arXiv subjects

Yang Wu

Publications and source records attributed to Yang Wu.

At least 127 records · Page 7Linked to original sources

arXiv2023

Revealing the two-dimensional electronic structure and anisotropic superconductivity in a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$

Van der Waals superlattices are important for tailoring the electronic structures and properties of layered materials. Here we report the superconducting properties and electronic structure of a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$. Anisotropic superconductivity with a transition temperature $T_c$ = 5.6 $\pm$ 0.1 K, which is higher than monolayer NbSe$_2$, is revealed by transport measurements on high-quality samples. Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the two-dimensional electronic structure and a charge transfer of 0.43 electrons per NbSe$_2$ unit cell from the blocking PbSe layer. In addition, polarization-dependent ARPES measurements reveal a significant circular dichroism with opposite contrast at K and K' valleys, suggesting a significant spin-orbital coupling and distinct orbital angular momentum. Our work suggests natural van der Waals superlattice as an effective pathway for achieving intriguing properties distinct from both the bulk and monolayer samples.

cond-mat.supr-con↗

arXiv2023

ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval

The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}). The zero-shot sketch-based image retrieval (\underline{ZS-SBIR}) is more generic and practical but poses an even greater challenge because of the additional knowledge gap between the seen and unseen categories. To simultaneously mitigate both gaps, we propose an \textbf{A}pproaching-and-\textbf{C}entralizing \textbf{Net}work (termed "\textbf{ACNet}") to jointly optimize sketch-to-photo synthesis and the image retrieval. The retrieval module guides the synthesis module to generate large amounts of diverse photo-like images which gradually approach the photo domain, and thus better serve the retrieval module than ever to learn domain-agnostic representations and category-agnostic common knowledge for generalizing to unseen categories. These diverse images generated with retrieval guidance can effectively alleviate the overfitting problem troubling concrete category-specific training samples with high gradients. We also discover the use of proxy-based NormSoftmax loss is effective in the zero-shot setting because its centralizing effect can stabilize our joint training and promote the generalization ability to unseen categories. Our approach is simple yet effective, which achieves state-of-the-art performance on two widely used ZS-SBIR datasets and surpasses previous methods by a large margin.

cs.CV↗

arXiv2022

Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval

Although sketch-to-photo retrieval has a wide range of applications, it is costly to obtain paired and rich-labeled ground truth. Differently, photo retrieval data is easier to acquire. Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i.e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo retrieval data (i.e., target domain). However, without co-training source and target data, source domain knowledge might be forgotten during the fine-tuning process, while simply co-training them may cause negative transfer due to domain gaps. Moreover, identity label spaces of source data and target data are generally disjoint and therefore conventional category-level Domain Adaptation (DA) is not directly applicable. To address these issues, we propose an Instance-level Heterogeneous Domain Adaptation (IHDA) framework. We apply the fine-tuning strategy for identity label learning, aiming to transfer the instance-level knowledge in an inductive transfer manner. Meanwhile, labeled attributes from the source data are selected to form a shared label space for source and target domains. Guided by shared attributes, DA is utilized to bridge cross-dataset domain gaps and heterogeneous domain gaps, which transfers instance-level knowledge in a transductive transfer manner. Experiments show that our method has set a new state of the art on three sketch-to-photo image retrieval benchmarks without extra annotations, which opens the door to train more effective models on limited-labeled heterogeneous image retrieval tasks. Related codes are available at https://github.com/fandulu/IHDA.

cs.CV↗

arXiv2022

Direct visualization of edge state in even-layer MnBi2Te4 at zero magnetic field

Being the first intrinsic antiferromagnetic(AFM) topological insulator(TI), MnBi2Te4 is argued to be a topological axion state in its even-layer form due to the antiparallel magnetization between the top and bottom layers. Here we combine both transport and scanning microwave impedance microscopy(sMIM) to investigate such axion state in atomically thin MnBi2Te4 with even-layer thickness at zero magnetic field. While transport measurements show a zero Hall plateau signaturing the axion state, sMIM uncovers an unexpected edge state raising questions regarding the nature of the 'axion state'. Based on our model calculation, we propose that the edge state of even-layer MnBi2Te4 at zero field is derived from gapped helical edge states of the quantum spin Hall effect with time-reversal-symmetry breaking, when a crossover from a three-dimensional TI MnBi2Te4 to a two-dimensional TI occurs. Our finding thus signifies the richness of topological phases in MnB2Te4 that has yet to be fully explored.

cond-mat.mes-hall↗

arXiv2022

Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media

Automatic detecting rumors on social media has become a challenging task. Previous studies focus on learning indicative clues from conversation threads for identifying rumorous information. However, these methods only model rumorous conversation threads from various views but fail to fuse multi-view features very well. In this paper, we propose a novel multi-view fusion framework for rumor representation learning and classification. It encodes the multiple views based on Graph Convolutional Networks (GCN), and leverages Convolutional Neural Networks (CNN) to capture the consistent and complementary information among all views and fuse them together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.

cs.CL↗

arXiv2022

Origami-inspired soft twisting actuator

Soft actuators have shown great advantages in compliance and morphology matched for manipulation of delicate objects and inspection in a confined space. There is an unmet need for a soft actuator that can provide torsional motion to e.g. enlarge working space and increase degrees of freedom. Towards this goal, we present origami-inspired soft pneumatic actuators (OSPAs) made from silicone. The prototype can output a rotation of more than one revolution (up to 435°), more significant than its counterparts. Its rotation ratio (=rotation angle/ aspect ratio) is more than 136°, about twice the largest one in other literature. We describe the design and fabrication method, build the analytical model and simulation model, and analyze and optimize the parameters. Finally, we demonstrate the potentially extensive utility of the OSPAs through their integration into a gripper capable of simultaneously grasping and lifting fragile or flat objects, a versatile robot arm capable of picking and placing items at the right angle with the twisting actuators, and a soft snake robot capable of changing attitude and directions by torsion of the twisting actuators.

cs.RO↗

arXiv2022

Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification

Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely valuable in applications. Meanwhile, higher flexibility in appearance always leads to more similar-looking confusing images, which is the weakness of the widely used retrieval methods. In this work, we shed light on how to handle these similar images. Specifically, we propose a novel retrieval-verification framework. Given an image, the retrieval module can search for similar images quickly. Our proposed verification network will then compare the input image and the candidate images by contrasting those local details and give a similarity score. An innovative ranking strategy is also introduced to take a good balance between retrieval and verification results. Comprehensive experiments are conducted to show the effectiveness of our framework and its capability in improving the state-of-the-art methods remarkably on both synthetic and realistic datasets.

cs.CV↗

arXiv2022

An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition

Recent works on multi-modal emotion recognition move towards end-to-end models, which can extract the task-specific features supervised by the target task compared with the two-phase pipeline. However, previous methods only model the feature interactions between the textual and either acoustic and visual modalities, ignoring capturing the feature interactions between the acoustic and visual modalities. In this paper, we propose the multi-modal end-to-end transformer (ME2ET), which can effectively model the tri-modal features interaction among the textual, acoustic, and visual modalities at the low-level and high-level. At the low-level, we propose the progressive tri-modal attention, which can model the tri-modal feature interactions by adopting a two-pass strategy and can further leverage such interactions to significantly reduce the computation and memory complexity through reducing the input token length. At the high-level, we introduce the tri-modal feature fusion layer to explicitly aggregate the semantic representations of three modalities. The experimental results on the CMU-MOSEI and IEMOCAP datasets show that ME2ET achieves the state-of-the-art performance. The further in-depth analysis demonstrates the effectiveness, efficiency, and interpretability of the proposed progressive tri-modal attention, which can help our model to achieve better performance while significantly reducing the computation and memory cost. Our code will be publicly available.

cs.CL↗

arXiv2022

Helical Luttinger liquid on the edge of a 2-dimensional topological antiferromagnet

Boundary helical Luttinger liquid (HLL) with broken bulk time-reversal symmetry belongs to a unique topological class which may occur in antiferromagnets (AFM). Here, we search for signatures of HLL on the edge of a recently discovered topological AFM, MnBi2Te4 even-layer. Using scanning superconducting quantum interference device, we directly image helical edge current in the AFM ground state appearing at its charge neutral point. Such helical edge state accompanies an insulating bulk which is topologically distinct from the ferromagnetic Chern insulator phase as revealed in a magnetic field driven quantum phase transition. The edge conductance of the AFM order follows a power-law as a function of temperature and source-drain bias which serves as strong evidence for HLL. Such HLL scaling is robust at finite fields below the quantum critical point. The observed HLL in a layered AFM semiconductor represents a highly tunable topological matter compatible with future spintronics and quantum computation.

cond-mat.mes-hall↗

arXiv2022

Regulating effect of biaxial strain on electronic, optical and photocatalytic properties in promising X2PAs (X = Si, Ge and Sn) monolayers

Photocatalytic water splitting is an effective way to obtain renewable clean energy. The challenge is to design tunable photocatalyst to meet the needs in different environments. At the same time, the oxygen and hydrogen evolution reactions (OER and HER) on the photocatalyst should be separated, which will be conducive to the separation of products. The electronic, optical and photocatalytic properties of Janus X2PAs (X = Si, Ge and Sn) monolayers are explored by first-principles calculation. All the strain-free X2PAs monolayers exhibit excellent photocatalytic properties with suitable band edge positions straddling the standard redox potential of water and large visible light absorption coefficients (up to 105 cm-1). Interestingly, the intrinsic internal electric field is favorable for separating photogenerated carriers to different surfaces of the monolayer. It contributes to realize the OER and HER separated on different sides of the monolayer. In particular, the energy band edge positions of X2PAs monolayers can be well adjusted by biaxial strain. Then it can effectively modulate photocatalytic reactions, suggesting X2PAs monolayers can be a piezo-photocatalytic switch between the OER, HER and full-reaction of redox for water splitting. This investigation not only highlights that the photocatalyst X2PAs monolayers with the separated OER and HER can be effectively tuned by the mechanical strain, but also provides a new strategy for designing highly adaptable and tunable piezo-photocatalysts.

cond-mat.mtrl-sci↗

arXiv2022

A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation

Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, without blur kernels estimation. In this paper, inspired by the Projective Motion Path Blur (PMPB) model and deformable convolution, we propose a novel constrained deformable convolutional network (CDCN) for efficient single image dynamic scene blind deblurring, which simultaneously achieves accurate spatially-variant motion blur kernels estimation and the high-quality image restoration from only one observed motion blurred image. In our proposed CDCN, we first construct a novel multi-scale multi-level multi-input multi-output (MSML-MIMO) encoder-decoder architecture for more powerful features extraction ability. Second, different from the DLVBD methods that use multiple consecutive frames, a novel constrained deformable convolution reblurring (CDCR) strategy is proposed, in which the deformable convolution is first applied to blurred features of the inputted single motion blurred image for learning the sampling points of motion blur kernel of each pixel, which is similar to the estimation of the motion density function of the camera shake in the PMPB model, and then a novel PMPB-based reblurring loss function is proposed to constrain the learned sampling points convergence, which can make the learned sampling points match with the relative motion trajectory of each pixel better and promote the accuracy of the spatially-variant motion blur kernels estimation.

cs.CV↗

arXiv2022

A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios with the channel fingerprint database established at 0.2 m intervals.

eess.SP↗

arXiv2022

Evolution of the electronic structure of ultrathin MnBi2Te4 Films

Ultrathin films of intrinsic magnetic topological insulator MnBi2Te4 exhibit fascinating quantum properties such as quantum anomalous Hall effect and axion insulator state. In this work, we systematically investigate the evolution of the electronic structure of MnBi2Te4 thin films. With increasing film thickness, the electronic structure changes from an insulator-type with a large energy gap to one with in-gap topological surface states, which is, however, still drastically different from the bulk material. By surface doping of alkali-metal atoms, a Rashba split band gradually emerges and hybridizes with topological surface states, which not only reconciles the puzzling difference between the electronic structures of the bulk and thin film MnBi2Te4 but also provides an interesting platform to establish Rashba ferromagnet that is attractive for (quantum) anomalous Hall effect. Our results provide important insights into the understanding and engineering of the intriguing quantum properties of MnBi2Te4 thin films.

cond-mat.mtrl-sci↗

arXiv2022

Ultrafast coherent interlayer phonon dynamics in atomically thin layers of MnBi2Te4

The atomically thin MnBi2Te4 crystal is a novel magnetic topological insulator, exhibiting exotic quantum physics. Here we report a systematic investigation of ultrafast carrier dynamics and coherent interlayer phonons in few-layer MnBi2Te4 as a function of layer number using time-resolved pump-probe reflectivity spectroscopy. Pronounced coherent phonon oscillations from the interlayer breathing mode are directly observed in the time domain. We find that the coherent oscillation frequency, the photocarrier and coherent phonon decay rates all depend sensitively on the sample thickness. The time-resolved measurements are complemented by ultralow-frequency Raman spectroscopy measurements, which both confirm the interlayer breathing mode and additionally enable observation of the interlayer shear mode. The layer dependence of these modes allows us to extract both the out-of-plane and in-plane interlayer force constants. Our studies not only reveal the interlayer van der Waals coupling strengths, but also shed light on the ultrafast optical properties of this novel two-dimensional material.

cond-mat.mes-hall↗

arXiv2022

MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations

Multimodal fine-grained sentiment analysis has recently attracted increasing attention due to its broad applications. However, the existing multimodal fine-grained sentiment datasets most focus on annotating the fine-grained elements in text but ignore those in images, which leads to the fine-grained elements in visual content not receiving the full attention they deserve. In this paper, we propose a new dataset, the Multimodal Aspect-Category Sentiment Analysis (MACSA) dataset, which contains more than 21K text-image pairs. The dataset provides fine-grained annotations for both textual and visual content and firstly uses the aspect category as the pivot to align the fine-grained elements between the two modalities. Based on our dataset, we propose the Multimodal ACSA task and a multimodal graph-based aligned model (MGAM), which adopts a fine-grained cross-modal fusion method. Experimental results show that our method can facilitate the baseline comparison for future research on this corpus. We will make the dataset and code publicly available.

cs.CL↗

arXiv2022

Quantifying Wetting Dynamics with Triboelectrification

Wetting is often perceived as an intrinsic surface property of materials, but determining its evolution is complicated by its complex dependence on roughness across the scales. The Wenzel state, where liquids have intimate contact with the rough substrate, and the Cassie-Baxter state, where liquids sit onto air pockets formed between asperities, are only two states among the plethora of wetting behaviors. Furthermore, transitions from the Cassie-Baxter to the Wenzel state dictate completely different surface performance, such as anti-contamination, anti-icing, drag reduction etc.; however, little is known about how transition occurs during time between the several wetting modes. In this paper, we show that wetting dynamics can be accurately quantified and tracked using solid-liquid triboelectrification. Theoretical underpinning reveals how surface micro-/nano-geometries regulate stability/infiltration, also demonstrating the generality of our theoretical approach in understanding wetting transitions.

cond-mat.soft↗

arXiv2022

Experimental violation of the Leggett-Garg inequality with a single-spin system

Investigation the boundary between quantum mechanical description and classical realistic view is of fundamental importance. The Leggett-Garg inequality provides a criterion to distinguish between quantum systems and classical systems, and can be used to prove the macroscopic superposition state. A larger upper bound of the LG function can be obtained in a multi-level system. Here, we present an experimental violation of the Leggett-Garg inequality in a three-level system using nitrogen-vacancy center in diamond by ideal negative result measurement. The experimental maximum value of Leggett-Garg function is $K_{3}^{exp}=1.625\pm0.022$ which exceeds the Lüders bound with a $5σ$ level of confidence.

quant-ph↗

arXiv2022

UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection

Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era. Nevertheless, jointly conducting moment retrieval and highlight detection is an emerging research topic, even though its component problems and some related tasks have already been studied for a while. In this paper, we present the first unified framework, named Unified Multi-modal Transformers (UMT), capable of realizing such joint optimization while can also be easily degenerated for solving individual problems. As far as we are aware, this is the first scheme to integrate multi-modal (visual-audio) learning for either joint optimization or the individual moment retrieval task, and tackles moment retrieval as a keypoint detection problem using a novel query generator and query decoder. Extensive comparisons with existing methods and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and TVSum datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method under various settings. Source code and pre-trained models are available at https://github.com/TencentARC/UMT.

cs.CV↗