Nonlinear surface magneto-plasmonics in Kretschmann multilayers

The nonlinear magneto-plasmonics aims to utilize plasmonic excitations to control the mechanisms and taylor the efficiencies of the non-linear light frequency conversion at the nanoscale. We investigate the mechanisms of magnetic second harmonic generation in hybrid gold-cobalt-silver multilayer structures, which support propagating surface plasmon polaritons at both fundamental and second harmonic frequencies. Using magneto-optical spectroscopy in Kretschmann geometry, we show that the huge magneto-optical modulation of the second harmonic intensity is dominated by the excitation of surface plasmon polaritons at the second harmonic frequency, as shown by tuning the optical wavelength over the spectral region of strong plasmonic dispersion. Our proof-of-principle experiment highlights bright prospects of nonlinear magneto-plasmonics and contributes to the general understanding of the nonlinear optics of magnetic surfaces and interfaces.Comment: Main Manuscript: 5 pages, 3 figures. Supplementary Information: 10 pages, 7 figure

Long-term hail risk assessment with deep neural networks

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as "hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades

SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan