dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The rankings for the most relevant constructs were entered in SPSS Statistics Version 25. From the total of 186 'most relevant' constructs, one was excluded due to missing values. Since the rating of pictures is a very personal and subjective activity, data were treated as nominal. Treating the data as nominal leaves out the order in the categories of each variable; only the grouping of objects in categories is preserved. The dimension of the data was reduced via optimal scaling using Multiple Correspondence Analysis (MCA). Through MCA, or homogeneity analysis, each construct is represented as a two-dimensional scale, featured by discrimination measures and a mean value. The analysis creates a two-dimensional plot of the pictures 1-18 for all regions, which divides the 18 pictures into homogenous subgroups.
The interviews generated a rich dataset, but also shed light upon how passionately the interviewees spoke about the topics. Given our experiences, the wider use of RGT in research on the dynamics in sustainability transitions for (urban) landscapes may certainly be considered. Our research confirms that landscape has indeed a shared meaning. We introduced the notion of familiarity in order to express that, across the various urban regions, peoples' appreciation of landscapes is mediated by what they expect to see, e. g. the urban square as a social meeting point, the unattractive transport node that many nonetheless accept as a necessity, the historical innercity with green elements, the middle class suburban area. Some landscape elements are considered as overdone, they might be referred to as unhealthy or hostile to people, such as petrified multi-storage apartment buildings, sealed and dirty roads and parking spaces.
Background: As part of the multi-country I-O Optimise research initiative, this population-based study evaluated real-world treatment patterns and overall survival (OS) in patients treated for advanced non-small cell lung cancer (NSCLC) before and after public reimbursement of immuno-oncology (I-O) therapies in Alberta province, Canada. Methods: This study used data from the Oncology Outcomes (O2) database, which holds information for ~ 4. 5 million residents of Alberta. Eligible patients were adults newly diagnosed with NSCLC between January 2010 and December 2017 and receiving first-line therapy for advanced NSCLC (stage IIIB or IV) either in January 2010-March 2016 (pre-I-O period) or April 2016-June 2019 (post-I-O period). Time periods were based on the first public reimbursement of I-O therapy in Alberta (April 2017), with a built-in 1-year lag time before this date to allow progression to second-line therapy, for which the I-O therapy was indicated.
However, such analyses rely on robust and detailed realworld data on cancer patient treatment and outcomes. Data of such depth are scarce and typically rely on linkage between databases focused on population-level disease monitoring (e. g. , cancer registries) and detailed clinical data from electronic medical records (EMR) or claims sources. I-O Optimise is a multi-country, observational research initiative that utilizes real-world databases to provide valuable insights on the evolving treatment landscape for thoracic malignancies  [16]. The Oncology Outcomes (O2) database, which collects data on a variety of malignancies from the population of Alberta province in Canada, is one such database. The aim of the current study was to evaluate real-world treatment patterns and survival outcomes for patients treated for advanced NSCLC between 2010 and 2019 in Alberta using the O2 database.
Database overview The O2 database, in partnership with Alberta Health Services and Cancer Care Alberta, holds information for the entire province of Alberta, representing a population of approximately 4. 5 million residents. The database comprises a set of data from the Alberta Cancer Registry, also maintained by Alberta Health Services, linked to other relevant datasets (e. g. , Alberta Vital Statistics). O2 is a multisource database that integrates registry, EMR, administrative, claims and pharmacy data from 17 cancer centers, including two tertiary centers, four regional centers and 11 community sites. The Alberta Cancer Registry is responsible for recording and maintaining data on all new primary cancers, as well as all cancer deaths occurring within Alberta. Since 2004, the registry has employed the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) to classify all cancers by site and morphology and the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to record cancer deaths and cancer-related health problems.
Statistical methodology Descriptive statistics were used for reporting of patient demographic and clinical characteristics and treatment patterns. The Charlson Comorbidity Index, a score characterizing patient comorbidity burden  [20, 21] , was derived based on the prevalence of comorbidities prior to index treatment. Systemic anticancer therapies received by patients were coded in the O2 database and identified for this analysis using the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) classification system  [22]. Overall survival (OS) was estimated using the Kaplan-Meier method and was defined as the time from the start of first-line therapy for advanced NSCLC (for "progressed patients, " this was the date of receipt of systemic anticancer therapy per Additional file 1) to the date of death from any cause during the study period.
Discussion Using data from the Canadian O2 database, the current population-based analysis shows a rapid adoption of I-O therapies for the treatment of advanced NSCLC in Alberta, Canada after public reimbursement of these agents. In the post-I-O period, there was a profound increase in the proportion of patients receiving an I-O therapy in either the first-line (17% vs < 0. 5% in the pre-I-O period) or second-line (47% vs 8% in the pre-I-O period) setting. In addition, OS outcomes were significantly improved for patients treated in the post-I-O period, with a third of patients estimated to be alive 2 years after the start of first-line treatment compared with less than a quarter of those treated during the pre-I-O period. The observed uptake of I-O therapies in Alberta emulates real-world data on the adoption of these agents elsewhere.
Data acquisition with transmission electron microscopes Here we describe the basic steps to record tilt series with a transmission electron microscope. Because the use of transmission electron microscopes is particularly challenging, especially for users who are not familiar with electron microscopy, it is strongly suggested to collaborate with TEM specialists. 19. Fill up the cryo-trap dewar of the Tecnai microscope with liquid nitrogen allowing the temperature of the cryo-trap to go down to À196 C. Note: At À196 C, the cryo-trap will catch any sample contamination from remaining dirt within the column of the microscope. 20. Mount the grid on the sample holder. Note: Usually sample holders for electron tomography are allowing tilt series acquisition with a tilt angle range of +/À70. 21. Insert the sample holder in the stage of the electron microscope.
Note: Make sure that column valves are closed to not disrupt the vacuum of the microscope. Users should also verify that both the objective aperture and the cryo-box, which are 2 components of the electron microscope, are not inserted. 22. Open the SerialEM software which will provide an interface to record micrographs, as well as acquiring tilt series, of fungal biofilms. Note: Detailed instructions on how to use SerialEM are available on the following websites: a. https://bio3d. colorado. edu/SerialEM/. b. https://www. youtube. com/results?search_query=SerialEM. 23. Center the condenser aperture and bring the sample to eucentric height. 24. Collect a Full Grid Map (FGM) at low magnification (1503) using SerialEM. 25. Insert and center the objective aperture. 26. Find an area with fungal biofilm on the FGM and collect a Middle Magnification Map (MMM) at 25003.
Timing: 10 h Tomographic reconstruction is performed with the IMOD version 4. 9 software  (Mastronarde and Held, 2017). The fully detailed procedure is available online at the following address: https:// bio3d. colorado. edu/imod/doc/etomoTutorial. html. Here we cite the major steps allowing tomographic reconstruction for dual-axis tomography performed on C. albicans biofilms. For dual-axis tomography, tilt series should be recorded with two perpendicular tilt axes called A-and B-axis. Moreover, tomographic reconstruction is also a complicated process that could require the help of specialists of image processing. 32. Start the tomographic reconstruction by setting up ETomo which is the graphical user interface to the IMOD package. Note: To do so, users should sequentially enter the following information: (i) dataset name, (ii) indicate that tilt series were performed on plastic sections, (iii) select dual axis tomography, (iv) enter the size of the fiducial marker (10 nm) and (v) tick the box ''extract tilt angles from data''.
Pressing the ''Calculate Cross-Correlation'' button will launch the tiltxcorr program that will use cross-correlation to calculate XY shifts between successive images of the tilt series. For cross-correlation to take place the program first stretches the image with the larger tilt angle perpendicular to the tilt axis. The resulting file consists of a list of calculated shifts which will be applied to the dataset in the next step. Xftoxg will run once users will press the ''Generate Coarse Aligned Stack'' button. Xftoxg will use the output of tiltxcorr to compute a global set of alignments. Then, the program Newstack will apply the global set to the tilt series and generate a new image stack that the user is able to observe by pressing the ''View Aligned Stack In 3dmod'' button. 35. Generate a seed model on the zero-angle tilt micrograph based on gold particles positioning and then, track the gold particles on all other sections of the tilt series to generate the fiducial model.
37. Sample three regions of the tomogram (top, middle, and bottom parts of the tilt series) by pressing ''Create Sample Tomograms''. Then add the top and bottom surfaces from every reconstruction by pressing the ''Create Boundary Models''. Subsequently, press on ''Compute Z shift and Pitch Angles'' button. Note: This step is to fit the reconstruction into the smallest volume possible (Tomogram positioning section). Pressing the ''Compute Z shift and Pitch Angles'' button will allow tomopitch to reconstruct the flattest possible tomogram and fit it in the smallest volume possible. 38. Generate the tomogram of the axis A using Weighted Back Projection (WBP). Because we used dual-axis tomography, users should now reconstruct the tomogram corresponding to the second axis (B axis). 39. Start the reconstruction of the B axis by doing the pre-processing and the coarse alignment on the dataset corresponding to the axis B as you did for axis A.
Tilt series are recorded with the Tecnai F20 microscope by using the SerialEM software. Once the tilt series recorded, the tomographic reconstruction is conducted with the IMOD software. IMOD will create a fiducial model by using the position of gold particles, trace them across the tilt series and finally optimize the alignment of the series. Subsequently, IMOD will generate the full-size tomogram using the weighted back-projection method. In case of optimal contrast surface representations of the tomographic volume can be calculated in ChimeraX. Different features can be highlighted then with various colors using the ''measure & Colorblobs'' tool of ChimeraX. As an example, the inner cell wall (green), the outer cell wall (yellow) and the plasma membrane (magenta) were colored on a tomogram of fungal cells (Figure  7 ).
41. Once the seed model is completed, track gold particles throughout all sections of the tilt series to generate the fiducial model linked to the B-axis. Fill the gaps with the bead fixer. 42. Repeat steps 36-38 on the dataset corresponding to the B-axis. 43. Start the process of dual-axis tomogram combination by creating command files to run different programs used for the combine procedure. Press the ''Start Combine'' button. Then, press the ''Open Combined Volume'' to visualize the final tomogram. 44. Trim the volume of the final tomogram along the X, Y and Z axis.
ACKNOWLEDGMENTS T. M. is a recipient of the  Pasteur-Cantarini postdoctoral fellowship  from the  Institut Pasteur. Work in the laboratory of CdE  is supported by the  French Government 's  Investissement d'Avenir Program (Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ,  ANR-10-LABX-62-IBEID ). We thank the  UBI Core facility , and we are grateful for support for equipment from the  French Government Programme Investissements d'Avenir France BioImaging  ( FBI ,  N ANR-10-INSB-04-01 ).
Motion tracking A motion capture function is integrated in Virtual Compagnon. Motion Capture (MoCap) is a technique that allows to follow the position or orientation of an object or a person. This capture is rendered in real time via the rendering engine specific to the equipment and can be recorded in a format that can be used by 3D software. The different hardware available on the market allows to capture body movements, facial expressions, finger movements. Motion capture techniques can be classified into four categories: optical, mechanical, magnetic and gyroscopic. In this case, two different technologies were compared: VICON and the TEA Captiv solution. VICON is an optical motion capture system. To use it, markers are placed on the object or person to be tracked at certain points (Fig.
13a ). For tracking body movements, these markers are placed near the joints. To capture these points, infrared cameras are used (Fig. 13b ). The cameras send infrared rays that are reflected by the markers to the same cameras. The coordinates of the points are deduced by triangulation. A minimum of two cameras is required for the software to deduce them. Thus, when a point is visible, it appears on the processing software as of an illuminated pixel relative to the background. Each point has a unique identifier, which can be used in post-processing to recognize the different parts of the body. It is thus possible to reconstruct the skeleton by forming an axis connecting the points. The system is installed in the laboratory and is fixed and cannot be moved. The TEA Captiv is a mobile software and hardware solution developed by "Tech Ergo Appliquées" (TEA) and used by the industrial partner's.
Acknowledgment We would like to express my special thanks of gratitude to the R&D team of our industrial partner,  Bouygues Construction , for the support during this work. We are dearly obliged to thank  Bouygues Construction 's ergonomists, and all the members of MATFORM and the Gustave Eiffel training center teams for supporting us in this process. At last but not least, we would like to earnestly acknowledge the sincere efforts and valuable time given by the people, the novices and the professionals, who participated in the tests.
One extra flux meter was used to measure the heat transfer between the IEM VF and the existing wall; and three hot-sphere anemometers placed inside the cavities at mid-height (115 cm) to track the kinetic energy of the airflow blown inside each VF. The details on manufacturers, models, accuracies, ranges and time constants of the above sensors are summarized in Table  3. The analog (input and output) signals were digitalized via sensor-specific NI-DAQ acquisition modules and processed through a Virtual Instrument coded in LabVIEW. The sampling frequency was set at the maximum (0. 1 Hz). As a first trial, we aimed at imposing a PID feedback control on the air velocity inside the cavities by modulating the voltage to the arrays of fans individually. The dynamicity of the process turned out to exceed the sampling frequency, thus resulting in untimely and offsetted control actions.
Experimental setup The data set collected in the experimentation consists of 283,072 samples corresponding to measurements performed in the interval August 8, 2019-September 8, 2019 with a sampling rate of 0. 1 Hz. Each sample contains measurements related to all three considered VFs in terms of: (1) meteorological measurements (common to all VFs): external temperature, humidity, pressure, irradiance, wind speed and direction; (2) VF-specific measurements: air velocity, surface temperature at three different heights (0. 60, 1. 15 and 1. 68 m), external temperature and heat flux; and (3) timestamp of the measurements. Three regression algorithms were evaluated for the prediction of surface temperatures and thermal fluxes for the three VFs. In particular, two traditional machine learning algorithms and a deep learning algorithm were considered.
Combining artificial intelligence The versions of RT and SVR used in this work are available in Rapidminer (2022). With respect to deep learning algorithms, the long short-term memory (LSTM) algorithm  (Hochreiter and Schmidhuber, 1997) , which is a recurrent neural network that can learn long-term dependencies among sequential data, was considered. The LSTM network contains cycles that feed the network activations from a previous time step as new inputs to the network to influence predictions at the current time step. These activations are stored in the internal states of the network which can, in principle, hold long-term temporal contextual information. This mechanism allows RNNs to exploit a dynamically changing contextual window over the input sequence  [44]. LSTM was implemented using the Keras framework  [45] , which offers high-level application programming interfaces (APIs) for designing deep learning algorithms in Python.
This article deals with the detection of small-scale turbulence from in-situ meteorological measurements performed under superpressure balloons (SPBs). These balloons allow long duration flights (several months) at a prerequisite height level. The dataset is gathered from the Strateole-2 probationary campaign during which eights SPBs flew in the tropical tropopause layer at around 19 and 20. 5 km altitudes, from November 2019 to March 2020. Turbulence is not directly measured by the instrument set onboard the SPBs. Nonetheless, there is a potential to derive information about the occurrence of turbulence from the well-resolved in time measurements of pressure, temperature and position. It constitutes a challenge to extract that information from a measurement set that was not designed for quantifying turbulence, and the manuscript explains the methodology developed to overcome this difficulty.
The parameters describing the turbulence are either directly measured, such as the Thorpe lengths (an outer scale of turbulence) and the frequency of appearance of unstable layers, or are inferred on the basis of physical assumptions, like the TKE dissipation rates and the eddy diffusivity. Muhsin et al. (2020)  extended the two previous studies by analyzing the soundings of six stations of South-India, adding stations of Cochin (10°N, 76. 3°E), Coimbatore (10. 9°N, 76. 9°E),  Goa (15. 5°N, 73. 8°E) and Hyderabad (17. 5°N, 78. 6°E)  to the two previously mentioned. These data were acquired during four years, from August 2013 to December 2017. All these studies based on radiosondes measurements consistently show that the probability of occurrence of instability, either estimated from the gradient Richardson number Ri or from the squared Brunt-Väisälä frequency N 2 , is decreasing with altitude above 15 km altitude, i.
Four turbulence indexes coefficients are thus estimated. We have then averaged the 50% largest correlation coefficients, i. e. the quantiles 50-100 of the coefficients, for each the eight flights (table  4 ). These averaged coefficients give information, at least relatively, on the quality of the data and on the performance of the turbulence estimators: the larger the correlations, the smaller the impact of instrumental noise. 2 ). Systematically, we observe that: 1. the correlations are larger when using θ C rather than θ S , 2. there is a slight amelioration in using δz P rather than δz GPS , 3. the Spearman correlations (r S ) are slightly larger than the Pearson correlations (r P ). We shall therefore prefer the Spearman estimator, using δT C (and derived δθ C ) combined with δz P in evaluating both the correlations and the vertical gradients from which the turbulent indices are estimated, as the impact of instrumental noise appears to be less in this estimator.
First, a real-time video stream of the robot is presented to a visual display over a wired communication channel. For simplicity, we assume that the uplink channel is error and delay-free and the video input is delivered to the remote operator immediately. The remote controller, with the help of the visual input, sends control commands over the wireless communication channel, and the commands are received by both the robot and FoReCo. With the received commands, FoReCo performs two actions: 1) ML training: to solve Problem 1, FoReCo resorts to ML to derive f ({c j }, w), with w being the weights to learn (see §IV-B). To obtain w, FoReCo creates a dataset (see Fig. 6 ) with the commands it receives from the remote controller. The dataset contains a history of H commands, and FoReCo uses αH of them for training, and βH for testing; with α + β = 1.
This validation is performed by FoReCo in order to prevent forecasts that can lead to an accident, malfunction, or robot misuse. The valid forecast command ĉi+1 is then injected in the robot drivers (as illustrated in Fig. 3 ) with the latter assuming that it received a command on time. In the case a command arrives on time a(c i+1 ) ≤ a(c i ) + Ω + τ, FoReCo will just store the command in the dataset and later use it for training and forecasting purposes. Note that we refer to ĉi = c i if the command arrived on time ∆(c i ) ≤ τ, so it satisfies the constraint stated in (3). Thus, the forecasting receives as input { ĉ j } i j-R commands that arrived on time, and the forecasts of previous commands that did not arrive on time.
C. Training of Selected ML Forecasting Algorithms Next, we detail how FoReCo trains the selected ML algorithms described above. • VAR training: to train the VAR algorithm, we resort to Ordinary Least Squares (OLS), i. e. , the weights are computed as follows: w = argmin w αH ∑ i d ∑ k c k i -f k ({c j } i-1 i-R , w) 2 (9) over the training portion of the dataset αH. Here f k (•) refers to  (5). Note that minimizing the summation in (  9 ) is equivalent to minimizing the expression in (4), taking as distance d(c i , ĉi ) = ∑ k c k i -ĉk i 2. • seq2seq training: we train the seq2seq solution using Adam, a stochastic optimization method invariant to small gradients, as it is the case of our experimental study, e. g. , a the 3 rd robot joint takes values like c 3 i = 0. 001. We use Adam to iteratively minimize the error of the forecasts within a batch B i of commands from the training set, i.
e. , B i < αH. Hence, at each training step Adam minimizes: min w t l {c j } B i j=0 , w t = αH ∑ i d ∑ k c k i -f k ({c j } i-1 i-R , w t ) 2 B i (10) with l(•) denoting the loss function, and w t the updated weight vector at the step t of the training stage. Note that minimizing  (10)  is equivalent to minimizing (4) over the batch B i , rather than the whole training dataset αH, taking the sum of squared distances. At each step the weights are updated as follows: In this paper, we resort to the analytical model presented in  [7]  to derive wireless delays. This work models the MAC layer of IEEE 802. 11 with CSMA/CA, and studies how neighboring nodes and non-IEEE 802. 11 interfering sources impact the wireless delay. The work is based on a refinement  [41]  of Bianchi's characterization of IEEE 802.