3,601 research outputs found
Statistical Constraints on the Error of the Leptonic CP Violation of Neutrinos
A constraint on the error of leptonic CP violation, which require the phase
to be less than for it to be distinguishable on a
cycle, is presented. Under this constraint, the effects of neutrino detector 's
distance, beam energy, and energy resolution are discussed with reference to
the present values of these parameters in experiments. Although an optimized
detector performances can minimize the deviation to yield a larger
distinguishable range of the leptonic CP phase on a cycle, it is not
possible to determine an arbitrary leptonic CP phase in the range of
with the statistics from a single detector because of the existence of two
singular points. An efficiency factor is defined to characterize the
distinguishable range of . To cover the entire possible
range, a combined efficiency factor corresponding to
multiple sets of detection parameters with different neutrino beam energies and
distances is proposed. The combined efficiency factors of various
major experiments are also presented.Comment: 9 pages, 5 figure
Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles
Celebrities' whereabouts are of pervasive importance. For instance, where
politicians go, how often they visit, and who they meet, come with profound
geopolitical and economic implications. Although news articles contain travel
information of celebrities, it is not possible to perform large-scale and
network-wise analysis due to the lack of automatic itinerary detection tools.
To design such tools, we have to overcome difficulties from the heterogeneity
among news articles: 1)One single article can be noisy, with irrelevant people
and locations, especially when the articles are long. 2)Though it may be
helpful if we consider multiple articles together to determine a particular
trip, the key semantics are still scattered across different articles
intertwined with various noises, making it hard to aggregate them effectively.
3)Over 20% of the articles refer to the celebrities' trips indirectly, instead
of using the exact celebrity names or location names, leading to large portions
of trips escaping regular detecting algorithms. We model text content across
articles related to each candidate location as a graph to better associate
essential information and cancel out the noises. Besides, we design a special
pooling layer based on attention mechanism and node similarity, reducing
irrelevant information from longer articles. To make up the missing information
resulted from indirect mentions, we construct knowledge sub-graphs for named
entities (person, organization, facility, etc.). Specifically, we dynamically
update embeddings of event entities like the G7 summit from news descriptions
since the properties (date and location) of the event change each time, which
is not captured by the pre-trained event representations. The proposed CeleTrip
jointly trains these modules, which outperforms all baseline models and
achieves 82.53% in the F1 metric.Comment: Accepted to ICWSM 2024, 12 page
Spatio-temporal Joint Modelling on Moderate and Extreme Air Pollution in Spain
Very unhealthy air quality is consistently connected with numerous diseases.
Appropriate extreme analysis and accurate predictions are in rising demand for
exploring potential linked causes and for providing suggestions for the
environmental agency in public policy strategy. This paper aims to model the
spatial and temporal pattern of both moderate and extremely poor PM10
concentrations (of daily mean) collected from 342 representative monitors
distributed throughout mainland Spain from 2017 to 2021. We firstly propose and
compare a series of Bayesian hierarchical generalized extreme models of annual
maxima PM10 concentrations, including both the fixed effect of altitude,
temperature, precipitation, vapour pressure and population density, as well as
the spatio-temporal random effect with the Stochastic Partial Differential
Equation (SPDE) approach and a lag-one dynamic auto-regressive component
(AR(1)). Under WAIC, DIC and other criteria, the best model is selected with
good predictive ability based on the first four-year data (2017--2020) for
training and the last-year data (2021) for testing. We bring the structure of
the best model to establish the joint Bayesian model of annual mean and annual
maxima PM10 concentrations and provide evidence that certain predictors
(precipitation, vapour pressure and population density) influence comparably
while the other predictors (altitude and temperature) impact reversely in the
different scaled PM10 concentrations. The findings are applied to identify the
hot-spot regions with poor air quality using excursion functions specified at
the grid level. It suggests that the community of Madrid and some sites in
northwestern and southern Spain are likely to be exposed to severe air
pollution, simultaneously exceeding the warning risk threshold
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