Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

Deep Learning for Forecasting Stock Returns in the Cross-Section

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201

Generative deep fields : arbitrarily sized, random synthetic astronomical images through deep learning

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond.Peer reviewe

TextGAIL: Generative Adversarial Imitation Learning for Text Generation

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. Our approach uses contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.Comment: AAAI 202

Simultaneous dual-frequency radio observations of S5 0716+714: A search for intraday variability with the Korean VLBI Network

This study aims to search for the existence of intraday variability (IDV) of BL Lac object S5 0716+714 at high radio frequencies for which the interstellar scintillation effect is not significant. Using the 21-meter radio telescope of the Korean VLBI Network (KVN), we present results of multi-epoch simultaneous dual-frequency radio observations. Single-dish observations of S5 0716+714 were simultaneously conducted at 21.7 GHz (K-band) and 42.4 GHz (Q-band), with a high cadence of 30-60 minute intervals.We observed four epochs between December 2009 and June 2010. Over the whole set of observation epochs, S5 0716+714 showed significant inter-month variations in flux density at both the K- and Q-bands, with modulation indices of approximately 19% for the K-band and approximately 36% for the Q-band. In all epochs, no clear intraday variability was detected at either frequency. The source shows monotonic flux density increase in epochs 1 and 3 and monotonic flux density decrease in epochs 2 and 4. In the flux density increasing phases, the flux densities at the Q-band increase more rapidly. In the decreasing phase, no significant flux density difference is seen at the two frequencies. The situation could be different close to flux density peaks that we did not witness in our observations. We find an inverted spectrum with mean spectral indices of -0.57+-0.13 in epoch 1 and -0.15+-0.11 in epoch 3. On the other hand, we find relatively steep indices of +0.24+-0.14 and +0.17+-0.18 in epochs 2 and 4, respectively. We conclude that the frequency dependence of the variability and the change of the spectral index are caused by source-intrinsic effects rather than by any extrinsic scintillation effect.Comment: 6 pages and 4 figures and 4 table