arXiv subjects

Chong Wang

Publications and source records attributed to Chong Wang.

At least 199 records · Page 11Linked to original sources

arXiv2024

Electrically Controlled Anomalous Hall Effect and Orbital Magnetization in Topological Magnet MnBi2Te4

In this work, we propose an intrinsic mechanism to understand the even-odd effect, namely the opposite signs of the anomalous Hall resistance and the different shapes of hysteresis loops for even and odd septuple layers (SLs), of MBE-grown MnBi2Te4 thin films with electron doping. In particular, we show that the non-zero hysteresis loops in the anomalous Hall and magnetic circular dichroism measurements for even-SLs MnBi2Te4 films are originated from two different anti-ferromagnetic (AFM) states with opposite magnetoelectric coefficients that give rise to different energies of zeroth Landau levels of the surface states in this model. The complex form of the anomalous Hall hysteresis loop in even-SLs MnBi2Te4 films can be understood from two magnetic transitions, a transition from one AFM state to the other AFM state followed by a second transition to the ferromagnetic state. Our model also provides a microscopic understanding of the electrical switching between two AFM states via the axion electrodynamics in even-SL MnBi2Te4 films. We further study orbital magnetization and magnetoelectric coefficient in MnBi2Te4 films, and find an even-odd oscillation behavior of the magnetoelectric coefficient.

cond-mat.mtrl-sci↗

arXiv2024

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI

Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However, conventional DUN aims to reconstruct all the missing information within the entire null space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation, usually leading to unsatisfactory reconstruction. In this work, we propose a Progressive Divide-And-Conquer (PDAC) strategy, aiming to break down the subsampling process in the actual severe degradation and thus perform reconstruction sequentially. Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network. Specifically, each iterative stage in PDAC focuses on recovering a distinct moderate degradation according to the decomposition. Furthermore, as part of the PDAC iteration, such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask. Following this prediction, the sampling mask is further integrated via a severity conditioning module to ensure awareness of the degradation severity at each stage. Extensive experiments demonstrate that our proposed method achieves superior performance on the publicly available fastMRI and Stanford2D FSE datasets in both multi-coil and single-coil settings.

eess.IV↗

arXiv2024

Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks

Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learning techniques have demonstrated impressive performance in image shadow removal, their robustness against adversarial attacks remains largely unexplored. Furthermore, many existing attack frameworks typically allocate a uniform budget for perturbations across the entire input image, which may not be suitable for attacking shadow images. This is primarily due to the unique characteristic of spatially varying illumination within shadow images. In this paper, we propose a novel approach, called shadow-adaptive adversarial attack. Different from standard adversarial attacks, our attack budget is adjusted based on the pixel intensity in different regions of shadow images. Consequently, the optimized adversarial noise in the shadowed regions becomes visually less perceptible while permitting a greater tolerance for perturbations in non-shadow regions. The proposed shadow-adaptive attacks naturally align with the varying illumination distribution in shadow images, resulting in perturbations that are less conspicuous. Building on this, we conduct a comprehensive empirical evaluation of existing shadow removal methods, subjecting them to various levels of attack on publicly available datasets.

cs.CV↗

arXiv2024

A photon-level broadband dual-comb interferometer for turbulent open-air trace gases detection application

Open-path dual-comb spectroscopy (DCS) significantly enhances our understanding of regional trace gases. However, due to technical challenges, cost considerations, and eye-safety regulations, its sensing range and flexibility remain limited. The photon-counting DCS demonstrated recently heralds potential innovations over open-path DCS. Nevertheless, a major challenge in open-air applications of this approach lies in accurately extracting information from the arrival time of photons that have traversed the turbulent atmosphere. Here, we demonstrate a photon-level dual-comb interferometer for field deployment in open-air environments, uniquely designed to counteract the impact of optical path-length variations caused by atmospheric turbulence and fiber-length wandering. Under variable optical path-length conditions, 20nm broadband absorption spectrum of H13C14N is acquired, with the power per comb line detected as low as 4 attowatt . Furthermore, this photon-level DCS achieves comb-line resolution with a quantum-noise-limited signal-to-noise (SNR). This paves the way for novel open-path DCS applications, including non-cooperative target sensing and sensing over a hundred-kilometers range, all within a portable, fieldable, eye-safety and low power consumption system.

physics.optics↗

arXiv2024

Using Large Language Models for Commit Message Generation: A Preliminary Study

A commit message is a textual description of the code changes in a commit, which is a key part of the Git version control system (VCS). It captures the essence of software updating. Therefore, it can help developers understand code evolution and facilitate efficient collaboration between developers. However, it is time-consuming and labor-intensive to write good and valuable commit messages. Some researchers have conducted extensive studies on the automatic generation of commit messages and proposed several methods for this purpose, such as generationbased and retrieval-based models. However, seldom studies explored whether large language models (LLMs) can be used to generate commit messages automatically and effectively. To this end, this paper designed and conducted a series of experiments to comprehensively evaluate the performance of popular open-source and closed-source LLMs, i.e., Llama 2 and ChatGPT, in commit message generation. The results indicate that considering the BLEU and Rouge-L metrics, LLMs surpass the existing methods in certain indicators but lag behind in others. After human evaluations, however, LLMs show a distinct advantage over all these existing methods. Especially, in 78% of the 366 samples, the commit messages generated by LLMs were evaluated by humans as the best. This work not only reveals the promising potential of using LLMs to generate commit messages, but also explores the limitations of commonly used metrics in evaluating the quality of auto-generated commit messages.

cs.SE↗

arXiv2024

Enhancement of Ising superconductivity in monolayer NbSe$_2$ via surface fluorination

Recently discovered Ising superconductors have garnered considerable interest due to their anomalously large in-plane upper critical fields ($B_{c2}$). However, the requisite strong spin-orbital coupling in the Ising pairing mechanism generally renders these superconductors heavy-element dominant with notably low superconducting transition temperatures ($T_c$). Here, based on the Migdal-Eliashberg theory and the mean-field Bogoliubov-de Gennes Hamiltonian, we demonstrate a significant enhancement of Ising superconductivity in monolayer NbSe$_2$ through surface fluorination, as evidenced by concomitant improvements in $T_c$ and $B_{c2}$. This enhancement arises from three predominant factors. Firstly, fluorine atoms symmetrically and stably adhere to both sides of the monolayer NbSe$_2$, thereby maintaining the out-of-plane mirror symmetry and locking carrier spins out-of-plane. Secondly, fluorination suppresses the charge density wave in monolayer NbSe$_2$ and induces a van Hove singularity in the vicinity of the Fermi level, leading to a marked increase in the number of carriers and, consequently, strengthening the electron-phonon coupling (EPC). Lastly, the appearance of fluorine-related, low-frequency phonon modes further augments the EPC. Our findings suggest a promising avenue to elevate $T_c$ in two-dimensional Ising superconductors without compromising their Ising pairing.

cond-mat.supr-con↗

arXiv2023

Fractional Chern Insulator in Twisted Bilayer MoTe$_2$

A recent experiment has reported the first observation of a zero-field fractional Chern insulator (FCI) phase in twisted bilayer MoTe$_2$ moiré superlattices [Nature 622, 63-68 (2023)]. The experimental observation is at an unexpected large twist angle 3.7$^\circ$ and calls for a better understanding of the FCI in real materials. In this work, we perform large-scale density functional theory calculation for the twisted bilayer MoTe$_2$, and find that lattice reconstruction is crucial for the appearance of an isolated flat Chern band. The existence of the FCI state at $ν= -2/3$ are confirmed by exact diagonalization. We establish phase diagrams with respect to the twist angle and electron interaction, which reveal an optimal twist angle of $3.5^\circ$ for the observation of FCI. We further demonstrate that an external electric field can destroy the FCI state by changing band geometry and show evidence of the $ν=-3/5$ FCI state in this system. Our research highlights the importance of accurate single particle band structure in the quest for strong correlated electronic states and provides insights into engineering fractional Chern insulator in moiré superlattices.

cond-mat.str-el↗

arXiv2023

Learning Support and Trivial Prototypes for Interpretable Image Classification

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space. Note that it is possible to make an analogy between ProtoPNet and support vector machine (SVM) given that the classification from both methods relies on computing similarity with a set of training points (i.e., trivial prototypes in ProtoPNet, and support vectors in SVM). However, while trivial prototypes are located far from the classification boundary, support vectors are located close to this boundary, and we argue that this discrepancy with the well-established SVM theory can result in ProtoPNet models with inferior classification accuracy. In this paper, we aim to improve the classification of ProtoPNet with a new method to learn support prototypes that lie near the classification boundary in the feature space, as suggested by the SVM theory. In addition, we target the improvement of classification results with a new model, named ST-ProtoPNet, which exploits our support prototypes and the trivial prototypes to provide more effective classification. Experimental results on CUB-200-2011, Stanford Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves state-of-the-art classification accuracy and interpretability results. We also show that the proposed support prototypes tend to be better localised in the object of interest rather than in the background region.

cs.CV↗

arXiv2023

Weighted Analytic Torsion for Weighted Digraphs

In 2020, Alexander Grigor'yan, Yong Lin and Shing-Tung Yau [4] introduced the Reidemeister torsion and the analytic torsion for digraphs by means of the path complex and the path homology theory. Based on the analytic torsion for digraphs introduced in [4], we consider the notion of weighted analytic torsion for vertex-weighted digraphs. For any non-vanishing real functions $f$ and $g$ on the vertex set, we consider the vertex-weighted digraphs with the weights $(f,g)$. We calculate the $(f,g)$-weighted analytic torsion by examples and prove that the $(f,g)$-weighted analytic torsion only depend on the ratio $f/g$. In particular, if the weight is of the diagonal form $(f,f)$, then the weighted analytic torsion equals to the usual (un-weighted) torsion.

math.AT↗

arXiv2023

Layer-dependent exciton polarizability and the brightening of dark excitons in few-layer black phosphorus

The evolution of excitons from 2D to 3D is of great importance in photo-physics, yet the layer-dependent exciton polarizability has not been investigated in 2D semiconductors. Here, we determine the exciton polarizabilities for 3- to 11-layer black phosphorus-a direct bandgap semiconductor regardless of the thickness-through frequency-resolved photocurrent measurements on dual-gate devices and unveil the carrier screening effect in relatively thicker samples. By taking advantage of the broadband photocurrent spectra, we are also able to reveal the exciton response for higher-index subbands under the gate electrical field. Surprisingly, dark excitons are brightened with intensity even stronger than the allowed transitions above certain electrical field. Our study not only sheds light on the exciton evolution with sample thickness, but also paves a way for optoelectronic applications of few-layer BP in modulators, tunable photodetectors, emitters and lasers.

cond-mat.mes-hall↗

arXiv2023

Spec-NeRF: Multi-spectral Neural Radiance Fields

We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from a set of color images filtered by different filters. The proposed method focuses on modeling the physical imaging process, and applies the estimated SSFs and radiance field to synthesize novel views of multispectral scenes. In this method, the data acquisition requires only a low-cost trichromatic camera and several off-the-shelf color filters, making it more practical than using specialized 3D scanning and spectral imaging equipment. Our experiments on both synthetic and real scenario datasets demonstrate that utilizing filtered RGB images with learnable NeRF and SSFs can achieve high fidelity and promising spectral reconstruction while retaining the inherent capability of NeRF to comprehend geometric structures. Code is available at https://github.com/CPREgroup/SpecNeRF-v2.

eess.IV↗

arXiv2023

Average Symmetry-Protected Topological Phases

Symmetry-protected topological (SPT) phases are many-body quantum states that are topologically nontrivial as long as the relevant symmetries are unbroken. In this work we show that SPT phases are also well defined for average symmetries, where quenched disorders locally break the symmetries, but restore the symmetries upon disorder averaging. An example would be crystalline SPT phases with imperfect lattices. Specifically, we define the notion of average SPT for disordered ensembles of quantum states. We then classify and characterize a large class of average SPT phases using a decorated domain wall approach, in which domain walls (and more general defects) of the average symmetries are decorated with lower dimensional topological states. We then show that if the decorated domain walls have dimension higher than $(0+1)d$, then the boundary states of such average SPT will almost certainly be long-range entangled, with probability approaching $1$ as the system size approaches infinity. This generalizes the notion of t'Hooft anomaly to average symmetries, which we dub "average anomaly". The average anomaly can also manifest as constraints on lattice systems similar to the Lieb-Schultz-Mattis (LSM) theorems, but with only average lattice symmetries. We also generalize our problem to "quantum disorders" that can admit short-range entanglement on their own, and develop a theory of such generalized average SPTs purely based on density matrices and quantum channels. Our results indicate that topological quantum phenomena associated with average symmetries can be at least as rich as those with ordinary exact symmetries.

cond-mat.str-el↗

arXiv2023

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach that addresses this low-reconstruction error issue for anomalous images. Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder. MemMCMAE masks large parts of the input image during its reconstruction, reducing the risk that it will produce low reconstruction errors because anomalies are likely to be masked and cannot be reconstructed. However, when the anomaly is not masked, then the normal patterns stored in the encoder's memory combined with the decoder's multi-level cross attention will constrain the accurate reconstruction of the anomaly. We show that our method achieves SOTA anomaly detection and localisation on colonoscopy, pneumonia, and covid-19 chest x-ray datasets.

eess.IV↗

arXiv2023

Gate-tunable antiferromagnetic Chern insulator in twisted bilayer transition metal dichalcogenides

A series of recent experimental works on twisted MoTe$_2$ homobilayers have unveiled an abundance of exotic states in this system. Valley-polarized quantum anomalous Hall states have been identified at hole doping of $ν= -1$, and the fractional quantum anomalous Hall effect is observed at $ν= -2/3$ and $ν= -3/5$. In this work, we investigate the electronic properties of AA-stacked twisted bilayer MoTe$_2$ at $ν=-2$ by $k$-space Hartree-Fock calculations. We find that the phase diagram is qualitatively similar to the phase diagram of a Kane-Mele-Hubbard with staggered onsite potential. A noteworthy phase within the diagram is the antiferromagnetic Chern insulator, stabilized by the external electric field. We attribute the existence of this Chern insulator to an antiferromagnetic instability at a topological phase transition between the quantum spin hall phase and a band insulator phase. We highlight that the antiferromagnetic Chern insulator phase is most evident at a twist angle of approximately $4^\circ$. Our research proposes the potential of realizing a Chern insulator beyond $ν=-1$, and contributes fresh perspectives on the interplay between band topology and electron-electron correlations in moiré superlattices.

cond-mat.mes-hall↗

arXiv2023

Tunable optical topological transitions of plasmon polaritons in WTe2 van der Waals films

Naturally existing in-plane hyperbolic polaritons and the associated optical topological transitions, which avoid the nano-structuring to achieve hyperbolicity, can outperform their counterparts in artificial metasurfaces. Such plasmon polaritons are rare, but experimentally revealed recently in WTe2 van der Waals thin films. Different from phonon polaritons, hyperbolic plasmon polaritons originate from the interplay of free carrier Drude response and interband transitions, which promise good intrinsic tunability. However, tunable in-plane hyperbolic plasmon polariton and its optical topological transition of the isofrequency contours to the elliptic topology in a natural material have not been realized. Here we demonstrate the tuning of the optical topological transition through Mo-doping and temperature. The optical topological transition energy is tuned over a wide range, with frequencies ranging from 429 cm-1 (23.3 microns) for pure WTe2 to 270 cm-1 (37.0 microns) at the 50% Mo-doping level at 10 K. Moreover, the temperature-induced blueshift of the optical topological transition energy is also revealed, enabling active and reversible tuning. Surprisingly, the localized surface plasmon resonance in skew ribbons shows unusual polarization dependence, accurately manifesting its topology, which renders a reliable means to track the topology with far-field techniques. Our results open an avenue for reconfigurable photonic devices capable of plasmon polariton steering, such as canaling, focusing and routing, and pave a way for low-symmetry plasmonic nanophotonics based on anisotropic natural materials.

physics.optics↗

arXiv2023

BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification

Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Indeed, many Chest X-ray (CXR) classifiers have already been modelled from datasets with noisy labels, but their training procedure is in general not robust to noisy-label samples, leading to sub-optimal models. Furthermore, CXR datasets are mostly multi-label, so current noisy-label learning methods designed for multi-class problems cannot be easily adapted. In this paper, we propose a new method designed for the noisy multi-label CXR learning, which detects and smoothly re-labels samples from the dataset, which is then used to train common multi-label classifiers. The proposed method optimises a bag of multi-label descriptors (BoMD) to promote their similarity with the semantic descriptors produced by BERT models from the multi-label image annotation. Our experiments on diverse noisy multi-label training sets and clean testing sets show that our model has state-of-the-art accuracy and robustness in many CXR multi-label classification benchmarks.

eess.IV↗

arXiv2023

Observation of Fractionally Quantized Anomalous Hall Effect

The integer quantum anomalous Hall (QAH) effect is a lattice analog of the quantum Hall effect at zero magnetic field. This striking transport phenomenon occurs in electronic systems with topologically nontrivial bands and spontaneous time-reversal symmetry breaking. Discovery of its putative fractional counterpart in the presence of strong electron correlations, i.e., the fractional quantum anomalous Hall (FQAH) effect, would open a new chapter in condensed matter physics. Here, we report the direct observation of both integer and fractional QAH effects in electrical measurements on twisted bilayer MoTe$_2$. At zero magnetic field, near filling factor $ν= -1$ (one hole per moiré unit cell) we see an extended integer QAH plateau in the Hall resistance $R_\text{xy}$ that is quantized to $h/e^2 \pm 0.1 \%$ while the longitudinal resistance $R_\text{xx}$ vanishes. Remarkably, at $ν=-2/3$ and $-3/5$ we see plateau features in $R_\text{xy}$ at $3h/2e^2 \pm 1\%$ and $5h/3e^2 \pm 3\%$, respectively, while $R_\text{xx}$ remains small. All these features shift linearly in an applied magnetic field with slopes matching the corresponding Chern numbers $-1$, $-2/3$, and $-3/5$, precisely as expected for integer and fractional QAH states. In addition, at zero magnetic field, $R_\text{xy}$ is approximately $2h/e^2$ near half filling ($ν= -1/2$) and varies linearly as $ν$ is tuned. This behavior resembles that of the composite Fermi liquid in the half-filled lowest Landau level of a two-dimensional electron gas at high magnetic field. Direct observation of the FQAH and associated effects paves the way for researching charge fractionalization and anyonic statistics at zero magnetic field.

cond-mat.mes-hall↗

arXiv2023

Twist-angle and thickness-ratio tuning of plasmon polaritons in twisted bilayer van der Waals films

Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating the plasmon topology in bilayers is elusive. Here, we fabricate bilayer structures of WTe2 films, which naturally host in-plane hyperbolic plasmons in the terahertz range. Plasmon topology is successfully modified by changing the TR and TA synergistically, manifested by the extinction spectra of unpatterned films and the polarization dependence of the plasmon intensity measured in skew ribbon arrays. Such TR- and TA-tunable topological transitions can be well explained based on the effective sheet optical conductivity by adding up those of the two films. Our study demonstrates TR as another degree of freedom for the manipulation of plasmonic topology in nanophotonics, exhibiting promising applications in bio-sensing, heat transfer and the enhancement of spontaneous emission.

physics.optics↗