dataesr/scientific-paragraphs-categorization huggingface dataset:
VII. CONCLUSION In this paper, we proposed a two stage approach that minimizes the energy cost of MGs running cloud DCs. In the first stage, the MGs optimally and fairly dispatch power between their respective DCs. We formulate the microgrid's power assignment to cloud DCs as a non-cooperative game, afterwards we calculate the optimal amount of power to be allocated to each DC according to its defined metrics. Based on their DCs power assignment, the MGs try, in the second stage, to minimize their energy cost by firstly, optimally purchasing power from the MGs and the main grid, and secondly by selling back their stored energy. We model this energy cost minimization problem as a constrained linear optimization problem and solve it using Matlab optimization tool. In order to validate our proposal, we compared it with our previously presented scheme called "BGS" as well as traditional and price-based approaches.
Global CP Approach To solve the problem defined in the previous section, a first idea is to directly reuse an existing combinatorial optimization engine. More precisely, we reuse a Constraint Programming (CP) tool, namely IBM Ilog CP Optimizer (CPO) that offers both a powerful declarative modeling language and efficient constraint-based scheduling techniques improved over the years. This approach is referred to as GCPS for Global Constraint Programming Search. The model exploited by GCPS considers several decision variables: The problem to solve is then defined as follows. The goal is to maximize the total reward (1). At most one mode is selected for each request  (2). The interval associated with each observation is present iff one mode containing it is selected  (3). Intervals associated with download opportunities are necessarily selected (4).
For one of the 200, 200, 30, 70 instances, the component sizes range from 1 to 456 activities, with a mean equal to 29. Experimental setup All the algorithms are implemented in Python 3. 8. 5. The Global Solver uses DOcplex 2. 22. 213. Two versions of this solver are tested, namely "GCPS 5 modes" and "GCPS 15 modes" that respectively take as an input the best 5 and 15 modes for each request. For UPCCAS and BPCCAS, the worker solvers use LKH3 to check whether a given set of activities can be scheduled in a CCA  [19]. Experiments were run on a 20-core Intel(R) Xeon(R) CPU E5-2660 v3 @ 2. 60GHz, 62GB RAM, with a time out of 1 minute, 5 minutes, or 30 minutes per run. Each run is allowed to use 10 CPUs, and DOcplex automatically parallelizes search over the number of cores specified. Comparisons Table  1  presents the results wrt the total rewards obtained for a representative set of instances, as well as results concerning mode generation.
Specifically, these galaxies are compiled from  Cresci et al. (2012 , zCOSMOS at z∼0. 3-0. 6), Hayden-Pawson et al. (2022 , KLEVER at z∼2. 2), Sanders et al. (2021 , MOSDEF at z∼2. 3-3. 3), Onodera et al. (2016 , z∼3. 3), and Troncoso et al. (2014b, AMAZE at z∼3. 3 ). The metallicities of galaxies at  > 1 have been consistently re-computed adopting the  Bian et al. (2018)   e -based strong-line calibrations (to account for potential evolution in the diagnostics), whereas we adopt the  Curti et al. (2020a)  calibrations for SDSS and zCOSMOS galaxies. For comparison, adopting instead the  Curti et al. (2020a)  calibrations for all galaxy samples produces, on average, a ∼ 0. 1 dex lower metallicity in z∼ 2 -3 galaxies. The various symbols in Fig. 7  mark, for each considered literature dataset, the average offset in O/H (and standard deviation as errorbars) from the FMR predictions for individual galaxies within the sample.
Different cosmological simulations can reproduce some of the galaxies on the MZR, but none of them seem capable of simultaneously matching all of them. • The three galaxies are widely scattered relative to the Fundamental Metallicity Relation, with two of them marginally consistent and one (ID4590 at z∼ 8. 5) strongly deviating (Fig. 7 ). This finding suggest that galaxies at such early epochs are undergoing rapidly evolving mechanisms, and not yet settled on the local scaling relations that are instead associated with smooth, secular evolutionary processes. In any case, much larger samples are required in order to draw strong conclusions on the evolution of such scaling relations. In conclusion, these first datasets already highlight the giant leap in the redshift parameter space brought to the field by the advent of restframe optical spectroscopy enabled by JWST, which is about to open a window on new modes of galaxy formation and evolution, helping to discover objects with properties radically different than all the other known galaxies.
2021; Shapley et al. 2021)  will soon provide a more robust re-calibration of the strong-line metallicity diagnostics for the highredshift Universe. INTERSTELLAR H2020/740120. HÜ gratefully acknowledges support by the Isaac Newton Trust and by the Kavli Foundation through a Newton-Kavli Junior Fellowship. SA acknowledges funding from grant PID2021-127718NB-I00 by the Spanish Ministry of Science and Innovation/State Agency of Research (MCIN/AEI). J. M. P and J. W. gratefully acknowledge support from the MERAC Foundation. AJB acknowledges funding from the "FirstGalaxies" Advanced Grant from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 789056). RS acknowledges support from an STFC Ernest Rutherford Fellowship (ST/S004831/1).
ACKNOWLEDGEMENTS We thank the anonymous referee for his/her comments which contributed to improving this paper. M. C. , R. M. , F. D'E. , L. S. , J. S. , W. B. , J. W. , T. J. L. , C. H-P. and J. S. B. acknowledge support by the  Science and Technology Facilities Council (STFC)  and  ERC Advanced  Grant  695671  "QUENCH". RM also acknowledges funding from a research professorship from the  Royal Society. S. C. acknowledges support from the  ERC Advanced  Grant.
4 this value is 0. 007 yr -1 for the tropical broadleaf evergreen tree (BET-Tr) PFT. In order to allow γ to vary with location, here it was prescribed as an extra ancillary variable with a unique value at every simulated land point. The input mortality datasets were derived from wood density data for Amazonia using relationships found in literature (see Sect. 2. 2). It was only through the variable γ in Eq. (  1 ) that wood density could influence the vegetation carbon simulated by JULES. The BET-Tr PFT is the dominant type in the Amazon  (Harper et al. , 2016 (Harper et al. , , 2018)) , therefore, for the purpose of this study only the mortality rate of this PFT was allowed to vary. To isolate the effects of introducing variable mortality into the dominant Amazonian PFT from other effects, we removed competition from the remaining 12 PFTs (8 natural and 4 agricultural, described in  (Harper et al.
5 gcm -3  (Siau, 1984; Chave et al. , 2009) , but a more realistic value for maximum species wood density is 1. 25 gcm -3 , given that only 0. 02% of species in the Global Wood Density Database have wood density greater than this value  (Zanne et al. , 2009). For the purposes of our large scale application, this value was chosen to ensure no grid cell was assigned a negative mortality rate. The disadvantage of this method is that the gradients of the two equations are effectively changed when ρ max is altered from the values presented in the original papers (formerly in M1, ρ max = 0. 9 gcm -3 and in M4, ρ max = 1. 0 gcm -3 ). Despite this, the equations were included in the experiment as extremes (M1 has a very large gradient and intercept compared to the others, whereas M4 has the lowest gradient and intercept) to explore the model sensitivity.
39); an improvement on M1. (Kraft et al. , 2010). Wood density values for each species were taken from the Global Wood Density Database database  (Zanne et al. , 2009). M3 was obtained from a logarithmic fit (R 2 =0. 62) of mortality against wood density measurements taken from 21 species (1-4 individuals per species) from two forest sites in Malaysia  (King et al. , 2006). The fact that the study sites did not match the location of this analysis is obviously a disadvantage; however this equation is included in the mortality model used by  Sakschewski et al. (2015)  in LPJmL-FIT, specifically for Amazonia. This equation merits inclusion here because it affords the opportunity to test whether a non-linear relationship can better reproduce biomass variation. Finally, in M4, the gradient parameter was tuned in the original publication  (Maréchaux and Chave, 2017 ) so that AGB, NPP, leaf area index and stem density outputs best reproduced observational data from two plots in French Guiana; this equation is not based on direct observations of mortality data.
Wood density data and regridding Three maps of wood density for the Amazon forest were used in this study. The derivation method impacted the range of wood density values present in the maps, so the three datasets provided another means of testing the sensitivity of JULES outputs. The first map of wood density was created by calculating the unweighted mean wood density for all species occurring in each grid cell. Mean species values of wood density were taken from an update to the Global Wood Density Database  (Zanne et al. , 2009). Species occurrence was predicted using species distribution models (SDMs) developed from occurrence records from the Global Biodiversity Information Facility (GBIF) (GBIF. org, 2021) and climatic layers. Henceforth this is called the "SDM-based" map. The fact that mean wood density for each grid cell is not weighted by species abundance contributes to the relatively low variation between cells, as extreme values are averaged out.
This method was chosen because species abundance data with wide coverage is difficult to source for Amazonia. Similar results were achieved in a map which only included the hyperdominant Amazonian species  (Ter Steege et al. , 2013) , suggesting that weighting by abundance may not increase the wood density variation dramatically. To create the second map, global climate covariates (TerraClimate  (Abatzoglou et al. , 2018) ), disturbance layers and a subset of wood density pixels were used to train a regularised random forest (RF) model to predict wood density over the region, utilising the "ranger" R package. Wood density for each 25 km pixel was calculated as the unweighted average value for all species with measured occurrence in the pixel, with occurrence data from the GBIF (GBIF. org, 2021).
New cells were masked when over 50% of the overlapping source cells were blank due to missing data; this threshold best represented the forest extent in the original maps. The final wood density map was derived by spatial interpolation (kriging) of the mean wood density from 414 intact forest plots, using plot data from another study  (Mitchard et al. , 2014). This map will be referred to as the kriged map. Mitchard et al. (2014)  recorded mean wood density values weighted by species basal area, primarily from RAINFOR  (Peacock et al. , 2007)  and ATDN  (Ter Steege et al. , 2013)  data, with species wood density values from the Global Wood Density Database  (Zanne et al. , 2009). Notably, there are no data from disturbed forests. These observations were interpolated across the study area by ordinary kriging with a spherical variogram model ("PyKrige" package in Python).
JULES simulations over the Amazon forest JULES was run over all Amazonian grid cells. Maps of mortality across Amazonia were generated by applying each of the four equations in Table  1  to each of three observational wood density datasets (Sect. 2. 3). This resulted in 12 different mortality configurations, which were fed into JULES as ancillary data in 12 separate simulations. The results were compared against a control simulation, where mortality was spatially constant with the value for the BET-Tr PFT used in the original configuration of JULES. In this work, the earth system configuration of JULES, JULES-ES was used  (Sellar et al. , 2019). This is the configuration used in the TRENDY version 8 ensemble, a group of models used to simulate net biome production under standard protocols for annual Global Carbon Budget assessments  (Friedlingstein et al.
Model evaluation A map of vegetation carbon created by  Avitabile et al. (2016) , based on remote sensing and field data, was used to test the success of adding spatially variable mortality rates into JULES and is henceforth called the Avitabile map. It is a fusion of two pantropical biomass maps  (Saatchi et al. , 2011; Baccini et al. , 2012) , which were derived from Geoscience Laser Altimeter System LiDAR remote sensing data, and calibrated with field observations. The maps are fused using bias removal and weighted linear averaging in a model that was calibrated against an extensive reference dataset. The model parameters AGB was converted into vegetation carbon using a conversion factor of 0. 5  (Chave et al. , 2005). The maps were then regridded following the method in Sect. 2. 3. The regridded Avitabile and ESA CCI maps are shown in Fig.
B1. Model evaluation was also completed for intact forests, defined as grid cells containing forests with less than 20% disturbance. Maps of disturbed forest fraction for 2017 were calculated from degraded forest (JRC  (Vancutsem et al. , 2021) ), secondary forest (MapBiomas Amazonia C2 (MapBiomas, 2021)) and total forest extent (MapBiomas) data. They were regridded using the method outlined in Sect. 2. 3. The raw data and masked forest extent are shown in Fig. C1. An additional experiment was conducted to test whether the observed vegetation carbon could be captured by any range of wood density. A linear regression was applied to the simulated vegetation carbon and the mortality rates of the corresponding grid cells produced from M2-4. M1 was excluded because it led to vegetation dieback.
, 2014). It was found to have a lower root mean square error than the two input maps and nearly zero bias. A comparison of existing biomass maps  (Zhang et al. , 2019)  for South America shows that the Avitabile map is in broad agreement with other datasets  (Hu et al. , 2016; Zhang et al. , 2019). The uncertainties in the eastern Amazon are still relatively large (∼30 MgCha -1 ) and this estimate does not include the uncertainty associated with classifying the errors in the input map, potentially a large contribution  (Avitabile et al. , 2016). The lack of reference data from disturbed forests in the Avitabile et al. study may cause errors in the arc of deforestation, where this map records more carbon than the ESA CCI product (see Fig. B1 ). Future analysis could consider comparing JULES outputs to the upcoming GEDI L4B map of AGB, which will incorporate a larger number of observations of the tropics than used in the sources of the Avitabile map  (Dubayah et al.
Implementing the improvements to PFT mortality schemes in vegetation models outlined in this paper would expand the potential for accurately modelling the spatial distribution of vegetation carbon and the size of the vegetation carbon sink. This method also opens up several avenues for further improvements, such as coupling mortality to growth rates, adding spatial diversity to drought and fire responses, and adding temporal dynamics to mortality rates via trait composition change. The JULES code and the Rose suite (suite u-cg769) for these simulations is available on the Met Office Science Repository System (MOSRS; https://code. metoffice. gov. uk/ trac/jules). Free registration is required to access MOSRS.
Statistical comparison of the control JULES simulation and the nine non-zero simulations of vegetation carbon with different mortality configurations after highly disturbed forests are filtered out and the vegetation carbon derived from the Avitabile map. The absolute bias (MgCha -1 ), CRMSD (MgCha -1 ) and Pearson correlation coefficient are shown. UKESM, Grant NE/N017951/1). FJF and JC were supported by the "Investissement d'Avenir" grants managed by the Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001). DF and SS were supported by ESA Climate Change Initiative RECCAP2 (contract no. 4000123002/18/I-NB).
Code and data availability. JULES output for the 12 mortality configurations and control configuration, for present day, has been archived at https://doi. org/10. 5281/zenodo. 6388019. The 12 mortality maps and the kriged wood density map are also found in this repository. The SDM-and RF-based wood density maps have not yet been made publicly accessible because their creators, FJF and JC and collaborators, are preparing to publish them separately in upcoming manuscripts.
Finally, the paper describes the architecture of a prototype of ProMISE (Provision Model-based Inferential Search Engine). A data set of Italian legislative documents, published on the Web using the URI and XML NormeInRete  (Francesconi 2006)  standards has been created, and domain ontologies have been used to annotate the semantic content of the provisions. The DALOS multilingual European consumer protection law ontology  (Agnoloni et al. 2009 ) was used in the testing environment. The result is a set of RDF triples representing normative provisions stored in the OpenLink Virtuoso Database Server 8. Since the associated reasoner has limitations, external reasoners such as Pellet 9 (a Java-based OWL-DL reasoner) can be used. Finally, the paper comments on a Web application providing the users with semantic web search facilities able to query the system according to the Provision Model.
Nazarenko et al. (2021) ), but 7 SPARQL is a recursive acronym for SPARQL Protocol and RDF Query Language, an RDF query language. See https:// www. w3. org/ TR/ rdf-sparql-query/ 8 https:// virtu oso. openl inksw. com/ 9 https:// github. com/ stard og-union/ pellet 10 http:// www. akoma ntoso. org/ 11 https:// www. oasis-open. org/ commi ttees/ tc_ home. php? wg_ abbrev= legal ruleml also to improved quality of legislation, as it may effectively contribute to the detection of legislative errors, for instance contradictions in regulation  (Araszkiewicz et al. 2021). Francesconi (2014)  has also been cited in the legal-theoretical literature on legislative techniques (e. g. Kłodawski 2021). 3 Eunomos, a legal document and knowledge management system for the web to provide relevant, reliable and up-to-date information on the law (Boella et al.
Approach: each issue is treated as a module in an overall process. Each module uses tools relevant to the task and data, which are largely semiautomatic, interactive support tools, so that a legal knowledge engineer uses the tool to filter or structure the data as it passes into the machine-readable representation. The aim is that the interfaces do not require specialist skill, e. g. , text classification, rule application, NLP, and so on. Some of the tasks include adding legislation to the database, checking XML parses, adding cross-references, classifying references, checking document domain attribution, adding terms and concepts to the ontology, commenting on the text, linking the ontology to the source text, and adding explanations or interpretations. The core system of EUNOMOS has three layers: -a database of Italian national laws that are processed for similarity (Cosine Similarity with TF-IDF) and for domain topic (SVM) then converted into XML using the ITTIG XML parser to convert the NormaInRete XML format; -an ontology of legal terms from the Legal Taxonomy Syllabus, which are augmented with lexical semantic relations and contextual or interpretive information, then linked to the law (using the TULE parser (Lesmo 2009)); and -a user interface for querying the database with respect to the ontology, keywords, and other metadata.
Discussion As can be gathered from the catalogue of issues and approaches, EUNOMOS has a global, ambitious aim, covering a heterogeneous range of familiar issues and technologies which bear on one another. An integrated workbench would be a very useful contribution; as an outline of the issues, approaches, and technologies available at the time, the paper serves a useful purpose. However, the practical and scientific contributions are rather limited. It is useful to have issues and approaches sketched in one place, even if they individually would not appear to be novel, being of long standing in AI and Law. On the practical side, the data and software have been acquired by a company and are unavailable for any further academic research and development. On the scientific side, the chief aims were to reuse standard approaches (e.
A proliferation of community-wide datasets, predictive tasks with straightforward evaluation criteria, and leader boards created institutional rewards for incremental improvements in predictive accuracy. The AI and Law community was slow to adopt this corpus-based approach 16 for several reasons. Attorneys' ability to predict outcomes of disputes typically increases with legal experience, but it is much less central to legal expertise than articulating the most cogent argument for a given outcome. Human attorneys' training typically emphasizes the ability to generate, understand, and evaluate conflicting textual arguments under various factual scenarios. While corpus-based methods for texts have a lengthy history, particularly for author identification and stylometry (Holmes and Kardos 2003), the shallow statistical methods commonly used in these applications were ill-suited for the argument-centered analysis characteristic of much legal problem solving.
perform their experiments on both a Japanese and an English language data set: the Japanese data collection is the Japanese National Pension Law Requisite-Effectuation Recognition dataset [JPL-RRE]; the English language data collection is the Japanese Civil Code dataset [JCC-RRE]. The authors compare their approach to a non-neural baseline consisting of a sequence of Conditional Random Fields (CRFs)  (Lafferty et al. 2001 ), but they also investigate three distinct variations of their Bi-LSTM architecture to run against the JPL-RRE (Japanese) dataset and five to run against the JCC-RRE (English) dataset. They initially harness sequences of singular Bi-LSTMs or Bi-LSTMs with external features from CRFs, and progress to using multi-layered Bi-LSTMs, finally performing experiments on one that uses a single multi-connected layer and another that uses two multi-connected layers.
This result was recently replicated for more contemporary ML techniques in  Steging et al. (2021). The use of ML techniques in AI and Law fell into abeyance for a while but, as discussed in Section 4, developments in ML techniques and the much increased availability of large amounts of data has led to an upsurge of interest in such techniques. Much of this has been directed towards legal tasks other than decision support and argumentation, but predicting case outcomes has received a good degree of 1 3 Thirty years of artificial intelligence and law: the third… attention. A very well publicised example was Aletras et al. (  2016 ) which attracted a great deal of press attention 27. Aletras et al. (  2016 ) used a Support Vector Machine (Vapnik 1999) to classify decision of the European Court of Human Rights (ECHR) and achieved an accuracy of 79%.
To ensure a fair comparison between the competing methods and LamBERTa, the authors noted that each of the competing models (with the exception of Transformer), require word vector initialization. To satisfy this need, the models were firstly provided with Italian Wikipedia pre-trained Glove embeddings, and secondly, each model was fine-tuned over the individual ICC texts following the same data labeling schemes as the LamBERTa models. Experimental results demonstrate the superiority of LamBERTa models against the selected competitors for ICC case law retrieval construed as a classification task. In addition, these results show much promise for the development of AI tools to ease the work of jurists, which is one of the main focuses of the AI and Law research area.