SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models

Hyperspectral Image (HSI) classification is an important issue in remote sensing field with extensive applications in earth science. In recent years, a large number of deep learning-based HSI classification methods have been proposed. However, existing methods have limited ability to handle high-dimensional, highly redundant, and complex data, making it challenging to capture the spectral-spatial distributions of data and relationships between samples. To address this issue, we propose a generative framework for HSI classification with diffusion models (SpectralDiff) that effectively mines the distribution information of high-dimensional and highly redundant data by iteratively denoising and explicitly constructing the data generation process, thus better reflecting the relationships between samples. The framework consists of a spectral-spatial diffusion module, and an attention-based classification module. The spectral-spatial diffusion module adopts forward and reverse spectral-spatial diffusion processes to achieve adaptive construction of sample relationships without requiring prior knowledge of graphical structure or neighborhood information. It captures spectral-spatial distribution and contextual information of objects in HSI and mines unsupervised spectral-spatial diffusion features within the reverse diffusion process. Finally, these features are fed into the attention-based classification module for per-pixel classification. The diffusion features can facilitate cross-sample perception via reconstruction distribution, leading to improved classification performance. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods. For the sake of reproducibility, the source code of SpectralDiff will be publicly available at https://github.com/chenning0115/SpectralDiff

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product uncertainty quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS

A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution

Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process

Laser-Induced Damage Initiation and Growth of Optical Materials

The lifetime of optical components is determined by the combination of laser-induced damage initiation probability and damage propagation rate during subsequent laser shots. This paper reviews both theoretical and experimental investigations on laser-induced damage initiation and growth at the surface of optics. The damage mechanism is generally considered as thermal absorption and electron avalanche, which play dominant roles for the different laser pulse durations. The typical damage morphology in the surface of components observed in experiments is also closely related to the damage mechanism. The damage crater in thermal absorption process, which can be estimated by thermal diffusion model, is typical distortion, melting, and ablation debris often with an elevated rim caused by melted material flow and resolidification. However, damage initiated by electron avalanche is often accompanied by generation of plasma, crush, and fracture, which can be explained by thermal explosion model. Damage growth at rear surface of components is extremely severe which can be explained by several models, such as fireball growth, impact crater, brittle fracture, and electric field enhancement. All the physical effects are not independent but mutually coupling. Developing theoretical models of multiphysics coupling are an important trend for future theoretical research. Meanwhile, more attention should be paid to integrated analysis both in theory and experiment

Absolute co-supplement and absolute co-coclosed modules

A module M is called an absolute co-coclosed (absolute co-supplement) module if whenever M ≅ T/X the submodule X of T is a coclosed (supplement) submodule of T. Rings for which all modules are absolute co-coclosed (absolute co-supplement) are precisely determined. We also investigate the rings whose (finitely generated) absolute co-supplement modules are projective. We show that a commutative domain R is a Dedekind domain if and only if every submodule of an absolute co-supplement R-module is absolute co-supplement. We also prove that the class Coclosed of all short exact sequences 0→A→B→C→0 such that A is a coclosed submodule of B is a proper class and every extension of an absolute co-coclosed module by an absolute co-coclosed module is absolute co-coclosed.Scientific and Technical Research Council of Turke

Chlamydia trachomatis Co-opts the FGF2 Signaling Pathway to Enhance Infection

The molecular details of Chlamydia trachomatis binding, entry, and spread are incompletely understood, but heparan sulfate proteoglycans (HSPGs) play a role in the initial binding steps. As cell surface HSPGs facilitate the interactions of many growth factors with their receptors, we investigated the role of HSPG-dependent growth factors in C. trachomatis infection. Here, we report a novel finding that Fibroblast Growth Factor 2 (FGF2) is necessary and sufficient to enhance C. trachomatis binding to host cells in an HSPG-dependent manner. FGF2 binds directly to elementary bodies (EBs) where it may function as a bridging molecule to facilitate interactions of EBs with the FGF receptor (FGFR) on the cell surface. Upon EB binding, FGFR is activated locally and contributes to bacterial uptake into non-phagocytic cells. We further show that C. trachomatis infection stimulates fgf2 transcription and enhances production and release of FGF2 through a pathway that requires bacterial protein synthesis and activation of the Erk1/2 signaling pathway but that is independent of FGFR activation. Intracellular replication of the bacteria results in host proteosome-mediated degradation of the high molecular weight (HMW) isoforms of FGF2 and increased amounts of the low molecular weight (LMW) isoforms, which are released upon host cell death. Finally, we demonstrate the in vivo relevance of these findings by showing that conditioned medium from C. trachomatis infected cells is enriched for LMW FGF2, accounting for its ability to enhance C. trachomatis infectivity in additional rounds of infection. Together, these results demonstrate that C. trachomatis utilizes multiple mechanisms to co-opt the host cell FGF2 pathway to enhance bacterial infection and spread