dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation (SPETABARU) We have implemented our speculation method on top of a lightweight C++ RS that was originally less than 3,000 lines. 1 We use modern C++17 and advanced meta-programming to look at data dependencies at compile time. The RS supports the data access modes of read, write, atomic_write, and commute (for commutative operations). We also use a dynamic array view, which allows for having an unlimited number of known dependencies at execution time. It is possible to use lambda/anonymous functions as tasks, as shown in Code 1. Code 1: SPETABARU example. The example of an uncertain task is given in Code 2. Compared to a regular task, the keyword SpMaybeWrite replaces SpWrite and the function returns a boolean to inform the RS if a modification occurs. Code 2: Example of creating an uncertain task in SPETABARU.
Software/Hardware We used an Intel Xeon E5-2680 v3 with a frequency of 2. 5 GHz and 2 sockets of 12 cores each. We compiled using the GNU compiler version 7. 2, and we bound the threads to the cores following a compact strategy, e. i. the threads are pinned to contiguous cores. We use SPETABARU available on the public master branch at tag v0. 15. 2 1 Task A : write(val1),write(val2),write(val3) 2 Task B uncertain : maybe-write(val1) -> false 3 Task C uncertain : maybe-write(val1) -> true 4 Task D uncertain : maybe-write(val2) -> true 5 Task E uncertain : maybe-write(val3) -> true 6 Task F :.
CONCLUSION In the current paper, we provided the first results of using speculation in task-based RS's. We described a general pattern and the algorithm of a predictive-oriented approach. Our RS, SPETABARU, is able to execute speculative task flows, and we demonstrate that the obtained speedup for both the MC and REMC is close to the theoretical one of 30%, when the probability to accept changes is 1/2. The mechanism we proposed can be easily incorporated in any other runtime system. However, the fact that we used the C++ language is an important asset because it makes it possible to generate functions capable of copying   (selecting) user's data. The implementation of our system using a low-level language would require using advanced programming from the user perspective such as function pointers.
ACKNOWLEDGEMENTS Work by  Berenger Bramas  was partially done at the  Max Planck Computing and Data Facility (MPCDF), Garching, Germany. Experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine.
We developed a holistic intelligent decision-support system (IDSS) to provide decision-support for all steps in planning, managing and optimizing water quality monitoring programs (WQMPs). The IDSS is connected to a previously developed database, Enki TM. The IDSS integrates tacit and explicit knowledge on WQMPs to standardize decision making and to make decisions transparent and transferable. The optimization features of the IDSS were tested on a lake and a river WQMP from two case studies in Canada. We illustrate how the IDSS provides decision support to understanding the underlying rationale of the existing WQMPs, validating and storing data, selecting optimization procedures proposed in the literature, applying the optimization procedures and finalizing the optimization procedure. We demonstrated that the IDSS/Enki TM is necessary to take and document decisions during all phases of a WQMP to obtain a clear idea of when and why changes are made and determine actionable tasks in the optimization process.
The conceptual model of such an Intelligent Decision-Support System (IDSS) was developed and connected to a previously developed database Enki TM  [12]. The purpose of this paper is to test some of the features of the IDSS implemented and test decision-support workflows prior to implementation. More precisely, the optimization process proposed by the IDSS is tested herein using the data of two watershed case studies from the province of Quebec, Canada. The optimization process is based on a selection of optimization objectives elicited through a participative approach conducted on both case studies  [16]. The IDSS was designed to propose existing optimization methods from the literature which correspond to different types of optimization objectives, such as evaluating the number of sampling sites, water quality parameters and sampling frequency  [13, [17] [18] [19].
Methodology The global workflow of the entire optimization process of a WQMP is illustrated in Figure  1:  (1) Identify stakeholder concerns through a participative approach to elicit knowledge needs supported by a public participation geographical information system (PPGIS), (2) identify new monitoring and optimization objectives and validate the attainment of past monitoring objectives, (3) understand the rationale behind the existing WQMP, (4) integrate data from the existing WQMPs into the database connected to the IDSS,  (5)  interrogate the IDSS on optimization suggestions based on the input from the first three points,  (6)  propose optimized WQMPs and validate proposals with the decision-makers of the existing WQMPs. Stakeholders, as illustrated in this figure, are defined as citizens and representatives of organized stakeholders (e.
5), 6. Is instrumental in the redesign of the WQMPs (Results and discussion Section 3). The IDSS was tested with two watersheds in the province of Quebec, Canada. The optimization objectives were specific to each watershed. The first to be tested was Lac Saint-Charles, a lake WQMP (size of the watershed: 198 km 2 -hereafter W1). This lake is the main drinking water reservoir of the city of Québec where an extensive cyanobacteria monitoring program was implemented in 2011 to obtain a spatio-temporal portrait of the cyanobacteria community in the lake  [21]. This WQMP is operated by the Association pour la protection de l'environnement du lac Saint-Charles et des Marais du Nord (APEL) and financed by the city of Québec. Given the cost of the analysis of the cyanobacteria species of nearly 300 samples per year, the main optimization objective here was to evaluate whether a reduction in sampling frequency, sampling points and the number of samples per sampling station for cyanobacteria (up to three at different water depths) would result in a loss of information.
The general characteristics of the W1 WQMP are presented in Figure  2. The IDSS was tested with two watersheds in the province of Quebec, Canada. The optimization objectives were specific to each watershed. The first to be tested was Lac Saint-Charles, a lake WQMP (size of the watershed: 198 km 2 -hereafter W1). This lake is the main drinking water reservoir of the city of Québec where an extensive cyanobacteria monitoring program was implemented in 2011 to obtain a spatio-temporal portrait of the cyanobacteria community in the lake  [21]. This WQMP is operated by the Association pour la protection de l'environnement du lac Saint-Charles et des Marais du Nord (APEL) and financed by the city of Québec. Given the cost of the analysis of the cyanobacteria species of nearly 300 samples per year, the main optimization objective here was to evaluate whether a reduction in sampling frequency, sampling points and the number of samples per sampling station for cyanobacteria (up to three at different water depths) would result in a loss of information.
The general characteristics of the W1 WQMP are presented in Figure  2. The second watershed was the watershed of the river, Rivière du Nord (hereafter W2), north-east of Montreal. In this watershed (size of the watershed: 2222 km 2 ), a river monitoring program was implemented in 2009 to assess water quality to protect public health and follow nutrient concentrations and erosion  [22]. This WQMP is operated by the watershed organization of the Rivière du Nord river  (Abrinord)  and financed by several municipalities within the territory. Given the vast territory, financial limitations and new monitoring objectives, the optimization objective was to evaluate whether a reduction in the number of sampling sites would be possible. In addition, the objective was to find out whether sufficient samples had been taken in wet weather conditions and whether the sampling frequency was sufficient to respond to the initial monitoring objectives.
The objective of capturing wet weather conditions is to capture water quality impairment due to runoff from non-point pollution sources  [23]. The general characteristics of the W2 WQMP are presented in Figure  3. The second watershed was the watershed of the river, Rivière du Nord (hereafter W2), north-east of Montreal. In this watershed (size of the watershed: 2222 km 2 ), a river monitoring program was implemented in 2009 to assess water quality to protect public health and follow nutrient concentrations and erosion  [22]. This WQMP is operated by the watershed organization of the Rivière du Nord river  (Abrinord)  and financed by several municipalities within the territory. Given the vast territory, financial limitations and new monitoring objectives, the optimization objective was to evaluate whether a reduction in the number of sampling sites would be possible.
Understanding the Initial WQMPs and Data Validation and Integration Figure  4  illustrates the questions raised by the IDSS in order to understand and evaluate the existing WQMP. These questions include queries on the initial incentives and stakeholders that led to the WQMP and the underlying objectives. Further questions focused on the rationale behind the choice of sampling sites and water quality parameters, sampling frequency, laboratories, probes, etc. All the answers to these questions were documented in the system. Understanding the initial WQMP is closely linked to the data validation and integration procedure. Figure  5  provides an illustration of the dashboard of the W1 WQMP, as well as the features that may be consulted to understand the WQMP and the available data sets.
Understanding the Initial WQMPs and Data Validation and Integration Figure  4  illustrates the questions raised by the IDSS in order to understand and evaluate the existing WQMP. These questions include queries on the initial incentives and stakeholders that led to the WQMP and the underlying objectives. Further questions focused on the rationale behind the choice of sampling sites and water quality parameters, sampling frequency, laboratories, probes, etc. All the answers to these questions were documented in the system. Understanding the initial WQMP is closely linked to the data validation and integration procedure. Figure  5  provides an illustration of the dashboard of the W1 WQMP, as well as the features that may be consulted to understand the WQMP and the available data sets. The procedures to understand the rationale of the initial WQMPs, as well as the data validation and integration process were tested on the lake data from W1 and on the river data from W2.
Also, the continuous updating of the data was tested on users from both APEL and Abrinord. The data validation and integration processes, based on six steps, are illustrated and described in Figure  6. The procedures to understand the rationale of the initial WQMPs, as well as the data validation and integration process were tested on the lake data from W1 and on the river data from W2. Also, the continuous updating of the data was tested on users from both APEL and Abrinord. The data validation and integration processes, based on six steps, are illustrated and described in Figure  6. All data for the W1 WQMP were batch imported from the Enki TM prototype, an ACCESS database, built and tested previously  [12]. Data included information from sampling contexts, laboratories, and probes from 2011 to 2015.
Data from 2016 were integrated directly into Enki TM during the testing phase. For the cyanobacteria monitoring program, 15 sampling sites were created and some 2000 sampling contexts and more than 100,000 probe and laboratory records were entered. Data from the W2 WQMP were stored in an Excel file with several sheets created in 2009 and continuously adapted and updated. This Excel file combined information on the sampling sites, field observations and the results of the laboratory analysis. However, not all information needed to complete the onboarding process to Enki TM was available in these files to complete step 1 (system setting) (Figure  6 ). For instance, the sampling site justifications had to be retrieved from old paperwork related to the W2 WQMP. Also, the results from the laboratories had been transferred manually into the Excel files.
Several incongruities were detected when comparing the original files from the laboratories with the Excel files serving as data storage for the WQMP. In addition, some of the changes in laboratories and analysis methods were not documented in these files. Therefore, we opted to integrate all sampling contexts manually by retrieving the missing information from different source files, such as probe data results and precipitation regimes. Laboratory data was then batch imported from the original Excel files of the laboratories. The origin of the data was attributed to the respective laboratories. All in all, 66 sampling stations and more than 2500 sampling contexts were created. Approximately 10,500 probe and laboratory records were batch imported and connected to the sampling contexts and sampling stations.
All data for the W1 WQMP were batch imported from the Enki TM prototype, an ACCESS database, built and tested previously  [12]. Data included information from sampling contexts, laboratories, and probes from 2011 to 2015. Data from 2016 were integrated directly into Enki TM during the testing phase. For the cyanobacteria monitoring program, 15 sampling sites were created and some 2000 sampling contexts and more than 100,000 probe and laboratory records were entered. Data from the W2 WQMP were stored in an Excel file with several sheets created in 2009 and continuously adapted and updated. This Excel file combined information on the sampling sites, field observations and the results of the laboratory analysis. However, not all information needed to complete the onboarding process to Enki TM was available in these files to complete step 1 (system setting) (Figure  6 ).
For instance, the sampling site justifications had to be retrieved from old paperwork related to the W2 WQMP. Also, the results from the laboratories had been transferred manually into the Excel files. Several incongruities were detected when comparing the original files from the laboratories with the Excel files serving as data storage for the WQMP. In addition, some of the changes in laboratories and analysis methods were not documented in these files. Therefore, we opted to integrate all sampling contexts manually by retrieving the missing information from different source files, such as probe data results and precipitation regimes. Laboratory.
We applied this model to the W2 WQMP to respond to the optimization objective: Verify whether sampling frequency was sufficient and whether a different type of sampling frequency should be adopted. We also applied the Kruskal-Wallis test to the data of W2 to verify whether the fecal coliform (FC), total phosphorous (TP) and total suspended solid (TSS) concentrations were different depending on the observed precipitation classes of W2  [25]. The objective was to verify whether sufficient data were collected in wet weather conditions, as this information would have an incidence on changing from a fixed sampling calendar as is presently the case in W2, to a flexible sampling calendar in order to obtain more data during wet weather conditions. The objective of sampling in wet weather conditions was to obtain water quality data following run-off.
This information was also entered in the sampling contexts of Enki TM. It has to be noted that the definitions of wet weather differ in the literature and are adapted to specific monitoring objectives, types of data analysis, land use characteristics and physical features of a given watershed. For instance, non-point source monitoring aims at monitoring effects of land use on water quality in a variety of weather conditions and point-source monitoring may aim at monitoring the retention capacity of a sewer system. The runoff in urbanized areas is greater than in forested areas. The effect on water quality of a given amount of rain will thus be more immediate and the criteria should be adapted accordingly. The same applies to differences in slope. The wet-weather conditions can be defined through time spans and precipitation (in mm).
Decision Support During the Optimization Procedures The IDSS provides decision support for questions related to the data used for the optimization. The essence of these questions is illustrated in Figure  8. The purpose of these questions is to support the person in charge of the statistical analysis and validate whether any kind of change in the WQMP has had an effect on data series, data integrity, and comparability of data. This also includes understanding outliers through the integration of field observations in the data series, thus contributing to decisions of necessary outlier elimination for some types of analysis. It also includes decisions to be made on reconstructing missing data. For W1, the optimization method which was applied can be consulted in Legendre and Gallagher (2001).
For W2, the optimization method can be consulted in  Beveridge et al. (2012)  and in Appendix A. person in charge of the statistical analysis and validate whether any kind of change in the WQMP has had an effect on data series, data integrity, and comparability of data. This also includes understanding outliers through the integration of field observations in the data series, thus contributing to decisions of necessary outlier elimination for some types of analysis. It also includes decisions to be made on reconstructing missing data. For W1, the optimization method which was applied can be consulted in  Legendre and Gallagher (2001). For W2, the optimization method can be consulted in  Beveridge et al. (2012)  and in Appendix A.
Results and Discussion In the first step, the challenge was to understand the initial WQMPs and to integrate the data into the database Enki TM connected to the IDSS. The fact that the data were already entered in the prototype of Enki TM facilitated the process of integrating the data from W1. However, the purpose of the questions was to understand the rationale behind the selection of the lake sampling sites, the decisions taken on the sampling strategy at each site, as well as understand the sampling objectives which contributed greatly to documenting the rationale and making it transferable to WQMP managers and organizations that would like to increase the value of the data for other studies. This process also contributed to documenting the limitations of some sampling sites, such as shoreline sampling sites.
greatly contributed to obtaining a holistic view of the WQMP, as well as the specific rationale of decisions taken regarding the W2 WQMP. Data and metadata could now be efficiently consulted, visualized and retrieved. This contributed greatly to the choice of optimization methods: not only must they be based on specific optimization objectives (elicited by a participative approach), but also on the available data sets. The data integration steps showed that the data management question is not always clearly addressed at the outset of a WQMP. This may be due to the fact that challenges related to data management are underestimated and are only addressed when the data needs to be analyzed. In addition, for lack of resources, solutions, and knowledge on data management questions, data management is not always addressed adequately.
• During the process of understanding the rationale of the WQMP in the W1 watershed, many other monitoring objectives were identified for this lake. For any drastic change in the current strategy, the incidence on the other objectives must be evaluated. These objectives can be retrieved from the database, and the IDSS proposes the validation questions accordingly. The WQMP revealed that it is possible to obtain a consistent spatio-temporal portrait of the cyanobacteria community with the proposed sampling strategy and sampling frequency and recurrence. In addition, sufficient data were collected to attribute the species to the trophic state of the lake. It is also possible to eliminate one station where generally two samples were taken (C01), for a total of 20 samples per year. In addition, it was shown that the sampling could be reduced to a four-week interval.
Optimization of the W2 WQMP -Results and Discussion After the data validation and integration process, it was much easier to gain an overview of the W2 WQMP datasets and make choices concerning the optimization processes. For this WQMP, from 2328 sample contexts (sampling outings) taken on 66 stations, a maximum of 14 water quality parameters could be observed and field observations would include precipitation classes and ambient temperature. However, only for TSS, TP, FC and precipitation classes were sufficiently consistent data sets available for all these stations. The proportions of non-available data (NA) were only 6. 4% (TSS), 7. 2% (FC), and 18. 4% (TP) respectively. The missing information on precipitation classes which could not be retraced represented 12. 8%. Following validation with the WQMP managers, the optimization was conducted on these parameters and on all stations.
Conclusions A major issue in WQMPs is ensuring that decisions related to planning, management, and optimization follow some standard procedure and that decision making is guided by a set of questions that lead to a certain standardization or at least render the decision steps transparent to the people working for the WQMP. In this paper, we have strived to demonstrate how an IDSS connected to a database (Enki TM ) can significantly contribute to the challenge of optimizing WQMPs. We were able to show this for several steps of the optimization process: of decision during the planning, management, and optimization phases in order to gain a clear idea of when and why changes are made and obtain actionable tasks in the optimization process that are documented and monitored by dashboards. Several of the features of the IDSS were already implemented, and others can now be implemented since the decision-support trees were tested through the case studies.
Dielectrophoresis (DEP) and electrorotation (ROT) are two electrokinetic phenomena exploiting nonuniform electric fields to exert a force or torque on biological particles suspended in liquid media. They are widely used in lab-on-chip devices for the manipulation, trapping, separation, and characterization of cells, microorganisms, and other particles. The DEP force and ROT torque depend on the respective polarizabilities of the particle and medium, which in turn depend on their dielectric properties and on the field frequency. In this work, we present a new software, MyDEP, which implements several particle models based on concentric shells with adjustable dielectric properties. This tool enables the study of the variation in DEP and ROT spectra according to different parameters, such as the field frequency and medium conductivity.
Such predictions of particle behavior are very useful for choosing appropriate parameters in DEP experiments. The software also enables the study of the homogenized properties of spherical or ellipsoidal multishell particles and provides a database containing published cell properties. Equivalent electrical conductivity and relative permittivity of the cell alone and in suspension can be calculated. The software also offers the ability to create graphs of the evolution of the crossover frequencies with the electric field frequency. These graphs can be directly exported from the software.