Reviewerly/exHarmony huggingface dataset:
First insight into gold enrichment associated with Archean magmatic processes in the deep crust through melt inclusion studies: an example from the Abitibi Subprovince, Québec, Canada

Silicate melt inclusions (SMI) in rhyolitic volcanic rocks in the ~2699 &amp;#8211; 2697 Ma Bousquet Formation, Subprovince, Qu&amp;#233;bec were studied through integration of a variety of microanalytical methods (petrography, laser Raman microspectroscopy, LA-ICP-MS) to explore links between magmatic metal/volatile endowment and the high gold content of mineral deposits in the world-class Doyon-Bousquet-LaRonde mining district. The study is the first to present melt inclusion data from felsic volcanic rocks of Archean age.Rhyolitic SMI of primary origin were characterized from magmatic quartz phenocrysts from tholeiitic rhyolite sills and calc-alkaline flows near gold-rich volcanogenic massive sulfide deposits. Silicate melt inclusion trace element chemistry records a continuous transition from ocean ridge to volcanic arc tectonic affinity. SMI Sr-Y-La-Yb systematics are &amp;#160;inconsistent with Archean tonalite-trondhjemite-granodiorite (TTG; &amp;#8220;adakitic&amp;#8221;) compositional domains; rather, they are consistent with post-Archean TTG (&amp;#8220;calc-alkaline&amp;#8221;) suggesting significant compositional modification of TTG magmas through contamination and/or plagioclase fractionation during magma storage and ascent. &amp;#160;Thermobarometry suggests prolonged phenocryst residence at depth prior to eruption with SMI entrapment at ~10-12 km depth. Concentrations of Au in the SMI are variable and up to two orders of magnitude higher than in the host bulk volcanic rocks. This demonstrates that whole rock data are not representative of the composition of the original magmatic liquids and, thus, cautioning the traditional use of whole rock data as a proxy for volcanic assemblage fertility in such Archean environments. Moreover, SMI show melt co-entrapment with an immiscible, high density, carbonic fluid (CO2-dominant), indicating that rhyolitic melts were saturated in CO2. Saturation of this fluid phase may explain, in part, the variability observed in SMI metal contents, and demands consideration of the relative importance of early separation of magmatic volatile phases versus seafloor hydrothermal leaching of volcanic products in controlling the magmatic metal endowment of Archean exhalative ore-forming systems. &amp;#160;
Comparison of earthquake swarm detection methods: Case study at Mór Graben, Hungary

The seismicity of the Pannonian Basin can be described as moderate. The study area is located in the northern part of the Pannonian Basin, which is one of the most active area in terms of earthquakes.We found that earthquake swarms occur in the M&amp;#243;r graben quite regularly. The K&amp;#246;vesligethy Rad&amp;#243; Seismological Observatory deployed three temporary stations in the graben to monitor the local seismicity, and these stations operated for ca. 20 months. We can study the very small magnitude events because the detection capability is more sensitive from 2020.After relocating the events with a multiple-event location algorithm, we compare three different real swarm detection methods based on the filtered three-component waveforms, to find the best one to collect a complete swarm event list in the M&amp;#243;r Graben.Using the temporary and permanent stations of the K&amp;#246;vesligethy Rad&amp;#243; Seismological Observatory and the GeoRisk Ltd. networks we can identify more than a hundred swarms with small magnitudes.Our results show that the M&amp;#243;r Graben is still active, where some of the largest earthquakes occurred in Hungary in the past.&amp;#160;
Backgrounds for the existence formation and its overcoming ways

For the first time, this work explores the social factors that determine the birth and evolution of an individual’s being. Existence is a negative state of the human soul that depressingly affects the consciousness and behavior of a person. Based on the analysis of philosophical, psychological publications on existentialism and artistic existential literature, the authors identified the major factors that form an individual’s being. For the first time, the paper as in the philosophy of existentialism revealed the principal ways of relieving the existential stress of the individual, which allows developing a methodology for providing social, medical, psychological and pedagogical assistance to people with aggravated life.
Richard Bernstein and his concept of pragmatic fallibilism

ConstellationsVolume 30, Issue 1 p. 26-29 CORRESPONDENCE PIECE Richard Bernstein and his concept of pragmatic fallibilism María Pía Lara, Corresponding Author María Pía Lara [email protected] Correspondence María Pía Lara, Email: [email protected]Search for more papers by this author María Pía Lara, Corresponding Author María Pía Lara [email protected] Correspondence María Pía Lara, Email: [email protected]Search for more papers by this author First published: 24 February 2023 https://doi.org/10.1111/1467-8675.12659Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES Bernstein, R. J. (2005). The abuse of evil: The corruption of politics and religion since 9/11. Polity Press. Bernstein, R. J. (2010a). The pragmatic turn. Polity Press. Bernstein, R. J. (2010b). Richard Rorty's deep humanism. The pragmatic turn (pp. 13–27). Polity Press. Bernstein, R. J. (2011). Beyond objectivism and relativism: Science, hermeneutics, and praxis. University of Pennsylvania Press. Bernstein, R. J. (2013). Violence: Thinking without banisters. Polity Press. Bernstein, R. J. (2015). Cultural pluralism. Philosophy and Social Criticism, 41(4–5), 347–356. Bernstein, R. J., (2016a). The romance of philosophy. Pragmatic encounters (pp. 13–27). Routledge. Bernstein, R. J. (2016b). Pragmatic encounters. Routledge. Bernstein, R. J. (2017). From socialist feminism to the critique of global capitalism. In B. Bargu & C. Bottici (Eds.), Feminism, capitalism, and critique (pp. 17–41). Palgrave Macmillan. Bernstein, R. J. (2016). Jonathan Lear and Richard Rorty on irony. Ironic life. Polity Press. Bernstein, R. J. (2018). Why Read Hannah Arendt Now? Polity Press. Bernstein, R. J. (2023). The vicissitudes of nature: From Spinoza to Freud. Polity Press. Frega, R., Maddalena, G., & Bernstein, R. J. (2014). Interview with Richard J. Bernstein. European Journal of Pragmatism and American Philosophy, 6, VI–1. https://doi.org/10.4000/ejpap.517 James, W. (1977). A pluralistic universe. Harvard University Press. Marchetti, S. (2011). Richard J. Bernstein on ethics and philosophy between the linguistic and the pragmatic turn. European Journal of Pragmatism and American Philosophy, 3(2), 229–239. Menand, L. (2001). The metaphysical club: A story of ideas in America. Farrar, Straus and Giroux. Volume30, Issue1March 2023Pages 26-29 ReferencesRelatedInformation
Argumentum Ad Alia: argument structure of arguing about what others have said

Abstract Expertise, authority, and testimony refer to aspects of one of the most important elements of communication and cognition. Argumentation theory recognises various forms of what we call the argumentum ad alia pattern, in which speakers appeal to what others have said, including Position to Know scheme, Witness Testimony scheme, Expert Opinion scheme and the classical ad verecundiam . In this paper we show that ad alia involves more than merely an inferential step from what others (a person in position to know, a witness, an expert) have said, and that studying this type of argumentation requires going beyond the argument structure of propositional contents to account for different speech activities. We also demonstrate that using the words of others, attacking what has been said, and reasoning from how others reason constitute a rich repertoire of communication strategies which we need to model in order to be able to analyse them manually and to process them computationally.
Ecosystem “Distant”: Teacher-student dyad

The paper describes the analysis of the teacher’s role as a guarantor of maintaining the quality of the educational process in a higher educational institution in a distance learning format. The author provides a detailed description of the Distant ecosystem, its positive and negative sides. The basis of the research is the collaborative method of teaching. The author considers learning features with the help of distance technologies, both from the view of a student and from the teacher’s one. The study interpreted a lecturer’s personality as a conductor of collaborative and motivating roles. Particular attention the author paid to the collaborative teaching method, which turned out being the most effective in distance learning. This method heads to maximum interaction between the considered dyad of communicants in the educational process. The results of the study allow to determine the major factors affecting the quality of the educational process with the use of distance technologies. Also, the research defined the key third-party elements to either hinder to achieve the maximum quality of education during distance learning.
On Unsupervised Learning from Environmental Data

Predictive learning from data usually is formulated as a problem of finding the best connection between input and output spaces by optimizing well-defined cost or risk functions.In geo-environmental studies input space is usually constructed from the geographical coordinates and features generated from different sources of available information (feature engineering), by applying expert knowledge, using deep learning technologies and taking into account the objectives of the study. Often, it is not known in advance if the input space is complete or contains redundant features. Therefore, unsupervised learning (UL) is essential in environmental data analysis, modelling, prediction and visualization. UL also helps better understand the data and phenomena they describe as well as in interpreting/communicating modelling strategies and the results in the decision-making process.The main objective of the present investigation is to review some important topics in unsupervised learning from environmental data: 1) quantitative description of the input space (&amp;#8220;monitoring network&amp;#8221;) structure using global and local topological and fractal measures, 2) dimensionality reduction, 3) unsupervised feature selection and clustering by applying a variety of machine learning algorithms (kernel-based, ensemble learning, self-organizing maps) and visualization tools.Major attention is paid to the simulated and real spatial data (pollution, permafrost, geomorphological and wind fields data).&amp;#160; Considered case studies have different input space dimensionality/topology and number of measurements. It is confirmed that UL should be considered an integral part of a generic methodology of environmental data analysis. Comprehensive comparisons and discussions of the results conclude the research.&amp;#160;&amp;#160;
Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting

With fast parallel computing hardware, particularly GPUs, becoming more accessible in the geosciences the now efficiently running deep learning techniques are ready to handle larger amounts of recorded observation and satellite derived data and are able to learn complex structures across time-series. Thus, a suitable deep learning setup is able to generate highly-resolved weather forecasts in real-time and on demand. Forecasts of irradiance and radiation can be challenging in machine learning as they embrace a high degree of diurnal and seasonal variation.Continuously extended PV/solar power production grows into one of our most important fossil-fuel free energy sources. Unlike the just recently emerging PV power observations, solar irradiance offers long time-series from automized weather station networks. Being directly linked to PV outputs, forecasting highly resolved solar irradiance from nowcasting to short-range plays a crucial role in decision support and managing PV.In this study, we investigate the suitability of several deep learning techniques adopted and developed to a set of heterogeneous data sources on selected locations. We compare the forecast results to traditional &amp;#8211; however computationally expensive - numerical weather prediction models (NWP) and rapid update cycle models. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.: CAMS), radiation time series from remote sensing, and observation time time-series (site observations and close sites). The amount of time-series data can be extended by a synthetic data generator, a part of our deep learning framework. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory) model using a climatological background model or NWP for post-processing, a Graph NN model, and an analogs based deep learning method. Furthermore, a novel neural network model based on two other ideas, the IrradianceNet and the PhyDNet, was developed. IrradPhyDNet combines the skills of IrradianceNet and PhyDNet and showed improved performance in comparison to the original models.Results obtained by the developed methods yield, in general, high forecast-skills. For selected case studies of extreme events (e.g. Saharan dust) all novel methods could outperform the traditional methods. &amp;#160;Different combinations of inputs and processing-steps are part of the analysis.
GIS-based analysis for regional exploration in the geothermal province of intraplate volcanism in the Center of Mexico

The regional evaluation of the intraplate volcanism province is attempted using the Play Fairway Analysis. This province is characterized by recent alkaline volcanism that is not related with the previous Farallon Plate subduction of the present Cocos Plate subduction. This volcanism is constrained to the main structures related with the Basin and Range processes of extensional tectonism.A geographic information system is constructed by including geological and geophysical data. Thematic maps are developed with the information that is considered relevant for the location of areas with favorable conditions to host geothermal systems, with or without surface manifestations, i.e. including the blind geothermal systems. This work includes information of the occurrence of recent volcanism (age less than 2 Ma), geological structures, seismic activity, shallow Curie Point Depth, surface heat flow, crustal thickness, hydrothermal manifestations (hydrothermal alteration, thermal fluids discharge, etc.). Data integration is performed using a &amp;#8220;Knowledge driven&amp;#8221; model with three different geothermal play models producing play fairway evaluations. The presence of several areas with high favorability was identified to lead future detailed exploration in the field.
Identifying and Describing Billions of Objects: an Architecture to Tackle the Challenges of Volume, Variety, and Variability

Persistent identifiers are applied to an ever-increasing diversity of research objects, including data, software, samples, models, people, instruments, grants, and projects. There is a growing need to apply identifiers at a finer and finer granularity. The systems developed over two decades ago to manage identifiers and the metadata describing the identified objects struggle with this increase in scale. Communities working with physical samples have grappled with these challenges of the increasing volume, variety, and variability of identified objects for many years. To address this dual challenge, the IGSN 2040 project explored how metadata and catalogues for physical samples could be shared at the scale of billions of samples across an ever-growing variety of users and disciplines. This presentation outlines how identifiers and their describing metadata can be scaled to billions of objects. In addition, it analyses who the actors involved with this system are and what their requirements are. This analysis resulted in the definition of a minimum viable product and the design of an architecture that addresses the challenges of increasing volume and variety. The system is also easy to implement because it reuses commonly used Web components. Our solution is based on a Web architectural model that utilises Schema.org, JSON-LD and sitemaps. Applying these commonly used architectural patterns on the internet allows us not only to handle increasing volume, variety and variability but also enable better compliance with the FAIR Guiding Principles.
Geochemical transport through the critical zone: Statistics and reconstruction

Detecting and quantifying geochemical transport through the critical zone at the continental to regional scale requires reliable &amp;#160;statistical procedures that can be uniquely interpreted. We present methods that provide different views on the same data sets and &amp;#160;formulate rules for their application and interpretation. The statistical analysis of cumulative distribution functions (CDFs) uses cumulative probability (CP) plots for spatially representative multi-element and multi-media data sets, preferably containing &gt;1000 sites.Mathematical models demonstrate how contamination can influence elemental CDFs of different &amp;#160;sample media. For example large-scale diffuse soil contamination leads to a distinctive shift of the low-concentration end of the distribution of the studied element in its top-soil CP plot, whereas high local contamination influences the high-concentration end. But also bio-geochemical processes can generate recognizable changes in elemental CDFs.A related and partly unsolved problem is the correct interpretation of compositional data in terms of their transport through &amp;#160;the critical zone.&amp;#160;&amp;#160;
AI based 1D and 3D P- and S-Wave Velocity Models for the Alpine Mountain Chain from Local Earthquake Data

The increase in seismic data availability as well as the improvement of automated picking algorithms allows us to reassess the seismicity and velocity structure in many regions around the globe with higher accuracy. Using the seismic recordings from a total of more than 1100 stations of the AlpArray Seismic Network and other permanent and temporary stations within the area we work towards a comprehensive 3D P- and S-wave crustal velocity model for the European Alpine region using Local Earthquake Tomography. Phase arrival times of local seismicity are determined by the widely used deep neural network PhaseNet.We present first a P- &amp; S-wave minimum 1D model of the Greater Alpine region computed with the established linearized inversion algorithm VELEST and compare it to our new 1D model using a bayesian Markov chain Monte Carlo (McMC) algorithm exploring a broader model space. Pg and Pn phase arrivals in the epicentral distance ranges from 0-130km and 300-600km, respectively, are included while picks within the triplication zone from 130-300km are not considered due to difficult phase identification. Both models match within the error margin of the McMC result, while the discrepancy is largest in the lower crust where the resolution decreases due to the chosen epicentral distance ranges.&amp;#160;With our minimum 1D model as starting model we compute a 3D P-wave model using the SIMULPS code. As the remaining residual distributions of the 1D and 3D model show, the removal of outliers in the pick catalog is more accurate when based on the 3D residuals due to insufficient incorporation of velocity variations along epicentral distance and backazimuth in the 1D model. The most prominent first order structures of the 3D model are in agreement with previous local studies of the area and the model already can be used to consistently improve crustal correction terms on an orogenic scale for teleseimic tomographies and thus sharpen the seismic image of the upper mantle. Furthermore, it will allow to the associate the phases Pg, Pmp &amp; Pn to picked onset times in the crustal triplication zone more accurately. Due to their ray paths these picks are of special importance to the resolution in the lower crust and will contribute significantly to the final 3D P- and S-wave model. Absolute velocities along the Moho interface are higher than in previous studies and therefore in better accordance with values expected from petrology. &amp;#160;
Application of multi‐party computation and error correction with image enhancement and convolution neural networks based on cloud computing

Abstract With the development of technology, people work hard on image processing and artificial intelligence which require a lot of computer resources. Instead of simply speeding up calculations with processors, people develop the technology of the Cloud with the Internet which performs high‐quality calculations and has quite a huge amount of storage. Thus, in this paper, the authors use a kind of encryption methods, Secure Multi‐Party Computation (SMPC), to protect the important information which is based on the absence of a trusted party and does not use specific keys or methods to maintain the confidentiality of data. Furthermore, the authors also use the error correction, Berlekamp–Welch (BW) algorithm, to double‐check the correction of the information. To apply the two algorithms to image enhancement and convolution neural networks (CNN), the authors will also discuss the homomorphism in all operations, including addition, multiplication, division etc., and find the best way to encrypt and decrypt the information. Finally, the authors will implement the system with cloud computing to decrease the consumption of computer resources and build a system that can do image enhancement and CNN.
Polytope: Feature Extraction for Improved Access to Petabyte-Scale Datacubes

ECMWF currently produces about 120 TiB of raw weather data from its real-time forecasts every day. With model improvements and higher resolution forecasting however, this raw data is expected to grow to over a petabyte per day over the next few years. Whilst these improvements will in theory help scientists better forecast weather events, distributing such vast amounts of data efficiently will prove to be increasingly difficult with the current data access mechanisms.&amp;#160; To tackle this challenge, ECMWF is developing a novel feature extraction concept, named &amp;#8220;Polytope&amp;#8221;. By leveraging tools in the field of higher dimensional computational geometry, Polytope will be able to efficiently cut a wide range of intricate n-dimensional shapes (polytopes) from ECMWF&amp;#8217;s high-dimension (6D/7D) weather datacubes. Polytope can be used to perform server-side feature extraction, providing significant data reductions before delivering the data to the user. This not only improves the efficiency of access to petabyte-scale datacubes, but also removes significant post-processing complexity from the user leading to an overall data usability improvement. Practical examples include a user requesting weather data over a 4-dimensional flight path, which crosses three spatial axes as well as a temporal axis. In this example, instead of providing data over the entire bounding box of the path, Polytope will only return the few precise bytes of interest to the user.&amp;#160; This work is an important contribution to, and is funded by, the EU&amp;#8217;s Destination Earth initiative. Within Destination Earth, Polytope will enable efficient access to petabyte-scale datacubes generated by very high-resolution digital twins. This presentation will introduce the Polytope concept and demonstrate its usage for different types of feature extraction.&amp;#160;