arXiv subjects

Lan Chen

Publications and source records attributed to Lan Chen.

At least 55 records · Page 3Linked to original sources

arXiv2024

Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining

Current works focus on addressing the remote sensing change detection task using bi-temporal images. Although good performance can be achieved, however, seldom of they consider the motion cues which may also be vital. In this work, we revisit the widely adopted bi-temporal images-based framework and propose a novel Coarse-grained Temporal Mining Augmented (CTMA) framework. To be specific, given the bi-temporal images, we first transform them into a video using interpolation operations. Then, a set of temporal encoders is adopted to extract the motion features from the obtained video for coarse-grained changed region prediction. Subsequently, we design a novel Coarse-grained Foregrounds Augmented Spatial Encoder module to integrate both global and local information. We also introduce a motion augmented strategy that leverages motion cues as an additional output to aggregate with the spatial features for improved results. Meanwhile, we feed the input image pairs into the ResNet to get the different features and also the spatial blocks for fine-grained feature learning. More importantly, we propose a mask augmented strategy that utilizes coarse-grained changed regions, incorporating them into the decoder blocks to enhance the final changed prediction. Extensive experiments conducted on multiple benchmark datasets fully validated the effectiveness of our proposed framework for remote sensing image change detection. The source code of this paper will be released on https://github.com/Event-AHU/CTM_Remote_Sensing_Change_Detection

cs.CV↗

arXiv2024

Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition

Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple cases, however, the model performance may be limited by monotonous modality expressions, sub-optimal fusion, and readout mechanisms. In this paper, we propose a novel dual-stream framework for event stream-based pattern recognition via differentiated fusion, termed EFV++. It models two common event representations simultaneously, i.e., event images and event voxels. The spatial and three-dimensional stereo information can be learned separately by utilizing Transformer and Graph Neural Network (GNN). We believe the features of each representation still contain both efficient and redundant features and a sub-optimal solution may be obtained if we directly fuse them without differentiation. Thus, we divide each feature into three levels and retain high-quality features, blend medium-quality features, and exchange low-quality features. The enhanced dual features will be fed into the fusion Transformer together with bottleneck features. In addition, we introduce a novel hybrid interaction readout mechanism to enhance the diversity of features as final representations. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on multiple widely used event stream-based classification datasets. Specifically, we achieve new state-of-the-art performance on the Bullying10k dataset, i.e., $90.51\%$, which exceeds the second place by $+2.21\%$. The source code of this paper has been released on \url{https://github.com/Event-AHU/EFV_event_classification/tree/EFVpp}.

cs.CV↗

arXiv2024

Belief Information based Deep Channel Estimation for Massive MIMO Systems

In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. However, current MIMO systems rely on inserting pilot signals to achieve accurate channel estimation. As the increase of transmit stream, the pilots consume a significant portion of transmission resources, severely reducing the spectral efficiency. In this correspondence, we propose a belief information based mechanism. By introducing a plug-and-play belief information module, existing single-antenna channel estimation networks could be seamlessly adapted to multi-antenna channel estimation and fully exploit the spatial correlation among multiple antennas. Experimental results demonstrate that the proposed method can either improve 1 ~ 2 dB channel estimation performance or reduce 1/3 ~ 1/2 pilot overhead, particularly in bad channel conditions.

cs.IT↗

arXiv2024

Explainable Multimodal Emotion Recognition

Multimodal emotion recognition is an important research topic in artificial intelligence, whose main goal is to integrate multimodal clues to identify human emotional states. Current works generally assume accurate labels for benchmark datasets and focus on developing more effective architectures. However, emotion annotation relies on subjective judgment. To obtain more reliable labels, existing datasets usually restrict the label space to some basic categories, then hire plenty of annotators and use majority voting to select the most likely label. However, this process may result in some correct but non-candidate or non-majority labels being ignored. To ensure reliability without ignoring subtle emotions, we propose a new task called ``Explainable Multimodal Emotion Recognition (EMER)''. Unlike traditional emotion recognition, EMER takes a step further by providing explanations for these predictions. Through this task, we can extract relatively reliable labels since each label has a certain basis. Meanwhile, we borrow large language models (LLMs) to disambiguate unimodal clues and generate more complete multimodal explanations. From them, we can extract richer emotions in an open-vocabulary manner. This paper presents our initial attempt at this task, including introducing a new dataset, establishing baselines, and defining evaluation metrics. In addition, EMER can serve as a benchmark task to evaluate the audio-video-text understanding performance of multimodal LLMs.

cs.MM↗

arXiv2024

Evidence for Multiferroicity in Single-Layer CuCrSe$_2$

Multiferroic materials, which simultaneously exhibit ferroelectricity and magnetism, have attracted substantial attention due to their fascinating physical properties and potential technological applications. With the trends towards device miniaturization, there is an increasing demand for the persistence of multiferroicity in single-layer materials at elevated temperatures. Here, we report high-temperature multiferroicity in single-layer CuCrSe$_2$, which hosts room-temperature ferroelectricity and 120 K ferromagnetism. Notably, the ferromagnetic coupling in single-layer CuCrSe$_2$ is enhanced by the ferroelectricity-induced orbital shift of Cr atoms, which is distinct from both types I and II multiferroicity. These findings are supported by a combination of second-harmonic generation, piezo-response force microscopy, scanning transmission electron microscopy, magnetic, and Hall measurements. Our research provides not only an exemplary platform for delving into intrinsic magnetoelectric interactions at the single-layer limit but also sheds light on potential development of electronic and spintronic devices utilizing two-dimensional multiferroics.

cond-mat.mtrl-sci↗

arXiv2024

MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition

Multimodal emotion recognition plays a crucial role in enhancing user experience in human-computer interaction. Over the past few decades, researchers have proposed a series of algorithms and achieved impressive progress. Although each method shows its superior performance, different methods lack a fair comparison due to inconsistencies in feature extractors, evaluation manners, and experimental settings. These inconsistencies severely hinder the development of this field. Therefore, we build MERBench, a unified evaluation benchmark for multimodal emotion recognition. We aim to reveal the contribution of some important techniques employed in previous works, such as feature selection, multimodal fusion, robustness analysis, fine-tuning, pre-training, etc. We hope this benchmark can provide clear and comprehensive guidance for follow-up researchers. Based on the evaluation results of MERBench, we further point out some promising research directions. Additionally, we introduce a new emotion dataset MER2023, focusing on the Chinese language environment. This dataset can serve as a benchmark dataset for research on multi-label learning, noise robustness, and semi-supervised learning. We encourage the follow-up researchers to evaluate their algorithms under the same experimental setup as MERBench for fair comparisons. Our code is available at: https://github.com/zeroQiaoba/MERTools.

cs.HC↗

arXiv2024

Occupation Life Cycle

This paper explores the evolution of occupations within the context of industry and technology life cycles, highlighting the critical yet underexplored intersection between occupational trends and broader economic dynamics. Introducing the Occupation Life Cycle (OLC) model, we delineate five stages (i.e., growth, peak, fluctuation, maturity, and decline) to systematically explore the trajectory of occupations. Utilizing job posting data from one of China's largest recruitment platforms as a novel proxy, our study meticulously tracks the fluctuations and emerging trends in the labor market from 2018 to 2023. Through a detailed examination of representative roles, such as short video operators and data analysts, alongside emerging occupations within the artificial intelligence (AI) sector, our findings allocate occupations to specific life cycle stages, revealing insightful patterns of occupational development and decline. Our findings offer a unique perspective on the interplay between occupational evolution and economic factors, with a particular focus on the rapidly changing Chinese labor market. This study not only contributes to the theoretical understanding of OLC but also provides practical insights for policymakers, educators, and industry leaders facing the challenges of workforce planning and development in the face of technological advancement and market shifts.

cs.CY↗

arXiv2024

Structural transition, electric transport, and electronic structures in the compressed trilayer nickelate La4Ni3O10

Atomic structure and electronic band structure are fundamental properties for understanding the mechanism of superconductivity. Motivated by the discovery of pressure-induced high-temperature superconductivity at 80 K in the bilayer Ruddlesden-Popper nickelate La3Ni2O7, the atomic structure and electronic band structure of the trilayer nickelate La4Ni3O10 under pressure up to 44.3 GPa are investigated. A structural transition from the monoclinic P21/a space group to the tetragonal I4/mmm around 12.6-13.4 GPa is identified, accompanying with a drop of resistance below 7 K. Density functional theory calculations suggest that the bonding state of Ni 3dz2 orbital rises and crosses the Fermi level at high pressures, which may give rise to possible superconductivity observed in resistance under pressure in La4Ni3O10. The trilayer nickelate La4Ni3O10 shows some similarities with the bilayer La3Ni2O7 and has unique properties, providing a new platform to investigate the underlying mechanism of superconductivity in nickelates.

cond-mat.supr-con↗

arXiv2024

Gap and magnetic engineering via doping and pressure in tuning the colossal magnetoresistance in (Mn$_{1-x}$Mg$_x$)$_3$Si$_2$Te$_6$

Ferrimagnetic nodal-line semiconductor Mn$_3$Si$_2$Te$_6$ keeps the records of colossal magnetoresistance (CMR) and angular magnetoresistance (AMR). Here we report tuning the electronic transport properties via doping and pressure in (Mn$_{1-x}$Mg$_x$)$_3$Si$_2$Te$_6$. As the substitution of nonmagnetic Mg$^{2+}$ for magnetic Mn$^{2+}$, ferrimagnetic transition temperature $T_C$ gradually decreases, while the resistivity increases significantly. At the same time, the CMR and AMR are both enhanced for the low-doping compositions (e.g., $x = 0.1$ and 0.2), which can be attributed to doping-induced broadening of the band gap and a larger variation range of the resistivity when undergoing a metal-insulator transition by applying a magnetic field along the $c$ axis. On the contrary, $T_C$ rises with increasing pressure due to the enhancement of the magnetic exchange interactions until a structural transition occurs at $\sim$13 GPa. Meanwhile, the activation gap is lowered under pressure and the magnetoresistance is decreased dramatically above 6 GPa where the gap is closed. At 20 and 26 GPa, evidences for a superconducting transition at $\sim$5 K are observed. The results reveal that doping and pressure are effective methods to tune the activation gap, and correspondingly, the CMR and AMR in nodal-line semiconductors, providing an approach to investigate the magnetoresistance materials for novel spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Ultrafast switching of sliding ferroelectricity and dynamical magnetic field in van der Waals bilayer induced by light

Sliding ferroelectricity is a unique type of polarity recently observed in a properly stacked van der Waals bilayer. However, electric-field control of sliding ferroelectricity is hard and could induce large coercive electric fields and serious leakage currents which corrode the ferroelectricity and electronic properties, which are essential for modern two-dimensional electronics and optoelectronics. Here, we proposed laser-pulse deterministic control of sliding ferroelectricity in bilayer h-BN by first principles and molecular dynamics simulation with machine-learned force fields. The laser pulses excite shear modes which exhibit certain directional movements of lateral sliding between bilayers. The vibration of excited modes under laser pulses is predicted to overcome the energy barrier and achieve the switching of sliding ferroelectricity. Furthermore, it is found that three possible sliding transitions - between AB (BA) and BA (AB) stacking - can lead to the occurrence of dynamical magnetic fields along three different directions. Remarkably, the magnetic fields are generated by the simple linear motion of nonmagnetic species, without any need for more exotic (circular, spiral) pathways. Such predictions of deterministic control of sliding ferroelectricity and multi-states of dynamical magnetic field thus expand the potential applications of sliding ferroelectricity in memory and electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Layer-dependent Raman spectroscopy of ultrathin Ta$_2$Pd$_3$Te$_5$

Two-dimensional topological insulators (2DTIs) or quantum spin Hall insulators are attracting increasing attention due to their potential applications in next-generation spintronic devices. Despite their promising prospects, realizable 2DTIs are still limited. Recently, Ta2Pd3Te5, a semiconducting van der Waals material, has shown spectroscopic evidence of quantum spin Hall states. However, achieving controlled preparation of few- to monolayer samples, a crucial step in realizing quantum spin Hall devices, has not yet been achieved. In this work, we fabricated few- to monolayer Ta$_2$Pd$_3$Te$_5$ and performed systematic thickness- and temperature-dependent Raman spectroscopy measurements. Our results demonstrate that Raman spectra can provide valuable information to determine the thickness of Ta2Pd3Te5 thin flakes. Moreover, our angle-resolved polarized Raman (ARPR) spectroscopy measurements show that the intensities of the Raman peaks are strongly anisotropic due to the quasi-one-dimensional atomic structure, providing a straightforward method to determine its crystalline orientation. Our findings may stimulate further efforts to realize quantum devices based on few or monolayer Ta$_2$Pd$_3$Te$_5$.

cond-mat.mtrl-sci↗

arXiv2023

MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance

Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within the edit region. Meanwhile, mask-free attention-based methods often exhibit editing leakage and misalignment in more complex compositions. In this work, we develop MAG-Edit, a training-free, inference-stage optimization method, which enables localized image editing in complex scenarios. In particular, MAG-Edit optimizes the noise latent feature in diffusion models by maximizing two mask-based cross-attention constraints of the edit token, which in turn gradually enhances the local alignment with the desired prompt. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in achieving both text alignment and structure preservation for localized editing within complex scenarios.

cs.CV↗

arXiv2023

Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from key issues like sematic gaps and small-scale backbone networks. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision-language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code will be made available at https://github.com/Event-AHU/SAFE_LargeVLM.

cs.CV↗

arXiv2023

The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills.

cs.CY↗

arXiv2023

VcT: Visual change Transformer for Remote Sensing Image Change Detection

Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the features of the change regions, however, these works are still limited mainly due to the ignorance of mining the unchanged background context information. It is known that one main challenge for change detection is how to obtain the consistent representations for two images involving different variations, such as spatial variation, sunlight intensity, etc. In this work, we demonstrate that carefully mining the common background information provides an important cue to learn the consistent representations for the two images which thus obviously facilitates the visual change detection problem. Based on this observation, we propose a novel Visual change Transformer (VcT) model for visual change detection problem. To be specific, a shared backbone network is first used to extract the feature maps for the given image pair. Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction. Top-K reliable tokens can be mined from the map and refined by using the clustering algorithm. Then, these reliable tokens are enhanced by first utilizing self/cross-attention schemes and then interacting with original features via an anchor-primary attention learning module. Finally, the prediction head is proposed to get a more accurate change map. Extensive experiments on multiple benchmark datasets validated the effectiveness of our proposed VcT model.

cs.CV↗

arXiv2023

Bilayer Kagome Borophene with Multiple van Hove Singularities

The appearance of van Hove singularities near the Fermi level leads to prominent phenomena, including superconductivity, charge density wave, and ferromagnetism. Here a bilayer Kagome lattice with multiple van Hove singularities is designed and a novel borophene with such lattice (BK-borophene) is proposed by the first-principles calculations. BK-borophene, which is formed via three-center two-electron (3c-2e) sigma-type bonds, is predicted to be energetically, dynamically, thermodynamically, and mechanically stable. The electronic structure hosts both conventional and high-order van Hove singularities in one band. The conventional van Hove singularity resulting from the horse saddle is 0.065 eV lower than the Fermi level, while the high-order one resulting from the monkey saddle is 0.385 eV below the Fermi level. Both the singularities lead to the divergence of electronic density of states. Besides, the high-order singularity is just intersected to a Dirac-like cone, where the Fermi velocity can reach 1340000 m/s. The interaction between the two Kagome lattices is critical for the appearance of high-order van Hove singularities. The novel bilayer Kagome borophene with rich and intriguing electronic structure offers an unprecedented platform for studying correlation phenomena in quantum material systems and beyond.

cond-mat.mtrl-sci↗

arXiv2023

Unconventional band structure via combined molecular orbital and lattice symmetries in a surface-confined metallated graphdiyne sheet

Graphyne (GY) and graphdiyne (GDY)-based materials represent an intriguing class of two-dimensional (2D) carbon-rich networks with tunable structures and properties surpassing those of graphene. However, the challenge of fabricating atomically well-defined crystalline GY/GDY-based systems largely hinders detailed electronic structure characterizations. Here, we report the emergence of an unconventional band structure in mesoscopically regular (~1 μm) metallated GDY sheets featuring a honeycomb lattice on Ag(111) substrates. Employing complementary scanning tunnelling and angle-resolved photoemission spectroscopies, electronic band formation with a gap of 2.5 eV is rigorously determined in agreement with real-space electronic characteristics. Extensive density functional theory calculations corroborate our observations as well as recent theoretical predictions that doubly degenerate frontier molecular orbitals on a honeycomb lattice give rise to flat, Dirac and Kagome bands close to Fermi level. These results illustrate the tremendous potential of engineering novel band structures via molecular orbital and lattice symmetries in atomically precise 2D carbon scaffolds.

cond-mat.mtrl-sci↗

arXiv2023

Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification

Recognizing target objects using an event-based camera draws more and more attention in recent years. Existing works usually represent the event streams into point-cloud, voxel, image, etc, and learn the feature representations using various deep neural networks. Their final results may be limited by the following factors: monotonous modal expressions and the design of the network structure. To address the aforementioned challenges, this paper proposes a novel dual-stream framework for event representation, extraction, and fusion. This framework simultaneously models two common representations: event images and event voxels. By utilizing Transformer and Structured Graph Neural Network (GNN) architectures, spatial information and three-dimensional stereo information can be learned separately. Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on two widely used event-based classification datasets. The source code of this work is available at: \url{https://github.com/Event-AHU/EFV_event_classification}

cs.CV↗