Cloud Radiative Effects on MJO Development in DYNAMO

Observed Madden–Julian oscillation (MJO) events are examined with the aid of regional model simulations to understand the role of cloud radiative effects in the MJO development. The importance of this role is demonstrated by the absence of the MJO in the model simulations that contain no cloud radiative effects. Comparisons of model simulations with and without the cloud radiative effects and observation help identify the major processes arising from those effects. Those processes develop essentially from heating in the upper troposphere due to shortwave absorption within anvil clouds in the upper troposphere and the convergence of longwave radiation in the middle to upper troposphere, with a peak at 300 hPa, during deep convection. First, that heating adds extra buoyancy and accelerates the rising motion in the upper troposphere in deep convection. The vertical acceleration in the upper troposphere creates a vacuum effect and demands for more deep convection to develop. Second, in response to that demand and required by mass balance arises the large-scale horizontal and vertical mass, moisture, and energy convergence. It strengthens deep convection and, with the feedback from continuing cloud radiative effect, creates conditions that can perpetuate deep convection and MJO development. That perpetuation does not occur however because those processes arising from the cloud radiative heating in the upper troposphere stabilize the troposphere until it supports no further deep convection. Weakening deep convection reduces cloud radiative effects. The subsequent reduction of the vacuum effect in the upper troposphere diminishes deep convection completing an MJO cycle. These results advance our understanding of the development of the MJO in the radiative–convective system over warm waters in the tropics. They show that while the embryo of intraseasonal oscillation may exist in the system its growth/development is largely dependent on cloud radiative effects and feedbacks

Competitive analysis of interrelated price online inventory problems with demands

This paper investigates the interrelated price online inventory problems in which decisions as to when and how much to replenish must be made in an online fashion to meet some demand even without concrete knowledge of future prices. The objective of the decision maker is to minimize the total cost with the demands met. Two different types of demand are considered carefully, which are linearly related demand to price and exponentially related demand to price. In this paper, the prices are online with only the price range variation known in advance, which are interrelated with the preceding price. Two models of price correlations are investigated. Namely an exponential model and a logarithmic model. The corresponding algorithms of the problems are developed and the competitive ratio of the algorithms are also derived by the solutions of linear programming

Competitive analysis of interrelated price online inventory problems with demands

This paper investigates the interrelated price online inventory problems in which decisions as to when and how much to replenish must be made in an online fashion to meet some demand even without concrete knowledge of future prices. The objective of the decision maker is to minimize the total cost with the demands met. Two different types of demand are considered carefully, which are linearly related demand to price and exponentially related demand to price. In this paper, the prices are online with only the price range variation known in advance, which are interrelated with the preceding price. Two models of price correla- tions are investigated. Namely an exponential model and a logarithmic model. The corresponding algorithms of the problems are developed and the competitive ratio of the algorithms are also derived by the solutions of linear programming

WeakPolyp: You Only Look Bounding Box for Polyp Segmentation

Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations are much cheaper and more accessible. Thus, to reduce labeling cost, we propose to learn a weakly supervised polyp segmentation model (i.e., WeakPolyp) completely based on bounding box annotations. However, coarse bounding boxes contain too much noise. To avoid interference, we introduce the mask-to-box (M2B) transformation. By supervising the outer box mask of the prediction instead of the prediction itself, M2B greatly mitigates the mismatch between the coarse label and the precise prediction. But, M2B only provides sparse supervision, leading to non-unique predictions. Therefore, we further propose a scale consistency (SC) loss for dense supervision. By explicitly aligning predictions across the same image at different scales, the SC loss largely reduces the variation of predictions. Note that our WeakPolyp is a plug-and-play model, which can be easily ported to other appealing backbones. Besides, the proposed modules are only used during training, bringing no computation cost to inference. Extensive experiments demonstrate the effectiveness of our proposed WeakPolyp, which surprisingly achieves a comparable performance with a fully supervised model, requiring no mask annotations at all.Comment: accepted by MICCAI 2023, codes are available at https://github.com/weijun88/WeakPoly

Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks

In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model, a team of drone base stations (DBSs) is dispatched to cooperatively serve clusters of ground users that have dynamic and unpredictable uplink access demands. In this scenario, the DBSs must cooperatively navigate in the considered area to maximize coverage of the dynamic requests of the ground users. This trajectory design problem is posed as an optimization framework whose goal is to find optimal trajectories that maximize the fraction of users served by all DBSs. To find an optimal solution for this non-convex optimization problem under unpredictable environments, a value decomposition based reinforcement learning (VDRL) solution coupled with a meta-training mechanism is proposed. This algorithm allows the DBSs to dynamically learn their trajectories while generalizing their learning to unseen environments. Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem. Simulation results show that, even without meta-training, the proposed VD-RL algorithm can achieve a 53.2% improvement of the service coverage and a 30.6% improvement in terms of the convergence speed, compared to baseline multi-agent algorithms. Meanwhile, the use of meta-learning improves the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must deal with a previously unseen task

Chromosomal DNA deletion confers phage resistance to Pseudomonas aeruginosa.

Bacteria develop a broad range of phage resistance mechanisms, such as prevention of phage adsorption and CRISPR/Cas system, to survive phage predation. In this study, Pseudomonas aeruginosa PA1 strain was infected with lytic phage PaP1, and phage-resistant mutants were selected. A high percentage (~30%) of these mutants displayed red pigmentation phenotype (Red mutant). Through comparative genomic analysis, one Red mutant PA1r was found to have a 219.6 kb genomic fragment deletion, which contains two key genes hmgA and galU related to the observed phenotypes. Deletion of hmgA resulted in the accumulation of a red compound homogentisic acid; while A galU mutant is devoid of O-antigen, which is required for phage adsorption. Intriguingly, while the loss of galU conferred phage resistance, it significantly attenuated PA1r in a mouse infection experiment. Our study revealed a novel phage resistance mechanism via chromosomal DNA deletion in P. aeruginosa

Competitive analysis of online inventory problem with interrelated prices

This paper investigates the online inventory problem with interrelated prices in which a decision of when and how much to replenish must be made in an online fashion even without concrete knowledge of future prices. Four new online models with different price correlations are proposed in this paper, which are the linear-decrease model, the log-decrease model, the logarithmic model and the exponential model. For the first two models, the online algorithms are developed, and as the performance measure of online algorithm, the upper and lower bounds of competitive ratios of the algorithms are derived respectively. For the exponential and logarithmic models, the online algorithms are proposed by the solution of linear programming and the corresponding competitive ratios are analyzed, respectively. Additionally, the algorithm designed for the exponential model is optimal, and the algorithm for the logarithmic model is optimal only under some certain conditions. Moreover, some numerical examples illustrate that the algorithms based on the dprice-conservative strategy are more suitable when the purchase price fluctuates relatively flat

Classification of coal gangue pile vegetation based on UAV remote sensing

The accurate classification of vegetation species is the basis for the evaluation of vegetation restoration effect of coal gangue pile. In this paper, the visible image of coal gangue pile in different seasons was obtained by UAV remote sensing technology. The color space conversion and texture filtering were used to adequately explore the rich features of color, structure and texture in the visible image. Then, the traditional artificial feature selection method was improved, which could quickly, simply and efficiently screen features information to obtain the optimal classification features, and the optimized results were fused with RGB images to obtain multi-feature fusion images. Finally, based on two stages of RGB images and multi-feature fusion images, the vegetation of coal gangue pile was classified by three supervised classification methods, including support vector machine (SVM), maximum likelihood (ML) and neural network (NN). Meanwhile, the accuracy of classification results was evaluated by confusion matrix and the dynamic changes of vegetation were analyzed. The results showed that the improved artificial feature selection method could screen out the optimal classification features of coal gangue pile vegetation in different seasons. The selected classification features can not only effectively reflect the differences of various ground features, but also reduce the redundancy of feature information to improve the accuracy and efficiency of image classification. The classification result based on Support Vector Machine Classification (SVM) combined with multi-feature fusion image had highest classification accuracy, and the overall classification accuracy could reach 90.60%, and the corresponding Kappa coefficient is 0.8780, which was 9.74% and 0.1265 higher than that of RGB image of the same period, respectively. And, the accuracy of MLC and NNC classification methods was less improved. Compared with the RGB images of the same period, the overall classification accuracy could be improved by 6.95% and 3.93%, respectively, and the corresponding Kappa coefficient could be improved by 0.0845 and 0.0541, respectively. At the same time, based on the result of optimal classification, this paper evaluated the vegetation restoration effect of coal gangue pile in Changcun from the perspectives of vegetation coverage and vegetation allocation pattern. The results showed that a variety of different vegetation allocation patterns were adopted by the coal gangue pile, and the vegetation coverage in autumn and summer is higher than 75%. The overall effect of vegetation restoration was better. This study could provide reference for the identification and classification of coal gangue piles vegetation information based on UAV visible light image, and meanwhile provide opinions or suggestions for the later management and maintenance of coal gangue piles vegetation restoration

Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning

We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective. Building upon the recent understanding that the lack of machine learning model's robustness is the tendency of the model's learning of spurious features, we aim to solve this problem from its root at the data perspective by removing the spurious features from the data before training. In particular, we introduce a software that helps the users to better prepare the data for training image classification models by allowing the users to annotate the spurious features at the pixel level of images. To facilitate this process, our software also leverages recent advances to help identify potential images and pixels worthy of attention and to continue the training with newly annotated data. Our software is hosted at the GitHub Repository https://github.com/HaohanWang/Robustar.Comment: This paper introduces the first release of our software. The paper is expected to be updated as we continue to develop the softwar