Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.Comment: 8 main pages, 3 pages for appendi

A Workflow to Predict the Present-day in-situ Stress Field in Tectonically Stable Regions

Knowledge of the present-day in-situ stress distribution is greatly important for better understanding of conventional and unconventional hydrocarbon reservoirs in many aspects, e.g., reservoir management, wellbore stability assessment, etc. In tectonically stable regions, the present-day in-situ stress field in terms of stress distribution is largely controlled by lithological changes, which can be predicted through a numerical simulation method incorporating specific mechanical properties of the subsurface reservoir. In this study, a workflow was presented to predict the present-day in-situ stress field based on the finite element method (FEM). Sequentially, it consists of: i) building a three-dimensional (3D) geometric framework, ii) creating a 3D petrophysical parameter field, iii) integrating the geometric framework with petrophysical parameters, iv) setting up a 3D heterogeneous geomechanical model, and finally, v) calculating the present-day in-situ stress distribution and calibrating the prediction with measured stress data, e.g., results from the extended leak-off tests (XLOTs). The approach was successfully applied to the Block W in Ordos Basin of central China. The results indicated that the workflow and models presented in this study could be used as an effective tool to provide insights into stress perturbations in subsurface reservoirs and geological references for subsequent analysis

Discovery and Full Genome Characterization of SARS-CoV-2 in Stool Specimen from a Recovered Patient, China

SARS-CoV-2 was found in a recovered patient’s stool specimen by combining quantitative reverse transcription PCR (qRT-PCR) and genome sequencing. The patient was virus positive in stool specimens for at least an additional 15 days after he was recovered, whereas respiratory tract specimens were negative. The discovery of the complete genome of SARS-CoV-2 in the stool sample of the recovered patient demonstrates a cautionary warning that the potential mode of the virus transmission cannot be excluded through the fecal-oral route after viral clearance in the respiratory tract