A review of parallel computing for large-scale remote sensing image mosaicking

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed

Compact all-fiber quartz-enhanced photoacoustic spectroscopy sensor with a 30.72 kHz quartz tuning fork and spatially resolved trace gas detection

An ultra compact all-fiber quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor using quartz tuning fork (QTF) with a low resonance frequency of 30.72 kHz was demonstrated. Such a sensor architecture has the advantages of easier optical alignment, lower insertion loss, lower cost, and more compact compared with a conventional QEPAS sensor using discrete optical components for laser delivery and coupling to the QTF. A fiber beam splitter and three QTFs were employed to perform multi-point detection and demonstrated the potential of spatially resolved measurements

Hardy-Littlewood-Sobolev inequalities with partial variable weight on the upper half space and related inequalities

In this paper, we establish a class of Hardy-Littlewood-Sobolev inequality with partial variable weight functions on the upper half space using a weighted Hardy type inequality. Overcoming the impact of weighted functions, the existence of extremal functions is proved via the concentration compactness principle, whereas Riesz rearrangement inequality is not available. Moreover, the cylindrical symmetry with respect to $t$-axis and the explicit forms on the boundary of all nonnegative extremal functions are discussed via the method of moving planes and method of moving spheres, as well as, regularity results are obtained by the regularity lift lemma and bootstrap technique. As applications, we obtain some weighted Sobolev inequalities with partial variable weight function for Laplacian and fractional Laplacian

A roadmap to engineering antiviral natural products synthesis in microbes

Natural products continue to be the inspirations for us to discover and acquire new drugs. The seemingly unstoppable viruses have kept records high to threaten human health and well-being. The diversity and complexity of natural products (NPs) offer remarkable efficacy and specificity to target viral infection steps and serve as excellent source for antiviral agents. The discovery and production of antiviral NPs remain challenging due to low abundance in their native hosts. Reconstruction of NP biosynthetic pathways in microbes is a promising solution to overcome this limitation. In this review, we surveyed 23 most prominent NPs (from more than 200 antiviral NP candidates) with distinct antiviral mode of actions and summarized the recent metabolic engineering effort to produce these compounds in various microbial hosts. We envision that the scalable and low-cost production of novel antiviral NPs, enabled by metabolic engineering, may light the hope to control and eradicate the deadliest viruses that plague our society and humanity

Plasma glow discharge as a tool for surface modification of catalytic solid oxides: A case study of La0.6Sr0.4Co0.2Fe0.8O3-δ perovskite

Performance of solid oxide fuel cells (SOFCs) is hindered by the sluggish catalytic kinetics on the surfaces of cathode materials. It has recently been reported that improved electrochemical activity of perovskite oxides can be obtained with the cations or the oxides of some metallic elements at the surface. Here, we used a cost-effective plasma glow charge method as a generic tool to deposit nano-size metallic particles onto the surface of SOFC materials. Ni nano-scale patterns were successfully coated on the La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) surface. The microstructure could be well controlled. The kinetics of oxygen exchange on the modified LSCF surface was promoted significantly, confirmed by electrical conductivity relaxation (ECR) measurement

Temporal Network of Depressive Symptoms across College Students with Distinct Depressive Trajectories during the COVID-19 Pandemic

BackgroundThere are marked differences in how individuals respond and adapt to depressive symptoms over time during the strain of public health emergencies; however, few studies have examined the interrelations between depressive symptoms in distinct depressive trajectories from the COVID-19 outbreak period to the COVID-19 control period. Therefore, this study conducted cross-lagged panel networks to investigate the temporal relationships between depressive symptoms across distinct depressive trajectories from the COVID-19 outbreak period (T1) to the COVID-19 control period (T2).MethodsA total of 35,516 young participants from the College Students' Behavior and Health Cohort during the COVID-19 pandemic were included in the current study. Depressive symptoms were self-reported using the nine-item Patient Health Questionnaire. Unique longitudinal relationships between symptoms during the COVID-19 pandemic were estimated using a cross-lagged panel network.ResultsLongitudinal relationships across distinct depressive trajectories were unique during the COVID-19 pandemic. Specifically, suicidal ideation at T1 in the chronic- and delayed-dysfunction groups was most predictive of other symptoms at T2, whereas "sleep" at T1 in the recovery group and "lack of energy" at T1 in the resistance group may be strongly related to the remission of other depressive symptoms at T2.ConclusionsThese exploratory findings demonstrate the directionality of relationships underlying individual symptoms in the youth and highlight suicidal ideation, sleep, and energy as potential influencers of other depressive symptoms across distinct depressive trajectories. Targeting those symptoms during the outbreak period of COVID 19 would theoretically have been beneficial in preventing and/or reducing the likelihood of spontaneous depression during the subsequent control period

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

In communications theory, the capacity of multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is fundamentally determined by wireless channels, which exhibit both diversity and correlation in spatial, frequency and temporal domains. It is further envisioned to exploit the inherent nature of channels, namely representation, to achieve geolocation-based MIMO transmission for 6G, exemplified by the fully-decoupled radio access network (FD-RAN). Accordingly, this paper first employs self-supervised learning to obtain channel representation from unlabeled channel, then proposes a channel generation assisted approach for determining MIMO precoding matrix solely based on geolocation. Specifically, we exploit the small-scale temporal domain variations of channels at a fixed geolocation, and design an ingenious pretext task tailored for contrastive learning. Then, a Transformer-based encoder is trained to output channel representations. We further develop a conditional diffusion generator to generate channel representations from geolocation. Finally, a Transformer-encoder-based decoder is utilized to reconstruct channels from generated representations, where the optimal channel is selected for calculating the precoding matrix for both single and dual BS transmission. We conduct experiments on a public ray-tracing channel dataset, and the extensive simulation results demonstrate the effectiveness of our channel representation method, and also showcase the performance improvement in geolocation-based MIMO transmission