dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We now investigate the space of optimal solutions. We first consider the generated dataset (Sections 6. 1 to 6. 3): we present the results in the order that our workflow follows (see Section 4), since it is a pipeline. In addition, we apply our method to a small network of international relations (Section 6. 4) to show its relevance on real-world data. Regarding the synthetic graphs, we present a selection of the most relevant results in Figures  4 to 9 , for 0 ∈ {2, 3, 4}. The complete results 2 as well as our source code 3  are available online, though. We first describe these plots generically here, for matters of convenience, before interpreting them. In these figures there are 3 subfigures (identified by a letter: a, b, c). Each subfigure is a block of 6 plots, and focuses on a specific variable of interest, represented on the y-axis of the plots.
This typology shows that the answers to our initial questions are multiple and depend on the considered graph. Our work highlights the necessity to develop a method allowing to handle these different cases. To show the relevance of the questions at the origin of our work, as well as the usefulness of our method, we further analyze a small real-world graph representing the relations between the main actors of the ongoing Syrian conflict. We choose this dataset because of its size, which eases the interpretation of the obtained CC solutions, but also because it depicts a very interesting situation, as the affiliations of the involved parties are multiple: they are positioned relative to terrorist group ISIS, the Syrian government, and various other geopolitical interests.
NanoGPS Oxyo ® Encoder The nanoGPS setup is represented in Figure  1a. It comprised a scale and a read head. The scale exhibited a 2D pattern, as shown on Figure  1b , and the read head included a monochrome camera, an objective, and an illumination system, and provided images of the scale. The acquired images by the read head were treated via nanoGPS software  [5]. The processing software included Fourier analysis and advanced cross-correlation analysis. It provided the position and orientation, along with the magnification between scale and image, expressed in pixel per µm. The nanoGPS read head used in this study was based on a 1920 × 1920 pixel sensor. It featured darkfield illumination based on green light-emitting diodes (LEDs). Scales comprised metallic patterns deposited on the transparent substrate and white backing that diffused the LED light.
We investigated two scales: • one 125 × 125 mm scale made of fused silica with white backing on the side opposite to the metal patterns, and • one 25 × 75 mm scale made out of float glass with white backing on the side with metal patterns. For this slide, the scale was read through its substrate. The nanoGPS software that interpreted the images of the scale into a position was encapsulated as a dynamic-link library. This software was calculation-intensive, and calculation time needed to extract the position information depended on processor performance and image size. While the time required for the processing of a full resolution image is about 1 s with a medium-performance PC, it decreased nearly linearly with the area of the image. In previously published works, we established: • precision on the x, y position was about 1 nm rms  [12].
Precision and Accuracy of NanoGPS OxyO as an Angle Encoder In order to evaluate nanoGPS OxyO precision as a function of image size, angular values were measured by the two systems in stationary conditions, i. e. , with the REAC table latched. The time needed by the program to process a full resolution (2000 × 2000 pixels) image was non-negligible (about 1 s). In this condition, the noise of the angular measurement was about 5 µrad, calculated as the standard deviation of 50 images. Image-processing time was almost linearly reduced, decreasing image size, but clearly noise was increased, and accuracy was degraded. For example, reducing the size to 480 × 480 pixels, image-processing time was decreased below 0. 1 s and noise was increased by a factor of 4 at least, which was consistent with the statistical reduction of the number of pixels by a factor of 16.
After a variable delay (3. 5-7. 75 seconds) during which the participants prepared the adequate saccade while fixating the central fixation point, a Go signal (Cue 2) prompted the participants to immediately execute the saccade. In the Control condition, participants were presented with a square at Cue 1, indicating that they would need to wait for the Go signal (Cue 2) to find out the required saccade direction and execute it immediately. Behavioral saccade onset latency data were collected, and spectral power features were extracted from the iEEG data across multiple time windows and all electrode sites. Power features were computed in 5 standard frequency bands: theta (θ)  [4] [5] [6] [7] [8] , alpha (α)  [8] [9] [10] [11] [12] [13] [14] [15] , beta (β)  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] , low-gamma (low γ)  and high gamma (high γ, HG).
Limitations and open questions Participants in our study were neurosurgical patients with drug-resistant epilepsy. To minimize the effect of epilepsy-related alterations and artifacts, we followed strict data exclusion procedures in line with our previous intracranial EEG work  [88] [89] [90] [91]. These consist primarily of systematic inspection of the data and exclusion of signals showing typical epileptic waveforms (e. g. , epileptic spikes). In addition, we excluded data from any electrode subsequently identified by the clinical staff as being part of the resection area. Moreover, it should also be noted that the advantages of depth stereotactic electroencephalography (SEEG) recordings (including high spatial and temporal resolution, high signal-to-noise ratio across a wide range of frequencies up to 140 Hz, 68), come at the cost of heterogenous spatial sampling among participants.
Method details Electrode implantation and stereotactic EEG recordings. Each participant was implanted with SEEG electrodes (diameter of 0. 8 mm). Depending on the implanted structure, electrodes were composed of 10 to 15 contacts that were 2-mm wide and 1. 5-mm apart (DIXI Medical Instrument, Besancon, France). Intracranial EEG signals were recorded from a total of 778 intracerebral sites across all participants (between 128 and 133 sites per participant). At the time of acquisition, a white-matter electrode was used as reference, and data were sampled at 1,024 Hz and bandpass filtered between 0. 1 and 250 Hz. Electrode locations were determined in each individual participant using the stereotactic implantation scheme. The coordinates of each electrode contact were given following these references: origin (anterior commissure), anteroposterior axis (anterior commissure-posterior commissure), and vertical axis (interhemispheric plane).
The electrodes were then localized in each individual participant using Talairach coordinates, which were then transformed to MNI coordinate system using standard procedures (i. e. , tal2mni. m MATLAB function) (Fig  1C ). We then automatically assigned electrodes to brain regions based on 3 distinct atlases: Brodmann areas, the Automated Anatomical Labeling (AAL; 95), and the Multiresolution Intrinsic Segmentation Template (MIST; 96). The mapping from coordinates to brain areas (using this atlas) is publicly available in several toolboxes, including the one we used here (Visbrain, http://visbrain. org/, see  [97] ). Delayed motor task. At the beginning of each trial, participants were asked to fixate a central fixation point that appeared at the center of the screen, along with 2 lateral points for 500 milliseconds.
Across all participants, approximately 75% of the trials were retained for further analysis. Behavioral analysis. Based on the EOG traces (see S2 Fig) , we computed saccade onset latencies for each trial and for all participants in the Control, Instructed, and Free conditions. Saccade onset latencies were identified from EOG traces recorded during each experimental conditions using a routine semiautomatic saccade detection procedure: A custom MATLAB (The Mathworks, Inc. ) program provided an initial automatic detection of saccade onsets and allowed for interactive manual adjustments of the marker latencies (see S2 Table ). In order to test whether RTs differed significantly across conditions, we used a 2-tailed paired Student t test and compared mean RTs for Control versus Instructed, Control versus Free, and Instructed versus Free conditions.
The statistical significance of the obtained conditional probabilities was evaluated by computing statistical thresholds using permutation tests (n = 1,000, p < 0. 001). A null distribution was generated by repeatedly (n = 1,000) computing conditional probabilities for each participant obtained after randomly permuting class labels (Left and Right). EOG data preprocessing. Oculomotor performance was followed online using horizontal and vertical electro-oculograms (EOG), allowing to measure the amplitude and the speed of saccades (see S2 Fig) , as well as the errors made by each participant. Four electrodes placed around the eyes to measure horizontal and vertical eye movements with a sampling frequency of 1,024 Hz. Saccade onsets were identified from EOG traces recorded during each experimental condition using a routine semiautomatic saccade detection procedure.
A custom MATLAB script identified saccade onsets by detecting the onset of the characteristic slope (by computing the derivative of the EOG signal). The automatic detection of saccade onset was then finetuned through interactive manual adjustments of the marker latencies. To make sure our results cannot be attributed to saccades made after the presentation of Cue 1 during the delay period, we used a 2-tailed paired Student t test at each moment in time to compare mean right and left EOG responses, as well as mean responses during Free and Instructed conditions across all participants and found no significant differences. SEEG data preprocessing. SEEG data preprocessing was conducted according to our routine procedures  [88, 89, [98] [99] [100]. These included signal bipolarization, in which each electrode site was re-referenced to its direct neighbor.
Several classification techniques were initially tested for the singlefeature classification procedure, including LDA, k-nearest-neighbor (KNN), and support vector machine (SVM). The classification accuracy results were very similar across the 3 methods. The LDA algorithm  [102]  was the fastest and was therefore chosen for this study given the computationally demanding permutation tests used to evaluate classifier performance. In brief, for a 2-dimensional problem, the LDA algorithm tries to find a hyperplane that maximizes the mean distance between the mean of the 2 classes while minimizing interclass variance. DA and statistical evaluation of decoding performance. Single-trial classification performance was evaluated in each participant separately. We used a standard stratified 10-fold cross-validation approach with Scikit-learn, a Python 3 package dedicated to machine learning analyses  [103].
In addition, we computed Pearson's rank correlation coefficients between RTs and the onset of HG activity for each trial in the Free, Instructed, and Control conditions during saccade execution. The onset of HG activity was determined in each trial by detecting the time point after which HG activity was greater than 2 standard deviations for at least 2 consecutive time bins. The statistical significance of correlations was established by using a 2-sided test whose null hypothesis is that 2 sets of data are uncorrelated. The p-value thus indicates the probability of an uncorrelated system producing datasets that have a Pearson rank correlation at least as extreme as the one computed from these datasets. Data mapping to a 3D standard cortical representation. To facilitate the interpretation of the results, all significant task-based feature modulations and decoding results were remapped from the intracranial electrode sites onto a standard cortical representation.
To achieve this, all electrode coordinates were transformed from individual Talairach space to standard MNI space using Visbrain  [97] , an open-source Python 3 package dedicated to brain signal visualization, to map the data from iEEG sites onto 3D images of transparent brains. This cortical representation technique is in line with methods used in previous iEEG studies  [88, 89, 107]  and allowed for brain-wide visualization of significant features and decoding performances.
Data and software availability Electrophysiological data were analyzed using Python 3, in conjunction with toolboxes including Visbrain  [97]  for data visualization and mlxtend  [106]  as well as Scikit-learn  [103]  for machine learning analyses. Data and custom Python analysis scripts are available upon reasonable request from Thomas Thiery (thomas. thiery@umontreal. ca). The numerical data used in all figures are included in S1 Data.
A model of a system of photovoltaic-thermal panels is built in a transient systems simulation program (TRNSYS) and a one-factor-at-a-time analysis is carried out for the cold-water main temperature, tank size, tank outlet flow and consumer demand curve. The results show that the variation of the outlet flow to the consumer has the highest impact on the electrical efficiency, of about 6. 8%. The next highest impact factor is the size of the tank with a variation of 4. 7%. Matching the profile of the consumer is also an important aspect. It was observed that the peak electrical efficiency occurs during peak consumer demand. Finally, the instantaneous variation of the thermal and electrical power of the system was analysed as a function of the temperature at the inlet of the photovoltaic-thermal panel.
The inverter has an electrical efficiency of 98%. The TRNSYS system is shown in Figure  6. A TRNSYS model was developed to replicate the system in Figure  4 , and it includes: • PVT collector Type 50d, modelled after DualSun Spring Hybrid panel. This type is a hybrid model that adds a PV module to the standard flat-plate collector. It simulates a combined collector and incorporates the analysis and work of Florschuetz  [4]  for flat plate collectors operated at peak power  [26].
• Weather data Type 15. It reads data from the Meteonorm database at regular time intervals, interpolating the data at the required timestep and transmits it to other TRNSYS components  [26]. The TRNSYS PVT component Type 50 was developed using the Hottel-Whillier-Bliss equations based on the work of based Florschuetz  [4]. The useful thermal output of the Type 50 is defined as: Q u = A c F R S -U L T f ,i -T a (9) where A c is the area of the collector (m 2 ), F R is the heat removal factor (-) and T f,i is the inlet temperature of the fluid ( • C). The electrical output of the system is defined as: Q e = A c Sη a α 1 - η r β r η a F R T f ,i -T a + S U L (1 -F R ) (10) where η a is the efficiency of the cell at ambient temperature (%), α is the effective absorbance of the cell (-), η r is the reference cell efficiency (%), and β r is the temperature coefficient of the cell (%/K).
Various Demand Curves The daily profile of hot water is dependent upon various parameters: type of building, the number of end-users, appliances, ambient condition, seasonal variations, the average daily hot water volume and the yearly demand  [30]. The default household hot water demand was obtained using the DHW-calc software, which can generate realistic DHW profiles for European countries for any required period of time and timestep  [31, 32]. A number of initial parameters are defined (total mean daily water volume, probability distribution, holidays).
Various Demand Curves The daily profile of hot water is dependent upon various parameters: type of building, the number of end-users, appliances, ambient condition, seasonal variations, the average daily hot water volume and the yearly demand  [30]. The default household hot water demand was obtained using the DHW-calc software, which can generate realistic DHW profiles for European countries for any required period of time and timestep  [31, 32]. A number of initial parameters are defined (total mean daily water volume, probability distribution, holidays). The hot water demand of the hotel was taken from a study with real measurement of the demand of one double room with two occupants in a city hotel, located in downtown Kyoto city  [33]. The curve is shown in Figure  12. The demand of a hotel is particular compared to the household, as we can observe that the peak demand occurs later in the evening, as typically hotel guests are out late.
Acknowledgements P. G. acknowledges the European  Union for Marie Curie Postdoctoral Fellowship  support (grantn umber-657765). This work was sponsored by  NWO Exacte en Natuurwetenschappen (Physical Sciences)  for the use of supercomputer facilities, with financial support from the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organizationf or Scientific Research, NWO). The X-ray diffractometer has been financed by  NWO. Chronoamperometry was carrieda tt he Inorganic Chemistry &C atalysis group at  UtrechtU  niversity;  Marisol Ta pia Rosales  is thankedf or practical assistance. The authors are thankful to  Dr. D ennis Hetterscheid  ( Leiden University ) for fruitful discussions.
This method aimed to decrease the uncertainty due to numerical estimation and the complexity of the model while the significant difference due to practical factors was still appreciated. $ = |&' -&' (! | • 100 ' ⁄ , ,-ℎ / $ < 5%, $ = $ 3 4 $ ≥ 5%, $ = $ 436 ( 6 ) With D is the relative difference between two MSE values with $ 3 4 is the insignificant relative difference and $ 436 is the significant relative difference. Model validation was then performed with calibrated n and kn. Value of Cm was calculated with given Co and then compared with its corresponding Cout. A set of influent and effluent data recorded from pilot operation under stable operational condition were used for model calibration In order to assess the calibration and validation processes, model evaluation statistics were recommend in addition to visual evaluation  [52].
Data gathered for model verification Residence time distribution (RTD) of the HRAP was previously investigated  [33]. The entire pilot HRAP can be considered as a continuous stirred-tank reactor (CSTR). The mean residence times of the HRAP (τ) were 2. 22 and 4. 53 days corresponding to the HRT of 4 and 8 days, respectively. Influent and effluent characteristics including COD, TKN, TN and TP during HRAP pilot operation were collected for model calibration and validation. As earlier reported by  Kadlec (2000) , hydraulic loading and influent concentration strongly influence model parameters. Moreover, it was indicated that an increasing concentration of algal bacterial biomass could lead to an increase in reaction rate  [1]. Therefore, in this study, experimental data of the pilot operation was separated into 4 modalities with different influent nutrient concentrations, biomass levels and hydraulic retention times (Table  3 ).
Pilot HRAP operation performance analysis In order to evaluate the relationship between experimental conditions and model calibrated parameters, it is important to understand the experimental pilot performance under different nutrient concentrations, biomass levels and hydraulic retention times conditions. It was also suggested that data assessment is a necessary step to obtain data set of good quality for model calibration and validation  [54]. Therefore, pilot HRAP operation data including influent and effluent wastewater, biomass and physiochemical characteristics were collected over the course of more than 8 months for evaluation. These data (Table  3 ) and calculated removal efficiencies (Table  4 ) were separated into various modalities with different conditions of nutrient load, HRT and biomass level.
This was commonly observed in HRAP system operation  [62, 63]. Throughout the pilot HRAP operation, Al-Bac biomass level, nutrient load and HRT provided significant impacts on biomass growth and wastewater treatment. Addition of high nutrient load favored bacterial growth, especially nitrifying bacteria which, at the same time, could negatively impact algal growth. High HRT resulted in high level of AL-Bac biomass in the reactor. However, no significant impact was recorded from high HRT of 8 days on treatment efficiency. High nutrient load resulted in poor TN and TP treatment while minor influence was observed in the case of COD and TKN removals. Nitrification was identified as the main mechanism in TKN and NH4-N removals, especially with high nutrient load wastewater. The data was generally sufficient for model application.
Culture conditions and bioreactor system The cultures were performed in a 500 ml total volume Xplorer® (HEL Ltd, UK) bioreactor that was modified specifically for this study. The working liquid volume was 330 mL. The bioreactor was first used with a commercial gas mixture containing H2/O2/CO2/N2 (molar %: 60:2:10:28) (Air Liquide, France) provided into the medium through a sparger. For the experiments carried out with individual gases, the bioreactor was then set up with three spargers in order to deliver air, pure CO2 and pure H2 independently. A fourth inlet gas was added in the headspace for pure N2 supply as a safety system. Each gas inlet was controlled by a gas mass flow meter. The bioreactor was equipped with pH, dissolved oxygen (DO), temperature and pressure controllers. The Xplorer® software handled the on-line monitoring and control systems of the reactor.
Statistical analyses Concentrations of metabolites and biomass are given as the mean value of 2 to 3 independent analyses. The error on the specific growth rate (determined as being the slope of ln[Optical Density] = f(t)) was calculated as the standard deviation (SD) of the slope. The instantaneous specific growth rates were calculated upon fitting process of the experimental data and the derivative calculation. For the yield determination, the state variables were plotted pairwise in a scatter plot within the considered period of the culture. A linear regression was applied to determine the yield (as the slope) and the error (as the SD of the slope). For the calculation of the CO2 to isopropanol conversion yield, the isopropanol amount was plotted versus the amount of consumed CO2, calculated from a classical mass balance from the analyses of inlet and outlet gas composition.
The composition of the biomass was determined according to the previously described methods  (Macias-Sánchez et al. , 2008; Phelippe, Gonccalves, Thouand, Cogne, & Laroche, 2019; Ritchie, 2006). The carbohydrate content was determined after two passes in high pressure disrupter (CellD, Constant System) at 270 MPa and centrifugation at 3,000g for 5 minutes. The protein concentration was determined in the total high-pressure lysate. The total pigment content was measured after centrifugation of intact cells and methanol extraction. The analysis' data gave that Nannochloropsis sp. contains ≈ 11% (wt. DW biomass) of carbohydrates, ≈ 43. 7% of proteins, ≈ 2. 0% of total pigments and ≈ 9. 0% of lipids; P. tricornutum contains ≈ 12. 4% of carbohydrates, ≈ 28. 7% of proteins, ≈ 1. 0% of total pigments and ≈ 3.