Bounded Projection Matrix Approximation with Applications to Community Detection

Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Theoretically, we establish the convergence properties of the proposed algorithm. Numerical experiments demonstrate the superiority of our algorithm over its competitors, such as the semi-definite relaxation method and spectral clustering

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

The within-field and between-field dispersal of weedy rice by combine harvesters

International audienceAbstractWeedy rice (Oryza sativa L.) severely decreases the grain yield and profitability of rice is one of the most significant problems in the majority of rice fields worldwide. Few reports focus on the dispersal of weedy rice, especially how it rapidly spreads to large areas and long distances. Here, we quantify for the first time the within- and between-field dispersal of weedy rice associated with combine harvesting operations. We randomly sampled 31 combine harvesters to determine where and how much weedy rice seeds remained on the machines at three locations in Jiangsu Province, China. Based on the sampling results, the field area over which weedy rice seeds were retained on the combine harvester during harvesting was estimated to assess the within-field dispersibility of weedy rice seeds remaining in the harvesters. A tracking experiment was also carried out by tracing the distribution of weedy rice seeds along harvest trails, to estimate the dispersal of weedy rice seeds within the field being harvested. Weedy rice seeds remained in the harvest pocket, on the pedrail, and the metal plate of the combine harvester. On average, more than 5000 weedy rice seeds which were 22.80% of remaining grains could potentially be transported into adjacent fields by the combine after each rice field infested with weedy rice had been harvested. Of the statistical models compared, a double exponential model simulating the variation in seed retention predicted that weedy rice seeds remaining on the metal plate could be dispersed over 6473.91 m2 or 3236.96 m into the next field during the harvesting operation. Within the field, the number of fallen weedy rice seeds and their dispersal distance were positively correlated to weedy rice panicle density with the combine dispersing most of seeds away from their mother plant thus creating new weed patches. Therefore, fields that were severely infested with weedy rice should be harvested cautiously and separately and seed remaining in a harvester should be avoided to prevent intra- and inter-field, and even cross-regional dispersal of weedy rice

Decoy State Quantum Key Distribution With Modified Coherent State

To beat PNS attack, decoy state quantum key distribution (QKD) based on coherent state has been studied widely. We present a decoy state QKD protocol with modified coherent state (MCS). By destruction quantum interference, MCS with fewer multi-photon events can be get, which may improve key bit rate and security distance of QKD. Through numerical simulation, we show about 2-dB increment on security distance for BB84 protocol.Comment: 4 pages, 4 figure

First-principles study of magnetic properties in V-doped ZnO

A comprehensive theoretical study of electronic and magnetic properties of V-doped ZnO in bulk as well as (112¯0)thin films has been performed using density functional theory. Vanadium atoms substituted at Zn sites show very little selectivity of site occupancy. More importantly, different geometries with ferromagnetic, ferrimagnetic, and antiferromagnetic configurations are found to be energetically nearly degenerate both in Zn1−xVxO bulk and subsurface layers of the thin film. On the other hand, V atoms couple ferromagnetically when they occupy surface sites of the thin film. The diverse magnetic behaviors in V-doped ZnO account for the many reported conflicting experimental results

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

tert-Butyl 2-borono-1H-pyrrole-1-carboxyl­ate

In the crystal structure of the title compound, C9H14BNO4, the boronic acid group and carbamate groups are nearly co-planar with the pyrrole ring, making dihedral angles of 0.1 (2) and 2.2 (2)°, respectively. Intra­molecular and inter­molecular O—H⋯O hydrogen bonds help to stabilize the structure, the latter interaction leading to inversion dimers.