5,106 research outputs found
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
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