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Recently it has been recognized that in compactified string/M theories that satisfy cosmological constraints, it is possible to derive some robust and generic predictions for particle physics and cosmology with very mild assumptions. When the matter and gauge content below the compactification scale is that of the minimal supersymmetric standard model (MSSM), it is possible to make precise predictions. In this case, we predict that there will be a single standard model-like Higgs boson with a calculable mass 105 GeV ≲ Mh ≲ 129 GeV depending on tanβ (the ratio of the Higgs vevs in the MSSM). For tanβ > 7, the prediction is 122 GeV ≲ Mh ≲ 129 GeV. [ABSTRACT FROM AUTHOR]

Current sequential recommender systems primarily focus on modeling users' dynamic interests over time by leveraging GNN-based approaches. However, most studies tend to overlook the impact of high-order data outside the sequence and its corresponding position information on users' interest, especially fail to maximize the collaborative signals between users or items. To alleviate the aforementioned problems, we propose a novel sequential recommendation model, called GNSR, which utilizes graph structures at various levels to enhance the node representation and ultimately improve recommendation performance. Specifically, we construct a high-dimensional continuous-time bipartite graph centered on target users and their historical sequences while preserving the interaction order. Subsequently, we create meta-path graphs through meta-path exploration. Additionally, to capture both effective high-dimensional collaborative signals and dynamic changes in users' interests throughout the sequence, we design a High-Dimensional Information Collaboration Module within GNSR, which enables a comprehensive understanding of user behavior. Finally, we conduct extensive experiments on three public datasets and demonstrate that the performance of GNSR improves by approximately 0.1% ∼ 3.2% over selected baselines. [ABSTRACT FROM AUTHOR]