dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the test dataset is used to assess the final, unbiased performance of the model. The validation dataset is unique concerning the test dataset because it is not kept hidden from the preparation of the model, however it is instead used to give an adequate performance of the ability of the last tuned model. At the very beginning only, these three sets of data are separated and should not be mixed at any point in time. Once the dataset is ready for training, the next step is algorithm selection and, finally, training. This step is iterative, where multiple algorithms are tried to obtain a trained model. For each algorithm, one has to optimize its hyperparameters on the training set and validate the model on the validation set, which is a time-consuming task (and implemented by a data scientist).
Multiple iterations are required for acquiring the best model, and keeping track of all iterations becomes a hectic process (often, this is manually done in excel sheets). To regenerate the best results, one must use the precise configuration (hyperparameters, model architecture, dataset, etc. ). Next, this trained model is tested using specific predefined criteria on the test set, and if the performance is acceptable, the model is ready for deployment. Once the model is ready, it is handed over to the operations team, which handles the deployment and monitoring process (usually implemented by an Operation engineer) such that model inference can be done. In the above process, all steps are incremental (similar to the waterfall software development style). These steps together form an ML pipeline as shown in Figure  1c.
Model and Application Monitoring The constant monitoring of the whole project stack, e. g. , model and application performance, as well as infrastructural circumstances and (most importantly) the data utilized by the model, is the foundation of a robust ML-based product. In utilizing metrics suggested in Section 5. 1. 5, many operational errors and shortcomings can be compensated in advance. With a focus on tracking the data through the pipeline operations  [11] , graph databases can help in managing and maintaining linkage between data objects and the respective assertions.
TP-3: Tracking ML is intrinsically approximate, and the development process is iterative. As the number of iterations increases, the information necessary for reproducing that experiment also grows. Tracking in ML is a process through which all modeling-related information, i. e. , the algorithm, its hyperparameters, and the observed performance based on some metrics, could be saved automatically in a database. This collection of meta-information helps compare algorithms, runs, and performance metrics. Tracking the training runs, configured hyperparameters, and the corresponding model quality metrics are the overall foundation of the model's quality. Especially when implementing multiple different ML models as a solution to a problem in parallel, comparing results of measurements may help in selecting feasible model candidates.
Polyaxon Polyaxon  [60]  is a tool that focuses on two things, i. e. , first, it promotes collaboration among ML teams, and second, it focuses on the complete life cycle management of ML projects. Unlike MLflow, it Deals with user management and offers code and data versioning support. The platform is not fully open-sourced, and it needs Kubernetes. Therefore, it is not fully architecture agnostic. It provides an interface to run any job on a Kubernetes cluster. The platform requires greater setup time and might be unnecessary for small teams.
Kedro The main focus of kedro  [61]  is pipeline construction and providing a basic project structure for ML projects (scaffolding). It offers the capabilities of defining pipelines by using a list of functions, and it also provides inbuilt file and database access wrappers. It also offers code versioning functionality with pipeline visualization (with an extra python package, 'Kedro-Viz'). However, with the pipeline visualization feature, there is no functionality to see real-time progress monitoring. + MM-1 MM-2 + DP-1 o + GR-2 GR-3 GR-4 Polyaxon (v1. 9. 5)  [60]  An enterprise-grade platform for building, training, and monitoring large-scale deep learning applications. + DPR-5 ++ TP-2 TP-3 TP-4 TP-5 ++ MM-1 MM-2 + DP-1 + OMP-1 + GR-1 GR-2 GR-3 * Kedro (v0. 17 TFX is an end-to-end platform for deploying production ML pipelines.
DVC DVC is a Version Control System for ML Projects  [68]. The tool is capable of tracking models, datasets (including large dataset 10-100 Gb vs. 2 Gb limit on Github), and pipelines. It is cloud-and storage-agnostic, i. e. , the datasets can be stored, accessed, and versioned locally or on some cloud platform. It is git compatible and can track experiments but does not offer a dashboard. It works by creating the file '. dvc' in the project root, where meta information such as storage location and format of the data is stored. For unstructured data, the changes are tracked as new versions by themselves, which requires high storage capacity. It is a very lightweight tool which offers great features with the minimum effort required for integrating it into any Gitflow workflow.
CML The tool is for implementing CI/CD/CT functionality in ML projects. It is ideal if one is using GitFlow for data science, i. e. , using git ecosystem and DVC for ML development lifecycle. Every time some code or data is changed, the ML model can be automatically retrained, tested, or deployed. It relies on GitHub actions and GitLab CI but offers an abstraction on the top for better usability. It is intended to save model metrics and other artifacts as comments in GitHub/Lab.
This might cause extra redundancy and lead to unwanted complications. Furthermore, tools are developed for different scalability requirements. Therefore, there is no single best tool for one particular phase for all the organizations. Another vital point to keep in mind when selecting tools is the integration support a tool offers. Some tools have their APIs only in python. In contrast, some offer support for JAVA and R. The support of different intermediate libraries required for model building is also an essential factor. Additionally, due to the lack of interactions between the production and development teams, some extra complications might occur in the future. The production team needs to use the tool stack, which will be in sync with the development team. For example, if the development team chooses a platform such as TFX, then the deployment team is somewhat limited to the tf serving compatible solutions.
Exemplar Workflow: Partially Automating Object Detection with MLOps Tools In this section, an example of basic level-1 MLOps workflow as discussed in Section 3. 8 for "Defect detection on objects with deep learning" is shown by coupling some of the tools from the previous section. This workflow aims to showcase how we can combine open-source tools for a use case where newer models are not required frequently and there is no CD. Such an example could be valuable for people in the early stages of ML development. Most organizations already have code versioning tools such as Git and GitHub in their tool stack. This example extends the push-based development process, which has already been adopted in the industry. With fewer integrations, even the older notebooks and data science code can be reused for designing such a simple workflow.
The model serving is just limited to saving ML models as a python function at a fixed location. Furthermore, these tools do not require any significant additional installations, and most of them are already commonly used for software development. This ensures reusability, and in order to automate an old model training script, one only has to integrate MLflow API and set up a new DVC repository. Regarding the automation level of a workflow, also note that an MLOps workflow can be distinguished based on the maturity level of the data science process followed in an organization and also on how many end users interact with the ML model. A lower maturity level represents a small team working on data science with manual ML workflow execution (in most cases, an individual data scientist). In such scenarios, a model is not deployed to multiple users, and there is no monitoring of the deployed service.
Critical Discussion Many organizations have mastered developing a model that works on a static dataset, but realizing real value from a model in the production environment is still a challenge. This challenge exists because the process of deploying the already developed model is not straightforward. In addition to the incremental complexity of the development environment, there is also dependency on the data. Information regarding the development environment, dataset, and hyperparameters is necessary for reproducing the same model. As the number of experiments increases, the amount of information required to be stored increases dramatically. All this information can not be logged manually, and there is a need for a tools stack that can ease the work of a data scientist and promote shareability between different data science teams.
This is visible from the workflow defined in Figure  3. For example, suppose Git and DVC are not used for data and code versioning. In that case, the development team has to perform versioning by manually saving different folders with different datasets or outsourcing the complete data handling to a third party. Another complexity involved with images is the quality assurance of the data. Image quality assessment (IQA) is usually divided into two sections: Full Reference-based IQA (FR-IQA) and No Reference-based IQA (NR-IQA). The methods under NR-IQA require an additional learned model to quantify the quality of the images and have higher complexity. On the other hand, FR-IQA methods are less complex and have faster implementations. Some of the most common FR-IQA methods are presented in Table  1.
For this workflow, the SSIM metric was chosen to perform automated quality assurance of the data due to the fact that it outperforms standard metrics such as MSE and PSNR. After data preprocessing and versioning, the following requirement is producing reproducible training experiments. This is achieved through MLflow's tracking functionality. In the above case, the YoloV5 large model is used, and the Hyperopt library optimizes hyperparameters. With very few tweaks, the other versions of YOLOv5 object detection models can also be used. This is also true if one decides to use any other optimization strategy such as genetic algorithms for hyperparameter optimization. The portrayed automated workflow helps in organizing this and other important information in a relational database. This information is vital for knowing what worked and ensuring reproducibility.
These metrics are the key to obtaining a continual learning solution. From the comparison of different tools depicted in Table  5 , it can be seen that no single tool has the capability of realizing a fully automated MLOps workflow. Different tools have overlapping features, increasing redundancy. For example, DVC is not just a tool for data versioning; instead, it can also be used to track machine learning experiments, but due to no dashboard to visualize the experiments, MLflow is often chosen over DVC for this task. Furthermore, there is also an expansion of the skills required by different actors in the ML lifecycle to create any real business value. For example, earlier, the role of data scientist was limited to experimentation and modeling. However, it can be seen from Figure  2  that close collaboration with operation or software engineers is needed for the successful deployment of an ML model.
In future work, the potentials of AutoML capabilities for MLOps workflows and their deep integration and orchestration within such operations could be investigated. By revealing the combinations of state-of-the-art software, their infrastructural restrictions and requirements could be focused on. As the tool variety for supporting the different phases in ML projects is constantly evolving, many operational tools are expected to be refined in the future. Exciting and novel offerings for interconnection capabilities with other tools and a more holistic metric support may be worth further investigation. The implementation of expert interviews regarding their experience with interconnecting and integrating various tools for creating complete MLOps pipelines and ecosystems are of interest, too.
This led to the accumulation of an acceptance-corrected livetime of 44. 9 hr, obtained after selecting all runs with pointing direction within 3 o from the HGPS position of HESS J1702-420 (l = 344. 30 o , b = -0. 18 o ) and averaging over a 0. 5 o circle centered at the same location. Observations were processed with the H. E. S. S. analysis package (HAP), applying a Hillas-type shower reconstruction  (Hillas 1985)  and the multi-variate analysis (MVA) technique  (Becherini et al. 2012)  for efficient γ/hadron discrimination. Preselection and MVA discrimination cuts were optimized to improve the collection area at high energies (E > 1 TeV), assuming a γ-ray spectral index of 2. 3. The reduced data and instrument response functions 3  (IRFs) were exported to FITS files complying with the standard format developed by  Deil et al.
(2017a). Finally, all high-level analysis results -that is sky maps and spectra -were obtained using gammapy (version 0. 17), an open source python library for γ-ray astronomy  (Deil et al. 2017b; Nigro et al. 2019; Deil et al. 2020). The analysis cross-check was also performed with gammapy, by applying the same high-level analysis pipeline to data that were reduced according to an alternative low-level chain of calibration, reconstruction and γ-hadron separation methods  (Ohm et al. 2009 ). Independent crosschecks for the classical morphological and spectral analyses presented in Section 2. 2 and Appendix A were also carried out using the standard HAP software.
As a result of the region's observation history (see the introduction of Section 2), standard run selection criteria led to a heterogeneous set of observations in terms of array response, zenith angles and source offsets from the pointing direction. We therefore separated observations obtained before and after the 2017 camera upgrade, for which different IRFs have to be used, and grouped together observations with similar zenith and offset values. More details on the four groups of observations that were defined are reported in Table  1. We stacked observations within each group, thus obtaining four independent datasets. This choice represents a good compromise between the timesaving, due to a decrease in the number of degrees of freedom, and information loss, due to the IRFs averaging, that are connected with the data stacking procedure.
g. ,  Mohrmann et al. (2019) ). With this setup, we performed a joint-likelihood analysis of the four independent datasets, each one having its own FoV Obtained by averaging over a 0. 5 o -radius circle centered at the center of the HGPS position of HESS J1702-420. We observe that runs belonging to the second and third groups were included to better contrain the background level in the source region. The fourth group contains all (and only) runs taken after the 2017 camera upgrade  (Ashton et al. 2020). Table  1 : Details on the four groups of H. E. S. S. observations, with similar pointing zenith angle and source offsets from the pointing direction, that were used for the 3D likelihood analysis. background model but all sharing the same source model. This means that we summed the four dataset-specific log-likelihood values, and maximized the total resulting likelihood with respect to the model parameters.
The γ-ray spectra of both components are shown in Figure  2 , together with spectral points computed under a power law assumption and reoptimizing all the nuisance parameters of the model -see Table H. 1 for details. We adapted the binning of the spectral energy distributions to obtain approximately equal counts in each bin. HESS J1702-420B is the brightest component up until roughly 40 TeV, where HESS J1702-420A eventually starts dominating with its Γ ≈ 1. 5 power law spectrum up to 100 TeV. The second to last spectral point of HESS J1702-420A (HESS J1702-420B), covering the reconstructed energy range 64 -113 TeV (36 -113 TeV), is significant at 4. 0σ (3. 2σ) confidence level. We explicitly point out that, based on this dataset, it is impossible to tell whether HESS J1702-420A and HESS J1702-420B 10 1 10 2 E γ [TeV] 10 -13 10 -12 E γ 2 × dN/dE γ [TeV cm -2 s -1 ] HESS J1702-420B HESS J1702-420A Fig.
Multi wavelength observations Even in the absence of a multi wavelength detection of HESS J1702-420, low-energy observations can help to constrain the TeV emission scenarios. Section 3. 1 summarizes a dedicated analysis of archival Fermi-LAT data in the HESS J1702-420 region, while Section 3. 2 reports our considerations on the surrounding ISM and Section 3. 3 discusses archival Suzaku measurements in the context of the new H. E. S. S. results.
2). This choice of integration region reflected the approximate shape of the TeV source, from  Aharonian et al. (2008). Based on the new H. E. S. S. observations and improved analyses presented in this paper (Section 2), we repeated the ISM analysis, with the same radio dataset and approach as in  Lau et al. (2018)  but adopting a smaller extraction window to focus on HESS J1702-420A. Our conclusions agree with  Lau et al. (2018) , in that dense target material, although present at various distances along the line of sight (see Figure  H. 3), does not exhibit any obvious correlation with the VHE γ-ray maps (see  Figure H. 4). In particular, no hydrogen cloud clearly correlates with HESS J1702-420A or HESS J1702-420B.
The upper left (right) panel of the figure shows the measured (model-predicted) counts map, obtained after stacking the four individual datasets and integrating over the energy axis above 2 TeV. Diagonal line hatches represent portions of the RoI that were excluded from the likelihood computation (Section 2. 1. 1). The measured counts map is well matched by the prediction, since the spatial distribution of the significance of model residuals (lower left panel) does not contain significant structures. The histogram of significance values (lower right panel) closely matches a standard normal distribution, as expected if residuals are only due to statistical Poisson fluctuations. Additionally, the spatial distribution of model residuals in three independent energy bands is shown in Figure H. 1.
A horizontal field direction demands the DC current in opposite directions in the two coil pairs. Changing the ratio of currents circulating in each pair allows to create a field B 0 inclined by any angle  in the ZY plane. The homogeneity of the central field region is largely improved with the addition of two compensation coil pairs (B1c and B2c), connected in series with their respective main coils to preserve the proportionality between their magnetic fields when the current is changing. Optimization. The big advantage of the proposed ''iron-free'' coil system is the possibility to calculate the magnetic field in any point of space using known analytical solutions. This makes a numerical modeling and optimization of the magnetic coils highly effective and fast. To simulate the magnetic field created by each DC coil and to calculate, the effective relaxation rate, we used the package Radia  [32]  working under ''Wolfram Mathematica''  [33].
For the optimization of the coil sizes and their positioning, we used a package of global optimization in Mathematica based on the ''Differential Evolution'' algorithm  [34]. The magnetic field in the central region is built as a superposition of fields created by 8 DC coils. The twofold rotation symmetry of the coil assembly around the -axis reduces the number of independent coils to 4. The field in an arbitrary point due to a circular coil of finite size depends on the outer radius of the coil  out , the height and width of the coil section, h and w, the position of the coil center p along the coils symmetry axis, the current density I and the inclination of the symmetry axis. Based on this 4-coil pair system, we build a numerical field map in the toroidal region of interest  where the polarizer and the analyzer cells are installed and calculate the volume averaged relaxation rate given by Eq.
The first dataset corresponds to the inputs of MAELIA ( http: //maelia-platform. inra. fr/ ), a spatial agent-based simulation platform that was used to support an iterative design and assessment of scenarios to redesign cropping systems. The second dataset corresponds to the outputs of MAELIA simulations and the associated indicators at the farm, group and territory level. The data comprise multiple shape and csv files characterizing the edaphic-climatic heterogeneity of the territory and cropping systems, farmers' crop management rules (IF-THEN rules) and general information about the farms (e. g. crops, agricultural equipment, average crop yields). Data is reported for the baseline situation and three exchange scenarios containing different innovative cropping systems codesigned by scientists, agricultural advisers and the farmers.
Value of the Data • This dataset offers a unique set of detailed and spatially explicit data on 7 farms including a description of crop management strategies through decision rules. • This dataset can be used by researchers to perform a multi-criteria assessment of crop diversification scenarios considering socio-ecological and economic dynamics. • As MAELIA is an open-source platform ( http://maelia-platform. inra. fr/ ) this dataset can be used to define and simulate new scenarios. • This data allows evaluating self-sufficiency, sustainability and vulnerability of cropping systems from field and farm to group of farms levels. • This data permits to obtain several socio-economic (e. g. gross margin) and environmental indicators (e. g. nitrogen use and quantity of pesticide active ingredient applied) to evaluate performance at various scale (from field to territory).
Data Description We provide two datasets in this paper that were used in the research article "Fostering local crop-livestock integration via legume exchanges using an innovative integrated assessment and modelling approach: MAELIA"  [1]. Firstly, a complete dataset of the inputs necessary to run MAELIA ( http://maelia-platform. inra. fr/ ), a high-resolution agent-based platform for IAM (Integrated Assessment Modelling) of agricultural landscapes considered as socio-agroecological systems  [2]. Secondly, the raw outputs of MAELIA simulations and the respective indicators at the farm, group and territory level. Overall, it corresponds to an integration of generic data and local knowledge, as well as the data for the simulated baseline situation and the three scenarios considered, as described below in detail.
MAELIA input dataset This dataset includes spatially explicit data, in the format of shapefiles ( *. shp ), concerning the administrative divisions, soil mapping units, meteorological zones (8 × 8 km) and, for each farm, field blocks (herein islets) and fields. To avoid any sort of identification, we have anonymised fields that could directly be linked to the farmer. It also includes local and expert-based data that were gathered through direct collaboration with the parties involved in the study, such as the farmers and advisors of local chamber of agriculture. Lastly, it contains the observed crop sequences within each field, crop management strategies described through decision rules, equipment used, production (yield) and economic information (prices and costs). The description of each variable, the unit of measurement, the nature of data and respective units are presented in Tables 1 , 2 , 3 , 4 , 5 , 6 , 7 and 8.