Scene Text Eraser

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images

U-Th-Pb isotopic systematics of lunar meteorite Asuka-31

U-Th-Pb isotopic systematics indicate that Asuka-31 is of lunar origin and was formed 3940±8Ma. The Pb isotopic composition is extremely nonradiogenic compared with those of typical Apollo mare basalts. The Pb-Pb, U-Pb, and Th-Pb ages are concordant at 3.94Ga. The U-Pb data from maskelynitized plagioclase does not plot on the internal isochrons defined by other mineral separates, indicating that it was disturbed by a later shock event(s). The U-Th-Pb systematics of Asuka-31,combined with previous results, suggest a scenario for the early Pb isotope evolution of the Moon. According to the currently popular hypothesis for lunar origin, following a collision between the Earth and another large planetesimal, the Moon was formed mainly from the Earth\u27s mantle and partially from the other impactor. The primary ^U/^Pb (μ) value of the Moon had been increased four to five times that of the Earth\u27s mantle value (∿8) by volatilization of Pb during the impact. Further depletion of Pb within the Moon\u27s mantle is believed to have occurred during lunar core formation. The chalcophile behavior of Pb and large partition coefficient of Pb in silicate minerals compared to those of U and Th helped to decrease μ values of early cumulates that formed from the magma ocean and settled in the deep lunar mantle. The μ values of later cumulates gradually increased as a result of extensive fractionation. We suggest that Asuka-31 originated from partial melting of early cumulates enriched with sulfides

Focusing on what to decode and what to train: Efficient Training with HOI Split Decoders and Specific Target Guided DeNoising

Recent one-stage transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOI) task by leveraging the detection of DETR. However, the current methods redirect the detection target of the object decoder, and the box target is not explicitly separated from the query embeddings, which leads to long and hard training. Furthermore, matching the predicted HOI instances with the ground-truth is more challenging than object detection, simply adapting training strategies from the object detection makes the training more difficult. To clear the ambiguity between human and object detection and share the prediction burden, we propose a novel one-stage framework (SOV), which consists of a subject decoder, an object decoder, and a verb decoder. Moreover, we propose a novel Specific Target Guided (STG) DeNoising training strategy, which leverages learnable object and verb label embeddings to guide the training and accelerate the training convergence. In addition, for the inference part, the label-specific information is directly fed into the decoders by initializing the query embeddings from the learnable label embeddings. Without additional features or prior language knowledge, our method (SOV-STG) achieves higher accuracy than the state-of-the-art method in one-third of training epochs. The code is available at this https://github.com/cjw2021/SOV-STG

Probabilistic web image gathering

We propose a new method for automated large scale gath-ering of Web images relevant to speci¯ed concepts. Our main goal is to build a knowledge base associated with as many concepts as possible for large scale object recognition studies. A second goal is supporting the building of more accurate text-based indexes for Web images. In our method, good quality candidate sets of images for each keyword are gathered as a function of analysis of the surrounding HTML text. The gathered images are then segmented into regions, and a model for the probability distribution of regions for the concept is computed using an iterative algorithm based on the previous work on statistical image annotation. The learned model is then applied to identify which images are visually relevant to the concept implied by the keyword. Implicitly, which regions or the images are relevant is also determined. Our experiments reveal that the new method performs much better than Google Image Search and a sim-ple method based on more standard content based image retrieval methods

Trace element and isotopic characteristics of inclusions in the Yamato ordinary chondrites Y-75097, Y-793241 and Y-794046

Igneous inclusions and hosts of the Yamato ordinary chondrites Y-75097 (L6), Y-793241 (L6) and Y-794046 (H5) were analyzed for lithophile trace elements, and Rb-Sr, rare gas and oxygen isotopes, together with preliminary petrographic examinations. On a three oxygen-isotope plot, all the inclusions lie near the H-chondrite field. The Y-75097 host and inclusion were severely shocked and the Rb-Sr systematics were disturbed by a 500Ma event which was defined by the K-Ar age. The Y-793241 host and inclusion are unshocked and have an old K-Ar age of 4270±170Ma and undisturbed Rb-Sr systematics for the bulk meteorite. Both Y-75097 and Y-793241 inclusions have similar chemical compositions and mineral assemblages consisting mainly of olivine (Fa_), and minor plagioclase (An_), chlor-apatite, merrillite and chromite. Olivines in both inclusions equilibrated with those of their L6 hosts. The two inclusion mantles consisting of mainly olivine and plagioclase show a highly fractionated REE pattern with middle REE depletion and a large positive Eu anomaly (50-100 times chondritic) (V-shaped). A model calculation suggests that this remarkable REE fractionation was produced by thermal equilibration with the phosphate-rich cores of inclusions during the igneous formation and the metamorphic event. The Y-794046 inclusion comprises abundant anhedral olivines (Fa_), fractured pyroxenes (Fs_) and microcrystalline plagioclase (An_Ab_Or_). The inclusion did not equilibrate with its host which has less Fe-rich olivines (Fa_) and more Fe in pyroxenes (Fs_). The inclusion shows an unfractionated REE pattern. We suggest that the three inclusions formed by melting of differentiated precursor materials carrying unfractionated REE. They were then incorporated into the L-or H-chondrite parent bodies and subjected to the early thermal metamorphism, which eventually overprinted the fractionated REE in the Y-75097 and Y-793241 inclusions by solid/solid equilibrium partitioning. The Y-794046 inclusion was subjected to less extensive equilibration, so that REE abundances remained unfractionated