249 research outputs found
The impact of human relationship on bankruptcy-related evolution of inter-firm trade network
This paper studies the impact of human relationship on the evolution of inter-firm trade network emerged from bankruptcy. Based on the extracted properties of Japanese firm data in 10 years, we propose an agent-based model and conduct series of simulation experiments to evaluate several aspects of human relationship effects. The simulation results indicate that human relationship delays the bankrupt spread and promotes the average performance of firms. By examining different scenarios, we found the influential features of human relationship that are likely to help firms to survive in the bankrupt propagation process
Wave breaking for the generalized Fornberg-Whitham equation
This paper aims to show that the Cauchy problem of the Burgers equation with
a weakly dispersive perturbation involving the Bessel potential (generalization
of the Fornberg-Whitham equation) can exhibit wave breaking for initial data
with large slope. We also comment on the dispersive properties of the equation
EdgeYOLO: An Edge-Real-Time Object Detector
This paper proposes an efficient, low-complexity and anchor-free object
detector based on the state-of-the-art YOLO framework, which can be implemented
in real time on edge computing platforms. We develop an enhanced data
augmentation method to effectively suppress overfitting during training, and
design a hybrid random loss function to improve the detection accuracy of small
objects. Inspired by FCOS, a lighter and more efficient decoupled head is
proposed, and its inference speed can be improved with little loss of
precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8%
AP50 in MS COCO2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone2019-DET
dataset, and it meets real-time requirements (FPS>=30) on edge-computing device
Nvidia Jetson AGX Xavier. We also designed lighter models with less parameters
for edge computing devices with lower computing power, which also show better
performances. Our source code, hyper-parameters and model weights are all
available at https://github.com/LSH9832/edgeyolo
Experimental Quantum Fingerprinting
Quantum communication holds the promise of creating disruptive technologies
that will play an essential role in future communication networks. For example,
the study of quantum communication complexity has shown that quantum
communication allows exponential reductions in the information that must be
transmitted to solve distributed computational tasks. Recently, protocols that
realize this advantage using optical implementations have been proposed. Here
we report a proof of concept experimental demonstration of a quantum
fingerprinting system that is capable of transmitting less information than the
best known classical protocol. Our implementation is based on a modified
version of a commercial quantum key distribution system using off-the-shelf
optical components over telecom wavelengths, and is practical for messages as
large as 100 Mbits, even in the presence of experimental imperfections. Our
results provide a first step in the development of experimental quantum
communication complexity.Comment: 11 pages, 6 Figure
Using AI Methods for Health Behavior Change
Artificial intelligence (AI) has been applied to health behavior change research for over a decade. Current research programs include machine learning for delivering just-in-time adaptive interventions, computational modeling of behavior change processes, and the use of social AI for communication and persuasion. With new advances in AI, we propose an international workshop to bring together experts from all related disciplines to discuss and explore the potentials of AI for behavior change research. We discuss in this proposal the aims, planned activities, expected outcomes, and a promotion strategy for the workshop.</p
Development of risk prediction model for cognitive impairment in patients with coronary heart disease: A study protocol for a prospective, cross-sectional analysis
BackgroundIschemic heart disease and degenerative encephalopathy are two main sources of disease burden for the global elderly population. Coronary heart disease (CHD) and cognitive impairment, as representative diseases, are prevalent and serious illnesses in the elderly. According to recent research, patients with CHD are more likely to experience cognitive impairment and their cognitive ability declines more quickly. Vascular risk factors have been associated with differences in cognitive performance in epidemiological studies, but evidence in patients with CHD is more limited. Inextricably linked between the heart and the brain. Considering the unique characteristics of recurrent cognitive impairment in patients with CHD, we will further study the related risk factors. We tried to investigate the potential predictors of cognitive impairment in patients with CHD through a prospective, cross-sectional study.MethodsThe cross-sectional study design will recruit 378 patients with CHD (≥65 years) from Xiyuan Hospital of China Academy of Chinese Medical Sciences. The subjects' cognitive function is evaluated with MoCA scale, and they are divided into cognitive impairment group and normal cognitive function group according to the score results. Demographic data, disease characteristics (results of coronary CT/ angiography, number of stents implanted, status of diseased vessels), laboratory tests (biochemistry, coagulation, serum iron levels, pulse wave velocity), metabolites (blood samples and intestinal metabolites), and lifestyle (smoking, alcohol consumption, sleep, physical activity) will be assessed as outcome indicators. Compare the two groups and the correlation analysis will be performed on the development of mild cognitive impairment. Mann-Whitney U or X2 test was selected to describe and evaluate the variation, and logistics regression analysis was employed to fit the prediction model. After that, do the calibration curve and decision curve to evaluate the model. The prediction model will be validated by a validation set.DiscussionTo explore the risk factors related to mild cognitive impairment (MCI) in patients with CHD, a new predictive model is established, which can achieve advanced intervention in the occurrence of MCI after CHD. Owing to its cross-sectional study design, the study has some limitations, but it will be further studied by increasing the observation period, adding follow-up data collection or prospective cohort study. The study has been registered with the China Clinical Trials Registry (ChiCTR2200063255) to conduct clinical trials
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