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This archive is the artifact for the CF-2020 paper "Approximating Trigonometric Functions for Posits Using the CORDIC Method" There are two methods to install the artifact: (1) Installation using Dockerfile: Pull the docker image from the docker hub: $ docker pull jpl169/cordicwithposit $ docker run -it jpl169/cordicwithposit (2) Manual installation with Ubuntu 18.04" Install the required packages: $ sudo apt−get update $ sudo apt−get install −yq −−no−install−recommends apt−utils $ sudo apt−get install −yq build−essential python3 python3−pip libgmp3−dev libmpfr−dev git $ python3 −m pip install numpy matplotlib Install SoftPosit library $ git clone https://gitlab.com/cerlane/SoftPosit.git $ cd SoftPosit/build/Linux−x86_64−GCC/ $ make $ cd ../../.. Finally, untar the artifact and build the code: $ export SOFTPOSITPATH=<path to SoftPosit directory> $ tar −xvf CordicWithPosit.tar.gz $ cd CordicWithPosit && make (2) Experiments There are three experiments included in the artifact. (a) Graph Generation This experiment creates three graphs describing the accuracy of sin, cos, and atan of our CORDIC compared to the naive floating point implementation of CORDIC. This script generates three graphs, sin.pdf, cos.pdf, and atan.pdf graph directory. $ python3 runGraphGeneration.py (b) Accuracy Experiment (Fast). This experiment is the short version of the accuracy evaluation script. $ python3 runSimplifiedAccAnalysis.py $ python3 GenerateTableForSimplifiedAcc.py (c) Accuracy Experiment (Full). This experiment is the full accuracy evaluation script. $ python3 runAccAnalysis.py $ python3 GenerateTableForAcc.py

This package contains clustering functionalities for the Center for Sustainable Energy Systems (ZNES) based on the tsam package. Notes: The package can be installed using the pip installer e.g. using pip install -e znes_clustering

AbstractAimKnowing a species' response to historical climate shifts helps understanding its perspectives under global warming. We infer the hitherto unresolved postglacial history of Pinus cembra. Using independent evidence from genetic structure and demographic inference of extant populations, and from palaeoecological findings, we derive putative refugia and re‐colonisation routes.LocationEuropean Alps and Carpathians.Taxa Pinus cembra. MethodsWe genotyped nuclear and chloroplast microsatellite markers in nearly 3000 individuals from 147 locations across the entire natural range of P. cembra. Spatial genetic structure (Bayesian modelling) and demographic history (approximate Bayesian computation) were combined with palaeobotanical records (pollen, macrofossils) to infer putative refugial areas during the Last Glacial Maximum (LGM) and re‐colonisation of the current range.ResultsWe found distinct spatial genetic structure, despite low genetic differentiation even between the two disjunct mountain ranges. Nuclear markers revealed five genetic clusters aligned East–West across the range, while chloroplast haplotype distribution suggested nine clusters. Spatially congruent separation at both marker types highlighted two main genetic lineages in the East and West of the range. Demographic inference supported early separation of these lineages dating back to a previous interstadial or interglacial c. 210,000 years ago. Differentiation into five biologically meaningful genetic clusters likely established during postglacial re‐colonisation.Main ConclusionsCombining genetic and palaeoecological evidence suggests that P. cembra primarily survived the LGM in ‘cold period’ refugia south of the Central European Alps and near the Carpathians, from where it expanded during the Late Glacial into its current Holocene ‘warm period’ refugia. This colonisation history has led to the distinct East–West structure of five genetic clusters. The two main genetic lineages likely derived from ancient divergence during an interglacial or interstadial. The respective contact zone (Brenner line) matches a main biogeographical break in the European Alps also found in herbaceous alpine plant species.

Study location We used data from long-term annual monitoring that commenced in 2003 in Booderee National Park (BNP), Jervis Bay Territory, south-eastern Australia. BNP is on Indigenous land and is jointly managed by the Wreck Bay Aboriginal Community and Parks Australia. The 6600 ha park has a temperate climate, with an average annual rainfall of 1213 mm, spread evenly across the year (Bureau of Meteorology, 2021). Average temperatures range from 18.6-25.1°C in summer (January) to 9.9-16.1°C in winter (July) (Bureau of Meteorology, 2021). BNP supports a range of vegetation types such as heathlands, wetlands, forests, and woodlands. Two major fires have occurred in BNP over the past 20 years (in 2003 and 2017), with each burning approximately half of the park. A fox baiting program has been in place in BNP since 1999 and was intensified in 2003 to reduce the deleterious impact of this introduced predator on native prey species (Dexter et al., 2013). Data collection We surveyed small and medium-sized mammals annually each summer for 14 years at 109 permanent sites starting in 2003, with another 20 sites added in 2008. The sites were surveyed along 100 m transects with 2 large (30 x 30 x 60 cm) cage traps at the beginning and end of transects, small (20 x 20 x 50 cm) cage traps every 20 m between the large cage traps, and 10 Elliott traps (10 x 10 x 30 cm; Elliott Scientific Equipment, Upwey, Victoria) every 10 m (Figs 2) (Lindenmayer et al., 2008, 2016). Approximately 50% of the sites were surveyed each year (with the other 50% being surveyed the next year), depending on weather conditions (Lindenmayer et al., 2016). We recorded the number of individuals of both species caught at each site in a given year. We collected data on environmental and disturbance variables at each of our 129 sites. These data included visual estimates of the percentage of understorey and leaf litter cover in four 1 x 1 m subplots within 20 x 20 m survey plots during semi-annual vegetation surveys (Lindenmayer et al., 2008). We selected understorey and leaf litter as representative variables of the primary bush rat habitat, which are also components of common brushtail possum habitat (Callander, 2018; Cruz et al., 2012). We constructed a predictive model to fill the data gaps for those years when sites were not surveyed (MacGregor et al., 2020). We used monthly rainfall averages collected at the nearby Point Perpendicular weather station for the trapping period at each site (Bureau of Meteorology, 2021). We transformed both the vegetation variables and rainfall were transformed into quadratic functions using the poly() function in R (R Core Team, 2021). We used data on fire occurrence recorded on-ground since 2003, and fire history data collected by Booderee National Park over the past 50 years (Foster et al., 2017), specifically the number of years since the last fire at a site. To minimise possible inaccuracies stemming from incorrect fire dates, or the occurrence of unreported fires, we grouped the number of years since fire into 10-year blocks (i.e., 0-10, 11-20, 21-30, 30+ years). Statistical analysis We used Bayesian regression models with a hurdle step to test the response of species abundances to the selected variables using the brms package ver. 2.16.3 (Bürkner, 2017; 2108; Feng, 2021) implemented in R (R Core Team, 2021). These regression models employed Markov Chain Monte Carlo simulations, with four chains and a warm-up of 1000 iterations before sampling another 1000 iterations. We assessed model convergence by ensuring all Rhat values were <1.1 (Bürkner, 2017; 2108). The hurdle step consisted of two components: the first modelled the presence/absence of the response variable, and the second, conditional on the species being present, modelled the conditional abundance using a zero-truncated Poisson (Feng, 2021). We combined these two components in an analysis of unconditional abundance (Feng, 2021). We created a regression model with bush rats as the response variable, and a regression model with common brushtail possums as the response variable. Both regression models included time, understorey cover, leaf litter cover and rainfall as covariates within the conditional abundance component. These variables were included to assess the variation in bush rat and common brushtail possum abundances with environmental variables (H1). Years since fire was included in the conditional abundance and hurdle step of both regression models as an explanatory variable, as it is a prominent disturbance within BNP, and past research has indicated that fire has a significant effect on small vertebrate populations (Arthur et al., 2012). The other species was also input into the conditional abundance and hurdle step of both regression models (i.e., common brushtail possums into the bush rat model, bush rats into the common brushtail possum model) as an explanatory variable to assess the co-occurrence effect between species (H2). We also included site as a random effect, and used the log of the number of Elliott traps as a control for the bush rat models, and the number of open cage traps as a control for the common brushtail possum models. The control variables account for varying trapping effort, and were selected based on the main trap-type that captures the relevant species (i.e., Elliott traps for bush rats, cage traps for common brushtail possums). We performed a model selection procedure for both of the regression models, based on the selection for explanatory variables only. We chose not to perform model selection on the covariates (i.e., the environmental variables) as we were testing variation in species abundance in relation to the environment, and not predicting significant changes in abundance that we were with the co-occurrence and disturbance variables. Using model selection, we assessed the relevancy of our exploratory variables to changes in species abundance (H3). The chosen model was the most parsimonious, which was based on the simplest model which was within 2 leave-one-out cross validation (LOOIC) scores of the best fitting model. We created ten variations of the regression models for each species, and assessed the fit of each variation using LOOIC (Tables 1 and 2) (Vehtari et al., 2017). LOOIC estimates the out-of-sample predictive fit by measuring the predictive accuracy for each data point using a variation of the expected log pointwise predictive density equation (Vehtari et al., 2017). LOOIC was selected as the appropriate method over other model selection methods as it is informative and was created for Bayesian models (Burnham & Anderson, 2002; Vehtari et al., 2017). Co-occurring species often overlap in resource use and can interact in complex ways. However, shifts in environmental conditions or resource availability can lead to changes in patterns of species co-occurrence, which may be exacerbated by global escalation of human disturbances to ecosystems, including conservation directed alterations. We investigated the relative abundance and co-occurrence of two naturally sympatric mammal species following two forms of environmental disturbance: wildfire and introduced predator control. Using 14 years of abundance data from repeat surveys at long-term monitoring sites in south-eastern Australia, we examined the association between a marsupial, the common brushtail possum Trichosurus vulpecula, and a co-occurring native rodent, the bush rat Rattus fuscipes. We asked: Is the increase in abundance of common brushtail possums following control of an introduced predator associated with a decline in abundance of the bush rats? Using Bayesian regression models, we tested hypotheses that the abundance of each species would vary with changes in environmental and disturbance variables, and that the negative association between bush rats and common brushtail possums was stronger than the association between bush rats and disturbance. Our analyses revealed that bush rat abundance varied greatly in relation to environmental and disturbance variables, whereas common brushtail possums showed relatively limited variation in response to the same variables. There was a negative association between common brushtail possums and bush rats, but this association was weaker than the initial decline and subsequent recovery of bush rats in response to wildfires. Using co-occurrence analysis, we can readily infer negative relationships in abundance between co-occurring species, but to understand the impacts of such associations, and plan appropriate conservation measures, we require more information on interactions between the species and environmental variables. Co-occurrence can be a powerful and novel method to diagnose threats to communities and understand changes in ecosystem dynamics.

Evolutionary events may impact the geological carbon cycle via transient imbalances in silicate weathering, and such events have been implicated as causes of glaciations, mass extinctions, and oceanic anoxia. However, suggested evolutionary causes often substantially predate the environmental effects to which they are linked—problematic when carbon cycle perturbations must be resolved in less than a million years. What is more, the geochemical signatures of such perturbations are recorded as they occur in widely distributed marine sedimentary rocks that have been densely sampled for important intervals in Earth history, whereas the fossil record—particularly on land—is governed by the availability of sedimentary basins that are patchy in both space and time, necessitating lags between the origination of an evolutionary lineage and its earliest occurrence in the fossil record. Here, we present a simple model of the impact of preservational filtering on sampling to show that an evolutionary event that causes an environmental perturbation via weathering imbalance should not appear earlier in the rock record than the perturbation itself and, if anything, should appear later rather than simultaneously. The Devonian Hangenberg glaciation provides an example of how evolutionary events might be more fruitfully considered as potential causes of environmental perturbations. Just as the last samplings of species lost in mass extinction are expected to come before the true environmental event, first appearance should be expected to post-date the geological expression of a lineage's environmental impact with important implications for our reading of Earth history. Funding provided by: N/A*Crossref Funder Registry ID: Award Number:

Plasma heating in magnetic confinement fusion devices can be performed by launching a microwave beam with frequency in the range of the cyclotron frequency of either ions or electrons, or close to one of their harmonics. The Electron Cyclotron Resonance Heating (ECRH) is characterized by the small size of the wavelength that allows one to study the wave properties using the geometrical optics approximations. This means that the microwave beam can be simulated by a large amount of rays. Being all the ray computations independent, this problem is well suited to be solved in the grid relying on the EGEE infrastructure. The framework has been implemented using the standard DRMAA API provided by GridWay.

The IUCN Red List of Threatened Species, along with data on body mass, was used to generate a species list of 298 large extant terrestrial mammals (IUCN, 2019; Jones et al., 2009; Myers et al., 2020; Smith et al., 2003; Table S1). 'Large mammals' were defined as those exceeding 15 kg maximum recorded body mass, a threshold that allows the inclusion of key predators, their prey, and other large herbivores, and that is consistent with other studies (Ferreira et al. 2020; Ripple et al., 2014; Salom-Peréz et al. 2021; Wolf et al., 2018). To identify intact and near-intact large mammal assemblages, data from the IUCN Red List (IUCN, 2019) were used for current species ranges and data from Faurby & Svenning (2015) for historical ranges. Faurby & Svenning (2015) modeled mammal species ranges as the ranges would have been today in the absence of human influence. AD 1500 was chosen as the cutoff for historical ranges, following the rationale provided by Morrison et al. (2007). Briefly, this marks a globally synchronous period after which there were the most profound anthropogenic changes to Earth's terrestrial area; it is the same demarcation used by the IUCN Red List as the cutoff for examining 'recent' extinctions. Moreover, all except six of the large mammal species (>15 kg) present in AD 1500 are still extant and have opportunities for in situ conservation. The six extinct species (EX) were removed from the analysis since there is no opportunity for their restoration; one species listed as extinct in the wild (EW; Elapharus davidianus) was retained. Intact and near-intact large mammal assemblages were identified by first converting current species range polygons into ~100 km² rasters to match the historical species range data. To ensure small ranges were not missed, any grid cells that overlapped the polygon were included. These raster layers of current and historical species ranges were then downscaled and refined to 10 km² using the Land Cover product published by the European Space Agency Climate Change Initiative (ESA CCI) (Bontemps et al. 2013). To perform this step, the ESA CCI landcover was first resampled to 10 km² and each landcover class was linked to habitat preferences from the IUCN Red List (IUCN, 2019). For example, the ESA CCI landcover class 'Tree cover, broadleaved, evergreen, closed to open (>15%)' was linked to the IUCN habitat preference of 'Forest'. The individual 100 km² current and historical species range rasters were then downscaled by resampling them to 10 km² and removing grid cells that did not match the habitat preferences linked to the matching 10km² landcover data. This process removes unsuitable habitat form each species' range, limiting errors of commission. Historical range maps were also downscaled using current landcover to ensure areas no longer suitable for a species were not included as potential restoration areas. Thus, the spatial scale at which decisions were made about presence or absence of a particular large mammal species was at a fine-grained scale of 10 km2. Only later in the analysis was the ecoregion boundary coverage overlayed on the relevant grid cells. To identify areas where a species is no longer present but might have occurred in the absence of human influence, each species' current refined range was then subtracted from its refined historical range. These areas of loss for each species were then combined, yielding a raster of the number of species missing per grid cell. Using this raster, places having all species present were identified as intact large mammal assemblage areas. Those with 1–3 missing species were identified as near-intact large mammal assemblages as. This range was chosen because the objective was to take a pragmatic approach to identify places for restoration to a complete assemblage. This decision serves as a reasonable starting place for operationally defining the term 'near-intact'. The rationale wa if restoration of the assemblage will require targeted, species-based reintroductions, major effort required will be needed for each species. Conversely, areas with more than three species missing are more likely to be degraded, isolated, or have significant hunting pressures and make near-term restoration less feasible. Grid cell output was summarized within ecoregion boundaries (Dinerstein et al., 2017; Table S2). Assemblages of large mammal species play a disproportionate role in the structure and composition of natural habitats. Loss of these assemblages destabilizes natural systems, while their recovery can restore ecological integrity. Here we take an ecoregion-based approach to identify landscapes that retain their historically present large mammal assemblages, and map ecoregions where reintroduction of 1–3 species could restore intact assemblages. Intact mammal assemblages occur across more than one-third of the 730 terrestrial ecoregions where large mammals were historically present, and 22% of these ecoregions retain complete assemblages across >20% of the ecoregion area. Twenty species, if reintroduced or allowed to recolonize through improved connectivity, can trigger restoration of complete assemblages over 54% of the terrestrial realm (11,116,000 km2). Each of these species have at least two large, intact habitat areas (>10,000 km2) in a given ecoregion. Timely integration of recovery efforts for large mammals strengthens area-based targets being considered under the Convention on Biological Diversity.

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