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Yongjun Zhang

Publications and source records attributed to Yongjun Zhang.

At least 73 records · Page 4Linked to original sources
arXiv2024

Direction dependent switching of carrier-type enabled by Fermi surface geometry

While charge carriers can typically be designated as either electron- or hole- type, depending on the sign of the Hall coefficient, some materials defy this straightforward classification. Here we find that LaRh$_6$Ge$_4$ goes beyond this dichotomy, where the Hall resistivity is electron-like for magnetic fields along the $c$-axis but hole-like in the basal plane. Together with first-principles calculations, we show that this direction-dependent switching of the carrier type arises within a single band, where the special geometry leads to charge carriers on the same Fermi surface orbiting as electrons along some directions, but holes along others. The relationship between the Fermi surface geometry and occurrence of a Hall sign reversal is further generalized by considering tight-binding model calculations, which show that this type of Fermi surface corresponds to a more robust means of realizing this phenomenon, suggesting an important route for tailoring direction dependent properties for advanced electronic device applications.

cond-mat.str-el↗
arXiv2023

Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and collectively feature 59,737 bridges. Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset.

cs.CV↗
arXiv2023

LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News

Large language and vision models have transformed how social movements scholars identify protest and extract key protest attributes from multi-modal data such as texts, images, and videos. This article documents how we fine-tuned two large pretrained transformer models, including longformer and swin-transformer v2, to infer potential protests in news articles using textual and imagery data. First, the longformer model was fine-tuned using the Dynamic of Collective Action (DoCA) Corpus. We matched the New York Times articles with the DoCA database to obtain a training dataset for downstream tasks. Second, the swin-transformer v2 models was trained on UCLA-protest imagery data. UCLA-protest project contains labeled imagery data with information such as protest, violence, and sign. Both fine-tuned models will be available via \url{https://github.com/Joshzyj/llvms4protest}. We release this short technical report for social movement scholars who are interested in using LLVMs to infer protests in textual and imagery data.

cs.CV↗
arXiv2023

Hierarchical Memory Learning for Fine-Grained Scene Graph Generation

As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the dataset due to the crowd-sourced labeling, and the long-tail problem is also pronounced. Given this tricky situation, many existing SGG methods treat the predicates equally and learn the model under the supervision of mixed-granularity predicates in one stage, leading to relatively coarse predictions. In order to alleviate the negative impact of the suboptimum mixed-granularity annotation and long-tail effect problems, this paper proposes a novel Hierarchical Memory Learning (HML) framework to learn the model from simple to complex, which is similar to the human beings' hierarchical memory learning process. After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates. In order to realize this hierarchical learning pattern, this paper, for the first time, formulates the HML framework using the new Concept Reconstruction (CR) and Model Reconstruction (MR) constraints. It is worth noticing that the HML framework can be taken as one general optimization strategy to improve various SGG models, and significant improvement can be achieved on the SGG benchmark (i.e., Visual Genome).

cs.CV↗
arXiv2023

NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data

Understanding how local environments influence individual behaviors, such as voting patterns or suicidal tendencies, is crucial in social science to reveal and reduce spatial disparities and promote social well-being. With the increasing availability of large-scale individual-level census data, new analytical opportunities arise for social scientists to explore human behaviors (e.g., political engagement) among social groups at a fine-grained level. However, traditional statistical methods mostly focus on global, aggregated spatial correlations, which are limited to understanding and comparing the impact of local environments (e.g., neighborhoods) on human behaviors among social groups. In this study, we introduce a new analytical framework for analyzing multi-variate neighborhood effects between social groups. We then propose NeighVi, an interactive visual analytics system that helps social scientists explore, understand, and verify the influence of neighborhood effects on human behaviors. Finally, we use a case study to illustrate the effectiveness and usability of our system.

cs.HC↗
arXiv2023

ATP-Dependent Mismatch Recognition in DNA Replication Mismatch Repair

Mismatch repair is a critical step in DNA replication that occurs after base selection and proofreading, significantly increasing fidelity. However, the mechanism of mismatch recognition has not been established for any repair enzyme. Speculations in this area mainly focus on exploiting thermodynamic equilibrium and free energy. Nevertheless, non-equilibrium processes may play a more significant role in enhancing mismatch recognition accuracy by utilizing adenosine triphosphate (ATP). This study aimed to investigate this possibility. Considering our limited knowledge of actual mismatch repair enzymes, we proposed a hypothetical enzyme that operates as a quantum system with three discrete energy levels. When the enzyme is raised to its highest energy level, a quantum transition occurs, leading to one of two low-energy levels representing potential recognition outcomes: a correct match or a mismatch. The probabilities of the two outcomes are exponentially different, determined by the energy gap between the two low energy levels. By flipping the energy gap, discrimination between mismatches and correct matches can be achieved. Within a framework that combines quantum mechanics with thermodynamics, we established a relationship between energy cost and the recognition error.

physics.bio-ph↗
arXiv2023

Signatures of a gapless quantum spin liquid in the Kitaev material Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$

The honeycomb-lattice cobaltate Na$_3$Co$_2$SbO$_6$ has recently been proposed to be a proximate Kitaev quantum spin liquid~(QSL) candidate. However, non-Kitaev terms in the Hamiltonian lead to a zigzag-type antiferromagnetic~(AFM) order at low temperatures. Here, we partially substitute magnetic Co$^{2+}$ with nonmagnetic Zn$^{2+}$ and investigate the chemical doping effect in tuning the magnetic ground states of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. X-ray diffraction characterizations reveal no structural transition but quite tiny changes on the lattice parameters over our substitution range $0\leq x\leq0.4$. Magnetic susceptibility and specific heat results both show that AFM transition temperature is continuously suppressed with increasing Zn content $x$ and neither long-range magnetic order nor spin freezing is observed when $x\geq0.2$. More importantly, a linear term of the specific heat representing fermionic excitations is captured below 5~K in the magnetically disordered regime, as opposed to the $C_{\rm m}\propto T^3$ behavior expected for bosonic excitations in the AFM state. Based on the data above, we establish a magnetic phase diagram of Na$_3$Co$_{2-x}$Zn$_x$SbO$_6$. Our results indicate the presence of gapless fractional excitations in the samples with no magnetic order, evidencing a potential QSL state induced by doping in a Kitaev system.

cond-mat.str-el↗
arXiv2023

EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation

This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS). Large-size remote sensing images (LRIs) can lead to GPU memory exhaustion due to their extremely large size, which has been handled in previous works through either global-local fusion or multi-stage refinement, both of which are limited in their ability to fully exploit the abundant information available in LRIs. Unlike them, EHSNet features three memory-friendly modules to utilize the characteristics of LRIs: a long-range dependency module to develop long-range spatial context, an efficient cross-correlation module to build holistic contextual relationships, and a boundary-aware enhancement module to preserve complete object boundaries. Moreover, EHSNet manages to process holistic LRISS with the aid of memory offloading. To the best of our knowledge, EHSNet is the first method capable of performing holistic LRISS. To make matters better, EHSNet outperforms previous state-of-the-art competitors by a significant margin of +5.65 mIoU on FBP and +4.28 mIoU on Inria Aerial, demonstrating its effectiveness. We hope that EHSNet will provide a new perspective for LRISS. The code and models will be made publicly available.

cs.CV↗
arXiv2023

Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic

The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. The classifier achieves an F1 score of 0.64 and an accuracy rate of 93%. We provide the final model and a new training dataset with 18,425 tweets for researchers to study fake news in the Chinese language during the COVID-19 pandemic. We also introduce a new dataset generated by our classifier that tracks the dynamics of fake news in the Chinese language during the early pandemic.

cs.CY↗
arXiv2023

Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel Imbalance Knowledge-Driven Multi-modal Network (IKD-Net) to extract features from raw multi-modal heterogeneous data directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized using a holistic loss function. While we were developing IKD-Net, we also established a new dataset called the National Agriculture Imagery Program and 3D Elevation Program Combined dataset in California (N3C-California), which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission.

eess.IV↗
arXiv2023

Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes demonstrate our framework consistently outperforms the state-of-the-art methods with a large margin. Code will be available publicly.

cs.CV↗
arXiv2023

GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery

Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and manually labeled surface water annotations that are distributed globally and contain water bodies exhibiting a wide variety of types (e.g., rivers, lakes, and ponds in forests, irrigated fields, bare areas, and urban areas). Each image is of the size 12,800 $\times$ 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models. Furthermore, we also design a strong baseline with the novel pyramid consistency loss (PCL) to initially explore this challenge. Finally, we implement the cross-dataset and pilot area generalization experiments, and the superior performance illustrates the strong generalization and practical application of GLH-water. The dataset is available at https://jack-bo1220.github.io/project/GLH-water.html.

cs.CV↗
arXiv2023

RIFT2: Speeding-up RIFT with A New Rotation-Invariance Technique

Multimodal image matching is an important prerequisite for multisource image information fusion. Compared with the traditional matching problem, multimodal feature matching is more challenging due to the severe nonlinear radiation distortion (NRD). Radiation-variation insensitive feature transform (RIFT)~\cite{li2019rift} has shown very good robustness to NRD and become a baseline method in multimodal feature matching. However, the high computational cost for rotation invariance largely limits its usage in practice. In this paper, we propose an improved RIFT method, called RIFT2. We develop a new rotation invariance technique based on dominant index value, which avoids the construction process of convolution sequence ring. Hence, it can speed up the running time and reduce the memory consumption of the original RIFT by almost 3 times in theory. Extensive experiments show that RIFT2 achieves similar matching performance to RIFT while being much faster and having less memory consumption. The source code will be made publicly available in \url{https://github.com/LJY-RS/RIFT2-multimodal-matching-rotation}

cs.CV↗
arXiv2023

Pressure tuning domain-wall chirality in noncentrosymmetric magnetic Weyl semimetal CeAlGe

Topological magnetic Weyl semimetals have been proposed to host controllable chiral domain walls which bear a great prospect in device applications. To exploit them in applications, it is important to have a proper way to tune and manipulate these domain walls. One possible means is through magnetoelastic coupling. The involvement of rare earth in the lately proposed $R$Al$X$ ($R$=rare earth, $X$=Si and Ge) family magnetic Weyl semimetals may provide such a platform. Here we present transport and thermodynamic properties of CeAlGe under hydrostatic pressure. We find that pressure enhances the antiferromagnetic exchange in CeAlGe but essentially retains its magnetic structure. Large topological Hall effect with pronounced loop shape is observed within the magnetically ordered state, and it splits into two regions under pressure. Such an unusual electromagnetic response is inferred to be a consequence of chiral magnetic domain walls. The unprecedented concomitance of its evolution under pressure and the reentrance of antiferromagnetic order strongly suggest the capability of switching on/off this electromagnetic response in noncentrosymmetric magnetic Weyl semimetals via magnetoelastic coupling.

cond-mat.str-el↗
arXiv2023

Suppression of the antiferromagnetic order by Zn doping in a possible Kitaev material Na$_2$Co$_2$TeO$_6$

Very recently, a 3$d$ based honeycomb cobaltate Na$_2$Co$_2$TeO$_6$ has garnered tremendous attention due to the proposed proximity to the Kitaev spin-liquid state as its 4$d$/5$d$ counterparts. Here, we use Zn to substitute Co in a broad range and perform systematic studies on Na$_2$Co$_{2-x}$Zn$_x$TeO$_6$ by structural, magnetic, and thermodynamic measurements, and track the doping evolution of its magnetic ground states. Due to the extremely close radii of Zn$^{2+}$ and high-spin Co$^{2+}$ ions, the substitution can be easily achieved. X-ray diffractions reveal no structural transition but only minor changes on the lattice parameter $c$ over a wide substitution range $0 \leq x \leq 1.5$. Magnetic susceptibility and specific heat measurements both suggest an antiferromagnetic ground state which is gradually suppressed with doping. It can survive with $x$ up to $\sim1.0$. Then it evolves into a spin-glass phase with short-range order that is rapidly supplanted by a magnetically disordered state when $x \geq 1.3$. By summarizing all these data, we construct a magnetic phase diagram of Na$_2$Co$_{2-x}$Zn$_x$TeO$_6$. Our results demonstrate that the Zn doping can effectively suppress the magnetic order and induce a possibe quantum paramagnetic state. These may serve as a platform to investigate the Kitaev physics in this system.

cond-mat.str-el↗
arXiv2022

LiDAR-guided Stereo Matching with a Spatial Consistency Constraint

The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When the percentage of LiDAR points in the simulated datasets was 0.16%, the matching accuracy of our method achieved a subpixel level, while that of the original stereo matching algorithm was 3.4 pixels. The experimental results show that LGSM is suitable for indoor, street, aerial, and satellite image datasets and provides good transferability across semi-global matching and AD-Census. Furthermore, the qualitative and quantitative evaluations demonstrate that LGSM is superior to two state-of-the-art optimizing cost volume methods, especially in reducing mismatches in difficult matching areas and refining the boundaries of objects.

cs.CV↗
arXiv2022

Asymmetric Hash Code Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing learning as the current mainstream method has achieved satisfactory retrieval performance. On one hand, various deep neural networks are used to extract semantic features of remote sensing images. On the other hand, the hashing techniques are subsequently adopted to map the high-dimensional deep features to the low-dimensional binary codes. This kind of methods attempts to learn one hash function for both the query and database samples in a symmetric way. However, with the number of database samples increasing, it is typically time-consuming to generate the hash codes of large-scale database images. In this paper, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL generates the hash codes of query and database images in an asymmetric way. In more detail, the hash codes of query images are obtained by binarizing the output of the network, while the hash codes of database images are directly learned by solving the designed objective function. In addition, we combine the semantic information of each image and the similarity information of pairs of images as supervised information to train a deep hashing network, which improves the representation ability of deep features and hash codes. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency. The source code is available at https://github.com/weiweisong415/Demo AHCL for TGRS2022.

cs.CV↗
arXiv2021

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the embeddings of entities, and the connections between entities are weak. Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings. However, existing NARL models either only utilize one-hop neighbors, ignoring the information in multi-hop neighbors, or utilize multi-hop neighbors by hierarchical neighbor aggregation, destroying the completeness of multi-hop neighbors. In this paper, we propose a NARL model named RMNA, which obtains and filters horn rules through a rule mining algorithm, and uses selected horn rules to transform valuable multi-hop neighbors into one-hop neighbors, therefore, the information in valuable multi-hop neighbors can be completely utilized by aggregating these one-hop neighbors. In experiments, we compare RMNA with the state-of-the-art TRL models and NARL models. The results show that RMNA has a competitive performance.

cs.LG↗