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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “Wuhan”

Empirical validation of network learning with taxi GPS data from Wuhan, Chinaphysics.soc-ph

Study validates method using taxi GPS data to monitor network performance.

problem Monitoring transportation network performance with limited data.

method Multi-agent inverse optimization method using taxi GPS probe data.

result Forecasted travel times correlate with observed travel times (0.23-0.56).

transportation network taxi GPS inverse optimization network monitoring Wuhan

2019-11-09abs ↗pdf ↗

Inference in Stochastic Epidemic Models via Multinomial Approximationsstat.ME

New method for epidemic model inference using multinomial approximations.

problem Inference in stochastic epidemic models with partial observations.

method Recursive multinomial approximations to integrate over unobserved variables.

result Accuracy demonstrated through real and simulated data.

epidemic modeling stochastic models multinomial approximations inference reproduction number

2020-06-24abs ↗pdf ↗

Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italystat.AP

Study compares ARIMA, ETS, NNAR and hybrids for COVID-19 hospitalizations.

problem Forecasting the second wave of COVID-19 hospitalizations in Italy.

method ARIMA, ETS, NNAR, and hybrid models.

result Hybrid models outperform single models in capturing epidemic patterns.

COVID-19 forecasting hospitalizations Italy ARIMA

2020-10-22abs ↗pdf ↗

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imageseess.IV

CoroNet detects COVID-19 from chest X-rays with high accuracy.

problem Detecting COVID-19 from chest X-rays using limited testing kits.

method Proposes CoroNet, a deep neural network based on Xception architecture trained on a combined dataset of COVID-19 and pneumonia X-rays.

result CoroNet achieved an overall accuracy of 89.6% and precision/recall rates of 93%/98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal).

COVID-19 chest x-ray deep learning diagnosis pneumonia

2020-04-10abs ↗pdf ↗