dataesr/scientific-paragraphs-categorization huggingface dataset:
D. Final form of the χ 2 Including all of the contributions from the previous sections and defining σ ijk d (δσ ijk stat ) as the data central value (statistical error) corresponding to the σ ij th (E k |θ) prediction, the complete form of our χ 2 distribution becomes: χ 2 (θ, f ) = i,j,k w ijk (φj (f ) σ ij th (E k |θ)-σ ijk d ) 2 (δσ ijk stat ) 2 + (θ -θ 0 ) T Σ -1 θ (θ -θ 0 ) (10) + j (fj -1) 2 (δfj ) 2 where φ j (f ) is the product of the nuisance scaling factors that are relevant for j-th dataset as described in Sec. V B. θ 0 and Σ θ are the central values and the covariance matrix of the priors for the parameters of interest, respectively. Equation 10 represents the full capability of our tuning machinery. However, the priors we applied for the present work were uncorrelated and so only the diagonal entries of Σ θ were used.
The details on the priors applied in this analysis are described in Sec. V C. The contribution of each point to the likelihood can be (de-)emphasized using weights w ijk to set the relative importance of different datasets (or of individual data points within a dataset). Such weighting schemes have been used extensively in generalpurpose event generator tunes for the LHC (for an example, see  [85] ). In this particular analysis, the weights are used to include or exclude datasets only (w ijk ∈ {0, 1}).
A. Partial fits Two main subsets were identified in the global dataset in order to study tensions: inclusive and exclusive datasets. The fits consider the G18 02a CMC as the base configuration and include nuisance parameters to take into account the correlation between datasets from the same experiment, see Sec. V for more details. No priors on M RES A and M QE A are applied as we are interested to see the impact of each subset on the prediction. The fit to inclusive data only is not sensitive to the scaling multiplicity parameters for the non-resonant background, therefore those parameters are fixed to their default values during the fit. Partial fit results for inclusive and exclusive data are presented in Tab. V. The tune against inclusive data only achieves much better agreement with in-clusive data than the previous GENIE G18 02a default, see Fig.
6. This difference between the old and new inclusive tune is due to 1) the inclusion of only hydrogen and deuterium datasets and 2) the effect of the nuisance parameters. [86] Particularly, without exclusive data, a small reduction of the resonant cross section is already observed in the CC RES region. The result for M RES A is consistent with previous results without the addition of priors  [78]. W cut is pulled to the lower edge of the parameter range: the parameter uncertainty could not be estimated as the χ 2 minimum was found on the contour. As expected, the fit to exclusive data only is able to correctly describe exclusive datasets for one and two pion production. The low cross-section data for one pion production forces all the relevant parameters to decrease with respect to the default values see Fig.
7a. At the same time, two pion production data forces R CC2π νp , R CC2π νn and W cut to increase in order to match two pion production data, see Fig. 7b. The agreement with ν µ CC inclusive data is worse, see Fig. 6 , but the compatibility is still acceptable given the large uncertainties on the data in that region. On the other hand, the partial fit does not obtain a good prediction for M RES A. W cut is fixed to its maximum value of 2 GeV to avoid nonphysical regions. The exclusive fit clearly shows a preference for a larger total cross section in the neutrino energy region between 1 and 10 GeV due to the high value of R CC2π νp and R CC2π νn. This is a tension between exclusive and inclusive datasets as the inclusive prefer a lower value in that E ν region. Since inclusive data constitute about 40% of all the data points, the inclusion of priors for the axial masses and S DIS becomes crucial to overcome the tension  [87].
The agreement with quasi-elastic data, included in the tune in order to constrain the fluxes of each experiment, remained the same, see Fig. 9. As discussed in Sec. VI, this decrease of the inclusive cross section at the SIS region is driven mainly by one pion production data. The impact on one (a) Comparison against νµCC1π + data. FIG. (7)  Comparison of ν µ CC exclusive channels against bubble chamber data. The default GENIE configuration corresponds to the G18 02a CMC. The exclusive tune is sensitive to the exclusive datasets only, see Tab. II. The predictions are computed with GENIE version 3. 2 using the parameters specified in Tab. V. The χ 2 values are calculated against all exclusive data shown in each plot. pion exclusive channels is shown for (anti)neutrino on proton, Fig. 10 , and neutrino on neutron, Fig.
Both CMC have been tuned against some H, 2 H data (filled markers). Each χ 2 is computed using all data available. In Tab. IX, the χ 2 values per dataset are specified. tune is similar for the existing configurations, the response of each model at the parameter level is not expected to be the same. Therefore, each tune is strongly affected by how the model is able to accommodate the data by modifying the tuned parameters. This reflects on the R m parameters and W cut which best fit values are incompatible in some cases, such as for R CC1π A , show agreement between the tunes and respect the applied priors.
The global fit is able to describe both inclusive and exclusive cross sections simultaneously. Tensions between inclusive and exclusive data have been re-encountered, and, as a consequence, the inclusive cross section at the 1-10 GeV energy region decreased with respect to the historical default value. The systematic treatment of correlations between datasets and the inclusion of priors were crucial to address tensions. After the tune, GENIE predictions of one pion production cross sections on free nucleons (ν µ CC pπ + , nπ + , pπ 0 and pπ + π -) show a decrease in the non-resonant background contribution, (θ i ) = χ 2 ( θ, f ) -χ 2 min respect the other parameters in the tune. The profiles for both tunes are shown for each case. The G18 02a profiles show that this configuration is less able to accommodate in the model variations on each parameter best fit values.
IX) Contributions to the default and best fit χ 2 for the datasets included. The data points with E ν < 0. 5 GeV, a total of 10 points, are considered in the χ 2 calculations of this table, but were not used in the fit. For the calculation of the χ 2 , the covariance matrix between the datasets is used instead of Eq. 10, which incorporates nuisance parameters which are not implemented in GENIE. This explains the difference when comparing with the χ 2 out of Professor from Tab. VI.
2016 , for a review). In these studies, a false discovery rate (FDR) is estimated to control for multiple testing and this FDR estimation assumes the uniformity of p-values under the null model  (Storey 2002). The criterion to filter loci was to have a minimum global MAF: loci with min p0+pτ 2 , 1 -p0+pτ 2 < 0. 05 were removed from the dataset. Thus, loci to be tested were chosen based on their genetic diversity, a bias that has to be taken into account in the test. This was done by discarding drift simulations that produced min p * 0 +p * τ 2 , 1 - p * 0 +p * τ 2 < 0. 05, where p * t is the frequency of the reference allele in the sample at time t in the simulation.
Analysis of simulated data For each simulation replicate, data were analysed as described in the two previous sections. In addition, the effective population size was also estimated from F C (following Waples 1989) for comparison. For a given scenario, the true positive rate was estimated from the test results at loci under selection. The false positive rate was estimated from the test results at the neutral loci on the linkage group that does not include the locus under selection. Neutral loci on the linkage group with the locus under selection were used to characterize the footprint of selection due to hitch-hiking by estimating the positive rate as a function of the distance to the locus under selection. In order to quantify the variance of the selection signal, loci were bootstrapped and the 95 % quantile interval for the proportion of positive tests was calculated.
Positive tests were defined with an arbitrary threshold of p-value<0. 001. In order to control for multiple testing, FDR estimates were quantified with q-values for each locus, following  Storey (2002) , with the qvalue R package  (Storey et al. 2019). Manhattan and QQ plots were generated with qqman R package  (Turner 2017). As explained in the previous section, the genetic diversity around the selected locus could influence the outlier test in a scenario of adaptation from standing variation in a predominantly sefing species (Fig. A2 ). Therefore, we examined the structure of genetic diversity at the beginning of the sweep in those scenarios. This allowed us to characterize the effects of historical recombination previous to the outset of the selective sweep. For each simulated population, the haplotypes, defined as the whole linkage group under selection, were classified in two groups: one group for haplotypes carrying the advantageous allele and one group for haplotypes with the neutral allele.
2019). Indeed, there is some evidence of selective sweeps in selfing species  (Bonhomme et al. 2015; Huber et al. 2014). In agreement with this, the temporal genome scan in a predominantly selfing population of A. thaliana from a previous study showed footprints of selection  (Frachon et al. 2017). Such positive results could have been favoured by the relatively large genetic variation of this population  (Baron et al. 2015; Frachon et al. 2017). Based on the simulation results we present here, we can assume that this population has been adapting from standing variation over a short period of time (eight generations) rather than from many new mutations occurring during (or shortly before) the studied period. The absence of evidence for selection in the M. truncatula dataset is manifest. It is reasonable to attribute this results to the extreme selfing rate estimate (σ = 0.
the platform manager, Alexandre Dehne Garcia, for being ever-helpful. We are grateful to Matteo Fumagalli, Christian Huber and two anonymous reviewers for their comments on this work. Version 4 of this preprint has been peer-reviewed and recommended by Peer Community In Evolutionary Biology (doi:10. 24072/pci. evolbiol. 100110) disequilibrium due to selfing. The simulation approach is very similar to the simple drift model used to build null distribution of the test described in the main text. The main difference is that initial allele frequency is set as a fixed parameter. Simulations are used to generate temporal data at 10 000 biallelic loci per pseudo-observed dataset. Initial allele frequencies (π 0 ) are fixed to 0. 5 for all loci. Allele frequency π in generation t were simulated with a binomial distribution as π t ∼ B(2N e , π t-1 )/2N e , where N e is the effective population size in number diploid individuals, for generations t ∈ [1, τ ].
Data and code accessibility The method described in this work is implemented in DriftTest, a C program available at Zenodo (Navascués and Vitalis 2020). An R script (R Core Team 2018) to reproduce the simulations and analyses in this work is available at Zenodo  (Becheler et al. 2018). SNP data for Medicago truncatula is available at Data INRAE  (Gay 2020).
The monitoring of turboGFP-LC3B signal in live cells upon starvation was performed using an Incu-Cyte system (https://www. sartorius. com/en/products/live-cell-imaging-analysis/live-cell-analysisinstruments). This live-imaging platform consists of a microscope residing in a standard tissue culture incubator maintained at 37 C and 5% CO 2 that allows automated image acquisition and analysis through the IncuCyte software. This protocol was tested using IncuCyte FLR and ZOOM models but more recent versions should also be compatible. The main difference between FLR and ZOOM is that the latter has both green  (Ex: 460 [440, 480]   Importantly, this protocol may be used to monitor autophagy using any high-throughput imaging platform (i. e. , N Cell Analyzer [GE] or Cytation Multi-Mode Reader [BioTek/Agilent]) but some adjustments may be required depending on the system.
Generate processing definition Timing: 45-60 min Creating a "processing definition" aims to ''train the software'' using the selected images to determine the optimal settings for automated object detection in all images generated in the experiment. This step is required only the first time the experiment is performed. Once defined and saved in the software, the same workflow can be applied to subsequent experiments. Note: This step is specific for the Zoom software and is not present in the FLR version. Note: You can also generate a new processing definition for previously analyzed experiments. 5. Add representative images to Image Collection. We recommend using at least 2-3 images for each condition representing different time points of the experiment. 6. Define parameters for object detection (Figure  1 ) for phase contrast and green fluorescence channels.
a. Define the parameters for the phase contrast channel. i. Click Preview Current and apply the Confluence Mask to visualize the preset values already present in the analysis software. ii. Refine the parameters within the confluence mask: Set up Segmentation adjustment, which aims at adjusting the Background/Cells slider bar. We used a 1. 1 value. Clean up by adjusting size. This parameters allows to either enlarge (by choosing a positive value) or shrink (by using a negative value) the mask by a specific number of pixels. We set up À1, which means that the mask was reduced by 1 pixel. Define minimal object area to filter out dead cells and debris (in the Zoom software, distance measurement can be made by clicking a ruler icon on the right side of the image). In our analysis, we excluded objects smaller than 200 mm 2.
Extracting and analyzing data This protocol explains how to exploit and further analyze data generated with the IncuCyte software. Timing: 1-3 h (depending on the number of wells and conditions) 7. Extract raw counts from IncuCyte software. a. Export the results for confluence (percent) calculations for phase contrast and green channels. 8. Copy raw data to Excel file and calculate the ratio of the percentage of confluence in the green channel by the confluence in the phase contrast channel using the following formula: Green confluence (percent)/Phase contrast confluence (percent) Alternatives: Instead of normalizing data with phase confluence, you can label cell nuclei using an appropriate red probe (e. g. IncuCyteâ Nuclight Rapid Red Dye) and normalize the number of fluorescent green objects [in this case edge split should be turned on to precisely determine the number of objects] by the number of nuclei (only applicable in IncuCyte models equipped with more than one fluorescence channel).
Normalize the obtained values with that of the first scan (time 0 h). Optional: Import your data into graphing software to create graphs. We used GraphPad Prism for statistical analysis and data visualization but this step may also be performed in Excel. 10. Generate a curve representing the levels of green fluorescence normalized by the number of cells (Figure  2 ). In the example shown Figure  2 , cells were starved in EBSS supplemented with 10% serum and non-essential amino acids for the indicated times. Upon starvation, there is a progressive decrease of turboGFP-LC3B fluorescence (Figure  2A ). A similar observation is made by immunoblotting endogenous LC3B (Figure  2B ) at various times following starvation, indicating that turboGFP-LC3B behaves similarly to the endogenous protein in starvation conditions.
QUANTIFICATION AND STATISTICAL ANALYSIS Data extracted from the IncuCyte software were exported to Excel to perform the normalization. Normalized values were copied to GraphPad 9 (Prism) to generate graphs. Results from at least 3 independent experiments are needed to perform statistical analysis. This may be done in GraphPad software by comparing each time point between several groups. This may be done using 2-way ANOVA followed by multiple testing correction (e. g. , Bonferroni test). For examples and more details on statistical analyses, see  Nowosad et al. , (2021).
The IncuCyte Zoom software does not allow analyzing fluorescence in single cells over time. However, the Incucyteâ Cell-by-Cell Analysis Software permits to perform the segmentation and treat exported measurement as single-cell data. This module is available for IncuCyteâ SX1, SX5 and S3 platforms as well as on other high-content imaging devices. Alternatively, recently developed microfluidic platforms (such as EVORION CellCity) can be used to refine the analysis and obtain single cell resolution. Furthermore, machine-learning approaches  (Morone et al. , 2020 ) may be helpful to classify fluorescent cells into different categories based on their fluorescence intensity or pattern.
With aging populations and increasing life expectancy, Home Health Care (HHC) has become an alternative treatment modality for the elderly who want autonomy and well-being as they live longer and longer. Facing this rise in demand, the organisation and coordination of HHC institutions has become increasingly complex and difficult. This paper addresses a short-term Home Health Care Routing and Scheduling Problem (HHCRSP), formulated by Mixed Integer Linear Programming (MILP). The proposed model considers tolerance-based soft constraints in view of both patients and caregivers, with the aim of maximising the total satisfaction of all HHC stakeholders. The problem is solved by the commercial solver CPLEX. We adopt a real dataset extracted from an existing HHC institution. The numerical result on 10 generated instances shows the efficient computing performance for an optimal solution for small and medium-large sizes.
Finally, the optimum solution is generated through CPLEX 2 by sending the caregivers with the maximisation of total satisfaction rates. This paper is structured as follows: Firstly, we review recent work on the HHCRSP. Then we highlight the originality of our investigation in section 2. Section 3 specifies the context and the concept of our problem based on an HHCRSP conceptual model. Section 4 describes the notation and mathematical formulation of the problem. In section 5, we illustrate the results of the experimentation with the CPLEX solver, based on a real dataset extracted from an existing HHC institution. In section 6, we conclude this research work and give our outlook for the future.
STATE OF THE ART Originally, the HHCRSP was an extension of the Vehicle Routing Problem (VRP). The complexity and the variability of business constraints, along with the diversity of HHC institutions, make the problem hard to solve  (Restrepo et al. , 2020)  Three resolution approaches are aimed at solving these problems with an MILP-based formulation. (1) The exact methods embedded in the solver (e. g. CPLEX, Gurobi): this approach finds optimal solutions for problems with a reasonable size and generally encountered difficulties when dealing with large use cases; therefore we focus not only on the applied solver but also on the maximum size of the problem being tested. (2) The approximated method is based on a metaheuristic algorithm; the goal being finding a solution approximating an exact method but in a more timely manner.
(3) The mixed method is the combination of (  1 ) and (  2 ). As for (1), CPLEX is used in  (Di Mascolo et al. , 2014; Nasir and Dang, 2018)  to solve the test problem having at most 16 or 20 services to be performed respectively. Gurobi is used in  (Xiao et al. , 2018)  for the problem with 23 services maximally. As for (2), a memetic algorithm is applied in  (Decerle et al. , 2017)  In this paper, we propose a mathematical model for the HHCRSP with consideration of business constraints in the view of both patients and caregivers to achieve a maximisation of total satisfaction regarding all stakeholders in the HHC system. We translate this satisfaction in a personalised way with regard to the profiles of stakeholders; each one (caregiver or care beneficiary) has his or her own referential acceptability when temporal constraints are not satisfied.
HHCRSP Conceptual model Patient Every working day, the patients and their requests must be registered into the database of the HHC structure. Each patient may request more than one care service per day. The multiple availability of a patient is denoted by the soft time windows with respect to each request. Each patient is characterised by their ID, their physical address, the inter-operation time, 2 tolerances and 2 satisfaction rates respectively in the case that waiting time and non-respect of inter-operation time occur. A set of patients' physical addresses will be translated into an itinerary matrix, which denotes the necessary travel time from one patient to another in a bird's-eye view without consideration of routing and traffic, taking 20 km/h as the average speed in a downtown area.
Data set generation The data set was extracted from an existing HHC institution. We generated 10 instances and submitted them to a CPLEX solver to complete our experimentation. We tested the computing time as well as the performance in terms of the 4 components of objective function reflecting the HHC stakeholders' satisfaction formulated in the model. The experimental test ran on a computer with Intel(R) Core (TM) i7-7500U CPU 2. 90GHz and 16. 0 GB RAM under the Windows 10 operating system. The run time limit was set at 4000 seconds for each execution. In all the instances, we randomly generated stakeholder satisfaction parameters and care services difficulties. On the caregiver's side:  [30, 50] , ξ k = 600 according to the regulation of the end user's institution. On the patient's side:  [30, 50].
Numerical results The general numerical results are shown in Table  1. We focus on the global performance in terms of computing time in seconds (CPU), and the gap between upper limit (UP) and retained objective function (OF) regarding the studied instances. These results demonstrate the performance of our model in reaching the optimum solution for each real datasetbased instance (gap ≈ 0); the computing time is satisfying although the dimension only goes up to the medium-large size. Then, in Fig. 3  we demonstrate the detailed results in terms of the 4 satisfaction components concerning the solution of each instance: waiting time (WT_P), respect of inter-operation time (IO_P), overtime workload (WL_K),.
CONCLUSION AND RESEARCH PERSPECTIVES This research work addresses an MILP model for solving the HHCRSP on the daily planning horizon. To consider the satisfaction of all HHC stakeholders, which has rarely been taken into account in previous work, we took various constraints into account from two perspectives: patients and caregivers. The test is based on a real operational dataset and the obtained optimum solution for each instance is generated though the use of CPLEX. The numerical results show a significant increase in performance in terms of the modeled satisfaction criteria in comparison with the ideal result. In addition, our MILP model is able to address the problem in a medium-large dimension within a short computing time (up to 236. 8 seconds in the use case: ML_s61_p32_mp17_k8). In future, the study should be conducted in a continuous experiment with a large instance, to see the limitation of the model in terms of CPU performance.