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Chong Wang

Publications and source records attributed to Chong Wang.

At least 217 records · Page 12Linked to original sources
arXiv2023

On a Quaternary Non-Local Isoperimetric Problem

We study a two-dimensional quaternary inhibitory system. This free energy functional combines an interface energy favoring micro-domain growth with a Coulomb-type long range interaction energy which prevents micro-domains from unlimited spreading. Here we consider a limit in which three species are vanishingly small, but interactions are correspondingly large to maintain a nontrivial limit. In this limit two energy levels are distinguished: the highest order limit encodes information on the geometry of local structures as a three-component isoperimetric problem, while the second level describes the spatial distribution of components in global minimizers. Geometrical descriptions of limit configurations are derived.

math.AP↗
arXiv2023

Superlattice Engineering of Topology in Massive Dirac Fermions

We show that a superlattice potential can be employed to engineer topology in massive Dirac fermions in systems such as bilayer graphene, moiré graphene-boron nitride, and transition-metal dichalcogenide (TMD) monolayers and bilayers. We use symmetry analysis to analyze band inversions to determine the Chern number $\mathscr C$ for the valence band as a function of tunable potential parameters for a class of $C_4$ and $C_3$ symmetric potentials. We present a novel method to engineer Chern number $\mathscr{C}=2$ for the valence band and show that the applied potential at minimum must have a scalar together with a non-scalar periodic part. We discover that certain forms of the superlattice potential, which may be difficult to realize in naturally occurring moiré patterns, allow for the possibility of non-trivial topological transitions. These forms may be achievable using an external superlattice potential that can be created using contemporary experimental techniques. Our work paves the way to realize the quantum Spin Hall effect (QSHE), quantum anomalous Hall effect (QAHE), and even exotic non-Abelian anyons in the fractional quantum Hall effect (FQHE).

cond-mat.mes-hall↗
arXiv2023

Fermi Surface Evolution and Anomalous Hall Effect in an Ideal Type-II Weyl Semimetal

Weyl semimetals (WSMs) are three-dimensional topological materials that exhibit fascinating properties due to the presence of Weyl nodes in their band structure. However, existing WSMs discovered so far often possess multiple pairs of Weyl nodes, posing a challenge in disentangling the contributions to transport phenomena from different energy bands. To overcome this challenge, we have identified field-induced ferromagnetic MnBi$_{2-x}$Sb$_{x}$Te$_{4}$ as an ideal type-II WSM with a single pair of Weyl nodes. By employing a combination of quantum oscillations and high-field Hall measurements, we have resolved the evolution of Fermi-surface sections as the Fermi level is tuned across the charge neutrality point, precisely matching the band structure of an ideal type-II WSM. Furthermore, the anomalous Hall conductivity exhibits a heartbeat-like behavior as the Fermi level is tuned across the Weyl nodes, a unique feature previously predicted for a type-II WSM. Our findings establish MnBi$_{2-x}$Sb$_{x}$Te$_{4}$ as an ideal platform for further investigation into Weyl physics.

cond-mat.mes-hall↗
arXiv2023

Differential algebras on digraphs and parametrized homology

The theory of path homology for digraphs was developed by Alexander Grigor'yan, Yong Lin, Yuri Muranov, and Shing-Tung Yau. In this paper, we consider the differential algebras on digraphs and define the parametrized homology of digraphs as an analog of the path homology. We prove the functoriality of the parametrized homology of digraphs in Theorem 4.2. We also prove some Kunneth-type formulae for the parametrized homology of digraphs in Theorem 4.4.

math.CO↗
arXiv2023

RoseMatcher: Identifying the Impact of User Reviews on App Updates

$\textbf{Context}$: The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. $\textbf{Objective}$: In this paper, we introduce $\textit{RoseMatcher}$, a novel automatic approach to match relevant user reviews with app release notes and identify matched pairs with high confidence. $\textbf{Methods}$: We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data to evaluate the effectiveness and accuracy of $\textit{RoseMatcher}$, and conducted deep content analysis on matched pairs. $\textbf{Results}$: Our evaluation shows that $\textit{RoseMatcher}$ can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. $\textbf{Conclusions}$: Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year.

cs.SE↗
arXiv2023

Core shells and double bubbles in a weighted nonlocal isoperimetric problem

We consider a sharp-interface model of $ABC$ triblock copolymers, for which the surface tension $σ_{ij}$ across the interface separating phase $i$ from phase $j$ may depend on the components. We study global minimizers of the associated ternary local isoperimetric problem in $\mathbb{R}^2$, and show how the geometry of minimizers changes with the surface tensions $σ_{ij}$, varying from symmetric double-bubbles for equal surface tensions, through asymmetric double bubbles, to core shells as the values of $σ_{ij}$ become more disparate. Then we consider the effect of nonlocal interactions in a droplet scaling regime, in which vanishingly small particles of two phases are distributed in a sea of the third phase. We are particularly interested in a degenerate case of $σ_{ij}$ in which minimizers exhibit core shell geometry, as this phase configuration is expected on physical grounds in nonlocal ternary systems.

math.AP↗
arXiv2023

Gate-tunable phonon magnetic moment in bilayer graphene

We develop a first-principles quantum scheme to calculate the phonon magnetic moment in solids. As a showcase example, we apply our method to study gated bilayer graphene, a material with strong covalent bonds. According to the classical theory based on the Born effective charge, the phonon magnetic moment in this system should vanish, yet our quantum mechanical calculations find significant phonon magnetic moments. Furthermore, the magnetic moment is highly tunable by changing the gate voltage. Our results firmly establish the necessity of the quantum mechanical treatment, and identify small-gap covalent materials as a promising platform for studying tunable phonon magnetic moment.

cond-mat.mtrl-sci↗
arXiv2023

Signatures of Fractional Quantum Anomalous Hall States in Twisted MoTe2 Bilayer

The interplay between spontaneous symmetry breaking and topology can result in exotic quantum states of matter. A celebrated example is the quantum anomalous Hall (QAH) state, which exhibits an integer quantum Hall effect at zero magnetic field thanks to its intrinsic ferromagnetism. In the presence of strong electron-electron interactions, exotic fractional-QAH (FQAH) states at zero magnetic field can emerge. These states could host fractional excitations, including non-Abelian anyons - crucial building blocks for topological quantum computation. Flat Chern bands are widely considered as a desirable venue to realize the FQAH state. For this purpose, twisted transition metal dichalcogenide homobilayers in rhombohedral stacking have recently been predicted to be a promising material platform. Here, we report experimental signatures of FQAH states in 3.7-degree twisted MoTe2 bilayer. Magnetic circular dichroism measurements reveal robust ferromagnetic states at fractionally hole filled moiré minibands. Using trion photoluminescence as a sensor, we obtain a Landau fan diagram which shows linear shifts in carrier densities corresponding to the v=-2/3 and -3/5 ferromagnetic states with applied magnetic field. These shifts match the Streda formula dispersion of FQAH states with fractionally quantized Hall conductance of -2/3$e^2/h$ and -3/5$e^2/h$, respectively. Moreover, the v=-1 state exhibits a dispersion corresponding to Chern number -1, consistent with the predicted QAH state. In comparison, several non-ferromagnetic states on the electron doping side do not disperse, i.e., are trivial correlated insulators. The observed topological states can be further electrically driven into topologically trivial states. Our findings provide clear evidence of the long-sought FQAH states, putting forward MoTe2 moiré superlattices as a fascinating platform for exploring fractional excitations.

cond-mat.mes-hall↗
arXiv2023

Dynamic Relevance Learning for Few-Shot Object Detection

Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods adopting training way similar to meta-learning have achieved promising performance, such as Meta R-CNN series. However, support data is only used as the class attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at https://github.com/liuweijie19980216/DRL-for-FSOD.

cs.CV↗
arXiv2023

Kekulé Moiré Superlattices

Moiré superlattices from stacks of van der Waals materials offer an exciting arena in the fields of condensed matter physics and materials science. Typically, these moiré superlattices consist of materials with identical or similar structures, and the long moiré period arises from a small twist angle or lattice mismatch. In this article, we discuss that long moiré period appears in a new moiré system by stacking two dissimilar van der Waals layers with large lattice mismatch, resulting in couplings between moiré bands from remote valleys in the momentum space. In this system, the first layer is reconstructed using a $\sqrt{3}$ by $\sqrt{3}$ supercell that resembles the Kekulé distortion in graphene, and such reconstruction becomes nearly commensurate with the second layer. This Kekulé moiré superlattice is realized in heterostructures of transition metal dichalcogenides and metal phosphorus trichalcogenides such as MoTe$_2$/MnPSe$_3$. By first-principles calculations, we demonstrate that the antiferromagnetic MnPSe$_3$ strongly couples the otherwise degenerate Kramers' valleys of MoTe$_2$, resulting in valley pseudospin textures that depend on Néel vector direction, stacking geometry, and external fields. With one hole per moiré supercell, we predict that the system can become a Chern insulator, of which the topology is tunable by external fields.

cond-mat.mes-hall↗
arXiv2023

Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations

People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their loss. When users want to renege on their past actions, they expect the recommender platforms to erase selective data at the model level. Ideally, given any specified user history, the recommender can unwind or "forget", as if the record was not part of training. To that end, this paper focuses on simple but widely deployed bi-linear models for recommendations based on matrix completion. Without incurring the cost of re-training, and without degrading the model unnecessarily, we develop Unlearn-ALS by making a few key modifications to the fine-tuning procedure under Alternating Least Squares optimisation, thus applicable to any bi-linear models regardless of the training procedure. We show that Unlearn-ALS is consistent with retraining without \emph{any} model degradation and exhibits rapid convergence, making it suitable for a large class of existing recommenders.

cs.LG↗
arXiv2023

Efficient Attention via Control Variates

Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied. Built upon previous progress of RFA, we characterize this gap through the lens of control variates and show that RFA can be decomposed into a sum of multiple control variate estimators for each element in the sequence. This new framework reveals that exact softmax attention can be recovered from RFA by manipulating each control variate. Besides, it allows us to develop a more flexible form of control variates, resulting in a novel attention mechanism that significantly reduces the approximation gap while maintaining linear complexity. Extensive experiments demonstrate that our model outperforms state-of-the-art efficient attention mechanisms on both vision and language tasks.

cs.LG↗
arXiv2023

Learning to Counterfactually Explain Recommendations

Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not interacted with the following items, we would not recommend it." Compared to the traditional explanation logic, counterfactual explanations are easier to understand, more technically verifiable, and more informative in terms of giving users control over recommendations. The major challenge of generating such explanations is the computational cost because it requires repeatedly retraining the models to obtain the effect on a recommendation caused by the absence of user history. We propose a learning-based framework to generate counterfactual explanations. The key idea is to train a surrogate model to learn the effect of removing a subset of user history on the recommendation. To this end, we first artificially simulate the counterfactual outcomes on the recommendation after deleting subsets of history. Then we train a surrogate model to learn the mapping between a history deletion and the corresponding change of the recommendation caused by the deletion. Finally, to generate an explanation, we find the history subset predicted by the surrogate model that is most likely to remove the recommendation. Through offline experiments and online user studies, we show our method, compared to baselines, can generate explanations that are more counterfactually valid and more satisfactory considered by users.

cs.AI↗
arXiv2023

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

State-of-the-art (SOTA) deep learning mammogram classifiers, trained with weakly-labelled images, often rely on global models that produce predictions with limited interpretability, which is a key barrier to their successful translation into clinical practice. On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity. We address these two issues with the proposal of BRAIxProtoPNet++, which adds interpretability to a global model by ensembling it with a prototype-based model. BRAIxProtoPNet++ distills the knowledge of the global model when training the prototype-based model with the goal of increasing the classification accuracy of the ensemble. Moreover, we propose an approach to increase prototype diversity by guaranteeing that all prototypes are associated with different training images. Experiments on weakly-labelled private and public datasets show that BRAIxProtoPNet++ has higher classification accuracy than SOTA global and prototype-based models. Using lesion localisation to assess model interpretability, we show BRAIxProtoPNet++ is more effective than other prototype-based models and post-hoc explanation of global models. Finally, we show that the diversity of the prototypes learned by BRAIxProtoPNet++ is superior to SOTA prototype-based approaches.

cs.CV↗
arXiv2023

Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning

Learning with noisy-labels has become an important research topic in computer vision where state-of-the-art (SOTA) methods explore: 1) prediction disagreement with co-teaching strategy that updates two models when they disagree on the prediction of training samples; and 2) sample selection to divide the training set into clean and noisy sets based on small training loss. However, the quick convergence of co-teaching models to select the same clean subsets combined with relatively fast overfitting of noisy labels may induce the wrong selection of noisy label samples as clean, leading to an inevitable confirmation bias that damages accuracy. In this paper, we introduce our noisy-label learning approach, called Asymmetric Co-teaching (AsyCo), which introduces novel prediction disagreement that produces more consistent divergent results of the co-teaching models, and a new sample selection approach that does not require small-loss assumption to enable a better robustness to confirmation bias than previous methods. More specifically, the new prediction disagreement is achieved with the use of different training strategies, where one model is trained with multi-class learning and the other with multi-label learning. Also, the new sample selection is based on multi-view consensus, which uses the label views from training labels and model predictions to divide the training set into clean and noisy for training the multi-class model and to re-label the training samples with multiple top-ranked labels for training the multi-label model. Extensive experiments on synthetic and real-world noisy-label datasets show that AsyCo improves over current SOTA methods.

cs.CV↗
arXiv2022

Architecture Decisions in AI-based Systems Development: An Empirical Study

Artificial Intelligence (AI) technologies have been developed rapidly, and AI-based systems have been widely used in various application domains with opportunities and challenges. However, little is known about the architecture decisions made in AI-based systems development, which has a substantial impact on the success and sustainability of these systems. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub. More specifically, we searched on SO with six sets of keywords and explored 32 AI-based projects on GitHub, and finally we collected 174 posts and 128 GitHub issues related to architecture decisions. The results show that in AI-based systems development (1) architecture decisions are expressed in six linguistic patterns, among which Solution Proposal and Information Giving are most frequently used, (2) Technology Decision, Component Decision, and Data Decision are the main types of architecture decisions made, (3) Game is the most common application domain among the eighteen application domains identified, (4) the dominant quality attribute considered in architecture decision-making is Performance, and (5) the main limitations and challenges encountered by practitioners in making architecture decisions are Design Issues and Data Issues. Our results suggest that the limitations and challenges when making architecture decisions in AI-based systems development are highly specific to the characteristics of AI-based systems and are mainly of technical nature, which need to be properly confronted.

cs.SE↗
arXiv2022

ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.

cs.CV↗
arXiv2022

Anomalous Second Harmonic Generation from Atomically Thin MnBi2Te4

MnBi2Te4 is a van der Waals topological insulator with intrinsic intralayer ferromagnetic exchange and A-type antiferromagnetic interlayer coupling. Theoretically, it belongs to a class of structurally centrosymmetric crystals whose layered antiferromagnetic order breaks inversion symmetry for even layer numbers, making optical second harmonic generation (SHG) an ideal probe of the coupling between the crystal and magnetic structures. Here, we perform magnetic field and temperature-dependent SHG measurements on MnBi2Te4 flakes ranging from bulk to monolayer thickness. We find that the dominant SHG signal from MnBi2Te4 is unexpectedly unrelated to both magnetic state and layer number. We suggest that surface SHG is the likely source of the observed strong SHG, whose symmetry matches that of the MnBi2Te4-vacuum interface. Our results highlight the importance of considering the surface contribution to inversion symmetry-breaking in van der Waals centrosymmetric magnets.

cond-mat.mtrl-sci↗