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Numerical precipitation prediction plays a crucial role in weather forecasting and has broad applications in public services including aviation management and urban disaster early-warning systems. However, numerical weather prediction (NWP) models are often constrained by a systematic bias due to coarse spatial resolution, lack of parameterizations, and limitations of observation and conventional meteorological models, including constrained sample size and long-tail distribution. To address these issues, we present a data-driven deep learning model, named the ordinal distribution autoencoder (ODA), which principally includes a precipitation confidence network and a combinatorial network that contains two blocks, i.e., a denoising autoencoder block and an ordinal distribution regression block. As an expert-free model for bias correction of precipitation, it can effectively correct numerical precipitation prediction based on meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and SMS-WARMS, an NWP model used in East China. Experiments in the two NWP models demonstrate that, compared with several classical machine-learning algorithms and deep learning models, our proposed ODA generally performs better in bias correction. [ABSTRACT FROM AUTHOR]

In plants, stomatal guard cells are one of the most dynamic cell types, rapidly changing their shape and size in response to environmental and intrinsic signals to control gas exchange at the plant surface. Quantitative and systematic knowledge of the biomechanical underpinnings of stomatal dynamics will enable strategies to optimize stomatal responsiveness and improve plant productivity by enhancing the efficiency of photosynthesis and water use. Recent developments in microscopy, mechanical measurements, and computational modeling have revealed new insights into the biomechanics of stomatal regulation and the genetic, biochemical, and structural origins of how plants achieve rapid and reliable stomatal function by tuning the mechanical properties of their guard cell walls. This review compares historical and recent experimental and modeling studies of the biomechanics of stomatal complexes, highlighting commonalities and contrasts between older and newer studies. Key gaps in our understanding of stomatal functionality are also presented, along with assessments of potential methods that could bridge those gaps. [ABSTRACT FROM AUTHOR]

The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLicity (Semantics-sensitive Integrated Matching for Picture Libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other region-based retrieval systems, an image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-non-textured, graph-photograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLicity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations. Index Terms--Content-based image retrieval, image classification, image segmentation, integrated region matching, clustering, robustness.

Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications including image enhancement for digital cameras, target recognition, image indexing for content-based retrieval, and 3D microscopic image analysis. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multiscale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with the state of the art algorithms, this new algorithm provides better accuracy at higher speed. Index Terms--Content-based image retrieval, image region segmentation, low depth-of-field, wavelet, multiresolution image analysis.