Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images

Individual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.National Key R&D Program of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationPeer Reviewe

Spatiotemporal Evolution of Urban Agglomeration and Its Impact on Landscape Patterns in the Pearl River Delta, China

An urban agglomeration is the engine of regional and national economic growth, but also causes many ecological and environmental issues that emerge from massive land changes. In this study, the spatiotemporal evolution of an urban agglomeration was quantified and its impacts on the urban and regional landscape patterns were evaluated. It showed that the urbanized land area of the Pearl River Delta Urban Agglomeration (PRDUA) in China nearly quadrupled, having linearly increased from 1819.8 km2 to 7092.2 km2 between 1985 and 2015. The average annual growth rate presented a bimodal wave-like pattern through time, indicating that the PRDUA has witnessed two rounds of the urbanization process. The growth modes (e.g., leapfrog, edge-expansion, infilling) were detected and they exhibited co-existing but alternating dominating patterns during urbanization, demonstrating that the spatiotemporal evolution of the urban development of the PRDUA follows the “spiral diffusion-coalescence” hypothesis. The morphology of the PRDUA presented an alternating dispersal-compact pattern over time. The city-level and regional-level landscape patterns changed synchronously with the spatiotemporal evolution of the PRDUA over time. The urbanization of the PRDUA increased both the complexity and aggregation of the landscape, but also resulted in an increasing fragmentation and decreasing connectivity of the natural landscape in the Pearl River Delta region. These findings are helpful for better understanding how urban agglomerations evolve and in providing insights for regional urban planning and sustainable land management.Natural Science Foundation of ChinaNational Key R&D Program of ChinaChina Postdoctoral Science FoundationJoint-PhD project of Shanghai Jiao Tong University and The University of MelbournePeer Reviewe

Uric acid levels correlate with disease activity in growth hormone-secreting pituitary adenoma patients

ObjectiveFew studies reported the effects of growth hormone-secreting pituitary adenoma (GHPA) on uric acid (UA) metabolism and the relationship between growth hormone (GH)/insulin-like growth factor-1 (IGF-1) levels and UA are controversial. This study aimed to evaluate the relationship between IGF-1 and UA in patients with GHPA and to further clarify whether UA levels are associated with GHPA disease activity by follow-up.MethodsA longitudinal study of 424 GHPA patients presenting to Beijing Tiantan Hospital, Capital Medical University between January 2015 and January 2023 was conducted. Spearman’s correlation tests were performed to examine the relationship between IGF-1 and UA at baseline. Univariate and multivariate linear regression analysis was conducted to investigate the independent association between UA and IGF-1. Changes in postoperative IGF-1 and UA levels were followed prospectively, and the differences in UA levels between the biochemical remission and nonremission groups were compared.ResultsAt baseline, male patients, the lower the age, the higher the IGF-1 and body mass index (BMI), and the higher the UA levels. IGF-1 was significantly associated with UA after controlling for sex, age, and BMI (r = 0.122, P = 0.012). In adjusted multiple linear regression analysis, IGF-1 was independently associated with UA, and UA levels increased significantly with increasing IGF-1. During postoperative follow-up, UA decreased gradually as IGF-1 levels decreased. At 12 months postoperatively, UA levels were significantly lower in the biochemical remission group than in the nonremission group (P = 0.038).ConclusionsIn patients with GHPA, UA levels are associated with disease activity. Changes in UA levels should be taken into account in the comprehensive treatment of GHPA, patients presenting with HUA should be given lifestyle guidance and appropriate urate-lowering treatment according to their condition to better improve their prognosis

Integrating identification and targeted proteomics to discover the potential indicators of postmortem lamb meat quality.

peer reviewedThe aim of this study was to identify the potential indicators of lamb meat quality by TMT and PRM-based proteomics combined with bioinformatic analysis. Lamb muscles were divided into three different meat quality groups (high, middle and low) according to tenderness (shear force, MFI value), colour (a* value, R630/580), and water-holding capacity (cooking loss, drip loss) at 24 h postmortem. The results showed that the abundance of phosphoglycerate kinase 1 (PGK1), β-enolase (ENO3), myosin-binding protein C (MYBPC1) and myosin regulatory light chain 2 (MYLPF) was significantly different in the three groups and could be used as potential indicators to characterize meat quality. Moreover, the postmortem processes of glycolysis, oxidative phosphorylation, and muscle contraction remarkably changed in different groups, and were the key biological pathways influencing meat quality. Overall, this study depicted the proteomic landscape of meat that furthers our understanding of the molecular mechanism of meat quality and provides a reference for developing non-destructive detection technology for meat quality