Related papers: CPO prediction: accuracy assessment and impact on …

Very Long Baseline Interferometry (VLBI) Intensive sessions are scheduled to provide operational Universal Time (UT1) determinations with low latency. UT1 estimates obtained from these observations heavily depend on the model of the…

As an important component of Earth Orientation Parameters (EOP), the prediction of Celestial Pole Offsets (CPO) holds significant importance for missions such as deep space exploration. To explore a better CPO prediction algorithm that…

To estimate real accuracy of EOP prediction real-time predictions made by the IERS Subbureau for Rapid Service and Prediction (USNO) and at the Institute of Applied Astronomy (IAA) EOP Service are analyzed. Methods of a priory estimate of…

A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole coordinates. The method is based on construction of a general polyharmonic model of the variations of the…

VLBI observations carried out by global networks provide the most accurate values of the precession-nutation angles determining the position of the celestial pole; as a rule, these results become available two to four weeks after the…

Tempo2 is a new software package for the analysis of pulsar pulse times of arrival. In this paper we describe in detail the timing model used by tempo2, and discuss limitations on the attainable precision. In addition to the intrinsic…

A new method is developed for prediction of UT1. The method is based on construction of a general harmonic model of the Earth rotation using all the data available for the last 80-100 years, and modified autoregression technique. A rigorous…

Two methods of prediction of the Pole coordinates and TAI-UTC were tested -- extrapolation of the deterministic components and ARIMA. It was found that each of these methods is most effective for certain length of prognosis. For short-time…

For future extremely large telescopes, error in extreme adaptive optics systems at small angular separations will be highly impacted by the lag time of the correction, which is typically on millisecond timescales; one solution is to apply a…

Scientific applications typically generate large volumes of floating-point data, making lossy compression one of the most effective methods for data reduction, thereby lowering storage requirements and improving performance in large-scale…

Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects between clustering and prediction (e.g. classification,…

The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly. In recent…

Prediction deviations of different uncertainties have varying impacts on downstream decision-making. Improving the prediction accuracy of critical uncertainties with significant impacts on decision-making quality yields better optimization…

A well known problem with Earth Orientation Parameters (EOP) prediction is that a prediction strategy proved to be the best for some testing time span and prediction length may not remain the same for other time intervals. In this paper, we…

This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path…

Large Language Models (LLMs) are increasingly embedded in enterprise workflows, yet their performance remains highly sensitive to prompt design. Automatic Prompt Optimization (APO) seeks to mitigate this instability, but existing approaches…

PLATO is designed to detect Earth-sized exoplanets around solar-type stars and to measure their radii with accuracy better than \(2\%\) via the transit method. Charge transfer inefficiency (CTI), a by-product of radiation damage to CCDs,…

Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings. Current approaches, however, suffer from considering overly conservative…

Although the widespread use of AI systems in today's world is growing, many current AI systems are found vulnerable due to hidden bias and missing information, especially in the most commonly used forecasting system. In this work, we…

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The…