DGI: Easy and Efficient Inference for GNNs

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.Comment: 10 pages, 10 figure

Regulatory Network and Prognostic Effect Investigation of PIP4K2A in Leukemia and Solid Cancers

Germline variants of PIP4K2A impact susceptibility of acute lymphoblastic leukemia (ALL) through inducing its overexpression. Although limited reports suggested the oncogenic role of PIP4K2A in cancers, regulatory network and prognostic effect of this gene remains poorly understood in tumorigenesis and leukemogenesis. In this study, we conducted genome-wide gene expression association analyses in pediatric B-ALL cohorts to discover expression associated genes and pathways, which is followed by the bioinformatics analyses to investigate the prognostic role of PIP4K2A and its related genes in multiple cancer types. 214 candidates were identified to be significantly associated with PIP4K2A expression in ALL patients, with known cancer-related genes rankings the top (e.g., RAC2, RBL2, and TFDP1). These candidates do not only tend to be clustered in the same types of leukemia, but can also separate the patients into novel molecular subtypes. PIP4K2A is noticed to be frequently overexpressed in multiple other types of leukemia and solid cancers from cancer cohorts including TCGA, and associated with its candidates in subtype-specific and cancer-specific manners. Interestingly, the association status varied in tumors compared to their matched normal tissues. Moreover, PIP4K2A and its related candidates exhibit stage-independent prognostic effects in multiple cancers, mostly with its lower expression significantly associated with longer overall survival (p < 0.05). Our findings reveal the transcriptional regulatory network of PIP4K2A in leukemia, and suggest its potentially important role on molecular subtypes of multiple cancers and subsequent treatment outcomes

MFFNet: Image Semantic Segmentation Network of Multi-level Feature Fusion

In the task of image semantic segmentation, most methods do not make full use of features of different scales and levels, but directly upsampling, which will cause some effective information to be dismissed as redundant information, thus reducing the accuracy and sensitivity of segmentation of some small categories and similar categories. Therefore, a multi-level feature fusion network (MFFNet) is proposed. MFFNet uses encoder-decoder structure, during the encoding stage, the context information and spatial detail information are obtained through the context information extraction path and spatial information extraction path respectively to enhance the inter-pixel correlation and boundary accuracy. During the decoding stage, a multi-level feature fusion path is designed, and the context information is fused by the mixed bilateral fusion module. Deep information and spatial information are fused by high-low feature fusion module. The global channel-attention fusion module is used to obtain the connections between different channels and realize global fusion of different scale information. The MIoU (mean intersection over union) of MFFNet network on the PASCAL VOC 2012 and Cityscapes validation sets is 80.70% and 76.33%, respectively, achieving better segmentation results

Marketing of biomove 3000 : exploratory research in factors affecting willingness to buy/try portable stroke rehabilation machine & marketing plan.

The primary objectives of this project was to find out consumers’ willingness to accept unsupervised and self administered after-stroke rehabilitation and to prepare a detailed marketing plan for Biomove 3000 machine in Singapore

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method

Effect of Mineral Composition and Particle Size on the Failure Characteristics and Mechanisms of Marble in the China Jinping Underground Laboratory

In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were analyzed by thin-section analysis and XRD tests, and the effect of intermediate principal stress on the mechanical properties of marble was investigated. Both SEM and microfracture analysis were coupled to reveal the failure mechanisms. The results highlight that the crack initiation strength, damage strength, peak strength, and elasticity modulus of Jinping marble exhibit an increasing trend with an increase in intermediate principal stress, while the peak strain initially increases and subsequently decreases. Moreover, this study established negative correlations between marble strength, brittleness characteristics, and fracture angle with grain size, whereas positive correlations were identified with the content of quartz, sodium feldspar, and the magnitude of the intermediate principal stress. The microcrack density in marble was found to increase with larger grain sizes and decrease with elevated quartz and sodium feldspar content, as well as with increasing intermediate principal stress. Notably, as the intermediate principal stress intensifies and grain size diminishes, the transgranular tensile failure of marble becomes more conspicuous. These research findings contribute to the effective implementation of disaster prevention and control strategies

Analysis of mRNA and lncRNA Expression Profiles of Breast Muscle during Pigeon (<i>Columba</i> <i>livia</i>) Development

The breast muscle is essential for flight and determines the meat yield and quality of the meat type in pigeons. At present, studies about long non-coding RNA (lncRNA) expression profiles in skeletal muscles across the postnatal development of pigeons have not been reported. Here, we used transcriptome sequencing to examine the White-King pigeon breast muscle at four different ages (1 day, 14 days, 28 days, and 2 years old). We identified 12,918 mRNAs and 9158 lncRNAs (5492 known lncRNAs and 3666 novel lncRNAs) in the breast muscle, and 7352 mRNAs and 4494 lncRNAs were differentially expressed in the process of development. We found that highly expressed mRNAs were mainly related to cell-basic and muscle-specific functions. Differential expression and time-series analysis showed that differentially expressed genes were primarily associated with muscle development and functions, blood vessel development, cell cycle, and energy metabolism. To further predict the possible role of lncRNAs, we also conducted the WGCNA and trans/cis analyses. We found that differentially expressed lncRNAs such as lncRNA-LOC102093252, lncRNA-G12653, lncRNA-LOC110357465, lncRNA-G14790, and lncRNA-LOC110360188 might respectively target UBE2B, Pax7, AGTR2, HDAC1, Sox8 and participate in the development of the muscle. Our study provides a valuable resource for studying the lncRNAs and mRNAs of pigeon muscles and for improving the understanding of molecular mechanisms in muscle development