learned implicitly in TCNNs. Also, the structured neurons in TCNNs are high-rank tensors rather than vectors or matrices. We compare TCNNs with current popular ConvNets, including ResNets, MobileNets, EfficientNets, RegNets, etc., on CIFAR10, CIFAR100, and Tiny ImageNet. The experiment shows that TCNNs have higher efficiency in terms of parameters. TCNNs also show higher robustness against white-box adversarial attacks on MNIST compared to ConvNets.

cs.CV↗

arXiv2022

Direct observation of topological surface state in the topological superconductor 2M-WS2

The quantum spin Hall (QSH) effect has attracted extensive research interest because of the potential applications in spintronics and quantum computing, which is attributable to two conducting edge channels with opposite spin polarization and the quantized electronic conductance of 2e2/h. Recently, 2M-WS2, a new stable phase of transition metal dichalcogenides with a 2M structure showing an identical layer configuration to that of the monolayer 1T' TMDs, was suggested to be a QSH insulator as well as a superconductor with critical transition temperature around 8 K. Here, high-resolution angle-resolved photoemission spectroscopy (ARPES) and spin-resolved ARPES are applied to investigate the electronic and spin structure of the topological surface states (TSS) in the superconducting 2M-WS2. The TSS exhibits characteristic spin-momentum-locking behavior, suggesting the existence of long-sought nontrivial Z2 topological states therein. We expect that 2M-WS2 with co-existing superconductivity and TSS might host the promising Majorana bound states.

cond-mat.supr-con↗

arXiv2022

Semantically Stealthy Adversarial Attacks against Segmentation Models

Segmentation models have been found to be vulnerable to targeted and non-targeted adversarial attacks. However, the resulting segmentation outputs are often so damaged that it is easy to spot an attack. In this paper, we propose semantically stealthy adversarial attacks which can manipulate targeted labels while preserving non-targeted labels at the same time. One challenge is making semantically meaningful manipulations across datasets and models. Another challenge is avoiding damaging non-targeted labels. To solve these challenges, we consider each input image as prior knowledge to generate perturbations. We also design a special regularizer to help extract features. To evaluate our model's performance, we design three basic attack types, namely `vanishing into the context,' `embedding fake labels,' and `displacing target objects.' Our experiments show that our stealthy adversarial model can attack segmentation models with a relatively high success rate on Cityscapes, Mapillary, and BDD100K. Our framework shows good empirical generalization across datasets and models.

cs.CV↗

arXiv2021

How to Accelerate Capsule Convolutions in Capsule Networks

How to improve the efficiency of routing procedures in CapsNets has been studied a lot. However, the efficiency of capsule convolutions has largely been neglected. Capsule convolution, which uses capsules rather than neurons as the basic computation unit, makes it incompatible with current deep learning frameworks' optimization solution. As a result, capsule convolutions are usually very slow with these frameworks. We observe that capsule convolutions can be considered as the operations of `multiplication of multiple small matrics' plus tensor-based combination. Based on this observation, we develop two acceleration schemes with CUDA APIs and test them on a custom CapsNet. The result shows that our solution achieves a 4X acceleration.

cs.AI↗

arXiv2019

P-CapsNets: a General Form of Convolutional Neural Networks

We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST\&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99\% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.

cs.CV↗

arXiv2018

Generalized Capsule Networks with Trainable Routing Procedure

CapsNet (Capsule Network) was first proposed by~\citet{capsule} and later another version of CapsNet was proposed by~\citet{emrouting}. CapsNet has been proved effective in modeling spatial features with much fewer parameters. However, the routing procedures in both papers are not well incorporated into the whole training process. The optimal number of routing procedure is misery which has to be found manually. To overcome this disadvantages of current routing procedures in CapsNet, we embed the routing procedure into the optimization procedure with all other parameters in neural networks, namely, make coupling coefficients in the routing procedure become completely trainable. We call it Generalized CapsNet (G-CapsNet). We implement both "full-connected" version of G-CapsNet and "convolutional" version of G-CapsNet. G-CapsNet achieves a similar performance in the dataset MNIST as in the original papers. We also test two capsule packing method (cross feature maps or with feature maps) from previous convolutional layers and see no evident difference. Besides, we also explored possibility of stacking multiple capsule layers. The code is shared on \hyperlink{https://github.com/chenzhenhua986/CAFFE-CapsNet}{CAFFE-CapsNet}.

cs.CV↗

arXiv2018

Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting

Detecting small, densely distributed objects is a significant challenge: small objects often contain less distinctive information compared to larger ones, and finer-grained precision of bounding box boundaries are required. In this paper, we propose two techniques for addressing this problem. First, we estimate the likelihood that each pixel belongs to an object boundary rather than predicting coordinates of bounding boxes (as YOLO, Faster-RCNN and SSD do), by proposing a new architecture called Filter-Amplifier Networks (FANs). Second, we introduce a technique called Loss Boosting (LB) which attempts to soften the loss imbalance problem on each image. We test our algorithm on the problem of detecting electrical components on a new, realistic, diverse dataset of printed circuit boards (PCBs), as well as the problem of detecting vehicles in the Vehicle Detection in Aerial Imagery (VEDAI) dataset. Experiments show that our method works significantly better than current state-of-the-art algorithms with respect to accuracy, recall and average IoU.

cs.CV↗

arXiv2015

New Epitaxial Thin Film Models and numerical approximation

This paper concerns new continuum phenomenological model for epitaxial thin film growth with three different forms of the Ehrlich-Schwoebel current. Two of these forms were first proposed by Politi and Villain [1996] and then studied by Evans, Thiel and Bartelt [2006]. The other one is completely new. Following the techniques used in Li and Liu [2003], we present rigorous analysis of the well-posedness, regularity and time stability for the new model. We also studied both the global and the local behavior of the surface roughness in the growth process. The new model differs from other known models in that it features a linear convex part and a nonlinear concave part, and thus by using a convex-concave time splitting scheme, one can naturally build unconditionally stable semi-implicit numerical discretizations with linear implicit parts, which is much easier to implement than conventional models requiring nonlinear implicit parts. Despite this fundamental difference in the model, numerical experiments show that the nonlinear morphological instability of the new model agrees well with results of other models published in Li and Liu [2003], which indicates that the new model correctly captures the essential morphological states in the thin film growth process.

math.NA↗

arXiv2013

Diffraction Interference Induced Superfocusing in Nonlinear Talbot Effect

We report a simple, novel sub-diffraction method, i.e. diffraction interference induced super-focusing in second-harmonic (SH) Talbot effect, to achieve focusing size of less than λ_pump/8 without involving evanescent waves or sub-wavelength apertures. By tailoring point spread functions with Fresnel diffraction interference, we observe periodic SH sub-diffracted spots over a hundred of micrometers away from the sample. Our demonstration is the first experimental realization of the proposal by Toraldo Di Francia pioneered 60 years ago for super-resolution imaging.

physics.optics↗