dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , <150min/wk and <75min/wk in moderate and vigorous PA, respectively), or regularly active (meeting WHO guidelines). Sleep duration was categorized as short (less than 6 hours), normal (6-9 hours) and excessive (> 9 hours). The smoker profile indicated whether the patient was currently an active smoker, had never smoked or was an active smoker in the past. Finally, we extracted from the data whether the patient had been clinically diagnosed with anxiety and depression. With regard to medical conditions, in addition to previous medical history, we also determined whether patients had diabetes (i. e. , medicated or glycaemia ≥ 125 mg/dL), hypercholesterolemia (medicated or total cholesterol ≥ 240 mg/dL), or hypertension (medicated or systolic/diastolic blood pressure ≥ 140/90mm Hg). Table  2  lists the proportion of cases in various categories reflecting, by and large, the standard structure of the Spanish labor force (with the exception of the lower presence of females and of more senior people in our study than in the overall labor market due to the types of companies served by the insurance provider) and its health status.
Treatment practices and policy uses We now describe several treatment practices and policy uses of the graphical structure, exploiting the ability of BNs to makes inferences, issue forecasts and support decisions: information, measurements and observations can be inserted and propagated throughout the network and modify the distributions in other nodes based on probabilistic computations using Bayes' formula  (5) , possibly applied several times. Accordingly, the network becomes a probabilistic database that summarizes the uncertainty about the CVD domain and may be queried to respond to diagnosis, treatment and policy issues.
Discussion We have built a discrete BN that relates modifiable and non-modifiable CVRFs and relevant medical conditions and illustrated its use to forecast various conditions, support health research, determine relevant findings and facilitate treatment and policy choices. We have acknowledged uncertainty in the probability tables through posterior distributions. Once the network is built (i. e. , its structure and probability tables), implementing its computations is relatively straightforward using software like GeNIe or R. As an example, the appendix describes one version in GeNIe. Based on this, we could build a decision support system to facilitate clinical and policy decisions. Two dynamic aspects not reflected in our model are relevant. First, additional data will be acquired annually by the insurance provider and the probability tables will be updated using Bayes' formula.
Locomotion System The first objective of the simulation is to accurately determine whether it is necessary or not for the character to take a step. This section introduces how the current state of the simulation is analyzed, leading to a walking state if necessary. It also explains the state machine that the model follows, to alternate stepping between legs to recover balance. Balance assessment using Time to Base of Support. The purpose of this work is for the character to recover and maintain balance, with or without locomotion. As such, the first concept to be clarified is the definition of balance : what factual observation can be made to determine whether a character is balanced or not. For this purpose, this work is based on an experimental dataset of participants undergoing external perturbations  [Chatagnon et al.
Only stepping motions are examined in these figures. The color gradient corresponds to the intensity of the push for each of the simulated (red) and real (blue) motions. Once the step is triggered, Figure  11  shows the similarity in the position over the duration of the step, which is directly related to both the simulated StepTime and feet goal position. The mean values and variance in Figure  12  have been studied to detect discrepancies. From a statistical standpoint, the hypothesis of the SPM t-test is never rejected, supporting that our model is able to reproduce accurately the real motions of human balance recovery. As a complementary analysis, various cases of different push angles, subject height and subject weight with corresponding morphology were tested in our simulation. A supplementary video is provided to demonstrate those configurations, and while no data is used to validate the results, this serves as an example of the potential uses of this simulation.
We note that one possibly way to improve on the RL results would be to use an adversarial motion prior (AMP), which can be shown to be a feasible way to guide RL to solve a motion task while respecting the available motion styles. In principle, a set of the captured push responses clips could then be used as the data to train an AMP policy. However, this type of approach remains tricky to train, given that the discriminator capability and the RL state distributions are both changing over time.
The models are constructed with open-source and free software (MODFLOW and PEST) using a Python interface [FloPy  (Bakker et al. 2016)  and PyEMU  (White et al. 2016) ]. This approach facilitates the integration of the groundwater model with other components such as, for example, river discharge or water use in economic models. In terms of novelties, this study allows to better understand how the Roussillon aquifer may react to new external stresses including the influence of climate change. As compared to previous modeling work in the region  (Chabart 1996) , the construction of a new model allows integrating new elements such as the importance of recharge from canals and provides a quantification of the uncertainty of the forecasts. This work opens also new perspectives for coupling the groundwater model with a novel economic model to aim for a better management of the water resource.
Model construction The MODFLOW groundwater modeling software is widely used and recognized in groundwater studies  (McDonald et al. 2003; Harbaugh 2005; Langevin et al. 2017a). In this paper, we use the MODFLOW 6 version and the opensource Python package FloPy  (Bakker et al. 2016 (Bakker et al. , 2020) )  to build the model, run it, and analyze the results.
5 × 10 -9 8 × 10 -9 2. 5 × 10 -10 Yes RIV Cond [ m 2 /s] Constant a 5 × 10 -3 1 × 10 -4 1 × 10 -3 No Irrigation [-] Zones 1 0. 8 2 Yes 1 3 201 Page 6 of 20 The rivers (Agly, Têt, Boulès, Réart, Canterrane, and Tech) are included using a Cauchy BC using the RIV package. The river stage is defined according to the DEM and the river bottom is set to 1. 5 m below the river stage (based on existing data and expert knowledge). The initial conductance parameter for all river beds is fixed to 0. 005 m 2 /s based on the conductance equation combining the geometry (width, thickness and length) and the hydraulic conductivity of the riverbed (see  Langevin et al. 2017b). Lagoons are implemented using the MODFLOW 6 Drain package (DRN), as we assume that these water bodies act only as outlets for the aquifer.
The flux is distributed homogeneously on the cells of both aquifers (QA and PA). Recharge The recharge is made up of two parts; one accounts for precipitations and the other accounts for the water that comes from irrigation of agricultural parcels and irrigation channels. The recharge from precipitation is estimated using simple soil water budget approaches to calculate effective precipitation  (Lanini et al. 2019 ; Healy 2010) combined with an infiltration coefficient related to the index of development and persistence of river networks (IDPR Mardhel et al. 2021) and a cartographic index that provides a qualitative estimate of infiltration versus runoff  (Martinsen et al. 2022). As the initial data (SAFRAN meteorological data reanalysis, Vidal et al. 2010) are coarse (grid of 8 km by 8 km), we applied a moving average to obtain the final recharge to avoid spatial artifacts.
Finally, these withdrawal values are redistributed between the different aquifers (QA and PA) using estimates made by HYDRIAD (2013) that indicate, by areas, the proportion of water withdrawn from each aquifer. All these BCs are implemented with the RCH package. Finally, we use groundwater withdrawal data regarding drinking water supply and industrial use collected by the Rhône-Méditerrannée-Corse Water Agency (AERMC) and the Roussillon Plain Association for the Protection and Management of Groundwater (SMNPR). This database contains all annual registered withdrawals from 1987 to 2018. For the reference year for the model (  2013 ), it identified 152 producing wells distributed over the study area. These withdrawals were implemented with the WEL package.
Inversion and uncertainty estimation The identification of parameters was carried out using PEST++ software  (White et al. 2020). PEST and PEST++ minimize an objective function defined as the combination of two components  (Doherty 2015) : where Φ m (Eq. 2) is the measurement misfit component, Φ r is the regularization component, and is the regularization factor. PEST++ automatically adjusts during inversion by aiming at a certain value of the user-defined objective function (PHIMLIM control variable). Φ m measures the discrepancies between the actual meas- urement data and the model output and is expressed as where for a specific observation point i, w i is the weight associated with the observation, h o i represents the head measurement and h c i the simulated head. n is the number of observation points.
Further studies and in situ measurements would be necessary to better constrain these exchanges. However, for a regional purpose, assuming homogeneous values was considered sufficient. Model limitations and improvements To study the impact of climate change on the Roussillon aquifer in a reasonable amount of time, a few simplifications in the model design were needed, such as the use of a steady-state model. An important simplification is that even with zero-recharge conditions, the rivers cannot become dry since we impose a fixed level as BC. The MODFLOW RIV package that we used accounts for the fact that the flow can be limited if the groundwater levels go below the bottom of the river (instead of continuously increasing), but it does not offer the possibility of drying the river and stopping the recharge completely.
Therefore, a possible improvement could be to use the more advanced package SFR  (Prudic et al. 2004; Leaf et al. 2021 ) that allows the implementation of the rivers in a more realistic manner. However, the implementation of this package would require additional data and parameters, not necessarily available, that would need to be given to the model. The way we implemented the rivers tends to mitigate the impact of the reduction of the recharge in the climate scenarios and is, therefore, conservative. We are thus confident that we do not overestimate the impact. When the models indicate a significant impact, it is likely that, in reality, the impacts will be even greater for a given climate and extraction scenario. Finally, the model does not simulate the transient and density transport processes.
Funding Open access funding provided by  University of Neuchâtel. This research was supported by the  University of Neuchâtel  and partially funded by the  French Ministry of Higher Education Research and Innovation , the  European Fund for Regional Development (FEDER)  through the  Occitania Region , the  Rhone Mediterranean and Corsica Water Agency , the  Perpignan Méditerranée Métropole Communauté Urbaine , the  Pyrénées Orientales department council  and the  French Geological Survey (BRGM) , as part of the regional project  Dem'Eaux Roussillon. Editorial Policies  for: Springer journals and proceedings: https:// www. sprin ger. com/ gp/ edito rial-polic ies, Nature Portfolio journals: https:// www. nature. com/ nature-resea rch/ edito rial-polic ies, Scientific Reports: https:// www. nature.
The optimal measurement bandwidth is different for measurements taken with and without the JPA not only because of the SNR improvement but also because of the different tunneling rates in each dataset, caused by a shift of the readout point and the 1D nature of the reservoir (see Appendix I). Further, the fidelity improves as Δt is increases since more j↑i are captured, see Fig. 2 (j). However, beyond an optimal value, the fidelity worsens because of the additional opportunities for the background noise to surpass the threshold. Spin mapping errors due to thermal excitation and relaxation are negligible due to their large time constant, being the relaxation time T 1 ¼ 5. 2 ms and the time constant for a thermal excitation t ↓ out ¼ 309 and 70 s without and with the JPA, respectively. We obtain a maximum spin readout fidelity F M ¼ 98.
From the fitted parameters, we determine the optimum threshold voltage for a maximum visibility of 98. 5%, as shown in Fig. 4(b). This data analysis can be carried out as a function of measurement time Δt. In Fig. 4 (c), the spin readout infidelity 1 -F M decreases as Δt increases due to a reduction of the measurement noise. However, at longer timescales, triplets start decaying into the (4,0) charge configuration, which decreases the readout fidelity. We find T 1 ¼ 228. 6 AE 0. 5 μs. Single-shot datasets were taken for varying rf powers, resulting in a maximum fidelity F M ¼ 99. 21 AE 0. 03% in 5. 6 μs, as shown in Fig. 4(d). From the difference between the sensor-limited readout fidelity [considering T 1 → ∞ (red crosses)] and F M (stars), we determine the sources of error for our optimum readout as being 0.
ACKNOWLEDGMENTS This research was supported by European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 951852 (QLSI), and by the UK's Engineering and Physical Sciences Research Council (EPSRC) via the Cambridge NanoDTC (EP/L015978/1), CDT in Delivering Quantum Technologies (EP/L015242/1), QUES2T (EP/N015118/1), and the Hub in Quantum Computing and Simulation (EP/T001062/1). V. N. C. -T. is supported by a Telefonica British-Spanish Society scholarship. M. F. G. -Z. is supported by the UKRI Future Leaders Fellow programme (MR/V023284/1). N. S. and J. W. A. R. acknowledge funding from the EPSRC Core-to-Core International Network Grant "Oxide Superspin" (No. EP/ P026311/1).
The approach used here uses the TSAI package for instantiating the networks and records training metrics using the Weights and Biases library, which is also used for hyperparameter optimization  [59, 60]. Figures  15(d ) and 15(e) show an improvement of the fidelity for measurements taken with and without a JPA. Here, the spin to charge errors are also included as described in Appendix L. We find a maximum fidelity F M ¼ 99. 1% for Δt ¼ 500 μs without using a JPA and F JPA M ¼ 99. 54% with a JPA for Δt JPA ¼ 250 μs. We observed that, when using the machine learning classification method, the measurement fidelity stays almost constant as the readout time Δt increases. This is because the optimized filters enhance the blip's edges features, mitigating the errors that appeared in the threshold method when the background noise surpasses the threshold.
For a fair comparison between the classification methods, we have calculated the accuracy in the fidelity for each method [see error bars in Figs. 15(d ) and 15(e)]. To do so, we have assumed that the experimental parameters have no error and used the same simulated set of readout traces. The fidelity error is calculated based on k-fold validation-we train the neural network on different sections of the dataset and predict the fidelity on the remaining sections. In the case of the threshold method approach, a set of 1 × 10 6 simulated traces is divided into 100 subsets of data, each one of equal size. Then, we obtain the optimal threshold for all the subsets except one and obtain the fidelity on the remaining subset. This way, we obtain a different fidelity for each subset. The final fidelity is calculated as the average of the different results and its error as their standard deviation.
This was carried out by taking the Hough transform of the threshold dataset, as described in Ref. [61]. These slopes indicate QD 2 is 7 times more coupled to the SEB than QD 3. These slopes were later built in the charge sensing controller of Appendix S. From Fig. 21 (b), we estimate the voltage shift in QD 1 when an electron is loaded into QD 2. This was done by fitting a cosh -2 distribution to the SEB transition for varying V g2 voltages [Eq. (Q1)]. From the fits, we extract the center and FWHM of the transition line, resulting in a voltage shift ΔV g1 of 1. 70 mV, which is almost 3 times the average FWHM of 0. 64 mV. We also note that the FWHM is constant as a function of V g2 , indicating that the peak is thermally rather than lifetime broadened  [44].
22(b). The main difference is that two peaks appear, one due to the singlets and the other due to the triplets outcomes, which we fit according to Eqs. (V1) and (V2), respectively. The noise is still calculated as the fitted average standard deviation, while the signal is the distance between the two peaks. Since the two methods adopted to measure the SNR were carried out at two different rf powers and on different days, the SNR values extracted at base temperature were used to calibrate the two datasets, resulting in the red data points on Fig. 22(c ). While the SEB peak shift remains greater than its FWHM, the SNR has the following temperature dependence: SNR ∝ 1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi T 2 fridge þ T 2 e q ; ðW1Þ where the value extracted in Appendix Q was used for T e , resulting in the fitted red curve.
Clinical and neuropsychological data We used the validated German version of QUIP-RS to assess ICB severity  40. The Apathy Evaluation Scale (AES) was used to capture apathy severity  41. These self-reported scores were obtained simultaneously. The Mini-Mental State Examination (MMSE) and Parkinson neuropsychometric dementia assessment (PANDA) evaluated cognitive functions. Furthermore, we assessed the Beck Depression Inventory (BDI-II) and the Barratt Impulsiveness Scale (BIS). The severity of motor symptoms in the unmedicated condition was assessed using the Unified Parkinson's Disease Rating Scale Part III (UPDRS III). Motor complications were assessed with the Unified Parkinson's Disease Rating Scale Part IV (UPDRS IV). Dopamine replacement therapy dosages were converted to levodopa equivalent daily doses and dopamine agonist equivalent doses, respectively  42.
Genetic analyses QIAamp DNA Blood Mini Kit (#51106, QIAGEN) was used to isolate DNA from a peripheral blood sample. For the assessment of DNA quality and concentration, an ND-1000 UV/Vis-Spectrophotometer (Peqlab) was used. For SNP genotyping of rs6280, 20 ng of DNA was analysed in triplicates using allelic discrimination assays (TaqMan SNP Genotyping Assays, Applied Biosystems by Invitrogen). Genotyping PCR was performed with a 7900HT Fast Real-Time PCR System (Applied Biosystems). Sequence Detection Software version 2. 3 (Applied Biosystems) was used to process fluorescence data  37   Image acquisition and preprocessing-MRI 54 PD patients underwent MRI imaging. Scanning was done on a 3 T Magnetom Prisma (Siemens, Erlangen). For the anatomical T 1weighted MPRAGE acquisition parameters were as follows: repetition time = 2300 ms, echo time = 2.
32 ms, flip angle = 8, field of view = 230 mm, slice thickness = 0. 9 mm, voxel size = 0. 9 × 0. 9 × 0. 9 mm, number of slices = 192. In order to detect anatomical differences, we calculated structural volume based on voxel-based morphometry with the CAT12 toolbox (https://neuro-jena. github. io/cat/). The T1-weighted images were segmented automatically into grey and white matter. The grey matter volumes were smoothed by an 8 × 8 × 8 FWHM Gaussian Kernel and the total intracranial volume (TIV) was calculated. Image acquisition and preprocessing-[18F]DOPA-PET Dynamic PET images were acquired in 3D mode with a 24detector ring scanner ECAT EXACT HRRT (Siemens, Erlangen) over 90 minutes. The subjects were lying in a quiet and dim room during image acquisition. All patients received 100 mg of Carbidopa 1 hour before intravenous injection of [18F]DOPA, with an average activity of 185 MBq.
Statistical analyses-behavioural and genetic data The distribution of normality for QUIP-RS and AES was tested using the Kolmogorov-Smirnow-Test in addition to visual inspection. In a first step, we examined the relationship between the severity of apathy and ICB. Therefore, we performed a Spearman correlation analysis with apathy and ICB as quantitative variables, i. e. , QUIP-RS and AES. We performed this analysis in PD patients and healthy controls, separately. Additionally, we investigated how far the severity of depression had an effect on ICB that was independent of apathy. To do this, we performed a partial rank correlation with QUIP-RS and BDI-II controlling for AES. In a second step, we examined how many patients of our cohort reached the established cut-offs for ICB and apathy. Positivity for ICB was defined according to previously established QUIP-RS cutoffs for the different ICB categories  40.
Positivity for apathy was defined by using a previously defined cut-off of the AES in PD (AES > 33)  41. In a third step, a two-way ANOVA was used to examine the interactive effect of apathy status (categorical variable) and DRD3 receptor polymorphism (categorical variable) on ICB severity (QUIP-RS, quantitative variable). Age and sex were included as covariates of no interest in this model. Using 1000 samples and the bias corrected and accelerated method (BCa), bootstrapped confidence intervals were calculated due to non-normal distribution of QUIP-RS. In a final step, group comparisons of neuropsychological and demographic data between DRD3-(wild type) and DRD3+ (risk type) as well as apathetic and non-apathetic were calculated based on Mann-Whitney-U-Tests. For differences in the distribution of biological sex, handedness and intake of dopamine agonists, we applied Fisher's exact test between the groups.
Statistical analyses-imaging data Apathy status and DRD3 polymorphism significantly predicted the severity of ICB. We performed a post-hoc analysis examining the independent effect of these two categorical predictors on striatal grey matter volume and dopamine synthesis capacity as neural correlates of the propensity to ICB. We tested how far grey matter volume and dopamine synthesis capacity were driven by the apathy status or DRD3 polymorphism and thereby led to more severe ICB as shown by the behavioural interaction. Therefore, we used multiple linear regression models. Due to the small sample size, different group sizes and nonparametric distribution of behavioural data, we performed voxel-based statistical nonparametric mapping using the SnPM13 package (http://www. nisox. org/Software/SnPM13/).
ACKNOWLEDGEMENTS This study was supported by grants of the  Deutsche Forschungsgemeinschaft (DFG) :  EI 892/3-1 ,  KFO-219 ,  SFB 1451  (project  431549029 ) and was supported by the  DFG (German Research Foundation) -Project -ID C03-431549029. H. T. was supported by the programme for rotation positions/ Faculty of Medicine/University of Cologne  and by the  Cologne Clinician Scientist Programme (CCSP)/Faculty of Medicine/University of Cologne. Funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  (Project no. 413543196 ). S. P. was supported by a stipend from the  Alexander von Humboldt Foundation.