269 research outputs found
Modeling Spacing Distribution of Queuing Vehicles in Front of a Signalized Junction Using Random-Matrix Theory
Modeling of headway/spacing between two consecutive vehicles has many
applications in traffic flow theory and transport practice. Most known
approaches only study the vehicles running on freeways. In this paper, we
propose a model to explain the spacing distribution of queuing vehicles in
front of a signalized junction based on random-matrix theory. We show that the
recently measured spacing distribution data well fit the spacing distribution
of a Gaussian symplectic ensemble (GSE). These results are also compared with
the spacing distribution observed for car parking problem. Why
vehicle-stationary-queuing and vehicle-parking have different spacing
distributions (GSE vs GUE) seems to lie in the difference of driving patterns
Shanghai cooperation organization and economic security in Belarus and China
The Shanghai Cooperation Organization (SCO) is an important regional organisation in Eurasia, encouraging collaboration among member governments in numerous disciplines. This research explores how the SCO improves economic security, focusing on Belarus and China. This article examines the SCO's economic activity and these two states' economic issues to demonstrate the organization's role in regional economic stability
Optical limiting using Laguerre-Gaussian beams
We demonstrate optical limiting using the self-lensing effect of a
higher-order Laguerre-Gaussian beam in a thin dye-doped polymer sample, which
we find is consistent with our model using Gaussian decomposition. The peak
phase shift in the sample required for limiting is smaller than for a
fundamental Gaussian beam with the added flexibility that the nonlinear medium
can be placed either in front of or behind the beam focus.Comment: 3 pages, 4 figure
Quantum Interference of Stored Coherent Spin-wave Excitations in a Two-channel Memory
Quantum memories are essential elements in long-distance quantum networks and
quantum computation. Significant advances have been achieved in demonstrating
relative long-lived single-channel memory at single-photon level in cold atomic
media. However, the qubit memory corresponding to store two-channel spin-wave
excitations (SWEs) still faces challenges, including the limitations resulting
from Larmor procession, fluctuating ambient magnetic field, and
manipulation/measurement of the relative phase between the two channels. Here,
we demonstrate a two-channel memory scheme in an ideal tripod atomic system, in
which the total readout signal exhibits either constructive or destructive
interference when the two-channel SWEs are retrieved by two reading beams with
a controllable relative phase. Experimental result indicates quantum coherence
between the stored SWEs. Based on such phase-sensitive storage/retrieval
scheme, measurements of the relative phase between the two SWEs and Rabi
oscillation, as well as elimination of the collapse and revival of the readout
signal, are experimentally demonstrated
DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
Noise removal of images is an essential preprocessing procedure for many
computer vision tasks. Currently, many denoising models based on deep neural
networks can perform well in removing the noise with known distributions (i.e.
the additive Gaussian white noise). However eliminating real noise is still a
very challenging task, since real-world noise often does not simply follow one
single type of distribution, and the noise may spatially vary. In this paper,
we present a new dual convolutional neural network (CNN) with attention for
image blind denoising, named as the DCANet. To the best of our knowledge, the
proposed DCANet is the first work that integrates both the dual CNN and
attention mechanism for image denoising. The DCANet is composed of a noise
estimation network, a spatial and channel attention module (SCAM), and a CNN
with a dual structure. The noise estimation network is utilized to estimate the
spatial distribution and the noise level in an image. The noisy image and its
estimated noise are combined as the input of the SCAM, and a dual CNN contains
two different branches is designed to learn the complementary features to
obtain the denoised image. The experimental results have verified that the
proposed DCANet can suppress both synthetic and real noise effectively. The
code of DCANet is available at https://github.com/WenCongWu/DCANet
Epidemiological and virological characteristics of pandemic influenza A (H1N1) 2009 in school outbreaks in China
Background: During the 2009 pandemic influenza H1N1 (2009) virus (pH1N1) outbreak, school students were at an
increased risk of infection by the pH1N1 virus. However, the estimation of the attack rate showed significant variability.
Methods: Two school outbreaks were investigated in this study. A questionnaire was designed to collect information by
interview. Throat samples were collected from all the subjects in this study 6 times and sero samples 3 times to confirm the
infection and to determine viral shedding. Data analysis was performed using the software STATA 9.0.
Findings: The attack rate of the pH1N1 outbreak was 58.3% for the primary school, and 52.9% for the middle school. The
asymptomatic infection rates of the two schools were 35.8% and 37.6% respectively. Peak virus shedding occurred on the
day of ARI symptoms onset, followed by a steady decrease over subsequent days (p = 0.026). No difference was found either
in viral shedding or HI titer between the symptomatic and the asymptomatic infectious groups.
Conclusions: School children were found to be at a high risk of infection by the novel virus. This may be because of a
heightened risk of transmission owing to increased mixing at boarding school, or a lack of immunity owing to socioeconomic
status. We conclude that asymptomatically infectious cases may play an important role in transmission of the
pH1N1 virus
A Markov Process Inspired Cellular Automata Model of Road Traffic
To provide a more accurate description of the driving behaviors in vehicle
queues, a namely Markov-Gap cellular automata model is proposed in this paper.
It views the variation of the gap between two consequent vehicles as a Markov
process whose stationary distribution corresponds to the observed distribution
of practical gaps. The multiformity of this Markov process provides the model
enough flexibility to describe various driving behaviors. Two examples are
given to show how to specialize it for different scenarios: usually mentioned
flows on freeways and start-up flows at signalized intersections. The agreement
between the empirical observations and the simulation results suggests the
soundness of this new approach.Comment: revised according to the helpful comments from the anonymous
reviewer
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