199 research outputs found
Analyze the Trend of Post Replies Based on Linear Regression Model-----take Tianyawebsiteas examples
In recent years, users spend more time on surfing the social networking than ever before. How to make the information spread rapidlywhen facing vast amounts of information?Scholars have conducted information dissemination in social networking. On the basis of previous research, the authors divide posts into popular posts and ordinary posts and then use the linear regression model to predict the replies at specified time. After comparing the difference between two types of posts, the authors concludethat ordinary posts could become popular posts if the posts could maintain a large number of replies within former five hours and increase replies by making use of community mechanism. This conclusion provides a reasonable proposal for enterprises and administrators to identify andrecommendpopular posts
Description of the newly observed states as molecular states
In this work, we study the strong decays of the newly observed
and assuming that
and as -wave and
molecular state, respectively. Since the was observed in the
invariant mass distributions, the partial decay width of
and into through
hadronic loops are evaluated with the help of the effective Lagrangians.
Moreover, the decay channel of is also included. The decay
process is described by the -channel , baryons and ,
mesons exchanges, respectively. By comparison with the LHCb
observation, the current results support the
with as pure molecule while the
with can not be well reproduced in the molecular state picture.
In addition, the spin-parity molecular assumptions for the
can't be conclusively determined. It may be a meson-baryon
molecule with a big component. Although the decay width of the
is of the order several MeV, it can be well
employed to test the molecule interpretations of and
Learning Trajectories are Generalization Indicators
This paper explores the connection between learning trajectories of Deep
Neural Networks (DNNs) and their generalization capabilities when optimized
using (stochastic) gradient descent algorithms. Instead of concentrating solely
on the generalization error of the DNN post-training, we present a novel
perspective for analyzing generalization error by investigating the
contribution of each update step to the change in generalization error. This
perspective allows for a more direct comprehension of how the learning
trajectory influences generalization error. Building upon this analysis, we
propose a new generalization bound that incorporates more extensive trajectory
information. Our proposed generalization bound depends on the complexity of
learning trajectory and the ratio between the bias and diversity of training
set. Experimental findings reveal that our method effectively captures the
generalization error throughout the training process. Furthermore, our approach
can also track changes in generalization error when adjustments are made to
learning rates and label noise levels. These results demonstrate that learning
trajectory information is a valuable indicator of a model's generalization
capabilities
Is the Age Structure of the Population One of the Determinants of the Household Saving Rate in China? A Spatial Panel Analysis of Provincial Data
In this paper, we use provincial panel data on China for the 2002-19 period to conduct a spatial autocorrelation analysis of household saving rates as well as a dynamic panel analysis of the determinants of household saving rates using a spatial Durbin model. To summarize our main findings, we find that, in China, the household saving rate shows significant positive spatial autocorrelation with an overall “high-high” and “low-low”clustering pattern, that, as predicted by the life-cycle hypothesis, the youth dependency ratio and the old-age dependency ratio have a negative and significant impact on the household saving rate, and that the logarithm of per capita household disposable income, the regional economic growth rate, the share of the urban population, the industrialization rate, and the income disparity between urban and rural areas also have a significant impact on the household saving rate
Deuterium- and Alkyne-Based Bioorthogonal Raman Probes for In Situ Quantitative Metabolic Imaging of Lipids within Plants
Plants can rapidly respond to different stresses by activating multiple signaling and defense pathways. The ability to directly visualize and quantify these pathways in real time using bioorthogonal probes would have practical applications, including characterizing plant responses to both abiotic and biotic stress. Fluorescence-based labels are widely used for tagging of small biomolecules but are relatively bulky and with potential effects on their endogenous localization and metabolism. This work describes the use of deuterium- and alkyne-derived fatty acid Raman probes to visualize and track the real-time response of plants to abiotic stress within the roots. Relative quantification of the respective signals could be used to track their localization and overall real-time responses in their fatty acid pools due to drought and heat stress without labor-intensive isolation procedures. Their overall usability and low toxicity suggest that Raman probes have great untapped potential in the field of plant bioengineering
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