Reviewerly/exHarmony huggingface dataset:
Bi-invariance for Uniform Strategies on Event Structures

A recurring problem in game semantics is to enforce uniformity in strategies. Informally, a strategy is uniform when the Player's behaviour does not depend on the particular indexing of moves chosen by the Opponent. In game semantics, uniformity is used to define a resource modality !, that can be exploited for the semantics of programming languages. In this paper we give a new account of uniformity for strategies on event structures. This work is inspired by an older idea due to Melli\`es, that uniformity should be expressed as "bi-invariance" with respect to two interacting group actions. We explore the algebraic foundations of bi-invariance, adapt this idea to the language of event structures and define a general notion of uniform strategy in this context. Finally we revisit an existing approach to uniformity, and show how this arises as a special case of our constructions.
Different Tectonics, Same Approach: Estimation of source parameters using the Coda Calibration Tool (CCT).

It is well known that the use of different methods (e.g., spectral fitting, empirical Green&amp;#8217;s functions) for compiling catalogs of source parameters (e.g., seismic moment, stress drop) can results in significant inconsistencies (Baltay et al., 2022). In this study, we present the application of coda-wave source parameters estimation by the Coda Calibration Tool (CCT) to different tectonic settings for closer analysis of the regional variations.&amp;#160; CCT implements the empirical methodology outlined in Mayeda et al., (2003), which provides stable source spectra and source parameters even for events recorded by sparse local and regional seismic networks (e.g., Morasca et al., 2022). The main strength of the method is the use of narrowband coda waves measurements, which show low sensitivity to source and path heterogeneity. Additionally, we use independent ground-truth (GT) reference spectra for which apparent stresses are independently calculated through the coda spectral ratio (Mayeda et al., 2007), to break the path and site trade-off.&amp;#160; The use of GT spectra eliminates the need to assume source scaling for the region, reducing the impact of a-priori model assumptions on the interpretation of scaling laws of source parameters and their variability. The CCT is a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) that significantly reduces the coda calibration effort and provides calibration parameters for future use in the same region for routine processing.Recently, several studies applied CCT in very different tectonic contexts, including (1) earthquakes in tectonically active regions (e.g., central Italy, Puerto Rico, southern California, Utah); (2) induced earthquakes in southern Kansas and northern Oklahoma; and (3) moderate-sized earthquakes in stable continental regions such as in Eastern Canada and the United Kingdom. There is excellent agreement between coda-derived Mw in all regions and available Mw from waveform modelling. In some cases, such as central Italy and Ridgecrest, the validation process also involved the comparison with estimates from different empirical techniques, such as spectral decomposition approaches applied to data sets sharing common events with CCT. Overall, there is a general consistency in the scaling laws obtained for different source parameters (e.g., seismic moment, corner frequency, radiated energy and apparent stress), with earthquakes in the UK and Canada having similar and higher apparent stresses than Utah, central Italy, Puerto Rico and southern California, while the induced regions are characterized by the lowest values. In conclusion, the application of a consistent methodological framework and the robustness demonstrated by the results of the seismic coda analysis allow comparison of source scaling relationships for different tectonic settings over a wide range of magnitudes.
<i>Fleisher’s Glossary of Mineral Species 2022</i> by Malcolm E. Back. Published by the Mineralogical Association of Canada as Education Publication Volume 1. 431 pages; 2022; $45 plus shipping from the Mineralogical Association of Canada, www.mineralogicalassociation.ca (spiralbound).

"Fleisher’s Glossary of Mineral Species 2022 by Malcolm E. Back. Published by the Mineralogical Association of Canada as Education Publication Volume 1. 431 pages; 2022; $45 plus shipping from the Mineralogical Association of Canada, www.mineralogicalassociation.ca (spiralbound).." Rocks & Minerals, 98(2), pp. 196–197
Cloud-based quantification of Spatial Explicit Uncertainty of Remote Sensing-based Benthic Habitat Classification and its utilization in the context of Active Learning

With the latest advances in cloud image processing, scientists and policy makers have found an effective and robust platform to process vast satellite data in order to be able to map the extent, monitor the condition and create effective protection policies for different ecosystems across the globe. Cloud-based techniques though lack information on the spatial explicit uncertainty of the mapping algorithms. In this study, we present a novel approach on the estimation of uncertainty in a benthic habitat classification context. We explore the benefits of such information in the context of better classification results through an ensemble classifier and the visualization of the uncertain areas in an attempt to provide better maps to the policy makers.&amp;#160;The study area consists of Komodo and Wakatobi islands in Indonesia while reference and satellite data come from the Allen Coral Atlas(ACA) project sampling and a six-year PlanetScope composite, free of clouds and optical deep waters Our semi-automated algorithm is divided in three sectors. The first one prepares the data in the context of sampling a number of subsets of reference points according to ACA map products and runs the first classification based on the first subset. The second one aims to help the model re-train itself in a data driven way by accepting training points of the remaining subsets that have mediocre to low uncertainty scores. The uncertainty score is calculated based on probabilistic principles and the theory of Information. The last stage consists of three ensemble classifiers with the inputs of the classification of the second sector. The ensemble classifiers produce three different map products based on mode, max likelihood and simple weighted average values, respectively.&amp;#160;According to the results, our workflow is able to minimize the noise of reference points, especially when they come from mapping products and non in-situ measurements. Furthermore, accuracy scores following retraining are better than the initial ones which verifies the hypothesis of removing training data with noise in an attempt to reduce the introduced bias in the classification model. Last but not least, the bi-product of classification uncertainty map can be utilized as a tool for better in-situ sampling planning and render a better understanding to policy makers regarding the validity of scientific reports such as change detection, satellite derived bathymetry and blue carbon accounting, among others.
Waves…a tool to explore our home! A successful experience in improving risk perception

In a geologically interesting land like Italy, and in particular Campania, educating and informing about the concept of risk in general, and specifically seismic risk, is of fundamental importance.The possibilities of seismic risk mitigation, in fact, depend not only on the scientific community but also on how well prepared and informed society is about the risk itself. It is, therefore, crucial to train the local population to increase disaster risk preparedness and resilience within our region.The Science Capital framework, developed by Prof. Louise Archer er al. (https://doi.org/10.1002/tea.21227), refers to a person&amp;#8217;s science-related resources, such as their science-related understanding, knowledge, attitudes, activities, and social contacts. It also offers a key to defining how everyone's store of scientific knowledge is enriched and influenced by their habits, family, and network of contacts. Understanding this context and its dynamics helps us to enhance the resources available for scientific culture, with a view to building a competent and inclusive educational community.&amp;#160;With this in minds, we built a didactic protocol dedicated to seismic risk perception taking into consideration the Science Capital framework.&amp;#8220;Waves&amp;#8230;a tool to explore our home!&amp;#8221; allows students to acquire both specific topics, such as the physical quantities involved in an earthquake, its generation and its dynamics, and general concepts, such as the perception of seismic risk and the impact of man in the prevention, in the possible induction and in the response to an earthquake.An evaluation phase was carried out to assess the learning experience and the effectiveness of the science communication technique.This work has been supported by CORE ("sCience and human factor for Resilient sociEty") project, funded from the European Union&amp;#8217;s Horizon 2020 - research and innovation program under grant agreement No 101021746 .&amp;#160;
ONTOLOGICAL, FULLY PROBABILISTIC KNOWLEDGE MODEL FOR HUMAN ACTIVITY RECOGNITION

Efficiency and scalability are obstacles that have not yet received a viable response from the human activity recognition research community. This paper proposes an activity recognition method. The knowledge model is in the form of ontology, the state-of-the-art in knowledge representation and reasoning. The ontology starts with probabilistic information about subjects’ low-level activities and location and then is populated with the assertion axioms learned from data or defined by the user. Unlike methods that choose only the most probable candidate from sensor readings, the proposed method keeps multiple candidates with the known degree of confidence for each one and involves them in decision making. Using this method, the system is more flexible to deal with unreliable data, readings from sensors, and the final recognition rate is improved. Besides, to resolve the scalability problem, a system is designed and implemented to do reasoning and storing in a relational database management system. Numerical evaluations and conceptual benchmarking prove the proposed system feasibility.
A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph

Publishing the research works on academic publications is an important part of the scientific process. Since the development of computer science research is very fast, researchers tend to publish the research works in a fast way, such as conferences whose review processes are faster than the journals. In the past decades, one conference usually focuses on a specific research field and the topic or method overlap between conferences is low. We have noticed that, in recent years, some topics or methods which were once studied in a small number of specific research fields have become popular in many other fields. Naturally, we come up with two research questions: (1) Do the conferences indeed become similar? and (2) How do conferences become similar? In this paper, we first use a trans-temporal heterogeneous graph network to model academic conferences in recent 20 years. Due to the large number of conferences, we categorize these conferences into 6 research fields for brevity. Then, we first quantitatively and qualitatively assess “Do the research fields become similar?” and then focus on exploring “How do research fields become similar?”. From the result, we find the reason for the research fields in computer science become similar is that AI becomes pervasive and researchers tend to apply the machine learning methods to different application fields. Since the methods become universal between different research fields, researchers should pay more attention to advanced information in other fields to motivate more interdisciplinary works. To assist the researchers to explore related interdisciplinary advanced information, it is crucial to measure the cross-field impact of papers using the citation information and recommend the paper which has a high cross-field impact on the related researchers. As for the newly published papers which do not have any citations, we also propose a cross-field impact prediction model to recommend the cutting-edge research works to related researchers accurately. Experiments conducted on real-world datasets verify the effectiveness of the proposed method.
An Extension of Multiquadric Method Based on Trend Analysis for Surface Construction

The representation of spatial discrete data is crucial 1 for the data analysis of meteorology, agriculture, geological ex-2 ploration and other fields.Among various methods for scattered 3 data interpolation, the multiquadric (MQ) method is the most 4 favorable in the field of surface construction.However, the 5 classical MQ method has accuracy concern on the boundary 6 and is time consuming for large data set.This study proposes 7 an algorithm by integrating the MQ method with trend surface 8 analysis.In which, the low-order polynomial trend surface 9 equation is firstly used to model the overall trend.Then, the 10 MQ equation is applied to fit the residual surface after removal 11 the trend from the data.Our implementation can eliminate the 12 distortion in data missing areas by the classical MQ method, 13 and the modeling efficiency can be improved significantly since 14 the local MQ method divide the residual surface into a group 15 of subsurfaces.The accuracy and efficiency of the proposed 16 algorithm are validated on a synthetic model.The performance 17 of the developed algorithm is further examined on the elevation 18 data collected in Tibet and the seabed of a strait in Norway.The 19 results show that with an equivalent resolution, the developed 20 algorithm can be much more efficient than the classical MQ 21 method and well-developed Kriging method.22 Index Terms-overall trend
Blockchain and Reverse Auction-based EVs Energy Trading Approach for Optimal Pricing

This paper proposes a reverse auction and blockchain-based electric vehicles (EVs) energy trading (ET) approach by adopting the smart grid. EVs can be classified into prosumer or consumer during the ET in the proposed approach. We further introduced the Interplanetary File System (IPFS)-based cost-efficient ET approach considering the sixth-generation (6G) wireless network to address the scalability and response time issues of the data transactions. Furthermore, the proposed approach leverages a reverse auction mechanism for optimal ET for EVs. The reverse auction mechanism maximizes the profit for EVs participating in the ET scheme using smart grid. Based on the reverse auction mechanism, a consumer can choose to trade energy with the prosumer or smart grid based on solar panel efficiency. Finally, the proposed approach is simulated considering the various performance metrics such as transaction efficiency, profit for consumers, and convergence to enable an optimal and efficient ET system.
RNN-Based Machine Translation for Indian Languages

Machine translation (MT) is an important part of natural language processing (NLP). The recent advancement in deep learning (DL) has enabled us to implement the MT techniques in neural networks. Deep neural networks are powerful models that give powerful results when we train the model on a large set of data. Word embeddings play an important role in representing the syntactic and semantic relationships between the words in the form of low-dimensional dense vectors. Word2Vec or GloVe vectors can be used to initialize these word embeddings. A many-to-many encoder-decoder model helps us encode the meaning of the source sentence in a single vector to be decoded and translated into another language. In this chapter, the Recurrent Neural Network (RNN) architecture is used to implement our sequence-to-sequence model with gated RNN units to design an MT model for Indian languages like Hindi and Gujarati.
NW Iran under pressure: Cristallization and metamorphic ages of the Shanderman eclogites.

The Tethyan oceans are the internal sotry-tellers of the amalgamation, tenure and break up of Pangea. All tethyan oceans have been mostly consumend and only remnants of them occur now along the margins of the Atlantic, Mediterranean, Black and Caspian seas, as well as in the Alpine-Himalayan and adjacent orogens. The Rheic (~500 to ~300 ma, some-times Ran or Proto-Tethys) closed during the amalgamation of Pangea and the Neo-Tethys (~270 to ~20 ma) is the main witness of its break-up. The Paleotethys is the ocean that shared an internal position during most of Pangea&amp;#8217;s tenure. There is no consensus about its origin, some suggest that opened during the latest stages of Pangea&amp;#8217;s amalgamation (Devonian-Carboniferous) whereas others considert it a remnant of the mostly subducted Rheic ocean after Gondwana-Laurussia collision. We have studied the Shanderman eclogites (NW Iran) and put them into their context within other HP rocks in the area because they a potential candidate to represent the Paleotethys ocean. They are metamorphosed oceanic rocks (protolith oceanic tholeiitic basalt with MORB composition). Eclogite occurs within a serpentinite matrix, accompanied by mafic rocks resembling a dismembered ophiolite. The eclogitic mafic rocks record different stages of metamorphism during subduction and exhumation. In this contribution we will show the new petrological, geochemical and geochronological results from this eclogites to shed light on the evolution of the tethyan oceans during the Paleozoic. The protolithic oceanic crust of Shanderman crystallized ~350 Ma, metamorphic age suggest that this piece of ocean subducted soon after forming, representing, perhaps, a subduction initiation or a ride-subduction event. We also found a metasomatic event at ~280 ma. Considering its relation with other HP rocks in Iran, we interpret that the Shanderman ophiolites are not a fragment of the Paleotethys but a fragment of the Rheic (Ran/Prototethys) ocean.
The Value of Legacy data - securing access and reuse of 25 years of data

Researchers need access to infrastructures to make their research data findable, accessible, interoperableand reusable, known as FAIR. The FAIR principles of data sharing transcend disciplines and encapsulatethe aims and ideals of Open Data advocates.As researchers look to make their research data FAIR, in some disciplines this may result in data beingshared in disparate solutions. If we look at the Geosciences for example, a researcher may use GitLab toshare their code, a hosted website for discoverability and separate storage for the preservation oflegacy data. Research groups often possess a rich heritage of data spanningperiods of several decades. However, this legacy almost never gets proper attention due to lackof funding, and thus lack of long-term maintenance plans. As a consequence, the legacy data aretypically unreadable and inaccessible due to obsolete formats and technologies in which they areprovided.This presentation will demonstrate how 25 years of wind data at the Technical University of Denmark(DTU) has been shared in accordance with the FAIR principles in their Figshare powered repository.We&amp;#8217;ll demonstrate how the long term availability of the data is secured and how discoverability is ensuredand data reuse is encouraged with robust metadata. This presentation will also touch on the importance ofdata reuse throughout the research lifecycle and showcase this for both an academic and lay audiencethrough the features of the DTU Data repository.
Progress of reprocessed long-term data records from Fengyun satellites

NSMC/CMA has been continuously pushing forward the reprocessing of Fengyun series dataset for better quality, especially L1 product. A comprehensive Fengyun historical data rescue and reprocessing task has been completed. At present, the early historical data of FY-1 archive was rescued and L1 data was reproduced providing full data record since 1988. The long-term L1 data reprocessing includes 7 instrument series on 13 satellites in FY-1, FY-2 and FY-3 missions. There are 3 optical imagers with 2 in polar orbit and 1 in geostationary orbit, 1 infrared sounder, 1 microwave imager, and 2 microwave sounders. After reprocessing, the sensor related instability issues caused by instrument degradation and instrument status changes are solved. The inter-platform consistency is also improved by radiometric reference transfer correction in each instrument series. The Fengyun fundamental climate data records (FCDRs) for 7 instruments have been evaluated by radiometric comparison with reference instruments or RTM simulations. The reprocessed L1 dataset has also supported long-term dataset generation for several geophysical parameters. This paper gives an overview of the data reprocessing efforts.&amp;#160;
Tephra compositional data: are we doing it right?

Tephrochronology is often used for dating of natural archives and for correlation between study sites. Volcanic ashes (tephra) extracted from cores used for climate reconstructions function as common time-marker horizons and become anchor points in comparisons of age models from different studies. Given these uses, tephrochronology is well placed to help overcome the chronological challenges that hinder sufficiently precise dating of palaeoclimate records.Tephras are identified based on their geographic and stratigraphic contexts, glass shard morphology, and geochemical composition. The geochemistry is most commonly analysed by electron probe microanalyser and presented as weight percentages of oxides of the nine or ten most abundant elements, often normalised to a 100 % total for ease of comparison. A simple exploration of such results and comparison to published data of previous tephra findings is usually enough for confident identification. However, compositional data of tephra findings from new studies continuously add to the complexity of tephrochronological investigations by increasing the amount of data available for comparison, including the addition of new potential candidates for identification. The increased likelihood of multiple candidates&amp;#8212;sometimes with overlapping geochemistries&amp;#8212;means that statistical data analyses are increasingly necessary.Tephrochronologists have used principal component analysis and discriminant function analysis in situations needing statistical approaches, but these methods&amp;#8217; validity often requires certain assumptions not to be violated. A rarely considered example of such an issue is that compositional data suffers from the constant-sum constraint and must be converted by log-ratio transformations for some statistical analyses to function properly. As there is presently no consensus on a tephra compositional data curation procedure including log-ratio transformations, we have explored several variations and compared the results to see if a formal recommendation for such a procedure is relevant for the tephra community.