The LSST AGN Data Challenge: Selection methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidatesComment: Accepted by ApJ. 21 pages, 14 figures, 5 table

York University at TREC 2005: Terabyte Track

York University participated in the terabyte track this year. Using the GOV2 collection, we used filtering techniques to shorten the amount of data to be indexed before indexing into eight partitions. As there were several different subsections of the terabyte track, we chose to participate in the ad hoc and named page retrieval runs. Our technique involved partitioned indexes across a single machine. We combined our results by first calculating the document frequency of a term across all the indexes, calculating the weight, then using the same weight in retrieving the top results from each index. This approach effectively tried to mimic the results that would be obtained if there were only one large index

Robust digital processing of speech signals

This book focuses on speech signal phenomena, presenting a robustification of the usual speech generation models with regard to the presumed types of excitation signals, which is equivalent to the introduction of a class of nonlinear models and the corresponding criterion functions for parameter estimation. Compared to the general class of nonlinear models, such as various neural networks, these models possess good properties of controlled complexity, the option of working in “online” mode, as well as a low information volume for efficient speech encoding and transmission. Providing comprehensive insights, the book is based on the authors’ research, which has already been published, supplemented by additional texts discussing general considerations of speech modeling, linear predictive analysis and robust parameter estimation

Assessing the techno-economic effects of replacing energy-inefficient street lighting with LED corn bulbs

The costs related to the operation of street lighting can be significant expenses for munic-ipalities; therefore, it is very important to take advantage of opportunities for improving energy efficiency. In this paper, the authors studied the effects of the implementation of energy efficiency measures in a street lighting system. Different scenarios, including replacing luminaires, replacing inefficient lamps, and installing a dimming control system, are analysed. The model includes a detailed analysis of the techno-economic characteristics of both LED street luminaires and LED corn bulbs available on the market. The results show that the replacement of low-power, high-intensity discharge lamps with LED corn bulbs is an economically favourable solution that provides desirable economic project parameters with relatively low investments. Moreover, in the case of a relatively low price of electricity for street lighting, it is the preferable solution in most scenarios