Anomalous bulk-boundary correspondence in dimerized topological insulators

The bulk-boundary correspondence is a generic feature of topological states of matter, reflecting the intrinsic relation between topological bulk and boundary states. For example, robust edge states propagate along the edges and corner states gather at corners in the two-dimensional first-order and second-order topological insulators, respectively. Here, we report two kinds of topological states hosting anomalous bulk-boundary correspondence in the extended two-dimensional dimerized lattice with staggered flux threading. At 1/2-filling, we observe isolated corner states with no fractional charge as well as metallic near-edge states in the C = 2 Chern insulator states. At 1/4-filling, we find a C = 0 topologically nontrivial state, where the robust edge states are well localized along edges but bypass corners. These robust topological insulating states significantly differ from both conventional Chern insulators and usual high-order topological insulators.

Floquet band engineering and topological phase transitions in 1T transition metal dichalcogenides

Using ab initio tight-binding approaches, we investigate Floquet band engineering of the 1T phase of transition metal dichalcogenides (MX2, M = W, Mo and X = Te, Se, S) monolayers under the irradiation with circularly polarized light. Our first principles calculations demonstrate that light can induce important transitions in the topological phases of this emerging materials family. For example, upon irradiation, Te-based MX2 undergoes a phase transition from quantum spin Hall (QSH) semimetal to time-reversal symmetry broken QSH insulator with a nontrivial band gap of up to 92.5 meV. On the other hand, Se- and S-based MX2 undergoes the topological phase transition from the QSH effect to the quantum anomalous Hall (QAH) effect and into trivial phases with increasing light intensity. From a general perspective, our work brings further insight into non-equilibrium topological systems.

Sign reversal of magnetoresistivity in massive nodal-line semimetals due to Lifshitz transition of Fermi surface

Topological nodal-line semimetals offer an interesting research platform to explore novel phenomena associated with its torus-shaped Fermi surface. Here, we study magnetotransport in the massive nodal-line semimetal with spin-orbit coupling and finite Berry curvature distribution which exists in many candidates. The magnetic field leads to a deformation of the Fermi torus through its coupling to the orbital magnetic moment, which turns out to be the main scenario of the magnetoresistivity (MR) induced by the Berry curvature effect. We show that a small deformation of the Fermi surface yields a positive MR $propto B^2$, different from the negative MR by pure Berry curvature effect in other topological systems. As the magnetic field increases to a critical value, a topological Lifshitz transition of the Fermi surface can be induced, and the MR inverts its sign at the same time. The temperature dependence of the MR is investigated, which shows a totally different behavior before and after the Lifshitz transition. Our work uncovers a novel scenario of the MR induced solely by the deformation of the Fermi surface and establishes a relation between the Fermi surface topology and the sign of the MR.

Ultrafast photoinduced nonthermal melting due to local dynamic instability

Laser-induced nonthermal melting in semiconductors has been studied for several decades, but the melting mechanism is still under debate. Based on real-time time-dependent density functional theory (rt-TDDFT) simulation, we reveal that the rapid nonthermal melting induced by photoexcitation in silicon originates from a local dynamic instability rather than a homogeneous inertial mechanism. Due to this local dynamic instability, any initial small random displacements can be amplified, create a local self-trapping mechanism for the excited carrier. This carrier self-trapping will amplify the initial randomness, cause locally nonthermal melting spots. Such locally melted spots gradually diffuse to the whole system achieving overall nonthermal melting within 200 fs. We also found that the initial hot carrier cooling towards the anti-bonding state is essential in order to realize this dynamic instability. This causes different cooling time depending on the excitation laser frequency, in accordance with the experimental observations. Our study provides an exquisite detail for the nonthermal melting mechanism.

Conditional Bures Metric for Domain Adaptation

As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to classification performance degeneration in real applications. In this work, we focus on the conditional distribution shift problem which is of great concern to current conditional invariant models. We aim to seek a kernel covariance embedding for conditional distribution which remains yet unexplored. Theoretically, we propose the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation for the CKB metric without introducing the implicit kernel feature map. It provides an interpretable approach to understand the knowledge transfer mechanism. The established consistency theory of the empirical estimation provides a theoretical guarantee for convergence. A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA. Extensive experiments and analysis show the superiority of our proposed model.

Detection of Deepfake Videos Using Long Distance Attention

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery.

Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes

Few-shot segmentation~(FSS) performance has been extensively promoted by introducing episodic training and class-wise prototypes. However, the FSS problem remains challenging due to three limitations: (1) Models are distracted by task-unrelated information; (2) The representation ability of a single prototype is limited; (3) Class-related prototypes ignore the prior knowledge of base classes. We propose the Prior-Enhanced network with Meta-Prototypes to tackle these limitations. The prior-enhanced network leverages the support and query (pseudo-) labels in feature extraction, which guides the model to focus on the task-related features of the foreground objects, and suppress much noise due to the lack of supervised knowledge. Moreover, we introduce multiple meta-prototypes to encode hierarchical features and learn class-agnostic structural information. The hierarchical features help the model highlight the decision boundary and focus on hard pixels, and the structural information learned from base classes is treated as the prior knowledge for novel classes. Experiments show that our method achieves the mean-IoU scores of 60.79% and 41.16% on PASCAL-$5^i$ and COCO-$20^i$, outperforming the state-of-the-art method by 3.49% and 5.64% in the 5-shot setting. Moreover, comparing with 1-shot results, our method promotes 5-shot accuracy by 3.73% and 10.32% on the above two benchmarks. The source code of our method is available at https://github.com/Jarvis73/PEMP.

VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots

In this paper, we investigate the task of hallucinating an authentic high-resolution (HR) human face from multiple low-resolution (LR) video snapshots. We propose a pure transformer-based model, dubbed VidFace, to fully exploit the full-range spatio-temporal information and facial structure cues among multiple thumbnails. Specifically, VidFace handles multiple snapshots all at once and harnesses the spatial and temporal information integrally to explore face alignments across all the frames, thus avoiding accumulating alignment errors. Moreover, we design a recurrent position embedding module to equip our transformer with facial priors, which not only effectively regularises the alignment mechanism but also supplants notorious pre-training. Finally, we curate a new large-scale video face hallucination dataset from the public Voxceleb2 benchmark, which challenges prior arts on tackling unaligned and tiny face snapshots. To the best of our knowledge, we are the first attempt to develop a unified transformer-based solver tailored for video-based face hallucination. Extensive experiments on public video face benchmarks show that the proposed method significantly outperforms the state of the arts.

Improving Weakly-supervised Object Localization via Causal Intervention

The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perceive the interval objects from the small and sparse discriminative attention map, yet ignoring the co-occurrence confounder (e.g., bird and sky), which makes the model inspection (e.g., CAM) hard to distinguish between the object and context. In this paper, we make an early attempt to tackle this challenge via causal intervention (CI). Our proposed method, dubbed CI-CAM, explores the causalities among images, contexts, and categories to eliminate the biased co-occurrence in the class activation maps thus improving the accuracy of object localization. Extensive experiments on several benchmarks demonstrate the effectiveness of CI-CAM in learning the clear object boundaries from confounding contexts. Particularly, in CUB-200-2011 which severely suffers from the co-occurrence confounder, CI-CAM significantly outperforms the traditional CAM-based baseline (58.39% vs 52.4% in top-1 localization accuracy). While in more general scenarios such as ImageNet, CI-CAM can also perform on par with the state of the arts.

Active plasmon interference control on ultrafast time scales by free electrons

Interference between light waves is one of the widely known phenomena in physics, which is widely used in modern optics, ranging from precise detection at the nanoscale to gravitational-wave observation. Akin to light, both classical and quantum interferences between surface plasmon polaritons (SPPs) have been demonstrated. However, to actively control the SPP interference within subcycle in time (usually less than several femtoseconds in the visible range) is still missing, which hinders the ultimate manipulation of SPP interference on ultrafast time scale. In this paper, the interference between SPPs launched by a hole dimer, which was excited by a grazing incident free electron beam without direct contact, was manipulated through both propagation and initial phase difference control. Particularly, using cathodoluminescence spectroscopy, the appearance of higher-order interference orders was obtained through propagation phase control by increasing separation distances of the dimer. Meanwhile, the peak-valley-peak evolution at a certain wavelength through changing the accelerating voltages was observed, which originates from the initial phase difference control of hole launched SPPs. In particular, the time resolution of this kind of control is shown to be in the ultrafast attosecond (as) region. Our work suggests that fast electron beams can be an efficient tool to control polarition interference in subcycle temporal scale, which can be potentially used in ultrafast optical processing or sensing.