Interactive Feature Embedding for Infrared and Visible Image Fusion

General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms

Bulk-Explosion-Induced Metal Spattering during Laser Processing

Spattering has been a problem in metal processing involving high-power lasers, like laser welding, machining, and recently, additive manufacturing. Limited by the capabilities of in situ diagnostic techniques, typically imaging with visible light or laboratory x-ray sources, a comprehensive understanding of the laser-spattering phenomenon, particularly the extremely fast spatters, has not been achieved yet. Here, using MHz single-pulse synchrotron-x-ray imaging, we probe the spattering behavior of Ti-6Al-4V with micrometer spatial resolution and subnanosecond temporal resolution. Combining direct experimental observations, quantitative image analysis, as well as numerical simulations, our study unravels a novel mechanism of laser spattering: The bulk explosion of a tonguelike protrusion forming on the front keyhole wall leads to the ligamentation of molten metal at the keyhole rims and the subsequent spattering. Our study confirms the critical role of melt and vapor flow in the laser-spattering process and opens a door to manufacturing spatter- and defect-free metal parts via precise control of keyhole dynamics

Long-term effects of restoration on the links between above-and belowground biodiversity in degraded Horqin sandy grassland, Northern China

Long-term ecological restoration plays an important role in the sustainable development of degraded grassland ecosystem. In this study, the levels of species diversity, genetic diversity and soil microbial diversity in restored grassland were measured by vegetation survey, DNA barcoding and soil microbial high-throughput sequencing technology, so as to explore the relationship between above- and belowground biodiversity and its driving factors in Horqin sandy grassland. In this study, the results found that herb are dominated in restoration grassland types. Plant species richness (SR) from post-non-grazing restoration plot (NGR) communities was significantly higher than other restoration communities (10 ± 1.1, p = 0.004). Genetic diversity indices of dominant plant species in chloroplast DNA (cpDNA), were remarkable greater than nuclear DNA (nrDNA) in each recovering sandy grassland plots (amplitude of difference was 44.8%–70.5% in allelic richness (AR), 81.9%–128.1% in expected heterozygosity (HE)). The soil bacterial and fungal richness from natural mobile dune grassland (NM) communities was notably lower than that from recovering grassland types (1641.9 ± 100.4, p < 0.001; 533 ± 16.6, p < 0.001). In this study, heterogeneous levels of genetic variability among different recovering sandy grassland types were detected. Correlation analyses revealed that there were positive correlations between species diversity and genetic diversity (SR & AR: r = 0.56, R2 = 0.31, p < 0.001; SR & HE: r = 0.33, R2 = 0.11, p = 0.045) and a negative correlation between soil microbial diversity and genetic diversity (r = -0.44, R2 = 0.19, p = 0.005). The final structural equation model explained 38% of the variance in SR, 57% in AR, 52% in soil microbial diversity (SD), 49% in aboveground biomass (AGB), 87% in soil organic carbon (SOC), 47% in soil alkali-hydrolyzable nitrogen (SAN) and 69% in soil available phosphorus (SOP). Long-term ecological restoration had significant direct positive effects on AGB, SOC, SAN, SOP, AR, SR and SD. There was a negative correlation between above- and belowground biodiversity and biological and abiotic factors. The results of this study have clarified the above- and underground biodiversity levels of sandy grassland and the relationship with driving factors under long-term ecological restoration measures, and will provide effective support for the management and sustainable development of sandy grassland