Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images

For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://github.com/MVME-HBUT/Faster-OreFSDet.Comment: 18 pages, 11 figure

Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.Comment: 14 pages, 15 figures, 5 tables, submitted to VLDB '2

Hyper-sampling imaging

In our research, we have developed a novel mechanism that allows for a significant reduction in the smallest sampling unit of digital image sensors (DIS) to as small as 1/16th of a pixel, through measuring the intra-pixel quantum efficiency for the first time and recomputing the image. Employing our method, the physical sampling resolution of DIS can be enhanced by 16 times. The method has undergone rigorous testing in real-world imaging scenarios

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds

Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images

Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress

Mouse islet-derived stellate cells are similar to, but distinct from, mesenchymal stromal cells and influence the beta cell function

AimsEvidence is accumulating of the therapeutic benefits of mesenchymal stromal cells (MSCs) in diabetes-related conditions. We have identified a novel population of stromal cells within islets of Langerhans – islet stellate cells (ISCs) – which have a similar morphology to MSCs. In this study we characterize mouse ISCs and compare their morphology and function to MSCs to determine whether ISCs may also have therapeutic potential in diabetes.MethodsISCs isolated from mouse islets were compared to mouse bone marrow MSCs by analysis of cell morphology; expression of cell-surface markers and extracellular matrix (ECM) components; proliferation; apoptosis; paracrine activity; and differentiation into adipocytes, chondrocytes and osteocytes. We also assessed the effects of co-culture with ISCs or MSCs on the insulin secretory capacity of islet beta cells. ResultsAlthough morphological similar, ISCs were functionally distinct from MSCs. Thus, ISCs were less proliferative and more apoptotic; they had different expression levels of important paracrine factors; and they were less efficient at differentiation down multiple lineages. Co-culture of mouse islets with ISCs enhanced glucose induced insulin secretion more effectively than co-culture with MSCs. ConclusionsISCs are a specific sub-type of islet-derived stromal cells that possess biological behaviors distinct from MSCs. The enhanced beneficial effects of ISCs on islet beta cell function suggests that they may offer a therapeutic target for enhancing beta cell functional survival in diabetes.</p

Integrated multi-omics analysis reveals the role of nitrogen application in seed storage protein metabolism and improvement of inferior grains in smooth bromegrass

This study explored the effects of nitrogen application on superior and inferior grains in smooth bromegrass (Bromus inermis) to provide insights for improving seed quality and yield. The study was conducted using a randomized block design with two nitrogen treatments (0 and 200 kg·N·ha-¹) during the 2021–2022 growing seasons. Seed dry weight, fresh weight, and storage protein content were measured at multiple stages after anthesis. PacBio full-length transcriptome sequencing generated a comprehensive transcriptome consisting of 124,425 high-quality transcripts, and metabolomic profiling were performed across developmsental stages. Genetic transformation in Arabidopsis was used to validate gene function. Nitrogen application significantly increased seed dry and fresh weights and storage protein content, particularly gliadin and glutelin. Metabolomic and transcriptomic analyses revealed that nitrogen treatment upregulated glutamate and asparagine levels and enhanced nitrogen transport and protein synthesis pathways. Two α-gliadin nitrogen-responsive genes, BiGli1 and BiGli2, were identified. Overexpression of these genes in Arabidopsis confirmed their role in regulating seed size and vigor. This study highlights the critical role of α-gliadin in enhancing seed quality, particularly in promoting the development of inferior grains, offering valuable insights for the development of high-yield seed varieties and the optimization of specialized forage seed production