Xiao Li

Xiao Li, Jaemoon Lee, Anand Rangarajan et al.

Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential growth in data production is surpassing the advancements in computing power, network capabilities, and storage capacities. To address this challenge, data compression or reduction techniques are crucial. These scientific datasets have underlying data structures that consist of structured and block structured multidimensional meshes where each grid point corresponds to a tensor. It is important that data reduction techniques leverage strong spatial and temporal correlations that are ubiquitous in these applications. Additionally, applications such as CFD, process tensors comprising hundred plus species and their attributes at each grid point. Reduction techniques should be able to leverage interrelationships between the elements in each tensor. In this paper, we propose an attention-based hierarchical compression method utilizing a block-wise compression setup. We introduce an attention-based hyper-block autoencoder to capture inter-block correlations, followed by a block-wise encoder to capture block-specific information. A PCA-based post-processing step is employed to guarantee error bounds for each data block. Our method effectively captures both spatiotemporal and inter-variable correlations within and between data blocks. Compared to the state-of-the-art SZ3, our method achieves up to 8 times higher compression ratio on the multi-variable S3D dataset. When evaluated on single-variable setups using the E3SM and XGC datasets, our method still achieves up to 3 times and 2 times higher compression ratio, respectively.

Jaemoon Lee, Ki Sung Jung, Qian Gong et al.

We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications. It uses a multidimensional block of tensors (spanning in space and time) for both input and output, capturing the spatiotemporal and interspecies relationship within a tensor. The tensor consists of species that represent different elements in a CFD simulation. To guarantee the error bound of the reconstructed data, principal component analysis (PCA) is applied to the residual between the original and reconstructed data. This yields a basis matrix, which is then used to project the residual of each instance. The resulting coefficients are retained to enable accurate reconstruction. Experimental results demonstrate that our approach can deliver two orders of magnitude in reduction while still keeping the errors of primary data under scientifically acceptable bounds. Compared to reduction-based approaches based on SZ, our method achieves a substantially higher compression ratio for a given error bound or a better error for a given compression ratio.

Tianyou Jiang, Mingshun Shao, Tianyi Zhang et al.

Automatic counting soybean pods and seeds in outdoor fields allows for rapid yield estimation before harvesting, while indoor laboratory counting offers greater accuracy. Both methods can significantly accelerate the breeding process. However, it remains challenging for accurately counting pods and seeds in outdoor fields, and there are still no accurate enough tools for counting pods and seeds in laboratories. In this study, we developed efficient deep learning models for counting soybean pods and seeds in both outdoor fields and indoor laboratories. For outdoor fields, annotating not only visible seeds but also occluded seeds makes YOLO have the ability to estimate the number of soybean seeds that are occluded. Moreover, we enhanced YOLO architecture by integrating it with HQ-SAM (YOLO-SAM), and domain adaptation techniques (YOLO-DA), to improve model robustness and generalization across soybean images taken in outdoor fields. Testing on soybean images from the outdoor field, we achieved a mean absolute error (MAE) of 6.13 for pod counting and 10.05 for seed counting. For the indoor setting, we utilized Mask-RCNN supplemented with a Swin Transformer module (Mask-RCNN-Swin), models were trained exclusively on synthetic training images generated from a small set of labeled data. This approach resulted in near-perfect accuracy, with an MAE of 1.07 for pod counting and 1.33 for seed counting across actual laboratory images from two distinct studies.

Hao Chen, Fuzhen Zhuang, Li Xiao et al.

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful mean for Computer Aided Diagnosis (CADx). This approach requires building a population graph to aggregate structural information, where the graph adjacency matrix represents the relationship between nodes. Until now, this adjacency matrix is usually defined manually based on phenotypic information. In this paper, we propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution, and uses the text similarity awareness mechanism to calculate the edge weights between nodes. The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information. In addition, a novel graph convolution network architecture using multi-layer aggregation mechanism is proposed. The structure can obtain deep structure information while suppressing over-smooth, and increase the similarity between the same type of nodes. Experimental results on two databases show that our method can significantly improve the diagnostic accuracy for Autism spectrum disorder and breast cancer, indicating its universality in leveraging multimodal data for disease prediction.