Thermal photogrammetric imaging:a new technique for monitoring dome eruptions

Structure-from-motion (SfM) algorithms greatly facilitate the generation of 3-D topographic models from photographs and can form a valuable component of hazard monitoring at active volcanic domes. However, model generation from visible imagery can be prevented due to poor lighting conditions or surface obscuration by degassing. Here, we show that thermal images can be used in a SfM workflow to mitigate these issues and provide more continuous time-series data than visible counterparts. We demonstrate our methodology by producing georeferenced photogrammetric models from 30 near-monthly overflights of the lava dome that formed at Volcán de Colima (Mexico) between 2013 and 2015. Comparison of thermal models with equivalents generated from visible-light photographs from a consumer digital single lens reflex (DSLR) camera suggests that, despite being less detailed than their DSLR counterparts, the thermal models are more than adequate reconstructions of dome geometry, giving volume estimates within 10% of those derived using the DSLR. Significantly, we were able to construct thermal models in situations where degassing and poor lighting prevented the construction of models from DSLR imagery, providing substantially better data continuity than would have otherwise been possible. We conclude that thermal photogrammetry provides a useful new tool for monitoring effusive volcanic activity and assessing associated volcanic risks

Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2-D image data, which is insufficient for 3-D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multisensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for nonstructured 3-D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground truth. The point cloud is dense and contains 3242964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3-D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256

An Image-Free Opto-Mechanical System for Creating Virtual Environments and Imaging Neuronal Activity in Freely Moving Caenorhabditis elegans

Non-invasive recording in untethered animals is arguably the ultimate step in the analysis of neuronal function, but such recordings remain elusive. To address this problem, we devised a system that tracks neuron-sized fluorescent targets in real time. The system can be used to create virtual environments by optogenetic activation of sensory neurons, or to image activity in identified neurons at high magnification. By recording activity in neurons of freely moving C. elegans, we tested the long-standing hypothesis that forward and reverse locomotion are generated by distinct neuronal circuits. Surprisingly, we found motor neurons that are active during both types of locomotion, suggesting a new model of locomotion control in C. elegans. These results emphasize the importance of recording neuronal activity in freely moving animals and significantly expand the potential of imaging techniques by providing a mean to stabilize fluorescent targets

A decade of detailed observations (2008-2018) in steep bedrock permafrost at the Matterhorn Hörnligrat (Zermatt, CH)

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines and started in 2006 with the goals of realizing observations that previously have not been possible. Specifically, the aims are to obtain measurements in unprecedented quantity and quality based on technological advances. This paper describes a unique >10-year data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500ma:s:l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make these data available in near real time and tightly monitor and control the running experiments. This data set (https://doi.org/10.1594/PANGAEA.897640,Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of 10 or more years. By documenting and sharing these data in this form we contribute to making our past research reproducible and facilitate future research based on these data, e.g., in the areas of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. Finally, the cross-validation of four different data types clearly indicates the dominance of thawing-related kinematics

Measurement of the inclusive isolated-photon cross section in pp collisions at √s = 13 TeV using 36 fb−1 of ATLAS data

The differential cross section for isolated-photon production in pp collisions is measured at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 36.1 fb. The differential cross section is presented as a function of the photon transverse energy in different regions of photon pseudorapidity. The differential cross section as a function of the absolute value of the photon pseudorapidity is also presented in different regions of photon transverse energy. Next-to-leading-order QCD calculations from Jetphox and Sherpa as well as next-to-next-to-leading-order QCD calculations from Nnlojet are compared with the measurement, using several parameterisations of the proton parton distribution functions. The predictions provide a good description of the data within the experimental and theoretical uncertainties. [Figure not available: see fulltext.

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)