A sustainable and effective catalyst system of (thio)ureas/organic bases toward synthesizing sustainable polyesters from renewable monomers.
Rana chensinensis (David, 1875) is a temperate anuran endemic to northern China. We examined differences in demographic traits of the populations from three elevations (1400, 1700, and 2000 m) along a montane river in 2002. We found that frogs from higher elevations had delayed maturity, a larger size at maturity, and slower growth rates compared with frogs at lower elevations. This life-history model is similar to observations of other amphibians living in montane areas. However, discordance with the expected model occurred between neighboring populations and the variation was sex-specific. Mid-elevation adult males were significantly older and larger than their low-elevation congeners, but they were statistically similar in age and size to frogs from high elevations; females from mid elevations were not statistically different in age and size from females from our low-elevation site, but they were significantly younger and smaller than high-elevation females. These variations may be related to sexual differences in life-history strategies, which might not covary systematically when elevational gradients are set at a finer scale. At each elevation, the sex ratio was skewed towards females; females also matured later, lived longer, and were larger. Age was a major factor related to size, but other factors played a role in shaping size differences both between populations and between sexes.
Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU.
Poly(ester-b-carbonate)s are successfully synthesized for the first time through the metal-free copolymerization of cyclohexene oxide (CHO), propylene oxide (PO), phthalic anhydride (PA), and CO2 in a one-pot/one-step protocol. Catalyzed by triethyl borane (TEB) and bis(triphenylphosphine)iminium chloride (PPNCl) Lewis pair, the diblock and triblock copolymers with little tapering are synthesized from the initiation of PPNCl and phthalic acid, respectively. Copolymers with a high molecular weight of up to 50 kDa can be readily obtained under mild conditions. By changing the content of chain components in quadripolymers, glass transition temperature (T g) values are adjusted between 86 and 115 °C. Moreover, the products appear extremely transparent with transparency of above 85% in the range of 600–1000 nm. This work first focuses on the synthesis of quadripolymers with high T gs (>90 °C) and tensile strength (up to 54.8 MPa), which have similar thermal, mechanical properties, and transparency as those of commercial polystyrene and thus may be candidate green materials to replace the nondegradable polystyrene in extensive application areas.
Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an Expectation-Maximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semisupervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN.
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