Research on Big Data Collection Mode of Ancient Village Culture Based on Network Crowdsourcing

In the long history of Chinese civilization, the village is the most basic social unit. However, in the wave of urbanization, the village culture is accelerating. Therefore, in the process of modernization, how to protect the village culture is an important issue that cannot be ignored. At the same time, with the advancement of the times, the introduction of modern information technology into the work of folk culture has become an important means of protecting traditional cultural heritage, but the historical and cultural materials of ancient villages are huge in number and form, which brings certain difficulties to the collection and collation of historical and cultural materials in villages. In order to solve the problems of large amount of data and difficulty of collection in ancient village digital culture protection, this paper combines ancient village cultural protection with inheritance and modern information technology, and proposes a big data collection mode based on crowdsourcing and supplemented by cloud service platform. Firstly, aiming at the status quo of cultural protection of ancient villages, we analyze the difficulties in data collection of ancient villages, and explain the necessity of combining cultural protection of ancient villages with network crowdsourcing. Then we design a network crowdsourcing mode and the ancient village cultural big data cloud service platform. At last, through the pilot verification, it verifies that the data collected by the crowdsourcing mode proposed in this paper has the advantages of high user participation, low cost, short time-consuming and quality control. This study effectively solves the problem of time-consuming and costly big data collection, and provides new feasible ideas for the digital inheritance and protection of ancient village culture. At the same time, the cloud platform can provide the deep excavation of ancient village culture, content indexing, literature compilation and publication, and thematic knowledge retrieval and so on. And this platform can serve as a basic tool for future cultural protection, heritage, research and foreign exchange in ancient villages

Progressive Scale-aware Network for Remote sensing Image Change Captioning

Remote sensing (RS) images contain numerous objects of different scales, which poses significant challenges for the RS image change captioning (RSICC) task to identify visual changes of interest in complex scenes and describe them via language. However, current methods still have some weaknesses in sufficiently extracting and utilizing multi-scale information. In this paper, we propose a progressive scale-aware network (PSNet) to address the problem. PSNet is a pure Transformer-based model. To sufficiently extract multi-scale visual features, multiple progressive difference perception (PDP) layers are stacked to progressively exploit the differencing features of bitemporal features. To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers. Experiments show that the PDP layer and SR module are effective and our PSNet outperforms previous methods. Our code is public at https://github.com/Chen-Yang-Liu/PSNetComment: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposiu

Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images). We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations. Different from previous methods that only consider 3D prior or 2D features, we incorporate additional 2D texture information and 3D prior by broadcasting CNN features to different point features along the sampled ray. To verify the effectiveness of the proposed method, we construct a challenging dataset containing six synthetic sub-datasets collected from the Carla platform and three real sub-datasets from Google Maps. Experiments show that the proposed method outperforms the CNN-based methods and the state-of-the-art INR-based segmentation methods in quantitative and qualitative metrics

Case Report: Identification of a novel PRR12 variant in a Chinese boy with developmental delay and short stature

Proline Rich 12 (PRR12) protein is primarily expressed in the brain and localized in the nucleus. The variants in the PRR12 gene were reported to be related to neuroocular syndrome. Patients with PRR12 gene presented with intellectual disability (ID), neuropsychiatric disorders, some congenital anomalies, and with or without eye abnormalities. Here, we report an 11-year-old boy with a novel PRR12 variant c.1549_1568del, p.(Pro517Alafs*35). He was the first PRR12 deficiency patient in China and presented with ID, short stature, and mild scoliosis. He could not concentrate on his studies and was diagnosed with attention deficit hyperactivity disorder (ADHD). The insulin-like growth factor 1 (IGH-1) was low in our patient, which may be the cause of his short stature. Patients with neuroocular syndrome are rare, and further exploration is needed to understand the reason for neurodevelopmental abnormalities caused by PRR12 variants. Our study further expands on the PRR12 variants and presents a new case involving PPR12 variants

Enhanced-efficiency fertilizers are not a panacea for resolving the nitrogen problem

Abstract Improving nitrogen (N) management for greater agricultural output while minimizing unintended environmental consequences is critical in the endeavor of feeding the growing population sustainably amid climate change. Enhanced-efficiency fertilizers (EEFs) have been developed to better synchronize fertilizer N release with crop uptake, offering the potential for enhanced N use efficiency (NUE) and reduced losses. Can EEFs play a significant role in helping address the N management challenge? Here we present a comprehensive analysis of worldwide studies published in 1980–2016 evaluating four major types of EEFs (polymer-coated fertilizers PCF, nitrification inhibitors NI, urease inhibitors UI, and double inhibitors DI, i.e. urease and nitrification inhibitors combined) regarding their effectiveness in increasing yield and NUE and reducing N losses. Overall productivity and environmental efficacy depended on the combination of EEF type and cropping systems, further affected by biophysical conditions. Best scenarios include: (i) DI used in grassland (n = 133), averaging 11% yield increase, 33% NUE improvement, and 47% decrease in aggregated N loss (sum of NO3-, NH3, and N2O, totaling 84 kg N/ha); (ii) UI in rice-paddy systems (n = 100), with 9% yield increase, 29% NUE improvement, and 41% N-loss reduction (16 kg N/ha). EEF efficacies in wheat and maize systems were more complicated and generally less effective. In-depth analysis indicated that the potential benefits of EEFs might be best achieved when a need is created, for example, by downward adjusting N application from conventional rate. We conclude that EEFs can play a significant role in sustainable agricultural production but their prudent use requires firstly eliminating any fertilizer mismanagement plus the implementation of knowledge-based N management practices

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification

IntroductionDue to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition. However, these methods ignore the unique spatial-temporal information of functional magnetic resonance imaging (fMRI) data, which is essential for FBN analysis.MethodsTo address this issue, we propose a spatial-temporal data-augmentation-based classification (STDAC) scheme that can fuse the spatial-temporal information, increase the samples, while improving the classification performance. Firstly, we propose a spatial augmentation module utilizing the spatial prior knowledge, which was ignored by previous augmentation methods. Secondly, we design a temporal augmentation module by random discontinuous sampling period, which can generate more samples than former approaches. Finally, a tensor fusion method is used to combine the features from the above two modules, which can make efficient use of spatial-temporal information of fMRI simultaneously. Besides, we apply our scheme to different types of classifiers to verify the generalization performance. To evaluate the effectiveness of our proposed scheme, we conduct extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and REST-meta-MDD Project (MDD) dataset.ResultsExperimental results show that the proposed scheme achieves superior classification accuracy (ADNI: 82.942%, MDD: 63.406%) and feature interpretation on the benchmark datasets.DiscussionThe proposed STDAC scheme, utilizing both spatial and temporal information, can generate more diverse samples than former augmentation methods for brain disorder classification and analysis