RESUMO
Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications. This work aims to design a novel and strong baseline for PSG. To achieve that, we first conduct an in-depth analysis to identify the bottleneck of the current PSG models, finding that inter-object pair-wise recall is a crucial factor that was ignored by previous PSG methods. Based on this and the recent query-based frameworks, we present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network (PPN) to learn and filter sparse pair-wise relationships between subjects and objects. Moreover, we also observed the sparse nature of object pairs for both. Motivated by this, we design a lightweight Matrix Learner within the PPN, which directly learns pair-wised relationships for pair proposal generation. Through extensive ablation and analysis, our approach significantly improves upon leveraging the segmenter solid baseline. Notably, our method achieves over 10% absolute gains compared to our baseline, PSGFormer. The code of this paper is publicly available at https://github.com/king159/Pair-Net.
RESUMO
The Panoptic Scene Graph Generation (PSG) challenge evaluates computer vision models to identify relations in images beyond object classification and localization, enabling a deeper understanding of scenes for real-world AI applications.
RESUMO
Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.
Assuntos
Neoplasias , Mutações Sintéticas Letais , Inteligência Artificial , Humanos , Neoplasias/genética , Redes Neurais de Computação , Mutações Sintéticas Letais/genéticaRESUMO
BACKGROUND: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. RESULTS: In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. CONCLUSIONS: After validating the performance of our method using simulation experiments, we further apply it to Alzheimer's disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer's disease.
Assuntos
Epistasia Genética , Estudo de Associação Genômica Ampla , Modelos Genéticos , Algoritmos , Doença de Alzheimer/genética , Área Sob a Curva , Sequência de Bases , Simulação por Computador , Humanos , Polimorfismo de Nucleotídeo Único/genética , Curva ROCRESUMO
Cross-sections for (n, 2n), (n, p) and (n, n'alpha) reactions have been measured on gallium isotopes at the neutron energies of 13.5-14.6MeV using the activation technique. Data are reported for the following reactions: 69Ga(n, 2n) 68Ga, 69Ga(n, p) 69mZn, 71Ga(n, p) (71m)Zn, and 71Ga(n, n'alpha) 67Cu. The neutron fluences were determined using the monitor reaction 93Nb(n, 2n) 92mNb.