Reviewerly/exHarmony huggingface dataset:
New stations to monitor gas hazard in the ongoing volcanic unrest crisis of La Fossa volcano (Vulcano Island, Italy)

&amp;lt;p&amp;gt;La Fossa volcano, located in the Vulcano Island of the Aeolian Archipelago, is the type locality of Vulcanian explosive eruptions. It last erupted in 1888-1890 and since then it is affected by an intense fumarolic activity from both the summit crater area and a hydrothermal site (Levante Beach) located very near to the main settlement of the island (Vulcano Porto). In Autumn 2021 a potential volcanic unrest crisis began with a strong increase of steam, CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emission from the high-T crater fumaroles, ground uplift and episodic anomalous seismicity. Vulcano Porto inhabited area is exposed to gas hazard either from the wind dispersed crater fumarolic plume (mostly CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) and from anomalous diffuse soil gas emissions in Levante Beach and other zones of Vulcano Porto village (mostly CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and eventually H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;S). The gas hazard of the village was considered so high that in December 2021 Civil Protection prohibited residents to stay at home during the night. In order to improve the monitoring of gas hazard we developed new stations continuously measuring the air concentration of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;. Each of these stations is operating with an electrochemical sensor for the measurement of SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and a photoacoustic sensor for the measurement of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;. Moreover, atmospheric pressure, temperature and humidity are monitored in parallel to the gas measurements. The measured data are sent continuously via mobile data connection to a dedicated server. By this means the measured parameters can be monitored remotely, without the need to access the site personally. Three stations were installed (at 1 m from the ground) in mid-December 2021 in three sites of Vulcano Porto; two of them were located at the base of La Fossa cone in the sector most exposed to the crater gas plume, while a third station was located in the heart of the village, near the church. Results show that CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; exceeds of few hundreds ppm the normal air value of 400 ppm in all the stations. In some occasions, during night in absence of wind or with light wind blowing from SW, some peaks of both CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; were recorded in all the stations (CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; max 1500 ppm; SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; max 2 ppm). Additionally a future server sided extension to our system is planned, which integrates an early warning system, that can send email alerts, if certain thresholds are exceeded.&amp;lt;/p&amp;gt;
An experimental investigation of seismic and acoustic harmonic tremor gliding and implication for churn-like flow

&amp;lt;p&amp;gt;The gradual shift over time of the spectral lines of harmonic seismic and/or acoustic tremor, that is commonly known as spectral gliding, has been largely observed at different volcanoes. Despite the clear advantage of the experimental approach in providing direct observation of degassing processes and of the related elastic radiation, experimental studies on gliding tremor are lacking. To fill this gap, we investigated different episodes of gliding of acoustic and seismic tremor observed during analogue degassing experiments performed under different experimental conditions, by systematically changing: 1) analogue magma viscosity (10-1,000 Pa s), 2) gas flux (5-180x10&amp;lt;sup&amp;gt;-3 &amp;lt;/sup&amp;gt;l/s) and 3) conduit surface roughness (fractal dimension of 2-2.99). The occurrence of gliding experimental seismic and acoustic tremor was linked to high gas flux rates and viscosities and generally associated with an increasing trend and often preceding a major burst. In a few cases we observed decreasing secondary sets of harmonic spectral lines. Results suggest that gliding episodes are mainly related to the progressive volume variation of shallow interconnected gas pockets. Spectral analyses performed on acoustic signals provided the theoretical length of the resonator. The latter was compared against the temporal evolution of the gas pockets, quantified from video analyses. The similarities between the observed degassing regime and churn-annular flow in high viscous fluids encourages further studies on churn dynamics in volcanic environments.&amp;lt;/p&amp;gt;
Young Adult TBT-S Client Skills Training

There is a biological basis for why eating is not easy in that food is not intrinsically rewarding, contributing to why the brain may code food as harmful. Lack of motivation for treatment may reflect a deficit in biologically induced reward/motivation system rather than willfulness. Clinicians can help clients to turn to Supports and others to identify external motivators since they are unable to experience intrinsic motivation. Providing structure around meals is temperament congruent because it is unlikely that individuals with active AN can eat intuitively given altered brain reward signaling to hunger.
Seismic Soundoff: Breaking down the life cycle of a well

In this episode, Sue Carr and Patrick Meroney discuss the importance of a shared definition of a well, the most important well component for geophysicists, the five life cycles of a well, and why well logs are key to successful drilling. Hear the full episode at https://seg.org/podcast/post/13689 .
&amp;#8220;Fully-automated&amp;#8221; clustering method for stress inversions (CluStress)

&amp;lt;p&amp;gt;As it is well-known, stress fields are responsible for earthquake formation. In order to analyse stress relations in a study area using focal mechanisms&amp;amp;#8217; (FMS) inversions, it is vital to consider three fundamental criteria:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;(1)&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160; The investigated area is characterized by a homogeneous stress field.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;(2)&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160; The earthquakes occur with variable directions on pre-existing faults.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;(3)&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160;&amp;amp;#160; The deviation of the fault slip vector from the shear stress vector is minimal (Wallace-Bott hypothesis).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The authors have attempted to develop a &amp;amp;#8220;fully-automated&amp;amp;#8221; algorithm to carry out the classification of the earthquakes as a prerequisite of stress estimations. This algorithm does not call for the setting of hyper-parameters, thus subjectivity can be reduced significantly and the running time can also decrease. Nevertheless, there is an optional hyper-parameter that is eligible to filter outliers, isolated points (earthquakes) in the input dataset.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation, they show the operation of this algorithm in case of synthetic datasets consisting of different groups of FMS and a real seismic dataset. The latter come from a survey area in the earthquake-prone Vrancea-zone (Romania). This is a relatively small region (around 30*70 km) in the external part of SE-Carpathians where the distribution of the seismic events is quite dense and heterogeneous.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;It shall be noted that though the initial results are promising, further developments are still necessary. The source codes are soon to be uploaded to a public GitHub repository which will be available for the whole scientific community.&amp;lt;/p&amp;gt;
Augmented Reality for the Visualization of the Impacts of Climate Change and&amp;#160;Response Measures

&amp;lt;p&amp;gt;There is an increasing demand for tailored climate services to assist informed decision-making. The scientific basis is there, with high-quality relevant models available for nearly any facet and topic within climate science. However, usage of these models requires a high degree of subject matter knowledge. Conversely, many simplified tools and reports either lose the interactivity that models offer or still require at least a working knowledge of the field. My project explores the potential of Augmented Reality (AR) to provide interactive and easy-to-use climate services to non-scientists. AR could allow for a whole new class of users to participate in the climate change debate, by offering them to assess climate impacts of different climate scenarios and trade-offs between various response measures. One of the key benefits of mobile AR is its accessibility, since it does not require any special dedicated equipment, working on almost all smartphones and being familiar to many users who already experienced AR in other popular applications.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;Two different AR approaches are used as the methodology. In-situ augmented reality is explored as a way to allow the user to assess climate impacts on their location, giving a more personal experience. Seeing the impacts on their own environment is more likely to leave an impression and influence users to make changes. On-table collaborative augmented reality simulations are used to provide a bird&amp;amp;#8217;s eye view of a larger area and to allow multiple-users to engage in the experience in a workshop style. In both approaches, the users are able to switch between different RCP climate scenarios and/or apply response measures, and instantly see the impacts of the change.&amp;lt;/p&amp;gt;
Applying Machine Learning Methods to predict rain using GNSS products and meteorological parameters

&amp;lt;p&amp;gt;Nowadays, weather forecast is an important factor of everyday life that we should be well prepared for. Especially the amount of rainfall can positively or negatively influence our lifestyle. While a moderate rainfall is supportive for agriculture or provision of potable water, too much rainfall can &amp;amp;#160;cause disasters like floods. Therefore, accessing the rain information in near-real-time is beneficial in all aspects. In recent years, GNSS meteorology has been widely utilized as a valuable tool to better interact with the weather conditions in the now-casting and forecasting applications. Nevertheless, rainfall cannot be estimated directly from the GNSS measurements, and therefore some other methods like Artificial Intelligence (AI) are employed to do so. One of the well-known methods in AI is Machine Learning (ML) which focuses on data in order to model or classify various cases such as anomaly detection, earthquake prediction, and rainfall classification. The main objective of this research is to develop a predictive model for accumulated rain every 3 hours for an area populated with 21 GNSS stations of the EUREF Permanent GNSS Network (EPN). For this purpose, we applied different ML methods. The period of interest ranges from 2017 January to 2021 October. The years 2017 to 2020 are used for training, and 2021 is utilized to evaluate the rain model. The temperature, atmospheric pressure, wind speed, wind direction, relative humidity, Zenith Wet Delay (ZWD), Gradients (GN-S, GE-W), Total Electron Contents (TEC) are selected as input parameters in ML. Besides, the rain product from Global Satellite Mapping of Precipitation (GSMaP) is considered as the reference of the model. Finally, the accumulated rain prediction models are derived every 3 hours over the area of interest.&amp;lt;/p&amp;gt;
How dilatancy-induced pore pressure changes control rupture and slip during failure experiments in crystalline rock.

&amp;lt;p&amp;gt;Failure and fault slip in crystalline rocks is associated with dilation. When pore fluids are present and drainage is insufficient, dilation leads to pore pressure drops, which in turn lead to strengthening of the material. We conducted laboratory rock fracture experiments with direct in-situ fluid pressure measurements which demonstrate that dynamic rupture propagation and fault slip can be stabilised (i.e., become quasi-static) by such a dilatancy strengthening effect. We also observe that, for the same effective pressures but lower pore fluid pressures, the stabilisation process may be arrested when the pore fluid pressure approaches zero and vaporises, resulting in dynamic shear failure.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We use acoustic emission locations and our fluid pressure sensors to further detail dilatancy-induced stable failure by tracking the progression of the rupture front (i.e., creation of the fault) and the active slip patches of the newly formed fault. In doing so, we are able to link local pore pressure records to the position of the rupture front where dilation is strongest. We see minimal slip in the wake of the rupture front. Once the fault is completed, we observe that the entire fault slips for up to a few minutes, driven by pore pressure recharge of the fault zone. Hence, we directly observe decoupling of rupture and &amp;amp;#8220;after&amp;amp;#8221;-slip that would otherwise &amp;amp;#8211; in a dynamic failure &amp;amp;#8211; occur simultaneously.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;All our observations are quantitatively explained by a spring-slider model combining slip-weakening behaviour, slip-induced dilation, and pore fluid diffusion. Using our data in an inverse problem, we estimate the key parameters controlling rupture stabilization: fault dilation rate and fault zone storage. These estimates are used to make predictions for the pore pressure drop associated with faulting, and where in the crust we may expect dilatancy stabilisation or vaporisation during earthquakes. For intact rock and well consolidated faults, we expect strong dilatancy strengthening between 4 and 6 km depth regardless of ambient pore pressure, and at greater depths when the ambient pore pressure approaches lithostatic pressure. In the uppermost part of the crust (&amp;lt;4 km), we predict vaporisation of pore fluids that limits dilatancy strengthening.&amp;lt;/p&amp;gt;
Communicating Uncertainties in Bi- and Multivariate Distributions

&amp;lt;p&amp;gt;Any model we use is an approximation of the real world and associated with some uncertainty about the transition of the model result into the real world. This issue is even stressed in situations where models are used to predict a set of variables over time. A prominent and widely used example are runs of climate models that generate ensembles of possible future paths of our climate including several correlated variables. Typically, these ensembles are presented and assessed in an univariate approach where e.g. spaghetti plots of single variables depict the variability within the future paths. What remains hidden are the dependencies among the variables. As the correlation measures might be ambiguous and summarize the possibly complex dependence structures in a single value, we use the concept of copulas to illustrate the variability of multivariate distributions. Going back to Sklar&amp;amp;#8217;s theorem any continuous multivariate distribution can be decomposed into its univariate margins and their copula describing the dependence between the univariate marginals. Copulas can serve two purposes in this context, to model and quantify i) the multivariate variability within the ensemble and ii) the variability in the dependence between the variables among the ensembles.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We illustrate the effect of different copula families on the inherent uncertainties in the ensemble based on synthetic data. Furthermore, we use climate predictions of the current century to identify and study the dependence and its variability in the data set. In order to increase the data and to avoid the expectation that we can do an assessment precisely for each year, the data is grouped by decades. Our approach is illustrated in R and an exploratory analysis is supported through an interactive Shiny application. The Shiny application also serves to communicate the multivariate uncertainty in the data.&amp;lt;/p&amp;gt;
Coordinate Descent

This chapter describes the coordinate descent approach, in which a single variable (or a block of variables) is updated at each iteration, usually based on partial derivative information for those variables, while the remainder are left unchanged. We describe two problems in machine learning for which this approach has potential advantages relative to the approaches described in previous chapters (which make use of the full gradient), and present convergence analyses for the randomized and cyclic versions of this approach. We show that convergence rates of block coordinate descent methods can be analyzed in a similar fashion to the basic single-component methods.
Identifying sequential characteristics of irradiance data sets applicable for the validation of sets for the assessment of the performance of renewable energy systems containing storage

&amp;lt;p&amp;gt;For the analysis of the expected performance of renewable energy systems of all sizes, nowadays various data bases offer the required time series of irradiances and wind speed. The validation of these sets is mainly based on various comparison schemes of averages or distribution functions on a yearly or monthly scale, forming a comprehensive toolbox for the respective quality assessment.&amp;amp;#160; This allows for a high confidence in predictions of system performance data, as-long-as system operation can be considered as &amp;amp;#8220;memory-less&amp;amp;#8221;.&amp;amp;#160; This is most prominently not the case for systems containing storage devices whose performance depend on the temporal characteristics of the time series - which had been less in focus for the validation of irradiance sets.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This contribution aims to discuss which sequential characteristics of the data series govern the storage sizing. This should result in the identification of parameters that are applicable for the validation of irradiance series for this task. As example, photovoltaic + battery systems are analyzed, that are driven by data extracted from different sources made available by the PVGIS (https://ec.europa.eu/jrc/en/pvgis ) service.&amp;lt;/p&amp;gt;
The OA Switchboard: How a Simple, Collaborative Solution Tackles Multiple Use Cases for Funders, Institutions and Publishers, Strategically and Practically

Yvonne Campfens, executive director of the OA Switchboard, presented an overview of the Switchboard and shared pre-recorded statements from a panel of user-stakeholders: Liz Bal and Jennifer Sanchez-Davies of Jisc, Alex Howat of the Microbiology Society, and Adam Der of the Max Planck Digital Library. Throughout her presentation, Campfens also shared quotes from interviews conducted with representatives of research funding organizations, publishers, and research institutions. The comments from the panelists and interviewees served to illustrate the potential benefits of the OA Switchboard for organizations which are producing, supporting, and transitioning to Open Access (OA) publishing.
Intelligent question and answer analysis model of power ICT based on BI-LSTM-CRF

With the deepening of the country’s digital transformation and the development of big data artificial intelligence software definition and other technologies, customers’ demand for IT services is constantly increasing. In the electric power industry, the front-end knowledge base for traditional customer service lacks self-learning ability, the accumulated customer service data in the middle cannot be analyzed and utilized effectively, the back-end quality inspection was not fully covered and lacked emotion recognition, etc. Apply artificial intelligence, information and communication technology and big data technology to power ICT customer service. It is proposed to construct a network model by combining conditional random airport with the long- and short-term memory neural network, and to realize the intelligent customer service model combined with human–computer interaction by constructing an intelligent knowledge base and the information system fault association analysis model, so as to improve the extraction performance of context information and establish a knowledge base question answering system in the field of power service. Through the comparative analysis of experimental data, the proposed model has achieved good results and can be effectively applied to the power industry to improve customer service satisfaction.
Joint alignment and compactness learning for multi-source unsupervised domain adaptation

Multi-source unsupervised domain adaptation (MUDA) has received increasing attention that leverages the knowledge from multiple relevant source domains with different distributions to improve the learning performance of the target domain. The most common approach for MUDA is to perform pairwise distribution alignment between the target and each source domain. However,existing methods usually treat each source domain identically in source-source and source-target alignment, which ignores the difference of multiple source domains and may lead to imperfect alignment. In addition, these methods often neglect the samples near the classification boundaries during adaptation process, resulting in misalignment of these samples. In this paper, we propose a new framework for MUDA, named Joint Alignment and Compactness Learning (JACL). We design an adaptive weighting network to automatically adjust the importance of marginal and conditional distribution alignment, and such weights are adopted to adaptively align each pair of source-target domains. We further propose to learn intra-class compact features for some target samples that lie in boundaries to reduce the domain shift. Extensive experiments demonstrate that our method can achieve remarkable results in three datasets (Digit-five, Office-31, and Office-Home) compared to recently strong baselines.