Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN

A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation

Generative Adversarial Networks (GANs) and their variants have achieved remarkable success on natural images. However, their performance degrades when applied to remote sensing (RS) images, and the discriminator often suffers from the overfitting problem. In this paper, we examine the differences between natural and RS images and find that the intrinsic dimensions of RS images are much lower than those of natural images. As the discriminator is more susceptible to overfitting on data with lower intrinsic dimension, it focuses excessively on local characteristics of RS training data and disregards the overall structure of the distribution, leading to a faulty generation model. In respond, we propose a novel approach that leverages the real data manifold to constrain the discriminator and enhance the model performance. Specifically, we introduce a learnable information-theoretic measure to capture the real data manifold. Building upon this measure, we propose manifold alignment regularization, which mitigates the discriminator's overfitting and improves the quality of generated samples. Moreover, we establish a unified GAN framework for manifold alignment, applicable to both supervised and unsupervised RS image generation tasks

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models

Cloning and characterization of maize ZmSPK1, a homologue to nonfermenting1-related protein kinase2

SnRK2s play important roles in plant stresses responses. One full-length cDNA encoding a SnRK2b homologue was isolated from maize by RT-PCR and named as ZmSPK1 (for stress-induced protein kinase). The ZmSPK1 protein has 364 amino acids with an estimated molecular mass of 41.8 KD and an isoelectric point of 5.8. The deduced protein sequence has the closest identities to the members of SnRK2b group. RT-PCR analysis showed that the ZmSPK1 expression was induced by mannitol, salt and abscisic acid (ABA). Furthermore, in different tissues the ZmSPK1 showed different expression patterns and was most abundant in reproductive organs. These results suggested that ZmSPK1 might play multiple roles in abiotic stress resistance pathways, as well as in plant reproductive development.Key words: Zea mays L., SnRK2b, expression pattern, abiotic stres

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Influence of polymerisation conditions on the properties of polymer/clay nanocomposite hydrogels

Free-radical polymerisation of acrylamide derivatives in the presence of exfoliated clay platelets has recently emerged as a new technique for the synthesis of strong and tough nanocomposite hydrogels (NCHs) with a unique hybrid organic/inorganic network structure. The central intent of many research studies in the field of NCHs conducted so far was to change hydrogel properties with the introduction of various clays and variation of the clay content. Here, we demonstrate that the properties of NCHs significantly vary depending on initiating conditions used for hydrogel synthesis via in situ polymerisation in solutions of high monomer concentrations (above 1 mol L-1 ). A unique, complementary combination of real-time dynamic rheology and pulsed NMR/MRI has been used to study the influence of the composition of a redox initiating system on the gelation process and hydrogel properties. The molar ratio of the persulphate initiator to tertiary amine activator affects the polymerisation kinetics, morphology and mechanical properties of the hydrogels. We further show that activator-dominated systems tend to produce hydrogels with higher storage modulus and lower water intake. This trend is attributed to the increase in the cross-linking degree. From the analysis of the water state in NCH and hydrogels prepared with and without an organic cross-linker, it was concluded that clay platelets did not form covalent bonds with polymer molecules but contributed to the formation of a physical network. There is evidence of self-crosslinking of polymer chains during acrylamide polymerisation at high monomer concentration. The composition of the initiating system influences the number of formed self-crosslinks

formal based operation strategy design for collaborative shared teleoperation system

The cooperative shared tele-operation technology is used to overcome the impact of satellite-earth communication delay and non-structural space environment. To get the appropriate collaborative shared teleoperation system operation sequence, the formal based operation strategy is proposed. Firstly, the task sequence algorithm is introduced. In the algorithm, GSPN (generalize stochastic Petri net) based task sequence models for teleoperation task are established and evaluated to work out the optimal task sequence. And the task sequence is turned into a working mode sequence. Then the working mode transform algorithm is designed in order to reach a fast and stable transform. Based on directed graph theory, the working mode topology graph is obtained. The optimal working mode transform steps can be derived by shortest path algorithm. Finally, unit replacement task is proposed as example to illustrate the operation strategy. The result in this paper can be used in cooperative shared teleoperation system design and on-orbit system task design. It also can be the auxiliary method for teleoperation operator

Rolling simulation method for acquiring pixel parameter of airborne 3D imaging LiDAR

Airborne 3D scannerless imaging LiDAR is an active detection means without scanner mechanism and has a broad application prospects in emergency rescue and flight safety. How to find appropriate pixel parameter is an critical problem for applying this approach. Low resolution may not meet requirement while high resolution will cause unnecessary power consumption. In this paper, a task-based rolling simulation method is proposed for acquiring pixel parameter of 3D scannerless imaging LiDAR. First, recognition level of aerial object is obtained for task requirement analysis. Then a scheme of generating 3D point clouds is given to acquire data of 3D scannerless imaging LiDAR. By applying Zernike moment's feature extraction algorithm and correlation coefficient method, the data features of the simulated point cloud is extracted and recognized to decide if the recognition results satisfy the task requirement. Based on numerical results from a case study, it has been demonstrated that, by implementing the proposed approach, the minimum pixels of airborne 3D imaging LiDAR satisfying task requirement can be achieved. © 2014 IEEE.Airborne 3D scannerless imaging LiDAR is an active detection means without scanner mechanism and has a broad application prospects in emergency rescue and flight safety. How to find appropriate pixel parameter is an critical problem for applying this approach. Low resolution may not meet requirement while high resolution will cause unnecessary power consumption. In this paper, a task-based rolling simulation method is proposed for acquiring pixel parameter of 3D scannerless imaging LiDAR. First, recognition level of aerial object is obtained for task requirement analysis. Then a scheme of generating 3D point clouds is given to acquire data of 3D scannerless imaging LiDAR. By applying Zernike moment's feature extraction algorithm and correlation coefficient method, the data features of the simulated point cloud is extracted and recognized to decide if the recognition results satisfy the task requirement. Based on numerical results from a case study, it has been demonstrated that, by implementing the proposed approach, the minimum pixels of airborne 3D imaging LiDAR satisfying task requirement can be achieved. © 2014 IEEE