dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The SMOTE algorithm has been adapted to deal with the special case in which the dataset labels are compositional, which had not been done before. The present study investigates its effectiveness on imbalanced datasets for three different models: Gradient Boosting tree, Neural Networks, and Dirichlet Regression. The evaluation was performed on both synthetic and real datasets, and several metrics, including accuracy, F1-score, RMSE, cross-entropy, and R 2 , were used to assess the performance of the models. The study suggests that the effectiveness of the SMOTE-CD technique depends on several factors, including the model, distance metric, dataset imbalance level, and class distribution. The SMOTE-CD technique can improve the performance of a model that does not perform too poorly, but it may not be effective for a model with very low performance.
Generated data X new 2 R q×p and Y new 2 R q×K with q the number of points created. 1: Compute the label sum vector S 2 R D as defined in Eq (3). 5: m argmin(S), the minority class of dataset. 6: Algorithm 2 SMOTE for compositional data Require: X 2 R n�p the features. Require: Y 2 R n�K the labels (compositional data). Require: k 2 N the number of neighbors to select for the k-Nearest Neighbors. Ensure: 2: M argmax(S), the majority class of dataset (hence S M is the sum of the majority class). 3: Initialize X new and Y new as empty matrices. 4: while min(S) < S M do.
the arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of arctic terrestrial ecosystems. the dataset, called aRCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45°N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0. 1 degree (~9 km) resolution. The indices are provided in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021.
Thus, at the ecosystem level, climate change has manifested throughout trends in both annual and seasonal temperature and precipitation patterns, but also throughout extreme events, such as transient periods of extreme winter warmth, summer drought or high wind speeds. Such trends may only become apparent in daily or sub-daily climatic datasets extending over sufficiently long time periods. Many scientific domains rely on high-quality climate information in an applicable format. For example, upscaling measurements of greenhouse gas fluxes  21  or modeling of species distributions  22  under past, current and future climates are typically carried-out on gridded climate datasets. Obviously, the realism of the outcome is partly dependent on the relevance of the used climate indices and the spatiotemporal resolution of the climate data.
Today, atmospheric reanalyses  23, 24  provide temporally and spatially consistent evolution on climate variables without being limited by the challenges arising from the uneven coverage of in-situ observations. Furthermore, by applying various downscaling methods, several spatially fine-grained bioclimatic datasets have been published in recent decades, e. g. , WorldClim  25  , TerraClimate  26  , CHELSA  27  or MERRAclim  28. However, the climatologies traditionally presented -such as annual, seasonal, or monthly averages -focus mainly on seasonally-averaged temperature and precipitation, which may fail to capture many ecologically important aspects of the Arctic climate. For example, snow cover duration  29, 30  , rain-on-snow events  31  , water vapor pressure deficit  32  or extreme wind events  33  are relevant for many biological, biogeochemical or geomorphological processes happening in the Arctic but may not be fully represented by the more commonly used climate statistics.
Climate datasets important for the Arctic and its terrestrial ecosystems may therefore still lack the relevant temporal resolution or the relevant bioclimatic indices to better capture extreme events, and their recurrence and impacts. Here, we present a new dataset of Arctic bioclimatic indices, called the Bioclimatic Atlas of the Arctic (ARCLIM). The ARCLIM dataset consists of 14 climate and event-type indicators that are particularly relevant for investigating the changes in the Arctic ecosystems driven by both trends (push) and stochastic events (pulse) accompanying climate change, such as growing season length, the number of rain-on-snow events or the heatwave magnitude index (Fig. 1 ). The ARCLIM variables are computed from the ERA5-Land dataset  34  , which includes global hourly data at 0.
Methods The ERA5-Land dataset. The ARCLIM dataset is derived purely from the ERA5-Land dataset  34. ERA5-Land, provided by European Centre for Medium-Range Weather Forecasts (ECMWF), is a downscaled re-simulation of the land component of global ERA5 reanalysis  23. The horizontal resolution of ERA5-Land is 0. 1°, downscaled from 0. 25° used in ERA5. The ERA5-Land dataset is produced by the ECMWF land surface model Carbon Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land  35  , which is forced by the meteorological fields from ERA5. Observations are indirectly incorporated via comprehensive assimilation of instrumental and remote sensing observations into the ERA5 reanalysis dataset  23. The ERA5-Land dataset is currently the most accurate modern reanalysis product in terms of horizontal and temporal resolution, making it a suitable dataset to use as a basis of the ARCLIM dataset.
Downloading and pre-processing the input data. The ERA5-Land dataset was downloaded from Copernicus Climate Data Store (CDS, cds. climate. copernicus. eu) for the period 1950-2021  36, 37. The list of all downloaded input variables is shown in Table  1. The variables were downloaded for the domain north of 45°N, in the native 9-km spatial resolution and with a one-hour temporal interval (Fig. 2 ). Due to the large data volume of the hourly ERA5-Land dataset, for each day, we computed daily mean, maximum, minimum and sum values of the variables. These daily statistics were archived to CSC (Finnish IT center for science) Allas object storage system (Fig. 2 ). For the instantaneous variables (2t, 2d,skt, snowc, u10, v10), daily mean, maximum and minimum values were archived, and for the accumulated variables (tp, sf) only the daily sum values were archived.
Furthermore, according to the ERA5-Land convention  34  , the accumulated variables are accumulated from 00 UTC to the next 24 hours (i. e. , the accumulation at 00 UTC represents the accumulation during the previous day). Thus, before archiving tp and sf variables, we shifted the time axis backward by one day so that the accumulations at 00 UTC correspond to the actual day, and not the previous day. Calculation of ARCLIM variables from the pre-processed ERA5-Land dataset. For each year during 1950-2021, we read the archived daily ERA5-Land data from CSC Allas and calculated the set of ARCLIM variables (Table  2 ). The reading of archived ERA5-Land files and the calculation of ARCLIM variables were performed with Python programming language and standard packages, such as Xarray  38. The calculation of ARCLIM utilised parallel computing with the Dask tool.
The calculations of the ARCLIM variables were done in the native 0. 1° by 0. 1° regular latitude-longitude ERA5-Land grid. Thus, no spatial interpolation was performed in any stage of the production of the ARCLIM dataset. As ERA5-Land is a land-only dataset, the ARCLIM variables were calculated only for terrestrial areas. Therefore, the grid cells representing the ocean are marked as missing values and are displayed as NaN in the Xarray datasets. After computing the annual values for each year during 1950-2021, we calculated the average conditions for the period 1991-2020 and the trends for 1951-2021 for each variable (Fig. 2 ). The magnitude and statistical significance of the trends were calculated using the Theil-Sen slope estimator  39, 40  and Mann-Kendall trend test, respectively, as implemented in the pyMannKendall Python package  41.
The p-values representing the statistical significance of the trends are provided in the dataset. The trends are calculated starting from 1951, because 1951 is the first complete year representing the winter conditions in the ERA5-Land dataset. Below, we provide short descriptions of the ARCLIM variables. The maps depicting both the average conditions of 1991-2020 and the trends over 1951-2021 for each variable can be found from the supplementary material (Figs. S1-S18 ). In addition to the 14 listed bioclimatic variables, ARCLIM includes annual mean 2-m temperature, annual precipitation, annual snowfall and annual mean 10-m wind speed (Table  2 ).
Thus, even a minor absolute lengthening of the thermal growing season can induce profound consequences in relative terms in the Arctic. Thermal growing degree day sum (GDD). The sum of daily mean temperatures which exceed the 5 °C threshold during the growing season. The growing season is defined as above by the GSL variable. Temperatures below the threshold within the growing season do not decrease the sum. Thermal growing degree day sum integrates the length and overall warmth of the warm season and is a commonly used variable to characterize the overall summer thermal conditions in ecological modeling of cold ecosystems  46, 48. Frost during the growing season (FGS). The sum of daily minimum skin temperatures which are below freezing during the growing season, as defined by GSL. Skin temperature is extracted from ERA5-Land dataset and represents the temperature of the uppermost surface layer of the Earth.
Data Records The ARCLIM dataset is available through Figshare repository  75. The dataset consists of three subsets: (1) the annual values for 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) trends and their associated p-values over 1951-2021. Note that the annual values (1) are aggregated over the whole year or over a specific time period of the year (e. g. , growing season, winter season, etc. ). Thus, for event-type variables, ARCLIM does not provide information on when a single event during the year has occurred. This information would require a daily temporal resolution that would multiply the volume of the data and was therefore not possible to provide in the public repository. The datafiles are provided both in a NETCDF4 and GeoTIFF format. The files for annual values follow the naming format arclim_variable, where "variable" refers to the abbreviation shown in Table  2  (e.
technical Validation The ARCLIM dataset is derived from ERA5-Land, which is a state-of-the-art global dataset for various land applications. As ERA5-Land is still a relatively new dataset, only a few studies so far have validated its performance against in-situ observations. Regarding snow depth, Muñoz-Sabater et al. 34  documented that ERA5-Land performs varyingly compared to ERA5. The ERA5-Land dataset outperforms ERA5 in the US and in complex terrain where ERA5-Land benefits from its higher spatial resolution. Instead, in Scandinavia, ERA5 was found to perform better than ERA5-Land. Räisänen  76  compared mean snow depth in March for 39 winter seasons (1982-2020) from ERA5-Land predictions with two Finnish station observations: Sodankylä and Helsinki. He found high interannual correlation (0.
92-0. 97), but a slight negative bias for Sodankylä (i. e. , observations had less snow than predicted by ERA5-Land). For precipitation, Xu et al. 77  showed that ERA5-Land and ERA5 exhibit similar performance in precipitation metrics on an annual basis, but both datasets were found to underestimate the mean rate of precipitation events over China. Given that total precipitation from ERA5 is used as an atmospheric forcing field in the production of ERA5-Land, their similar characteristics in precipitation metrics is perhaps not surprising. In Finland, winter (November-March) precipitation from ERA5-Land were found to be overestimated by 12% in Helsinki and by 18% in Sodankylä 76 compared to observations, but these discrepancies may be explained by the rain gauge undercatch which is prominent, especially in winter, when precipitation is mostly solid  78.
Despite these slight biases in precipitation, Räisänen  76  reported high interannual correlation (0. 91-0. 96) between ERA5-Land predictions and precipitation observed at meteorological stations in winter, even though ERA5 (and thus ERA5-Land) does not assimilate precipitation observations from Europe  23. To further assess the performance of the ERA5-Land dataset, we conducted a validation for summer (June-August) and winter (December-February) mean temperatures at 2-m height (T2m) using station-based data from the Global Historical Climatology Network Monthly (GHCN-M) database version 4  79. We used the station data which was homogenized (indicated by the suffix ". qcf " in the GHCN-M database) for non-climatic effects such as station relocations or time-of-observation biases. The homogenization was also the reason why we did not use daily station data for validation because the daily observations in GHCN-Daily are not homogenized  80.
As ARCLIM is primarily intended for monitoring the Arctic bioclimate and studying responses of northern land ecosystems to climate change, the validation was restricted for the areas north of 60°N. We calculated the average temperatures for summer (June-August) and winter (December-February) from both weather station observations and ERA5-Land predictions. Only those weather stations that had complete data for at least 60 years during the 1950-2021 period were included in the validation. The ERA5-Land data corresponding to the weather station locations were extracted using the grid cells that contain the location of the weather stations. We noted that part of the stations located at the coastline could not be used, as the nearest ERA5-Land grid point Fig. 2  The production of the ARCLIM dataset step by step.
We present the milestones achieved by the MICRO (MultI-messenger probe of Cosmic Ray Origins) project. This multi-institute project consists of a study of bursting astrophysical sources as candidate sources for ultra-high-energy cosmic rays (UHECRs). We aim at identifying source classes that correlate best with existing observational data (arrival direction distribution, energy spectrum and primary mass composition) and to constrain the origin of UHECRs on the basis of the largest datasets acquired to this day. The study of the secondary fluxes of neutrinos and gamma rays will provide a powerful test of the most suitable astrophysical scenarios. For these achievements, a public software enabling a joint fit of both composition and flux data as a function of energy and direction is being developed.
The objectives specifically addressed in the MICRO project will provide important answers to the leading questions of identifying the sources of UHECRs: (i) how do burst-like signatures such as Gamma Rays Bursts (GRBs) or Active Galactic Nuclei (AGN) flares fit the cosmic-ray data, (ii) how can we constrain the 3D distribution of sources from available UHECR observables, and (iii) could astrophysical high-energy neutrinos, some high-energy gamma rays, and UHECRs come from the same bursting sources. Two different subjects are addressed within the MICRO project: first the development of a public software, enabling a joint fit of both UHECRs composition and flux A list of contributors can be found here: https://micro-uhecr. github. io data as functions of energy and direction; second the modelling the source environment, studying how this can affect UHECR ejection.
The deviances are calculated comparing experimental data with model expectations, where the latter depend on fit parameters. The X max deviance calculation is performed at each energy bin of the corresponding X max distribution. Others modules used in the code are: Draw, which contains all the utilities to produce the plots, Xmax_tools, which is a library containing all the parametrizations for computing X max and σ(X max ). A module called Skymaps for taking into account the arrival directions in the combined fit is under development.
Including the hadronic interaction in UHECRs code A key part of the MICRO project regards the inclusion of the hadronic interaction, i. e. interactions with of UHECRs with other hadrons, in the UHECR propagation codes. Hadronic interactions are negligible for the extra-galactic propagation, due to the low density of the gas in the extra-galactic medium; however, for propagation inside the source environment, they are a crucial quantity to be taken into account. The computation of hadronic process is a new entry in UHECR propagation codes. In fact, as shown in  [14] , where hadronic interactions have been implemented in SimProp, hadronic interactions generate more secondary particles than photo-hadronic process, consequently more photons, neutrinos and a larger number of light nuclear fragments.
Modeling and simulation are crucial in high-performance computing (HPC), with numerous frameworks developed for distributed computing infrastructures and their applications. Despite node-level simulation of shared-memory systems and task-based parallel applications, existing works overlook non-uniform memory access (NUMA) effects, a critical characteristic of current HPC platforms. In this work, we introduce a modeling for complex NUMA architectures and enhance a simulator for dependency-based task-parallel applications. This facilitates experiments with varied data locality models: we refine a communication-oriented model leveraging topology information for data transfers, and devise a more intricate model incorporating a cache mechanism for last-level cache data storage. Dense linear algebra test cases are used to validate both models, demonstrating that our simulator reliably predicts execution time with minimal relative error.
Implementation of L3 caches The tile size in dense algebra is often chosen to fit tasks' datasets into the L3 cache but not the L2 cache, maximizing cache utilization. We model only the L3 caches, as including L2 would significantly increase simulation times without improving precision, as they aren't shared between cores and don't exhibit notable locality behavior. In our L3 cache implementation, we consider the actual cache size and tile size. The cache comprises slots, calculated by CacheSize T ileSize. Data insertion in the cache follows a least recently used (LRU) behavior for evictions, approximating actual cache associativity while ignoring aspects like conflict misses. During task execution, the data associated with the task is locked in the cache. Despite its simplicity and low simulation cost, this model provides satisfactory accuracy for dense linear algebra kernels.
Discussion The COMM+CACHE model refines data locality over the model from Section 5. 2, which assumes all data to be remote. By simulating the occupancy of L3 caches and data transfers between L3 caches and RAM, we enhance the accuracy of predicting application behavior. As tile sizes fit in the L3 cache but not L2, we model only the former, striking a balance between simulation precision and cost. This modeling is suitable for tiled dense linear algebra, while sparse linear algebra would require a more intricate task behavior modeling.
Results Our experiments were conducted on the processors mentioned in Section 4. The Intel system's limited locality effects contrast with AMD's complex interconnect of numerous NUMA nodes and caches. OpenBLAS 0. 3. 10 and LLVM OpenMP runtime were used, with thread binding on the core places and frequency governors set to powersave mode, to avoid uneven behavior of the software and hardware governors. Experiments in this paper, unless specified otherwise, utilized a matrix size of 16384 x 16384 (double precision), namely, 1 GB of data, and approximately 6000 tasks for the Cholesky case. The matrix size, larger than the L3 caches but allowing for substantial data reuse, will therefore require accurate simulation of cache-to-cache transfers. A task tile size of 512 x 512 was chosen, optimal for working sets fitting in L2+L3 caches but not L2 alone.
Precision results Figure  2  presents the Cholesky case results on the Intel platform. The TASK model only considers task computation time and its precision decreases beyond 18 cores, equivalent to the first NUMA domain (or socket). Beyond this, the TASK model exhibits approximately +3% precision error due to its disregard for data locality and transfers. The COMM model improves simulations beyond 18 cores by accounting for memory latencies due to platform contention, but becomes too pessimistic with many cores, as it overlooks data reuse in caches. The COMM+CACHE model, considering L3 cache data movements, consistently achieves under 1% average precision error across all core counts. The TASK model performs well on the Intel platform due to its single NUMA domain per socket, yielding a 0.
8% average precision error, implying minimal NUMA-related effects. However, on the AMD EPYC 7452 as shown in Figure  3 , with 16 NUMA nodes and 16 L3 caches, the TASK model's precision error increases to around +3% on the first socket and up to +10% when utilizing all cores. The increased data transfers due to multiple NUMA nodes on the AMD machine necessitate more accurate modeling. The COMM model lacks accuracy due to underestimation of intersocket communications, leading to up to -10% error. The COMM+CACHE model consistently provides reliable results, with less than 2% error, highlighting the importance of accurately modeling data reuse and L3 cache interconnections. A simplified version of this model, named "COMM+CACHE simple platform", ignoring the machine's hierarchical topology, becomes overly optimistic due to neglecting die-to-die network contention.
The QR factorization results in Figure  4  show that the TASK model is overly optimistic, and the COMM model is pessimistic. The COMM+CACHE model  accounts for L3 caching effects, but fails to consider bandwidth variation effects on the application kernels when many cores are used, making it optimistic in these scenarios. A more refined model within SimGrid, intertwining execution time and memory transfers, could address this but is beyond this paper's scope. Our models have demonstrated consistent accuracy across various linear algebra algorithms and different matrix sizes. For matrix sizes of 12288 x 12288, 16384 x 16384, 20480 x 20480 and 24576 x 24576, which means an increase in task and data transfer volumes due to larger matrices, the COMM+CACHE model remains reliable with an average precision error of 1.
Use case: experimenting with cache-aware schedulers The preceding analysis confirms that the COMM+CACHE model offers precise simulated execution times accounting for both NUMA and cache effects, enabling realistic and reproducible scheduling research for optimizing cache affinity, similar to prior work on GPU-based platforms  [30, 1]. We introduced a proofof-concept cache-aware OpenMP task scheduler that prioritizes tasks with data operands already in the CPU core's L3 cache. This reduces L3 cache misses and overall data transfers. Performance results, displayed in Figure  6 , reveal that the COMM+CACHE model closely simulates native executions. Utilizing the refined scheduler in the simulator exhibits a performance improvement that escalates with the number of cores used, demonstrating the heuristic's scalability benefits on a large multicore system.