arXiv subjects

Fei Guo

Publications and source records attributed to Fei Guo.

At least 37 records · Page 2Linked to original sources

arXiv2025

Lifespan estimate for the semilinear regular Euler-Poisson-Darboux-Tricomi equation

In this paper, we begin by establishing local well-posedness for the semilinear regular Euler-Poisson-Darboux-Tricomi equation. Subsequently, we derive a lifespan estimate with the Strauss index given by $p=p_{S}(n+\fracμ{m+1}, m)$ for any $δ>0$, where $δ$ is a parameter to describe the interplay between damping and mass. This is achieved through the construction of a new test function derived from the Gaussian hypergeometric function and a second-order ordinary differential inequality, as proven by Zhou \cite{Zhou2014}. Additionally, we extend our analysis to prove a blow-up result with the index $p=\max\{p_{S}(n+\fracμ{m+1}, m), p_{F}((m+1)n+\frac{μ-1-\sqrtδ}{2})\}$ by applying Kato$^{\prime}$s Lemma ( i.e., Lemma \ref{katolemma} ), specifically in the case of $δ=1$.

math.AP↗

arXiv2024

A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning

Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data to minimize the disparity between computational predictions and experimental results. However, there is still a lack of an automatic and precise gap-filling method for initial state GEMs before experimental data and annotated genomes become available. In this study, we introduce CLOSEgaps, a deep learning-driven tool that addresses the gap-filling issue by modeling it as a hyperedge prediction problem within GEMs. Specifically, CLOSEgaps maps metabolic networks as hypergraphs and learns their hyper-topology features to identify missing reactions and gaps by leveraging hypothetical reactions. This innovative approach allows for the characterization and curation of both known and hypothetical reactions within metabolic networks. Extensive results demonstrate that CLOSEgaps accurately gap-filling over 96% of artificially introduced gaps for various GEMs. Furthermore, CLOSEgaps enhances phenotypic predictions for 24 GEMs and also finds a notable improvement in producing four crucial metabolites (Lactate, Ethanol, Propionate, and Succinate) in two organisms. As a broadly applicable solution for any GEM, CLOSEgaps represents a promising model to automate the gap-filling process and uncover missing connections between reactions and observed metabolic phenotypes.

q-bio.MN↗

arXiv2024

Real and Reciprocal Space Characterization of the 3-Dimensional Charge Density Wave in Quasi-1-Dimensional CuTe

Low-dimensional materials are susceptible to electronic instabilities such as charge density waves (CDWs), originating from a divergence in the Lindhard electron response function, combined with a finite electron-phonon coupling strength. In this report, we present a detailed characterisation of the CDW in the quasi-one-dimensional material CuTe, including (1) direct visualization of lattice distortion seen with non-contact atomic force microscopy in real space, (2) the out-of-plane momentum dependency of the CDW gap size of the quasi-1-dimensional bands, by angle-resolved photoemission spectroscopy, (3) coherent dynamics of a photoexcited phonon mode seen by time- and angle-resolved photoemission spectroscopy, with frequency and wavevector q_CDW corresponding to the soft phonon modes predicted by theory. Furthermore, we find that the CDW gap closes through a transient band renormalisation. We thus confirm that, despite the quasi-1D characteristics of CuTe, it hosts inherently 3-dimensional CDWs.

cond-mat.str-el↗

arXiv2024

DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition

Few-shot action recognition is an emerging field in computer vision, primarily focused on meta-learning within the same domain. However, challenges arise in real-world scenario deployment, as gathering extensive labeled data within a specific domain is laborious and time-intensive. Thus, attention shifts towards cross-domain few-shot action recognition, requiring the model to generalize across domains with significant deviations. Therefore, we propose a novel approach, ``Distillation from Mixed-Source Domain", tailored to address this conundrum. Our method strategically integrates insights from both labeled data of the source domain and unlabeled data of the target domain during the training. The ResNet18 is used as the backbone to extract spatial features from the source and target domains. We design two branches for meta-training: the original-source and the mixed-source branches. In the first branch, a Domain Temporal Encoder is employed to capture temporal features for both the source and target domains. Additionally, a Domain Temporal Decoder is employed to reconstruct all extracted features. In the other branch, a Domain Mixed Encoder is used to handle labeled source domain data and unlabeled target domain data, generating mixed-source domain features. We incorporate a pre-training stage before meta-training, featuring a network architecture similar to that of the first branch. Lastly, we introduce a dual distillation mechanism to refine the classification probabilities of source domain features, aligning them with those of mixed-source domain features. This iterative process enriches the insights of the original-source branch with knowledge from the mixed-source branch, thereby enhancing the model's generalization capabilities. Our code is available at URL: \url{https://xxxx/xxxx/xxxx.git}

cs.CV↗

arXiv2024

Analysis of the hidden-charm pentaquark states based on magnetic moment and transition magnetic moment

In this work, we calculate the magnetic moments of the $P^{N^{0}}_ψ$ states and $P^{Δ^{0}}_ψ$ states with valence quark content $\bar{c}cudd $ in molecular model, diquark-diquark-antiquark model and diquark-triquark model, as well as the transition magnetic moments in the molecular model. At the same time, we also calculate magnetic moments and transition magnetic moments of $P^{Δ^{++}}_ψ$ states and $P^{Δ^{-}}_ψ$ states in the molecular model as additional products. Our results show that in the diquark-diquark-antiquark model, the magnetic moments of $ λ$ excitation state are usually larger than the magnetic moments of $ ρ$ excitation state. We find some interesting proportional relationships between the expressions of transition magnetic moments. The results provide important insights for future experimental observation of hidden-charm pentaquark states and help to distinguish their inner structures. With these efforts, our understanding of the properties for the hidden-charm pentaquark states will become more abundant.

hep-ph↗

arXiv2024

High-resolution MHz time- and angle-resolved photoemission spectroscopy based on a tunable vacuum ultraviolet source

Time and angle-resolved photoemission spectroscopy (trARPES) allows direct mapping of the electronic band structure and its dynamic response on femtosecond timescales. Here, we present a new ARPES system, powered by a new fiber-based femtosecond light source in the vacuum ultraviolet (VUV) range, accessing the complete first Brillouin zone for most materials. We present trARPES data on Au(111), polycrystalline Au, Bi2Se3 and TaTe2, demonstrating an energy resolution of 21 meV with a time resolution of <360 fs, at a high repetition rate of 1 MHz. The system is integrated with an extreme ultraviolet (EUV) high harmonic generation (HHG) beamline, enabling excellent tunability of the time-bandwidth resolution.

physics.ins-det↗

arXiv2024

Magnetic moments and axial charges of the octet hidden-charm molecular pentaquark family

In this work, we calculate the magnetic moment and axial charge of the octet hidden-charm molecular pentaquark family in quark model. The Coleman-Glashow sum-rule for the magnetic moments of pentaquark family is always fulfilled independently of SU(3) symmetry breaking. In the $ 8_{2f} $ flavor representation, the magnetic moments of hidden-charm molecular pentaquark states with spin configuration $J^{P}=\frac{1}{2}^{-}(\frac{1}{2}^{+}\otimes0^{-})$ are all equal to $μ_{c}=0.38μ_{N}$. The axial charges of octet hidden-charm molecular pentaquark states are quite small compared to the axial charge of nucleon. The axial charges of the pentaquark states with the spin configuration $J^{P}=\frac{1}{2}^{-}(\frac{1}{2}^{+}\otimes0^{-})$ in $8_{2f}$ flavor representation are all zero.

hep-ph↗

arXiv2024

Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-$\mathrm{M^2}$DF)

In recent years, few-shot action recognition has attracted increasing attention. It generally adopts the paradigm of meta-learning. In this field, overcoming the overlapping distribution of classes and outliers is still a challenging problem based on limited samples. We believe the combination of Multi-modal and Multi-view can improve this issue depending on information complementarity. Therefore, we propose a method of Multi-view Distillation based on Multi-modal Fusion. Firstly, a Probability Prompt Selector for the query is constructed to generate probability prompt embedding based on the comparison score between the prompt embeddings of the support and the visual embedding of the query. Secondly, we establish a Multi-view. In each view, we fuse the prompt embedding as consistent information with visual and the global or local temporal context to overcome the overlapping distribution of classes and outliers. Thirdly, we perform the distance fusion for the Multi-view and the mutual distillation of matching ability from one to another, enabling the model to be more robust to the distribution bias. Our code is available at the URL: \url{https://github.com/cofly2014/MDMF}.

cs.CV↗

arXiv2024

Non-aligned supervision for Real Image Dehazing

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called "Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Additionally, a channel attention network is employed to estimate the three-channel transmission. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. Phone-Hazy and code will be available at https://fanjunkai1.github.io/projectpage/NSDNet/index.html.

cs.CV↗

arXiv2023

Forced Exploration in Bandit Problems

The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have difficulty obtaining information about these distributions to design models for their problems, especially in non-stationary MAB problems. This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds. To this end, we propose a novel algorithm alternating between greedy rule and forced exploration. Our method can be applied to Gaussian, Bernoulli and other subgaussian distributions, and its implementation does not require additional information. We employ a unified analysis method for different forced exploration strategies and provide problem-dependent regret upper bounds for stationary and piecewise-stationary settings. Furthermore, we compare our algorithm with popular bandit algorithms on different reward distributions.

cs.LG↗

arXiv2023

Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CP$\mathbf{M^2}$C)

Recently, few-shot action recognition has significantly progressed by learning the feature discriminability and designing suitable comparison methods. Still, there are the following restrictions. (a) Previous works are mainly based on visual mono-modal. Although some multi-modal works use labels as supplementary to construct prototypes of support videos, they can not use this information for query videos. The labels are not used efficiently. (b) Most of the works ignore the motion feature of video, although the motion features are essential for distinguishing. We proposed a Consistency Prototype and Motion Compensation Network(CLIP-CP$M^2$C) to address these issues. Firstly, we use the CLIP for multi-modal few-shot action recognition with the text-image comparison for domain adaption. Secondly, in order to make the amount of information between the prototype and the query more similar, we propose a novel method to compensate for the text(prompt) information of query videos when text(prompt) does not exist, which depends on a Consistency Loss. Thirdly, we use the differential features of the adjacent frames in two directions as the motion features, which explicitly embeds the network with motion dynamics. We also apply the Consistency Loss to the motion features. Extensive experiments on standard benchmark datasets demonstrate that the proposed method can compete with state-of-the-art results. Our code is available at the URL: https://github.com/xxx/xxx.git.

cs.CV↗

arXiv2023

Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition

In the research field of few-shot learning, the main difference between image-based and video-based is the additional temporal dimension. In recent years, some works have used the Transformer to deal with frames, then get the attention feature and the enhanced prototype, and the results are competitive. However, some video frames may relate little to the action, and only using single frame-level or segment-level features may not mine enough information. We address these problems sequentially through an end-to-end method named "Task-Specific Alignment and Multiple-level Transformer Network (TSA-MLT)". The first module (TSA) aims at filtering the action-irrelevant frames for action duration alignment. Affine Transformation for frame sequence in the time dimension is used for linear sampling. The second module (MLT) focuses on the Multiple-level feature of the support prototype and query sample to mine more information for the alignment, which operates on different level features. We adopt a fusion loss according to a fusion distance that fuses the L2 sequence distance, which focuses on temporal order alignment, and the Optimal Transport distance, which focuses on measuring the gap between the appearance and semantics of the videos. Extensive experiments show our method achieves state-of-the-art results on the HMDB51 and UCF101 datasets and a competitive result on the benchmark of Kinetics and something 2-something V2 datasets. Our code is available at the URL: https://github.com/cofly2014/tsa-mlt.git

cs.CV↗

arXiv2023

ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization

In the realm of machine learning, the study of anomaly detection and localization within image data has gained substantial traction, particularly for practical applications such as industrial defect detection. While the majority of existing methods predominantly use Convolutional Neural Networks (CNN) as their primary network architecture, we introduce a novel approach based on the Transformer backbone network. Our method employs a two-stage incremental learning strategy. During the first stage, we train a Masked Autoencoder (MAE) model solely on normal images. In the subsequent stage, we apply pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels. This process allows the model to learn how to repair corrupted regions and classify the status of each pixel. Ultimately, the model generates a pixel reconstruction error matrix and a pixel anomaly probability matrix. These matrices are then combined to produce an anomaly scoring matrix that effectively detects abnormal regions. When benchmarked against several state-of-the-art CNN-based methods, our approach exhibits superior performance on the MVTec AD dataset, achieving an impressive 97.6% AUC.

cs.CV↗

arXiv2022

Systematic Constructions of Bent-Negabent Functions, 2-Rotation Symmetric Bent-Negabent Functions and Their Duals

Bent-negabent functions have many important properties for their application in cryptography since they have the flat absolute spectrum under the both Walsh-Hadamard transform and nega-Hadamard transform. In this paper, we present four new systematic constructions of bent-negabent functions on $4k, 8k, 4k+2$ and $8k+2$ variables, respectively, by modifying the truth tables of two classes of quadratic bent-negabent functions with simple form. The algebraic normal forms and duals of these constructed functions are also determined. We further identify necessary and sufficient conditions for those bent-negabent functions which have the maximum algebraic degree. At last, by modifying the truth tables of a class of quadratic 2-rotation symmetric bent-negabent functions, we present a construction of 2-rotation symmetric bent-negabent functions with any possible algebraic degrees. Considering that there are probably no bent-negabent functions in the rotation symmetric class, it is the first significant attempt to construct bent-negabent functions in the generalized rotation symmetric class.

cs.IT↗

arXiv2022

EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation

Automatic image segmentation technology is critical to the visual analysis. The autoencoder architecture has satisfying performance in various image segmentation tasks. However, autoencoders based on convolutional neural networks (CNN) seem to encounter a bottleneck in improving the accuracy of semantic segmentation. Increasing the inter-class distance between foreground and background is an inherent characteristic of the segmentation network. However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation. In this paper, we propose a light-weight end-to-end segmentation framework based on multi-task learning, termed Edge Attention autoencoder Network (EAA-Net), to improve edge segmentation ability. Our approach not only utilizes the segmentation network to obtain inter-class features, but also applies the reconstruction network to extract intra-class features among the foregrounds. We further design a intra-class and inter-class features fusion module -- I2 fusion module. The I2 fusion module is used to merge intra-class and inter-class features, and use a soft attention mechanism to remove invalid background information. Experimental results show that our method performs well in medical image segmentation tasks. EAA-Net is easy to implement and has small calculation cost.

cs.CV↗

arXiv2022

TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation

Automatic segmentation of medical images based on multi-modality is an important topic for disease diagnosis. Although the convolutional neural network (CNN) has been proven to have excellent performance in image segmentation tasks, it is difficult to obtain global information. The lack of global information will seriously affect the accuracy of the segmentation results of the lesion area. In addition, there are visual representation differences between multimodal data of the same patient. These differences will affect the results of the automatic segmentation methods. To solve these problems, we propose a segmentation method suitable for multimodal medical images that can capture global information, named TranSiam. TranSiam is a 2D dual path network that extracts features of different modalities. In each path, we utilize convolution to extract detailed information in low level stage, and design a ICMT block to extract global information in high level stage. ICMT block embeds convolution in the transformer, which can extract global information while retaining spatial and detailed information. Furthermore, we design a novel fusion mechanism based on cross attention and selfattention, called TMM block, which can effectively fuse features between different modalities. On the BraTS 2019 and BraTS 2020 multimodal datasets, we have a significant improvement in accuracy over other popular methods.

cs.CV↗

arXiv2022

Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation

We propose a novel style transfer method to quickly create a new visual product with a nice appearance for industrial designers' reference. Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product. Our model, Industrial Style Transfer (InST), consists of large-scale geometric warping (LGW) and interest-consistency texture transfer (ICTT). LGW aims to explore an unsupervised transformation between the shape masks of the source and target products for fitting large-scale shape warping. Furthermore, we introduce a mask smoothness regularization term to prevent the abrupt changes of the details of the source product. ICTT introduces an interest regularization term to maintain important contents of the warped product when it is stylized by using the art style image. Extensive experimental results demonstrate that InST achieves state-of-the-art performance on multiple visual product design tasks, e.g., companies' snail logos and classical bottles (please see Fig. 1). To the best of our knowledge, we are the first to extend the neural style transfer method to create industrial product appearances. Project page: \ulr{https://jcyang98.github.io/InST/home.html}. Code available at: \url{https://github.com/jcyang98/InST}.

cs.CV↗

arXiv2021

A Multi-Tenant Framework for Cloud Container Services

Container technologies have been evolving rapidly in the cloud-native era. Kubernetes, as a production-grade container orchestration platform, has been proven to be successful at managing containerized applications in on-premises datacenters. However, Kubernetes lacks sufficient multi-tenant supports by design, meaning in cloud environments, dedicated clusters are required to serve multiple users, i.e., tenants. This limitation significantly diminishes the benefits of cloud computing, and makes it difficult to build multi-tenant software as a service (SaaS) products using Kubernetes. In this paper, we propose Virtual-Cluster, a new multi-tenant framework that extends Kubernetes with adequate multi-tenant supports. Basically, VirtualCluster provides both control plane and data plane isolations while sharing the underlying compute resources among tenants. The new framework preserves the API compatibility by avoiding modifying the Kubernetes core components. Hence, it can be easily integrated with existing Kubernetes use cases. Our experimental results show that the overheads introduced by VirtualCluster, in terms of latency and throughput, is moderate.

cs.DC↗