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  • European Marine Science
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  • Authors: Loreto, Romolo;

    Loreto, Romolo, 2023. "Ichthyophagoi". Thematic Dictionary of Ancient Arabia. Online edition 2023.

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    NAKALA
    Other literature type . 2099
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      NAKALA
      Other literature type . 2099
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lukács, Réka; Guillong, Marcel; Szepesi, János; Szymanowski, Dawid; +9 Authors

    Highlights • Four rhyolitic explosive eruption events were distinguished from 13.1 Ma to 11.6 Ma. • Silicic volcanism occurred at termination of subduction in a thinning lithosphere. • Rhyolites show extreme magma differentiation and reduced-dry character. • Zircon trace element and Hf isotope fingerprint is an effective correlation tool. Abstract The Tokaj Mts. volcanism occurred in a thinning continental lithosphere regime at the final stage of the subduction process. Using high-precision zircon U-Pb dating, four major explosive eruption events were distinguished. Among them the 13.1 Ma Sátoraljaújhely and the 12.0 Ma Szerencs eruptions could have yielded large amount of volcanic material (possibly > 100 km3) and they were associated with caldera collapse as shown by the several hundred-metre-thick pyroclastic deposits and the long (>100 km) runout pyroclastic flow in case of the 13.1 Ma eruption. The 12.3 Ma Hegyköz and the 11.6 Ma Vizsoly eruptions were relatively smaller. The volcanic products can be readily distinguished by zircon and glass trace elements and trace element ratios, which can be used for fingerprinting and to correlate with distal deposits. The Rb, Ba, Sr content and strong negative Eu-anomaly of the glasses reflect extreme crystal fractionation, particularly for the Szerencs rhyolitic magma. The silicic volcanic products of the Tokaj Mts. show compositional similarities with the so-called ‘dry–reduced–hot’ rhyolite type consistent with an origin in an extensional environment, where the primary magmas were formed by near-adiabatic decompression melting in the mantle with subordinate fluid flux. In contrast, some of the older Bükkalja rhyolitic magmas evolved via more hydrous evolutionary paths, where amphibole played a role in the control of the trace element budget. The significant increase of zircon ε Hf values from −8.8 to + 0.2 in the rhyolitic pyroclastic rocks of Tokaj Mts. with time implies that mantle-derived magmas became more dominant. This can be explained by the specific tectonic setting, i.e. the final stage of subduction when the descending subducted slab became almost vertical, which exerted a pull in the upper lithosphere leading to thinning and accelerated subsidence as well as asthenospheric mantle flow just before the slab detachment.

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    Authors: L.G.S. Duarte; J.C. Eiras; L.A.C.P. da Mota;
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    Computer Physics Communications
    Article . 2024 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Computer Physics Com...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Computer Physics Communications
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: S. Oliver; S. Rodriguez Bosca; V. Giménez-Alventosa;

    Geometry construction is a fundamental aspect of any radiation transport simulation, regardless of the Monte Carlo code being used. Typically, this process is tedious, time-consuming, and error-prone. The conventional approach involves defining geometries using mathematical objects or surfaces. However, this method comes with several limitations, especially when dealing with complex models, particularly those with organic shapes. Furthermore, since each code employs its own format and methodology for defining geometries, sharing and reproducing simulations among researchers becomes a challenging task. Consequently, many codes have implemented support for simulating over geometries constructed via Computer-Aided Design (CAD) tools. Unfortunately, this feature is lacking in penRed and other PENELOPE physics-based codes. Therefore, the objective of this work is to implement such support within the penRed framework. Program Title: Parallel Engine for Radiation Energy Deposition (penRed) CPC Library link to program files:https://doi.org/10.17632/rkw6tvtngy.2 Developer's repository link:https://github.com/PenRed/PenRed Code Ocean capsule:https://codeocean.com/capsule/1041417/tree Licensing provisions: GNU Affero General Public License v3 Programming language: C++ standard 2011. Journal reference of previous version: V. Giménez-Alventosa, V. Giménez Gómez, S. Oliver, PenRed: An extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Computer Physics Communications 267 (2021) 108065. doi:https://doi.org/10.1016/j.cpc.2021.108065. Does the new version supersede the previous version?: Yes Reasons for the new version: Implements the capability to simulate on CAD constructed geometries, among many other features and fixes. Summary of revisions: All changes applied through the code versions are summarized in the file CHANGELOG.md in the repository package. Nature of problem: While Monte Carlo codes have proven valuable in simulating complex radiation scenarios, they rely heavily on accurate geometrical representations. In the same way as many other Monte Carlo codes, penRed employs simple geometric quadric surfaces like planes, spheres and cylinders to define geometries. However, since these geometric models offer a certain level of flexibility, these representations have limitations when it comes to simulating highly intricate and irregular shapes. Anatomic structures, for example, require detailed representations of organs, tissues and bones, which are difficult to achieve using basic geometric objects. Similarly, complex devices or intricate mechanical systems may have designs that cannot be accurately represented within the constraints of such geometric models. Moreover, when the complexity of the model increases, geometry construction process becomes more difficult, tedious, time-consuming and error-prone [2]. Also, as each Monte Carlo geometry library uses its own format and construction method, reproducing the same geometry among different codes is a challenging task. Solution method: To face the problems stated above, the objective of this work is to implement the capability to simulate using irregular and adaptable meshed geometries in the penRed framework. This kind of meshes can be constructed using Computer-Aided Design (CAD) tools, the use of which is very widespread and streamline the design process. This feature has been implemented in a new geometry module named “MESH_BODY” specific for this kind of geometries. This one is freely available and usable within the official penRed package1. It can be used since penRed version 1.9.3b and above. [1]V. Giménez-Alventosa, V. Giménez Gómez, S. Oliver, PenRed: an extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Comput. Phys. Commun. 267 (2021) 108065, https://doi.org/10.1016/j.cpc.2021.108065.[2]Paul P.H. Wilson, Timothy J. Tautges, Jason A. Kraftcheck, Brandon M. Smith, Douglass L. Henderson, Acceleration techniques for the direct use of CAD-based geometry in fusion neutronics analysis, Fusion Eng. Des. 85 (2010), https://doi.org/10.1016/j.fusengdes.2010.05.030.

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    CERN Document Server
    Other literature type . 2024
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    Computer Physics Communications
    Article . 2024 . Peer-reviewed
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      Computer Physics Communications
      Article . 2024 . Peer-reviewed
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    Authors: Aoyama, Tatsumi; Yoshimi, Kazuyoshi; Ido, Kota; Motoyama, Yuichi; +4 Authors

    H-wave is an open-source software package for performing the Hartree--Fock approximation (HFA) and random phase approximation (RPA) for a wide range of Hamiltonians of interacting fermionic systems. In HFA calculations, H-wave examines the stability of several symmetry-broken phases, such as anti-ferromagnetic and charge-ordered phases, in the given Hamiltonians at zero and finite temperatures. Furthermore, H-wave calculates the dynamical susceptibilities using RPA to examine the instability toward the symmetry-broken phases. By preparing a simple input file for specifying the Hamiltonians, users can perform HFA and RPA for standard Hamiltonians in condensed matter physics, such as the Hubbard model and its extensions. Additionally, users can use a Wannier90-like format to specify fermionic Hamiltonians. A Wannier90 format is implemented in RESPACK to derive ab initio Hamiltonians for solids. HFA and RPA for the ab initio Hamiltonians can be easily performed using H-wave. In this paper, we first explain the basis of HFA and RPA, and the basic usage of H-wave, including download and installation. Thereafter, the input file formats implemented in H-wave, including the Wannier90-like format for specifying the interacting fermionic Hamiltonians, are discussed. Finally, we present several examples of H-wave such as zero-temperature HFA calculations for the extended Hubbard model on a square lattice, finite-temperature HFA calculations for the Hubbard model on a cubic lattice, and RPA in the extended Hubbard model on a square lattice. Comment: 15 pages, 7 figures

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    Computer Physics Communications
    Article . 2024 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: arXiv Non-Exclusive Distribution
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      Computer Physics Communications
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    Authors: Kota Ido; Mitsuaki Kawamura; Yuichi Motoyama; Kazuyoshi Yoshimi; +4 Authors
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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: Villon, Sébastien; Iovan, Corina; Mangeas, Morgan; Vigliola, Laurent;

    Given the global biodiversity crisis, there is an urgent need for new tools to monitor populations of endangered marine megafauna, like sharks. To this end, Baited Remote Underwater Video Stations (BRUVS) stand as the most effective tools for estimating shark abundance, measured using the MaxN metric. However, a bottleneck exists in manually computing MaxN from extensive BRUVS video data. Although artificial intelligence methods are capable of solving this problem, their effectiveness is tested using AI metrics such as the F-measure, rather than ecologically informative metrics employed by ecologists, such as MaxN. In this study, we present both an automated and a semi-automated deep learning approach designed to produce the MaxN abundance metric for three distinct reef shark species: the grey reef shark (Carcharhinus amblyrhynchos), the blacktip reef shark (C. melanopterus), and the whitetip reef shark (Triaenodon obesus). Our approach was applied to one-hour baited underwater videos recorded in New Caledonia (South Pacific). Our fully automated model achieved F-measures of 0.85, 0.43, and 0.72 for the respective three species. It also generated MaxN abundance values that showed a high correlation with manually derived data for C. amblyrhynchos (R = 0.88). For the two other species, correlations were significant but weak (R = 0.35–0.44). Our semi-automated method significantly enhanced F-measures to 0.97, 0.86, and 0.82, resulting in high-quality MaxN abundance estimations while drastically reducing the video processing time. To our knowledge, we are the first to estimate MaxN with a deep-learning approach. In our discussion, we explore the implications of this novel tool and underscore its potential to produce innovative metrics for estimating fish abundance in videos, thereby addressing current limitations and paving the way for comprehensive ecological assessments.

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    Ecological Informatics
    Article . 2024 . Peer-reviewed
    License: CC BY NC ND
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      Ecological Informatics
      Article . 2024 . Peer-reviewed
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    Authors: Mark D. Goodsell; Ari Joury;

    We introduce BSMArt, a python program for the exploration of parameter spaces of theories Beyond the Standard Model. Especially designed for use with the SARAH family of tools, it is also sufficiently flexible to be used with a wide variety of external codes. BSMArt contains the first public release of the Active Learning scan by the same authors; but contains several additional scanning algorithms, ranging from the very simple to MultiNest and Diver. A BSMArt scan can be set up in a matter of minutes with only minimal editing of configuration files; installation scripts for all relevant tools and examples are provided. Comment: 33 pages; no figures. Code available at https://goodsell.pages.in2p3.fr/bsmart/

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    Authors: Dominik Beutel; Ivan Fernandez-Corbaton; Carsten Rockstuhl;
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      Computer Physics Communications
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    Authors: Ralf Wanzenböck; Florian Buchner; Péter Kovács; Georg K.H. Madsen; +1 Authors

    [Supplementary material]: Nature of problem: Find optimal atomistic structures retaining full flexibility in the choice of the optimization target, the methodological approach and its implementation (e.g. CPU- vs. GPU-heavy calculations). Enable interfacing with relevant software, including but not limited to density-functional-theory (DFT) codes and machine-learning (ML) solutions. [Solution method]: The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is implemented using Cholesky decompositions for efficiency. The core algorithm and application examples for specific problems are implemented in functional-style Python free of side effects, with data classes to keep track of the state of the evolution. Dask is used for job control to enable highly distributed workflows. Advanced strategies like BI-Population CMA-ES are easy to implement and illustrated in the examples. Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward. [Program summary]: Program Title: Clinamen2 CPC Library link to program files: https://doi.org/10.17632/x7syr2txsd.1 Developer's repository link: https://github.com/Madsen-s-research-group/clinamen2-public-releases Code Ocean capsule: https://codeocean.com/capsule/4950229 Licensing provisions: Apache-2.0 Programming language: Python This work was supported by the Austrian Science Fund (FWF) (SFB F81 TACO). Peer reviewed

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  • Authors: Loreto, Romolo;

    Loreto, Romolo, 2023. "Ichthyophagoi". Thematic Dictionary of Ancient Arabia. Online edition 2023.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lukács, Réka; Guillong, Marcel; Szepesi, János; Szymanowski, Dawid; +9 Authors

    Highlights • Four rhyolitic explosive eruption events were distinguished from 13.1 Ma to 11.6 Ma. • Silicic volcanism occurred at termination of subduction in a thinning lithosphere. • Rhyolites show extreme magma differentiation and reduced-dry character. • Zircon trace element and Hf isotope fingerprint is an effective correlation tool. Abstract The Tokaj Mts. volcanism occurred in a thinning continental lithosphere regime at the final stage of the subduction process. Using high-precision zircon U-Pb dating, four major explosive eruption events were distinguished. Among them the 13.1 Ma Sátoraljaújhely and the 12.0 Ma Szerencs eruptions could have yielded large amount of volcanic material (possibly > 100 km3) and they were associated with caldera collapse as shown by the several hundred-metre-thick pyroclastic deposits and the long (>100 km) runout pyroclastic flow in case of the 13.1 Ma eruption. The 12.3 Ma Hegyköz and the 11.6 Ma Vizsoly eruptions were relatively smaller. The volcanic products can be readily distinguished by zircon and glass trace elements and trace element ratios, which can be used for fingerprinting and to correlate with distal deposits. The Rb, Ba, Sr content and strong negative Eu-anomaly of the glasses reflect extreme crystal fractionation, particularly for the Szerencs rhyolitic magma. The silicic volcanic products of the Tokaj Mts. show compositional similarities with the so-called ‘dry–reduced–hot’ rhyolite type consistent with an origin in an extensional environment, where the primary magmas were formed by near-adiabatic decompression melting in the mantle with subordinate fluid flux. In contrast, some of the older Bükkalja rhyolitic magmas evolved via more hydrous evolutionary paths, where amphibole played a role in the control of the trace element budget. The significant increase of zircon ε Hf values from −8.8 to + 0.2 in the rhyolitic pyroclastic rocks of Tokaj Mts. with time implies that mantle-derived magmas became more dominant. This can be explained by the specific tectonic setting, i.e. the final stage of subduction when the descending subducted slab became almost vertical, which exerted a pull in the upper lithosphere leading to thinning and accelerated subsidence as well as asthenospheric mantle flow just before the slab detachment.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: L.G.S. Duarte; J.C. Eiras; L.A.C.P. da Mota;
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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: S. Oliver; S. Rodriguez Bosca; V. Giménez-Alventosa;

    Geometry construction is a fundamental aspect of any radiation transport simulation, regardless of the Monte Carlo code being used. Typically, this process is tedious, time-consuming, and error-prone. The conventional approach involves defining geometries using mathematical objects or surfaces. However, this method comes with several limitations, especially when dealing with complex models, particularly those with organic shapes. Furthermore, since each code employs its own format and methodology for defining geometries, sharing and reproducing simulations among researchers becomes a challenging task. Consequently, many codes have implemented support for simulating over geometries constructed via Computer-Aided Design (CAD) tools. Unfortunately, this feature is lacking in penRed and other PENELOPE physics-based codes. Therefore, the objective of this work is to implement such support within the penRed framework. Program Title: Parallel Engine for Radiation Energy Deposition (penRed) CPC Library link to program files:https://doi.org/10.17632/rkw6tvtngy.2 Developer's repository link:https://github.com/PenRed/PenRed Code Ocean capsule:https://codeocean.com/capsule/1041417/tree Licensing provisions: GNU Affero General Public License v3 Programming language: C++ standard 2011. Journal reference of previous version: V. Giménez-Alventosa, V. Giménez Gómez, S. Oliver, PenRed: An extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Computer Physics Communications 267 (2021) 108065. doi:https://doi.org/10.1016/j.cpc.2021.108065. Does the new version supersede the previous version?: Yes Reasons for the new version: Implements the capability to simulate on CAD constructed geometries, among many other features and fixes. Summary of revisions: All changes applied through the code versions are summarized in the file CHANGELOG.md in the repository package. Nature of problem: While Monte Carlo codes have proven valuable in simulating complex radiation scenarios, they rely heavily on accurate geometrical representations. In the same way as many other Monte Carlo codes, penRed employs simple geometric quadric surfaces like planes, spheres and cylinders to define geometries. However, since these geometric models offer a certain level of flexibility, these representations have limitations when it comes to simulating highly intricate and irregular shapes. Anatomic structures, for example, require detailed representations of organs, tissues and bones, which are difficult to achieve using basic geometric objects. Similarly, complex devices or intricate mechanical systems may have designs that cannot be accurately represented within the constraints of such geometric models. Moreover, when the complexity of the model increases, geometry construction process becomes more difficult, tedious, time-consuming and error-prone [2]. Also, as each Monte Carlo geometry library uses its own format and construction method, reproducing the same geometry among different codes is a challenging task. Solution method: To face the problems stated above, the objective of this work is to implement the capability to simulate using irregular and adaptable meshed geometries in the penRed framework. This kind of meshes can be constructed using Computer-Aided Design (CAD) tools, the use of which is very widespread and streamline the design process. This feature has been implemented in a new geometry module named “MESH_BODY” specific for this kind of geometries. This one is freely available and usable within the official penRed package1. It can be used since penRed version 1.9.3b and above. [1]V. Giménez-Alventosa, V. Giménez Gómez, S. Oliver, PenRed: an extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Comput. Phys. Commun. 267 (2021) 108065, https://doi.org/10.1016/j.cpc.2021.108065.[2]Paul P.H. Wilson, Timothy J. Tautges, Jason A. Kraftcheck, Brandon M. Smith, Douglass L. Henderson, Acceleration techniques for the direct use of CAD-based geometry in fusion neutronics analysis, Fusion Eng. Des. 85 (2010), https://doi.org/10.1016/j.fusengdes.2010.05.030.

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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: Aoyama, Tatsumi; Yoshimi, Kazuyoshi; Ido, Kota; Motoyama, Yuichi; +4 Authors

    H-wave is an open-source software package for performing the Hartree--Fock approximation (HFA) and random phase approximation (RPA) for a wide range of Hamiltonians of interacting fermionic systems. In HFA calculations, H-wave examines the stability of several symmetry-broken phases, such as anti-ferromagnetic and charge-ordered phases, in the given Hamiltonians at zero and finite temperatures. Furthermore, H-wave calculates the dynamical susceptibilities using RPA to examine the instability toward the symmetry-broken phases. By preparing a simple input file for specifying the Hamiltonians, users can perform HFA and RPA for standard Hamiltonians in condensed matter physics, such as the Hubbard model and its extensions. Additionally, users can use a Wannier90-like format to specify fermionic Hamiltonians. A Wannier90 format is implemented in RESPACK to derive ab initio Hamiltonians for solids. HFA and RPA for the ab initio Hamiltonians can be easily performed using H-wave. In this paper, we first explain the basis of HFA and RPA, and the basic usage of H-wave, including download and installation. Thereafter, the input file formats implemented in H-wave, including the Wannier90-like format for specifying the interacting fermionic Hamiltonians, are discussed. Finally, we present several examples of H-wave such as zero-temperature HFA calculations for the extended Hubbard model on a square lattice, finite-temperature HFA calculations for the Hubbard model on a cubic lattice, and RPA in the extended Hubbard model on a square lattice. Comment: 15 pages, 7 figures

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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: Kota Ido; Mitsuaki Kawamura; Yuichi Motoyama; Kazuyoshi Yoshimi; +4 Authors
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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: Villon, Sébastien; Iovan, Corina; Mangeas, Morgan; Vigliola, Laurent;

    Given the global biodiversity crisis, there is an urgent need for new tools to monitor populations of endangered marine megafauna, like sharks. To this end, Baited Remote Underwater Video Stations (BRUVS) stand as the most effective tools for estimating shark abundance, measured using the MaxN metric. However, a bottleneck exists in manually computing MaxN from extensive BRUVS video data. Although artificial intelligence methods are capable of solving this problem, their effectiveness is tested using AI metrics such as the F-measure, rather than ecologically informative metrics employed by ecologists, such as MaxN. In this study, we present both an automated and a semi-automated deep learning approach designed to produce the MaxN abundance metric for three distinct reef shark species: the grey reef shark (Carcharhinus amblyrhynchos), the blacktip reef shark (C. melanopterus), and the whitetip reef shark (Triaenodon obesus). Our approach was applied to one-hour baited underwater videos recorded in New Caledonia (South Pacific). Our fully automated model achieved F-measures of 0.85, 0.43, and 0.72 for the respective three species. It also generated MaxN abundance values that showed a high correlation with manually derived data for C. amblyrhynchos (R = 0.88). For the two other species, correlations were significant but weak (R = 0.35–0.44). Our semi-automated method significantly enhanced F-measures to 0.97, 0.86, and 0.82, resulting in high-quality MaxN abundance estimations while drastically reducing the video processing time. To our knowledge, we are the first to estimate MaxN with a deep-learning approach. In our discussion, we explore the implications of this novel tool and underscore its potential to produce innovative metrics for estimating fish abundance in videos, thereby addressing current limitations and paving the way for comprehensive ecological assessments.

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    Ecological Informatics
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      Ecological Informatics
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    Authors: Mark D. Goodsell; Ari Joury;

    We introduce BSMArt, a python program for the exploration of parameter spaces of theories Beyond the Standard Model. Especially designed for use with the SARAH family of tools, it is also sufficiently flexible to be used with a wide variety of external codes. BSMArt contains the first public release of the Active Learning scan by the same authors; but contains several additional scanning algorithms, ranging from the very simple to MultiNest and Diver. A BSMArt scan can be set up in a matter of minutes with only minimal editing of configuration files; installation scripts for all relevant tools and examples are provided. Comment: 33 pages; no figures. Code available at https://goodsell.pages.in2p3.fr/bsmart/

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    Computer Physics Communications
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      Computer Physics Communications
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    Authors: Dominik Beutel; Ivan Fernandez-Corbaton; Carsten Rockstuhl;
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      Computer Physics Communications
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    Authors: Ralf Wanzenböck; Florian Buchner; Péter Kovács; Georg K.H. Madsen; +1 Authors

    [Supplementary material]: Nature of problem: Find optimal atomistic structures retaining full flexibility in the choice of the optimization target, the methodological approach and its implementation (e.g. CPU- vs. GPU-heavy calculations). Enable interfacing with relevant software, including but not limited to density-functional-theory (DFT) codes and machine-learning (ML) solutions. [Solution method]: The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is implemented using Cholesky decompositions for efficiency. The core algorithm and application examples for specific problems are implemented in functional-style Python free of side effects, with data classes to keep track of the state of the evolution. Dask is used for job control to enable highly distributed workflows. Advanced strategies like BI-Population CMA-ES are easy to implement and illustrated in the examples. Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward. [Program summary]: Program Title: Clinamen2 CPC Library link to program files: https://doi.org/10.17632/x7syr2txsd.1 Developer's repository link: https://github.com/Madsen-s-research-group/clinamen2-public-releases Code Ocean capsule: https://codeocean.com/capsule/4950229 Licensing provisions: Apache-2.0 Programming language: Python This work was supported by the Austrian Science Fund (FWF) (SFB F81 TACO). Peer reviewed

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    Computer Physics Communications
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