Simple Stochastic Temporal Constraint Networks

Many artificial intelligence tasks (e.g., planning, situation assessment, scheduling) require reasoning about events in time. Temporal constraint networks offer an elegant and often computationally efficient framework for such temporal reasoning tasks. Temporal data and knowledge available in some domains is necessarily imprecise - e.g., as a result of measurement errors associated with sensors. This paper introduces stochastic temporal constraint networks thereby extending constraint-based approaches to temporal reasoning with precise temporal knowledge to handle stochastic imprecision. The paper proposes an algorithm for inference of implicit stochastic temporal constraints from a given set of explicit constraints. It also introduces a stochastic version of the temporal constraint network consistency problem and describes techniques for solving it under certain simplifying assumptions

Chern-Simons Matrix Models and Unoriented Strings

For matrix models with measure on the Lie algebra of SO/Sp, the sub-leading free energy is given by F_{1}(S)=\pm{1/4}\frac{\del F_{0}(S)}{\del S}. Motivated by the fact that this relationship does not hold for Chern-Simons theory on S^{3}, we calculate the sub-leading free energy in the matrix model for this theory, which is a Gaussian matrix model with Haar measure on the group SO/Sp. We derive a quantum loop equation for this matrix model and then find that F_{1} is an integral of the leading order resolvent over the spectral curve. We explicitly calculate this integral for quadratic potential and find agreement with previous studies of SO/Sp Chern-Simons theory.Comment: 28 pages, 2 figures V2: re-organised for clarity, results unchange

ERA5 reanalysis for the data interpretation on polarization laser sensing of high-level clouds

Interpreting the results of a high-level clouds (HLCs) lidar study requires a comparison with the vertical profiles of meteorological quantities. There are no regular radiosonde measurements of vertical profiles of meteorological quantities in Tomsk. The nearest aerological stations are several hundred kilometers away from the lidar and perform radiosonde measurements only a few times a day, whereas lidar experiments are performed continuously throughout the day. To estimate meteorological conditions at the HLC altitudes, we propose to use the ERA5 reanalysis. Its reliability was tested by comparing with the data from five aerological stations within a radius of 500 km around Tomsk. A labeled database of the lidar, radiosonde, and ERA5 data (2016–2020) for isobaric levels 1000–50 hPa was created. The temperature reconstruction error over the entire altitude range was characterized by an RMSE of 0.8–2.8 ◦C, bias of 0–0.9, and Corr ~1. The accuracy of the relative vertical profiles (RMSE 25–40%, Bias 10–22%, and Corr <0.7) and specific humidity (RMSE 0.2–1.2 g/kg, Bias ~0 g/kg, and Corr ~0) at the HLC altitudes were unsatisfying. The ERA5 data on wind direction and speed for the HLC altitudes were promising

Simple Stochastic Temporal Constraint Networks

Many artificial intelligence tasks (e.g., planning, situation assessment, scheduling) require reasoning about events in time. Temporal constraint networks offer an elegant and often computationally efficient framework for such temporal reasoning tasks. Temporal data and knowledge available in some domains is necessarily imprecise - e.g., as a result of measurement errors associated with sensors. This paper introduces stochastic temporal constraint networks thereby extending constraint-based approaches to temporal reasoning with precise temporal knowledge to handle stochastic imprecision. The paper proposes an algorithm for inference of implicit stochastic temporal constraints from a given set of explicit constraints. It also introduces a stochastic version of the temporal constraint network consistency problem and describes techniques for solving it under certain simplifying assumptions.</p

Simple Stochastic Temporal Constraint Networks

Many artificial intelligence tasks (e.g., planning, situation assessment, scheduling) require reasoning about events in time. Temporal constraint networks offer an elegant and often computationally efficient framework for formulation and solution of such temporal reasoning tasks. Temporal data and knowledge available in some domains is necessarily imprecise - e.g., as a result of measurement errors associated with sensors. This paper introduces stochastic temporal constraint networks thereby extending constraint-based approaches to temporal reasoning with precise temporal knowledge to handle stochastic imprecision. The paper proposes an algorithm for inference of implicit stochastic temporal constraints from a given set of explicit constraints. It also introduces a stochastic version of the temporal constraint network consistency verification problem and describes techniques for solving it under certain simplifying assumptions. 1. INTRODUCTION Many problems in artificial intelligence ..

The bactericidal effect of continuous wave laser with strongly absorbing coating at the fiber tip

The bactericidal effect of laser radiation with a quartz fiber-based transmission system with a strong absorption coating converter against bacteria associated with urological stones has been studied. Gram-negative rod Escherichia coli and the Gram-positive coccus Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecalis and Enterococcus faecium were used in this study. Each bacterial species was treated by continuous-wave near infrared laser coupled with bare fiber tip or strongly absorption coating fiber tip. After treatment, the temperature of bacterial suspension was measured. In addition, the temperature distribution was analyzed. It has been shown that using laser with a strongly absorption coating fiber tip results in significant bactericidal effect. The decrease of the amount of E. coli and S. epidermidis was 100% after treatment with an output power of 6W of radiation at a wavelength of 0.97μm for 40s. Number of S. aureus and Ent. faecium colony-forming unit was reduced to 70% after same exposure. The peak temperature of bacterial suspension was 86∘C after treatment by laser with a strongly absorption coating fiber tip. Laser with a strongly absorption coating fiber tip provides large-scale hydrodynamic flows directed away from the fiber tip. The laser with a strongly absorption coating fiber tip has bactericidal effect. The main role is associated with the effect of high temperature, which, in the form of flow in a liquid medium, affects bacteria

ERA5 Reanalysis for the Data Interpretation on Polarization Laser Sensing of High-Level Clouds

Interpreting the results of a high-level clouds (HLCs) lidar study requires a comparison with the vertical profiles of meteorological quantities. There are no regular radiosonde measurements of vertical profiles of meteorological quantities in Tomsk. The nearest aerological stations are several hundred kilometers away from the lidar and perform radiosonde measurements only a few times a day, whereas lidar experiments are performed continuously throughout the day. To estimate meteorological conditions at the HLC altitudes, we propose to use the ERA5 reanalysis. Its reliability was tested by comparing with the data from five aerological stations within a radius of 500 km around Tomsk. A labeled database of the lidar, radiosonde, and ERA5 data (2016–2020) for isobaric levels 1000–50 hPa was created. The temperature reconstruction error over the entire altitude range was characterized by an RMSE of 0.8–2.8 °C, bias of 0–0.9, and Corr ~1. The accuracy of the relative vertical profiles (RMSE 25–40%, Bias 10–22%, and Corr <0.7) and specific humidity (RMSE 0.2–1.2 g/kg, Bias ~0 g/kg, and Corr ~0) at the HLC altitudes were unsatisfying. The ERA5 data on wind direction and speed for the HLC altitudes were promising