Interpretable Traffic Event Analysis with Bayesian Networks

Although existing machine learning-based methods for traffic accident analysis can provide good quality results to downstream tasks, they lack interpretability which is crucial for this critical problem. This paper proposes an interpretable framework based on Bayesian Networks for traffic accident prediction. To enable the ease of interpretability, we design a dataset construction pipeline to feed the traffic data into the framework while retaining the essential traffic data information. With a concrete case study, our framework can derive a Bayesian Network from a dataset based on the causal relationships between weather and traffic events across the United States. Consequently, our framework enables the prediction of traffic accidents with competitive accuracy while examining how the probability of these events changes under different conditions, thus illustrating transparent relationships between traffic and weather events. Additionally, the visualization of the network simplifies the analysis of relationships between different variables, revealing the primary causes of traffic accidents and ultimately providing a valuable reference for reducing traffic accidents.Comment: 11 pages, 7 figure

Research on China’s fiscal and taxation policy of new energy vehicle industry technological innovation

Technological innovation in the new energy vehicle industry is conducive to the achievement of China’s major strategic goal of ‘carbon peak and carbon neutrality’. This research involved an empirical study on the relevant data of 14 listed new energy vehicle companies from 2012 to 2019. It used the entropy weight method to obtain the technological innovation index through the four indicators of research and development (R&D) investment, fixed asset investment, intangible assets, and patent application volume. Taking fiscal subsidies and tax burdens as independent variables, a fixed effect model was used to analyze the impact of fiscal and taxation policies on technological innovation in the new energy vehicle industry. The research results show that financial subsidies will encourage new energy vehicle companies to carry out technological innovation, the tax burden has no significant impact on the technological innovation of new energy vehicle enterprises, the scale and age of enterprises, as well as the proportion of R&D personnel to the total number of employees, will all encourage new energy vehicle companies to carry out technological innovation. Based on this, we put forward specific suggestions on further improving the fiscal subsidy and tax incentive policies

Relationships Between D-Dimer Levels and Stroke Risk as Well as Adverse Clinical Outcomes After Acute Ischemic Stroke or Transient Ischemic Attack: A Systematic Review and Meta-Analysis

Objective: Abnormal elevation of D-dimer levels is an important indicator of disseminated intravascular clotting. Therefore, we hypothesized that high D-dimer levels were associated with the risk of stroke and adverse clinical outcomes of patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA). Methods: The present meta-analysis aimed to systematically analyze the associations between D-dimer and the risk of stroke as well as the clinical outcomes of patients with post-stroke or TIA. Meanwhile, dose–response analyses were conducted when there were sufficient data available. Three electronic databases including Pubmed, the Embase database, and the Cochrane Library were searched by two investigators independently. All the pooled results were expressed as risk ratios (RRs). Results: Finally, 22 prospective cohort studies were included into this meta-analysis. The results suggested that high D-dimer levels were associated with increased risks of total stroke (RR 1.4, 95%CI 1.20–1.63), hemorrhagic stroke (RR 1.25, 95%CI 0.69–2.25), and ischemic Stroke (RR 1.55, 95%CI 1.22–1.98), and the dose-dependent relationship was not found upon dose–response analyses. Besides, the high D-dimer levels on admission were correlated with increased risks of all-cause mortality [RR 1.77, 95% confidence interval (CI) 1.26–2.49], 5-day recurrence (RR 2.28, 95%CI 1.32–3.95), and poor functional outcomes (RR 2.01, 95%CI 1.71–2.36) in patients with AIS or TIA. Conclusions: On the whole, high D-dimer levels may be associated with the risks of total stroke and ischemic stroke, but not with hemorrhagic stroke. However, dose–response analyses do not reveal distinct evidence for a dose-dependent association of D-dimer levels with the risk of stroke. Besides, high D-dimer levels on admission may predict adverse clinical outcomes, including all-cause mortality, 5-day recurrence, and 90-day poor functional outcomes, of patients with AIS or TIA. More studies are warranted to quantify the effect of D-dimer levels on the risk of stroke or TIA, so as to verify and substantiate this conclusion in the future

Galectin-3 regulates intracellular trafficking of EGFR through Alix and promotes keratinocyte migration.

The EGFR-mediated signaling pathways are important in a variety of cellular processes, including cell migration and wound re-epithelialization. Intracellular trafficking of EGFR is critical for maintaining EGFR surface expression. Galectin-3, a member of an animal lectin family, has been implicated in a number of physiological and pathological processes. Through studies of galectin-3-deficient mice and cells isolated from these mice, we demonstrated that the absence of galectin-3 impairs keratinocyte migration and skin wound re-epithelialization. We have linked this pro-migratory function to a crucial role of cytosolic galectin-3 in controlling intracellular trafficking and cell surface expression of EGFR after EGF stimulation. Without galectin-3, the surface levels of EGFR are markedly reduced, and the receptor accumulates diffusely in the cytoplasm. This is associated with reduced rates of both endocytosis and recycling of the receptor. We have provided evidence that this previously unreported function of galectin-3 may be mediated through interaction with its binding partner Alix, which is a protein component of the ESCRT (endosomal sorting complex required for transport) machinery. Our results suggest that galectin-3 is potentially a critical regulator of a number of important cellular responses through its intracellular control of trafficking of cell surface receptors

A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming

Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard. This problem is solved either by iterative algorithms, which suffer from slow convergence, or more recently by using deep learning tools, which require time-consuming pre-training process. In this paper, we propose a low-complexity meta-learning based gradient descent algorithm. A meta network with lightweight architecture is applied to learn an adaptive gradient descent update rule to directly optimize the beamformer. This lightweight network is trained during the iterative optimization process, which we refer to as \emph{training while solving}, which removes both the training process and the data-dependency of existing deep learning based solutions.Extensive simulations show that the proposed method achieves superior WSR performance compared to existing learning-based approaches as well as the conventional WMMSE algorithm, while enjoying much lower computational load