269 research outputs found
Theoretical Analysis of Binary Masks in Snapshot Compressive Imaging Systems
Snapshot compressive imaging (SCI) systems have gained significant attention
in recent years. While previous theoretical studies have primarily focused on
the performance analysis of Gaussian masks, practical SCI systems often employ
binary-valued masks. Furthermore, recent research has demonstrated that
optimized binary masks can significantly enhance system performance. In this
paper, we present a comprehensive theoretical characterization of binary masks
and their impact on SCI system performance. Initially, we investigate the
scenario where the masks are binary and independently identically distributed
(iid), revealing a noteworthy finding that aligns with prior numerical results.
Specifically, we show that the optimal probability of non-zero elements in the
masks is smaller than 0.5. This result provides valuable insights into the
design and optimization of binary masks for SCI systems, facilitating further
advancements in the field. Additionally, we extend our analysis to characterize
the performance of SCI systems where the mask entries are not independent but
are generated based on a stationary first-order Markov process. Overall, our
theoretical framework offers a comprehensive understanding of the performance
implications associated with binary masks in SCI systems
MC-MLP:Multiple Coordinate Frames in all-MLP Architecture for Vision
In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered
attention from researchers. This paper introduces MC-MLP, a general MLP-like
backbone for computer vision that is composed of a series of fully-connected
(FC) layers. In MC-MLP, we propose that the same semantic information has
varying levels of difficulty in learning, depending on the coordinate frame of
features. To address this, we perform an orthogonal transform on the feature
information, equivalent to changing the coordinate frame of features. Through
this design, MC-MLP is equipped with multi-coordinate frame receptive fields
and the ability to learn information across different coordinate frames.
Experiments demonstrate that MC-MLP outperforms most MLPs in image
classification tasks, achieving better performance at the same parameter level.
The code will be available at: https://github.com/ZZM11/MC-MLP
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
In this paper, we explore a principal way to enhance the quality of object
masks produced by different segmentation models. We propose a model-agnostic
solution called SegRefiner, which offers a novel perspective on this problem by
interpreting segmentation refinement as a data generation process. As a result,
the refinement process can be smoothly implemented through a series of
denoising diffusion steps. Specifically, SegRefiner takes coarse masks as
inputs and refines them using a discrete diffusion process. By predicting the
label and corresponding states-transition probabilities for each pixel,
SegRefiner progressively refines the noisy masks in a conditional denoising
manner. To assess the effectiveness of SegRefiner, we conduct comprehensive
experiments on various segmentation tasks, including semantic segmentation,
instance segmentation, and dichotomous image segmentation. The results
demonstrate the superiority of our SegRefiner from multiple aspects. Firstly,
it consistently improves both the segmentation metrics and boundary metrics
across different types of coarse masks. Secondly, it outperforms previous
model-agnostic refinement methods by a significant margin. Lastly, it exhibits
a strong capability to capture extremely fine details when refining
high-resolution images. The source code and trained models are available at
https://github.com/MengyuWang826/SegRefiner.Comment: NeurIPS 2023, Code: https://github.com/MengyuWang826/SegRefine
The distribution variation of pathogens and virulence factors in different geographical populations of giant pandas
Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas
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Viruses mobilize plant immunity to deter nonvector insect herbivores.
A parasite-infected host may promote performance of associated insect vectors; but possible parasite effects on nonvector insects have been largely unexplored. Here, we show that Begomovirus, the largest genus of plant viruses and transmitted exclusively by whitefly, reprogram plant immunity to promote the fitness of the vector and suppress performance of nonvector insects (i.e., cotton bollworm and aphid). Infected plants accumulated begomoviral βC1 proteins in the phloem where they were bound to the plant transcription factor WRKY20. This viral hijacking of WRKY20 spatiotemporally redeployed plant chemical immunity within the leaf and had the asymmetrical benefiting effects on the begomoviruses and its whitefly vectors while negatively affecting two nonvector competitors. This type of interaction between a parasite and two types of herbivores, i.e., vectors and nonvectors, occurs widely in various natural and agricultural ecosystems; thus, our results have broad implications for the ecological significance of parasite-vector-host tripartite interactions
MiRKAT-MC: A Distance-Based Microbiome Kernel Association Test With Multi-Categorical Outcomes
Increasing evidence has elucidated that the microbiome plays a critical role in many human diseases. Apart from continuous and binary traits that measure the extent or presence of a disease, multi-categorical outcomes including variations/subtypes of a disease or ordinal levels of disease severity are commonly seen in clinical studies. On top of that, studies with clustered design (i.e., family-based and longitudinal studies) are popular alternatives to population-based ones as they are able to identify characteristics on both individual and population levels and to investigate the trajectory of traits of interest over time. However, existing methods for microbiome association analysis are inadequate to handle multi-categorical outcomes, neither independent nor clustered data. We propose a microbiome kernel association test with multi-categorical outcomes (MiRKAT-MC). Our method is versatile to deal with both nominal and ordinal outcomes for independent and clustered data. In addition, it incorporates multiple ecological distances to allow for different association patterns between outcomes and microbiome compositions to be incorporated. A computationally efficient pseudo-permutation strategy is used to evaluate the statistical significance. Comprehensive simulations show that MiRKAT-MC preserves the nominal type I error and increases statistical powers under various scenarios and data types. We also apply MiRKAT-MC to real data sets with nominal and ordinal outcomes to gain biological insights. MiRKAT-MC is easy to implement, and freely available via an R package at https://github.com/Zhiwen-Owen-Jiang/MiRKATMC with a Graphical User Interface through R Shinny also available
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