Partitioning Clustering Based on Support Vector Ranking

Postprin

An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Huge challenges exist for old landslide detection because their morphology features have been partially or strongly transformed over a long time and have little difference from their surrounding. Besides, small-sample problem also restrict in-depth learning. In this paper, an iterative classification and semantic segmentation network (ICSSN) is developed, which can greatly enhance both object-level and pixel-level classification performance by iteratively upgrading the feature extractor shared by two network. An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides. Moreover, an iterative training strategy is elaborated to fuse features in semantic space such that both object-level and pixel-level classification performance are improved. The proposed ICSSN is evaluated on the real landslide data set, and the experimental results show that ICSSN can greatly improve the classification and segmentation accuracy of old landslide detection. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381 to 0.3743, and the object-level detection accuracy of old landslides is enhanced from 0.55 to 0.9. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875

1-[4-(2-Chloro­eth­oxy)-2-hy­droxy­phen­yl]ethanone

In the title compound, C10H11ClO3, obtained by the reaction of 2,4-dihy­droxy­acetophenone, potassium carbonate and 1-bromo-2-chloro­ethane, an intra­molecular O—H⋯O hydrogen bond occurs

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is increased from 0.501 to 0.565

1-[4-(4-Chloro­but­oxy)-2-hy­droxy­phen­yl]ethanone

In the title compound, C12H15ClO3, the eth­oxy group is nearly coplanar with the benzene ring, making a dihedral angle of 9.03 (4)°, and is involved in an intra­molecular O—H⋯O hydrogen bond to the neighbouring hy­droxy group

Extended Target Shape Estimation by Fitting B-Spline Curve

Taking into account the difficulty of shape estimation for the extended targets, a novel algorithm is proposed by fitting the B-spline curve. For the single extended target tracking, the multiple frame statistic technique is introduced to construct the pseudomeasurement sets and the control points are selected to form the B-spline curve. Then the shapes of the extended targets are extracted under the Bayes framework. Furthermore, the proposed shape estimation algorithm is modified suitably and combined with the probability hypothesis density (PHD) filter for multiple extended target tracking. Simulations show that the proposed algorithm has a good performance for shape estimate of any extended targets

Oral Vaccination with the Porcine Rotavirus VP4 Outer Capsid Protein Expressed by Lactococcus lactis Induces Specific Antibody Production

The objective of this study to design a delivery system resistant to the gastrointestinal environment for oral vaccine against porcine rotavirus. Lactococcus lactis NZ9000 was transformed with segments of vP4 of the porcine rotavirus inserted into the pNZ8112 surface-expression vector, and a recombinant L. lactis expressing VP4 protein was constructed. An approximately 27 kDa VP4 protein was confirmed by SDS-PAGE , Western blot and immunostaining analysis. BALB/c mice were immunized orally with VP4-expression recombinant L. lactis and cellular, mucosal and systemic humoral immune responses were examined. Specific anti-VP4 secretory IgA and IgG were found in feces, ophthalmic and vaginal washes and in serum. The induced antibodies demonstrated neutralizing effects on porcine rotavirus infection on MA104 cells. Our findings suggest that oral immunization with VP4-expressing L. lactis induced both specific local and systemic humoral and cellular immune responses in mice