Telescope software solutions

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Just click on the 'Update Information' button below to start the process. Starting with a self-excited thermoacoustic instability in the combustor, we find that a progressive increase in the swirler rotation rate transitions the system from thermoacoustic instability to the suppressed state through a state of intermittency. To model such transition while also quantifying the underlying synchronization characteristics, we extend the model of Dutta et al.

E 99, ] by introducing a feedback between the ensemble of mean-field phase oscillators and the basis expansion of the acoustic pressure governing equation. The link between the model and experimental results is quantitatively established by implementing an optimization algorithm for model parameter estimation.

We show that the model replicates the bifurcation characteristics, time series, probability density function PDF , and power spectral density PSD of the various dynamical states observed during the transition to the suppressed state, to excellent accuracy. Specifically, the model captures the change in the PDF of pressure and heat release rate fluctuations from a bimodal distribution during thermoacoustic instability to a unimodal distribution during suppression.

Finally, we discuss the global and local flame dynamics and show that the model qualitatively captures various aspects of spatio-temporal synchronization that underlies the transition.

NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail.

Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure.

This paper describes a numeric-oriented hierarchical transformer model to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers monetary, temporal, percentages etc.

We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context. However, when the channel symmetry is broken, an impurity quantum phase transition can occur at zero temperature.

Identification of an order parameter describing the impurity quantum phase transition is very difficult since it is beyond the conventional Landau-Ginzburg-Wilson theory.

By employing the natural orbitals renormalization group method, we study both two-channel and threechannel Kondo models, from the perspective of spin correlation between the impurity and electrons in electronic channels.

Here we demonstrate that by introducing the spin-correlation ratio as an order parameter we can characterize impurity quantum phase transitions driven by channel asymmetry. Half-Trek Criterion for Identifiability of Latent Variable Models We consider linear structural equation models with latent variables and develop a criterion to certify whether the direct causal effects between the observable variables are identifiable based on the observed covariance matrix.

Linear structural equation models assume that both observed and latent variables solve a linear equation system featuring stochastic noise terms. Each model corresponds to a directed graph whose edges represent the direct effects that appear as coefficients in the equation system.

Prior research has developed a variety of methods to decide identifiability of direct effects in a latent projection framework, in which the confounding effects of the latent variables are represented by correlation among noise terms.

This approach is effective when the confounding is sparse and effects only small subsets of the observed variables. In contrast, the new latent-factor half-trek criterion LF-HTC we develop in this paper operates on the original unprojected latent variable model and is able to certify identifiability in settings, where some latent variables may also have dense effects on many or even all of the observables.

Our LF-HTC is an effective sufficient criterion for rational identifiability, under which the direct effects can be uniquely recovered as rational functions of the joint covariance matrix of the observed random variables.

When restricting the search steps in the LF-HTC to consider subsets of latent variables of bounded size, the criterion can be verified in time that is polynomial in the size of the graph. Estimating the Photometric Redshifts of Galaxies and QSOs Using Regression Techniques in Machine Learning Measuring distances of cosmological sources such as galaxies, stars and quasars plays an increasingly critical role in modern cosmology.

Obtaining the optical spectrum and consequently calculating the redshift as a distance indicator could instantly classify these objects. As long as spectroscopic observations are not available for many galaxies and the process of measuring the redshift is time-consuming and infeasible for large samples, machine learning ML approaches could be applied to determine the redshifts of galaxies from different features including their photometric colors.

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The guide camera and your telescope mount communicate with each other to maintain a lock on your target by making subtle corrections to the tracking. This is accomplished by using a great piece of free software developed by Stark Labs. By using autoguiding software such as PHD2 Guiding , our computer can communicate with the telescope mount. This software can also do other useful astrophotography tasks such as drift alignment , which is helpful for those who cannot use Polaris for Polar Alignment.

In early , I upgraded my autoguiding system to include a more sensitive camera, and an improved guide scope:. PHD2 Guiding includes a user interface that allows you to enter in your specific connection type and autoguiding camera model. This combination works extremely well, and I spend very little time dealing with anything related to autoguiding these days. When autoguiding with the iOptron CEM60 mount, I can utilize the dithering and autoguiding features directly from my laptop computer to the mount.

This means that I no longer need to connect the ST4 cable to the guide port from the camera, as the ASCOM interface communicates with the mount directly.

It is important to make sure that the guide scope is properly focused to ensure accurate star tracking. Please consider visiting the Learn Astrophotography section of this site to explore my techniques during real-life situations in the backyard. Off-axis guiding or OAG , involves using a device that sends starlight to your guide camera from the optical axis of your primary imaging telescope.

The pick-off prism sent useful guide stars to my ZWO ASImm min i guide camera to make corrections to the telescope mount for long-exposure astrophotography. No matter which method of autoguiding you use, the goal is simple. To capture long exposure images with round, sharp stars. For those having issues with autoguiding early on, I urge you to ensure that your telescope mount is properly polar aligned and balanced with no cable snags before trying to diagnose autoguiding issues.