arXiv subjects

Qiang Chen

Publications and source records attributed to Qiang Chen.

At least 91 records · Page 5Linked to original sources

arXiv2023

A simplified method characterizing magnetic ordering modulated photo-thermoelectric response in noncentrosymmetric semimetal Ca3Ru2O7

Photo-Thermoelectric (PTE) response is usually one of the main working mechanisms for photodetectors. However, as another fast and easier way to measure thermoelectric characteristics of materials, it can also reveal important physics such as electric-phonon coupling, electron-electron correlation, etc. Recently, the spin entropy related to magnetic order transition which contributes to thermoelectric power is attracting more and more attention. Here, we demonstrate the PTE response can be reshaped when Ca3Ru2O7 undergoes meta-magnetic phase (MMP) transition driven by both temperature and magnetic field. Firstly, a sign change is observed crossing TS = 48 K and the linear polarization angle dependent PTE current maximizes along a-axis above TS while maximizes along b-axis below TS, which indicates that the antiferromagnetic spin order contributes to such spatial anisotropy. Secondly, in the temperature range of around 40 ~ 50 K, the PTE current is found to be sharply suppressed when external magnetic field is applied in plane along a-axis but is only gradually suppressed when applied field is along b-axis which gives out two critical fields. We attribute such suppression of PTE current under magnetic field to the suppression of the spin entropy in the phase transition between the antiferromagnetic state and the MMP state and the H-T phase diagrams of Ca3Ru2O7 is redrawn accordingly. Compared to previously work which trying to understand the magnetic phase transition in Ca3Ru2O7, such as neutron scattering, specific heat, and other advanced transport measurements, our work provides a more convenient yet efficient method, which may also find applications in other correlated spin materials in general.

cond-mat.str-el↗

arXiv2023

Highly tunable lateral homojunction formed in 2D layered CuInP2S6 via in-plane ionic migration

As basic building blocks for next-generation information technologies devices, high-quality p-n junctions based on van der Waals (vdW) materials have attracted widespread interest.Compared to traditional two dimensional (2D) heterojunction diodes, the emerging homojunctions are more attractive owing to their intrinsic advantages, such as continuous band alignments and smaller carrier trapping. Here, utilizing the long-range migration of Cu + ions under in-plane electric field, a novel lateral p-n homojunction was constructed in the 2D layered copper indium thiophosphate (CIPS). The symmetric Au/CIPS/Au devices demonstrate an electric-field-driven resistance switching (RS) accompanying by a rectification behavior without any gate control. Moreover, such rectification behavior can be continuously modulated by poling voltage. We deduce that the reversable rectifying RS behavior is governed by the effective lateral build-in potential and the change of the interfacial barrier during the poling process. Furthermore, the CIPS p-n homojuction is evidenced by the photovoltaic effect, with the spectral response extending up to visible region due to the better photogenerated carrier separation efficiency. Our study provides a facile route to fabricate homojuctions through electric-field-driven ionic migration and paves the way towards the use of this method in other vdW materials.

cond-mat.mes-hall↗

arXiv2023

Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.

cs.AI↗

arXiv2023

Measures of imaginarity and quantum state order

Complex numbers are widely used in both classical and quantum physics, and play an important role in describing quantum systems and their dynamical behavior. In this paper we study several measures of imaginarity of quantum states in the framework of resource theory, such as the measures based on $l_{1}$ norm, and convex function, etc. We also investigate the influence of the quantum channels on quantum state order for a single-qubit.

quant-ph↗

arXiv2022

DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection

Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issue. In this paper, we revisit these two assignment methods and find that bringing one-to-many assignment back to end-to-end fully convolutional detectors helps with model convergence. Based on this observation, we propose {\em \textbf{D}ual \textbf{A}ssignment} for end-to-end fully convolutional de\textbf{TE}ction (DATE). Our method constructs two branches with one-to-many and one-to-one assignment during training and speeds up the convergence of the one-to-one assignment branch by providing more supervision signals. DATE only uses the branch with the one-to-one matching strategy for model inference, which doesn't bring inference overhead. Experimental results show that Dual Assignment gives nontrivial improvements and speeds up model convergence upon OneNet and DeFCN. Code: https://github.com/YiqunChen1999/date.

cs.CV↗

arXiv2022

OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an ~10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.

eess.IV↗

arXiv2022

CAE v2: Context Autoencoder with CLIP Target

Masked image modeling (MIM) learns visual representation by masking and reconstructing image patches. Applying the reconstruction supervision on the CLIP representation has been proven effective for MIM. However, it is still under-explored how CLIP supervision in MIM influences performance. To investigate strategies for refining the CLIP-targeted MIM, we study two critical elements in MIM, i.e., the supervision position and the mask ratio, and reveal two interesting perspectives, relying on our developed simple pipeline, context autodecoder with CLIP target (CAE v2). Firstly, we observe that the supervision on visible patches achieves remarkable performance, even better than that on masked patches, where the latter is the standard format in the existing MIM methods. Secondly, the optimal mask ratio positively correlates to the model size. That is to say, the smaller the model, the lower the mask ratio needs to be. Driven by these two discoveries, our simple and concise approach CAE v2 achieves superior performance on a series of downstream tasks. For example, a vanilla ViT-Large model achieves 81.7% and 86.7% top-1 accuracy on linear probing and fine-tuning on ImageNet-1K, and 55.9% mIoU on semantic segmentation on ADE20K with the pre-training for 300 epochs. We hope our findings can be helpful guidelines for the pre-training in the MIM area, especially for the small-scale models.

cs.CV↗

arXiv2022

Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-coco

cs.CV↗

arXiv2022

Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce

In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for ``running'' in e-commerce, they would like to find products highly related to running, such as ``running shoes'' rather than ``shoes''. Nevertheless, many existing CSK collections rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. In this work, we define the task of supervised salience evaluation, where given a CSK triple, the model is required to learn whether the triple is salient or not. In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation. We conduct experiments in the dataset with several representative baseline models. The experimental results show that salience evaluation is a challenging task where models perform poorly on our evaluation set. We further propose a simple but effective approach, PMI-tuning, which shows promise for solving this novel problem. Code is available in \url{https://github.com/OpenBGBenchmark/OpenBG-CSK.

cs.CL↗

arXiv2022

SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training

The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in real-world applications, label noise inevitably exists in training data, damaging the effectiveness, robustness, and generalization of the models constructed on such data. Recently, remarkable achievements have been made to mitigate this dilemma in visual data, while only a few explore textual data. To fill this gap, we present SelfMix, a simple yet effective method, to handle label noise in text classification tasks. SelfMix uses the Gaussian Mixture Model to separate samples and leverages semi-supervised learning. Unlike previous works requiring multiple models, our method utilizes the dropout mechanism on a single model to reduce the confirmation bias in self-training and introduces a textual-level mixup training strategy. Experimental results on three text classification benchmarks with different types of text show that the performance of our proposed method outperforms these strong baselines designed for both textual and visual data under different noise ratios and noise types. Our code is available at https://github.com/noise-learning/SelfMix.

cs.CL↗

arXiv2022

Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning

Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}. We present a solution to overcome the overfitting problem, leading to better model generalization on learning novel classes. Our method decomposes backbone parameters into three successive matrices via the Singular Value Decomposition (SVD), then {\em only fine-tunes the singular values} and keeps others frozen. The above design allows the model to adjust feature representations on novel classes while maintaining semantic clues within the pre-trained backbone. We evaluate our {\em Singular Value Fine-tuning (SVF)} approach on various few-shot segmentation methods with different backbones. We achieve state-of-the-art results on both Pascal-5$^i$ and COCO-20$^i$ across 1-shot and 5-shot settings. Hopefully, this simple baseline will encourage researchers to rethink the role of backbone fine-tuning in few-shot settings. The source code and models will be available at https://github.com/syp2ysy/SVF.

cs.CV↗

arXiv2022

Improving Transferability for Domain Adaptive Detection Transformers

DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseline with a DETR-style detector on domain shift settings based on two findings. For one, mitigating the domain shift on the backbone and the decoder output features excels in getting favorable results. For another, advanced domain alignment methods in both parts further enhance the performance. Thus, we propose the Object-Aware Alignment (OAA) module and the Optimal Transport based Alignment (OTA) module to achieve comprehensive domain alignment on the outputs of the backbone and the detector. The OAA module aligns the foreground regions identified by pseudo-labels in the backbone outputs, leading to domain-invariant based features. The OTA module utilizes sliced Wasserstein distance to maximize the retention of location information while minimizing the domain gap in the decoder outputs. We implement the findings and the alignment modules into our adaptation method, and it benchmarks the DETR-style detector on the domain shift settings. Experiments on various domain adaptive scenarios validate the effectiveness of our method.

cs.CV↗

arXiv2022

Image Magnification Network for Vessel Segmentation in OCTA Images

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature. The retinal vessel segmentation in OCTA images is still an open problem, and especially the thin and dense structure of the capillary plexus is an important challenge of this problem. In this work, we propose a novel image magnification network (IMN) for vessel segmentation in OCTA images. Contrary to the U-Net structure with a down-sampling encoder and up-sampling decoder, the proposed IMN adopts the design of up-sampling encoding and then down-sampling decoding. This design is to capture more low-level image details to reduce the omission of small structures. The experimental results on three open OCTA datasets show that the proposed IMN with an average dice score of 90.2% achieves the best performance in vessel segmentation of OCTA images. Besides, we also demonstrate the superior performance of IMN in cross-field image vessel segmentation and vessel skeleton extraction.

eess.IV↗

arXiv2022

Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation

You can have your cake and eat it too. Microvessel segmentation in optical coherence tomography angiography (OCTA) images remains challenging. Skeleton-level segmentation shows clear topology but without diameter information, while pixel-level segmentation shows a clear caliber but low topology. To close this gap, we propose a novel label adversarial learning (LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL mainly consists of two designs: a label adversarial loss and an embeddable adjustment layer. The label adversarial loss establishes an adversarial relationship between the two label supervisions, while the adjustment layer adjusts the network parameters to match the different adversarial weights. Such a design can efficiently capture the variation between the two supervisions, making the segmentation continuous and tunable. This continuous process allows us to recommend high-quality vessel segmentation with clear caliber and topology. Experimental results show that our results outperform manual annotations of current public datasets and conventional filtering effects. Furthermore, such a continuous process can also be used to generate an uncertainty map representing weak vessel boundaries and noise.

eess.IV↗

arXiv2022

HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap

Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers from quantization errors caused by neglecting subpixel coordinates in image resizing and network downsampling. In this paper, we first quantitatively analyze the quantization error on benchmarks, which accounts for more than 1/3 of the whole prediction errors for state-of-the-art methods. To tackle this problem, we propose a novel Heatmap In Heatmap(HIH) representation and a coordinate soft-classification (CSC) method, which are seamlessly integrated into the classic hourglass network. The HIH representation utilizes nested heatmaps to jointly represent the coordinate label: one heatmap called integer heatmap stands for the integer coordinate, and the other heatmap named decimal heatmap represents the subpixel coordinate. The range of a decimal heatmap makes up one pixel in the corresponding integer heatmap. Besides, we transfer the offset regression problem to an interval classification task, and CSC regards the confidence of the pixel as the probability of the interval. Meanwhile, CSC applying the distribution loss leverage the soft labels generated from the Gaussian distribution function to guide the offset heatmap training, which makes it easier to learn the distribution of coordinate offsets. Extensive experiments on challenging benchmark datasets demonstrate that our HIH can achieve state-of-the-art results. In particular, our HIH reaches 4.08 NME (Normalized Mean Error) on WFLW, and 3.21 on COFW, which exceeds previous methods by a significant margin.

cs.CV↗

arXiv2022

MixFormer: Mixing Features across Windows and Dimensions

While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs self-attention within non-overlapped windows and shares weights on the channel dimension. We propose MixFormer to find a solution. First, we combine local-window self-attention with depth-wise convolution in a parallel design, modeling cross-window connections to enlarge the receptive fields. Second, we propose bi-directional interactions across branches to provide complementary clues in the channel and spatial dimensions. These two designs are integrated to achieve efficient feature mixing among windows and dimensions. Our MixFormer provides competitive results on image classification with EfficientNet and shows better results than RegNet and Swin Transformer. Performance in downstream tasks outperforms its alternatives by significant margins with less computational costs in 5 dense prediction tasks on MS COCO, ADE20k, and LVIS. Code is available at \url{https://github.com/PaddlePaddle/PaddleClas}.

cs.CV↗

arXiv2022

The consistent behavior of negative Poissons ratio with interlayer interactions

Negative Poissons ratio (NPR) is of great interest due to the novel applications in lots of fields. Films are the most commonly used form in practical applications, which involves multiple layers. However, the effect of interlayer interactions on the NPR is still unclear. In this study, based on first principles calculations, we systematically investigate the effect of interlayer interactions on the NPR by comparably studying single-layer graphene, few-layer graphene, h-BN, and graphene-BN heterostructure. It is found that they almost have the same geometry-strain response. Consequently, the NPR in bilayer graphene, triple-layer graphene, and graphene-BN heterostructure are consistent with that in single-layer graphene and h-BN. The fundamental mechanism lies in that the response to strain of the orbital coupling are consistent under the effect of interlayer interactions. The deep understanding of the NPR with the effect of interlayer interactions as achieved in this study is beneficial for the future design and development of micro-/nanoscale electromechanical devices with novel functions based on nanostructures.

physics.chem-ph↗

arXiv2021

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for the development of KGs. Knowledge Graph Embeddings (KGEs) embedding entities and relations of a KG into continuous vector spaces, have been proposed for these reasoning tasks and proven to be efficient and robust. But the plausibility and feasibility of applying and deploying KGEs in real-work applications has not been well-explored. In this paper, we discuss and report our experiences of deploying KGEs in a real domain application: e-commerce. We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems. While non existing KGE could meet all these desiderata, we propose a novel one, an explainable knowledge graph attention network that make prediction through modeling correlations between triples rather than purely relying on its head entity, relation and tail entity embeddings. It could automatically selects attentive triples for prediction and records the contribution of them at the same time, from which explanations could be easily provided and transferable rules could be efficiently produced. We empirically show that our method is capable of meeting all three desiderata in our e-commerce application and outperform typical baselines on datasets from real domain applications.

cs.AI↗