A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

Prognostic significance of the neutrophil-to-lymphocyte ratio with distal cholangiocarcinoma patients.

BACKGROUND: The present study aimed to investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in distal cholangiocarcinoma (DCC) following radical surgery. METHODS: The clinicopathological data of 59 patients with DCC were retrospectively reviewed. Patients were treated by radical surgery and diagnosed by postoperative pathology at the Second Affiliated Hospital of Kunming Medical University (Yunnan, China), between July 2015 and December 2017. The optimal cut-off value for the NLR was determined by generating receiver operating characteristic (ROC) curves. Kaplan-Meier survival analysis and Cox proportional hazards models were used to determine the risk factors and independent risk factors influencing the prognosis of patients with DCC. RESULTS: According to the ROC curve, the optimal cut-off value for the NLR was 2.933. The results of Kaplan-Meier survival analysis and the Cox proportional hazards model showed that carbohydrate antigen 125, NLR, perineural, vascular and fat invasion, regional lymph node metastasis, and the American Joint Committee on Cancer stage were risk factors for DCC; the only independent risk factor to affect the prognosis of DCC patients was the NLR. CONCLUSIONS: The preoperative NLR plays an important guiding role in evaluating the prognosis of patients with DCC, and an increase in the NLR is associated with poor patient prognosis.

Orthogonalizing EM: A design-based least squares algorithm.

We introduce an efficient iterative algorithm, intended for various least squares problems, based on a design of experiments perspective. The algorithm, called orthogonalizing EM (OEM), works for ordinary least squares and can be easily extended to penalized least squares. The main idea of the procedure is to orthogonalize a design matrix by adding new rows and then solve the original problem by embedding the augmented design in a missing data framework. We establish several attractive theoretical properties concerning OEM. For the ordinary least squares with a singular regression matrix, an OEM sequence converges to the Moore-Penrose generalized inverse-based least squares estimator. For ordinary and penalized least squares with various penalties, it converges to a point having grouping coherence for fully aliased regression matrices. Convergence and the convergence rate of the algorithm are examined. Finally, we demonstrate that OEM is highly efficient for large-scale least squares and penalized least squares problems, and is considerably faster than competing methods when n is much larger than p. Supplementary materials for this article are available online.