TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an Adaptive Global aGgregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the sub-optimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.Comment: MIR 2023, 15 page

Upregulation of PIP3-dependent Rac exchanger 1 (P-Rex1) promotes prostate cancer metastasis.

Excessive activation of G-protein-coupled receptor (GPCR) and receptor tyrosine kinase (RTK) pathways has been linked to prostate cancer metastasis. Rac activation by guanine nucleotide exchange factors (GEFs) plays an important role in directional cell migration, a critical step of tumor metastasis cascades. We found that the upregulation of P-Rex1, a Rac-selective GEF synergistically activated by Gbetagamma freed during GPCR signaling, and PIP3, generated during either RTK or GPCR signaling, strongly correlates with metastatic phenotypes in both prostate cancer cell lines and human prostate cancer specimens. Silencing endogenous P-Rex1 in metastatic prostate cancer PC-3 cells selectively inhibited Rac activity and reduced cell migration and invasion in response to ligands of both epidermal growth factor receptor and G-protein-coupled CXC chemokine receptor 4. Conversely, expression of recombinant P-Rex1, but not its \u27GEF-dead\u27 mutant, in non-metastatic prostate cancer cells, such as CWR22Rv1, increased cell migration and invasion through Rac-dependent lamellipodia formation. More importantly, using a mouse xenograft model, we showed that the expression of P-Rex1, but not its mutant, induced lymph node metastasis of CWR22Rv1 cells without an effect on primary tumor growth. Thus, by functioning as a coincidence detector of chemotactic signals from both GPCRs and RTKs, P-Rex1-dependent activation of Rac promotes prostate cancer metastasis

Entering the Era of Earth Observation-Based Landslide Warning Systems: A novel and exciting framework

Landslide early warning remains a grand challenge due to the high human cost of catastrophic landslides globally and the difficulty of identifying a diverse range of landslide triggering factors. There have been only a very limited number of success stories to date. However, recent advances in earth observation (EO) from ground, aircraft and space have dramatically improved our ability to detect and monitor active landslides and a growing body of geotechnical theory suggests that prefailure behavior can provide clues to the location and timing of impending catastrophic failures. In this paper, we use two recent landslides in China as case studies, to demonstrate that (i) satellite radar observations can be used to detect deformation precursors to catastrophic landslide occurrence, and (ii) early warning can be achieved with real-time in-situ observations. A novel and exciting framework is then proposed to employ EO technologies to build an operational landslide early warning system.This work was supported by the National Natural Science Foundation of China under grants 41801391, 41874005, and 41929001; the National Science Fund for Outstanding Young Scholars of China under grant 41622206; the Fund for International Cooperation under grant NSFCRCUK_NERC; Resilience to Earthquake-Induced Landslide Risk in China under grant 41661134010; the open fund of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2018-5-3-E); Sichuan Science and Technology Plan Project under grant 2019YJ0404; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under grant SKLGP2018Z019; the Spanish Ministry of Economy and Competitiveness, the State Agency of Research, and the European Funds for Regional Development under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P; and the Spanish Ministry of Education, Culture, and Sport under project PRX17/00439. This work was also partially supported by the U.K. Natural Environment Research Council through the Center for the Observation and Modeling of Earthquakes, Volcanoes, and Tectonics under come30001 and the Looking Inside the Continents From Space and Community Earthquake Disaster Risk Reduction in China projects under NE/K010794/1 and NE/N012151/1, respectively, and by the European Space Agency through the ESA-MOST DRAGON-4 project (32244 [4]). Roland Bürgmann acknowledges support by the NASA Earth Surface and Interior focus area

Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels

Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence predictive models require mining more inherent properties in videos, such as structural information. In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. Concerning the video as the spatial-temporal graph, the learning objectives of VideoHiGraph are emanated in a self-supervised manner for predicting unobserved hidden graphs via graph kernel manner. We learn a representation of the temporal coherence across frames in which pairwise similarity defines the structured hidden graph, such that a biased random walk graph kernel along the sub-graph can predict long-range correspondence. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The self-supervision of the model training is formed by the structural and temporal consistency of the graph. VideoHiGraph achieves superior performance and demonstrates its robustness across the benchmark of label propagation tasks involving objects, semantic parts, keypoints, and instances. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/VideoHiGraph

Annealing and Stretching Induced High Energy Storage Properties in All-Organic Composite Dielectric Films

High discharged energy density and charge⁻discharge efficiency, in combination with high electric breakdown strength, maximum electric displacement and low residual displacement, are very difficult to simultaneously achieve in single-component polymer dielectrics. Plenty of researches have reported polymer based composite dielectrics filled with inorganic fillers, through complex surface modification of inorganic fillers to improve interface compatibility. In this work, a novel strategy of introducing environmentally-friendly biological polyester into fluoropolymer matrix has been presented to prepare all-organic polymer composites with desirable high energy storage properties by solution cast process (followed by annealing or stretching post-treatment), in order to simplify the preparation steps and lower the cost. Fluoropolymer with substantial ferroelectric domains (contributing to high dielectric response) as matrix and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) with excellent linear polarization property (resulting in high breakdown strength) as filler were employed. By high-temperature annealing, the size of ferroelectric domains could be improved and interfacial air defects could be removed, leading to elevated high energy storage density and efficiency in composite. By mono-directional stretching, the ferroelectric domains and polyester could be regularly oriented along stretching direction, resulting in desired high energy storage performances as well. Besides, linear dielectric components could contribute to high efficiency from their strong rigidity restrain effect on ferroelectric component. This work might open up the way for a facile fabrication of promising all-organic composite dielectric films with high energy storage properties

Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique

Stabilization of cell-free RNA in blood samples using a new collection device

AbstractObjectiveTo investigate whether a new blood collection device stabilizes cell-free RNA (cfRNA) in blood post-phlebotomy when compared to collection using K3EDTA tubes.Design and methodsBlood samples were drawn from healthy donors into K3EDTA tubes and Cell-Free RNA BCTs (BCTs) and stored at room temperature (20–25°C). At specified time points (days 0–3), plasma was separated and cfRNA was extracted. Reverse transcription real-time PCR was used to quantify mRNA for c-fos, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and for 18S rRNA.ResultsBlood drawn into K3EDTA tubes showed a steady increase in RNA concentration over 3days of ex vivo incubation. Blood drawn into BCTs showed no statistically significant change in RNA copy number except for GAPDH on day 3.ConclusionsThe novel chemical cocktail contained in the new device allows for the stabilization of cfRNA in blood samples at room temperature, which potentially enhances the clinical utility of cfRNA