dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification was performed on 9 neurons for each condition which correspond roughly to 20,000 to 30,000 immunoreactive spots for each channels. Statistical significance was evaluated using Graphpad prism software. The level of significance (Mann-Whitney) is indicated by one (p < 0. 05), two (p < 0. 01), or three (p < 0. 001) symbols. Stochastic optical reconstruction microscopy. Stochastic optical reconstruction microscopy (STORM) imaging was performed on a Vutara microscope (Bruker) with a high-numerical aperture (NA) objective (60x, water, NA 1. 2, Olympus). 170 nm coverslips (Menzel glaser 18 mm diameter #1. 5) were mounted on a glass slide with a 15mm hole. The hole was filled with imaging buffer (Tris 50 mM, NaCl 10 mM, 10% glucose, 100 mM MEA, 70 U/mL glucose oxidase (Sigma G0543), 20 g/ mL catalase) and sealed with Picodent twinsil.
Samples were illuminated successively with a 647 and 488 nm laser and a 405 nm laser for the reactivation of the 488 fluorophores. Neurons were isolated with wide field mosaïc microscopy (Cool snap camera) and then imaged for STORM for a series of 30 000 images with a FLASH4 CMOS camera (20 ms, 20 × 20 microns). 3D-STORM imaging was done using the bi-plane module allowing the localisation in the xyz direction. The Srx software (Bruker) was used to localise particles in 3D. Localisation tables were exported and used in ICY software for statistical localisation analysis. Correction for chromatic aberration. TIRF images have been corrected using beads' alignment with rigid registration (plugin Rigid registration in Icy) and then, (inverse) rigid transformation of images. Correction for chromatic aberration in super-resolution microscopy (SIM and STORM) has been done using multispectral (blue/green/orange/dark red) Tetraspeck beads (Thermofisher T7279).
Acknowledgements We deeply thank  Dr. Carl G. Ebeling  ( Worldwide Application Scientist for Bruker Fluorescence Microscopy ), and  Jeff Stuckey  ( Product Marketing Manager, Bruker ) for their help and the use of the Bruker Vutara system and its SRX visualisation and analysis software for rendering localisation data. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of  IBiSA  and the  France-BioImaging infrastructure, supported by ANR grant  ( ANR-10-INBS-04 ). We thank the  NeurImag imaging facility of the Centre for Psychiatry and Neuroscience, the labex 'Who am I'  and the national research group in Microscopy of the living (GDR ImaBio). This work was financially supported by the  Institut Pasteur , the  CNRS , the  INSERM, ANR  grant  ANR-10-LABX-62-IBEID  and a grant from the  Institut pour la Recherche sur la Moelle épinière et l'Encéphale.
2. The bias correction is done separately for each individual month. That is, all daily values corresponding to a given calendar month, for the observed and the historical simulation, over the observational period are collected in two time series of equal length. For example, all the 31 daily precipitation values for the month of January from 1976 to, and including 2005, are used to calculate the January bias correction parameters. The years 1976 to 2005 are used for the bias correction of the stations of Samaru, Tahoua and Dori, while the years 1980 to 2005 are used for Makurdi, N'Tarla, and Tillabery. In each case we used all available observational data. For the analysis of the impact of climate change on crop yield, we used the historical climate data and different historical simulation period corresponding to the available historical crop yield data for the baseline (1981/85-2010).
Acknowledgments: We acknowledge the  Institute for Agricultural Research, Ahmadu Bello University (I. A. R/ABU) Zaria , and  Kaduna Agricultural Development Project ,  Benue Agricultural  and  Rural Development Authority (BNARDA); Center for Agriculture, Hydrology, Meteorology (AGRHYMET) ;  Agricultural Research Station N'Tarla, Institut D'Economie Rurale (IER) ,  Programme Coton ,  Station de Recherche Agronomique de N'Tarla, Mali, Food and Agriculture Organization of the United Nations (FAO) ,  AgMERRA  and  CORDEX Africa  for providing meteorological, crop data and soils data analyzed in this study. The authors are grateful to  O. Ogunsola  ( University of Oklahoma ),  Mrs Beida Lele  ( University of Oklahoma ),  B. Dogo  ( Kaduna State University, Nigeria ),  E. H. Abdou  ( University of Oklahoma ),  T.
Ground station software is used to control the UAV flight parameters during the survey. The flight control is run by DJI ® iOSD ® software. Although Hyper-DRELIO is able to perform an autonomous flight, take-off and landing are manually controlled. In order to help with preventing severe damage in case of technical problems, it is equipped with a parachute rescue system meant to ensure a softer landing, and that is released from the ground station. The imaging sensors and the Inertial Motion Unit (IMU) are mounted together on the same support and placed in a waterproof protective case (Figure  1 ). This sensor case is mounted on a two-axis gyrostabilized gimbal.
Imaging Module The imaging module (Table  1 ) is composed of a hyperspectral pushbroom camera and an auxiliary color RGB camera. The MicroHyperspec ® hyperspectral camera (Headwall ® ) has a VNIR (Visible and Near InfraRed: 380-1000 nm) imaging sensor parametrized to collect 250 spectral bands, with a spectral resolution of 1. 85 nm. The lens has a wide Field Of View (FOV = 48. 7°) and a focal length of 8. 2 mm. The small and lightweight (770 g) MicroHyperspec ® is based on the "pushbroom" technology, which relies on the displacement of the UAV to cover the study area. The array consists of 1000 pixels in cross-track direction. The onboard data acquisition system is based on the HyperBox developed by the Geodetic Engineering Laboratory  [23]. This system enables lines to be stacked in an along-track direction during the survey, with a new image recorded every 1000 lines.
Imaging Module The imaging module (Table  1 ) is composed of a hyperspectral pushbroom camera and an auxiliary color RGB camera. The MicroHyperspec ® hyperspectral camera (Headwall ® ) has a VNIR (Visible and Near InfraRed: 380-1000 nm) imaging sensor parametrized to collect 250 spectral bands, with a spectral resolution of 1. 85 nm. The lens has a wide Field Of View (FOV = 48. 7 • ) and a focal length of 8. 2 mm. The small and lightweight (770 g) MicroHyperspec ® is based on the "pushbroom" technology, which relies on the displacement of the UAV to cover the study area. The array consists of 1000 pixels in cross-track direction. The onboard data acquisition system is based on the HyperBox developed by the Geodetic Engineering Laboratory  [23]. This system enables lines to be stacked in an along-track direction during the survey, with a new image recorded every 1000 lines.
Proprioceptive Sensors: Navigation Module Proprioceptive sensors record the internal state of the platform, including the navigation parameters in position and attitude. The quality of the geometrical rectification of the hyperspectral data, which uses the navigation parameters, depends on the accuracy of these sensors. The Hyper-DRELIO is equipped with an Ekinox-D (SBG System ® ) Inertial Motion Unit (IMU) with 3-axis gyroscopes, 3-axis accelerometers, and 3-axis magnetometers. The position is recorded using a dual-frequency, dual-antenna RTK (Real Time Kinematics) GNSS receiver. GNSS/IMU data fusion uses a multisensor extended Kalman filter  [25]  proposed by SBG System ® for helicopters. This IMU has been selected to achieve a trade-off between its light weight (600 g, Table  1 ) and small size (10 × 8.
6 × 7. 5 cm) and its accuracy (0. 05 • for roll and pitch). The heading measurement from the dual GNSS antenna is used over the magnetometers data, as the former is more accurate than the latter. Because of technical constraints related to the span and design of the UAV, the dual GNSS antenna has an 85 cm baseline, which provides a heading estimation with about 0. 22 • of accuracy, against about 1 • with the magnetometers. Moreover, this system provides "true heading", i. e. , the direction where the platform 'nose' is pointing with respect to true north on the horizontal plane, both while the UAV is stationary and in motion. Indeed, for such platforms, depending on wind conditions, the heading may differ from the route (the direction along which the platform is moving). Navigation data are recorded at a frequency of 200 Hz.
Data Merging Multi-sensor data are recorded by an Intel ® NUC (Next Unit of Computing), a small box-shaped computer, equipped with Intel ® CoreTM i5 quad core processor and fitted with 220 GB Solid State Disk and 8 GB of RAM. The HyperBox consists of drivers written to handle both cameras and GPS + IMU  [23]  (Section 2). Pulses are sent by the imaging module to the navigation module upon the start of an image exposure. The synchronization is based on CPU (Central Processing Unit) timestamps, the acquisition drivers matching navigation lines and image lines (Figure  2 ). The CPU clock's drift is lower than 0. 09 s/h. For each cross-track hyperspectral line collected, the line number, the CPU time, and the UAV position and attitude are written in the header file of the hyperspectral raw image.
SfM Photogrammetric Processing When the flight is performed above solid ground, the photographs collected by the RGB camera are processed using the photogrammetric software Agisoft ® PhotoScan Professional (version 1. 2. 6, Agisoft®, St Petersburg, Russia). The procedure for the 3D surface reconstruction is based on the Structure from Motion (SfM) workflow for Multiview Stereophotogrammetry  [26, 27]. The DSM (Digital Surface Model) of the area is useful in the hyperspectral data georectification workflow to know exactly the height of the UAV above the ground and therefore to correct hyperspectral data of relief effects. Simultaneously acquiring RGB photographs from the hyperspectral imaging platform provides a DSM with higher spatial resolution than the existing DEMs available in national/global databases.
This first step combines the following inputs (Figure  4 ): 1. geometrical acquisition settings: focal length, pixel size, sensor length, mounting offsets; 2. the absolute position (X, Y, Z) and orientation (roll, pitch, heading) of the hyperspectral camera, recorded in the image header; 3. the DSM of the study area to take into account the true height of the UAV above the terrain. The absolute position of each imaged pixel is then computed into the selected reference coordinate system. Resampling by linear interpolation is finally applied to the hyperspectral images in order to obtain data on a regular grid (Figure  4c ), which later may be exported and further processed using other software. Here, this processing chain has been developed with MATLAB ® software. Typical hyperspectral imaging products can be generated from the georectified hyperspectral lines: spectra, hyperspectral cubes, etc.
The absolute position of each imaged pixel is then computed into the selected reference coordinate system. Resampling by linear interpolation is finally applied to the hyperspectral images in order to obtain data on a regular grid (Figure  4c ), which later may be exported and further processed using other software. Here, this processing chain has been developed with MATLAB ® software. Typical hyperspectral imaging products can be generated from the georectified hyperspectral lines: spectra, hyperspectral cubes, etc.
7 ha with a spatial resolution of about 9 cm and representing more than 4 Gb of data. at 100 m of altitude, which has been performed above Saint Aignan pond (Brittany, France), the georectification RMSE computed on nine control points is about 1. 9 m. Figure  8  presents a sample of each georectified dataset as hyperspectral image cubes, i. e. , with the third dimension of the cube depicting the radiometric intensity as a function of the wavelength. Some isolated lines appear misaligned when the navigation system fails to register the real movement of the imaging system, creating artifacts on the reconstructed hyperspectral image. RMSE value to be statistically refined for various flight conditions. As an example, for another flight at 100 m of altitude, which has been performed above Saint Aignan pond (Brittany, France), the georectification RMSE computed on nine control points is about 1.
9 m. Figure  8  presents a sample of each georectified dataset as hyperspectral image cubes, i. e. , with the third dimension of the cube depicting the radiometric intensity as a function of the wavelength. Some isolated lines appear misaligned when the navigation system fails to register the real movement of the imaging system, creating artifacts on the reconstructed hyperspectral image. The settings of both surveys are summarized in Table  2. At Porsmilin Beach, the weather was very sunny, without wind. The survey was performed along two flight lines at 50 m of altitude with the typical settings previously mentioned in Section 3. 1. The flight plan is depicted in Figure  6a. The spacing between flight lines was set to 30 m in order to have 33% of overlap. Due to sunshine conditions, the gain of the hyperspectral camera was set to 5.
Results of Hyperspectral Lines Georegistration Data are processed as described in Section 3. 3. As photogrammetry is ineffective above water surfaces, a DSM is only used above land surfaces, water surfaces being considered as flat surfaces with constant elevation. The results of hyperspectral lines georectification are depicted in Figure  7. To make data viewing easier, hyperspectral lines are represented as a 3-bands (RGB) composite image. For georegistration assessment purposes, as it is not possible to place control points over the water, the spatial accuracy of georegistration is assessed using natural, stable elements (e. g. , shoreline of the pond, features of the underwater landscapes. ) that are easily identifiable both in the georectified hyperspectral imagery and in orthophotographs from global databases.
As an example, for another flight at 100 m of altitude, which has been performed above Saint Aignan pond (Brittany, France), the georectification RMSE computed on nine control points is about 1. 9 m. Figure  8  presents a sample of each georectified dataset as hyperspectral image cubes, i. e. , with the third dimension of the cube depicting the radiometric intensity as a function of the wavelength. Some isolated lines appear misaligned when the navigation system fails to register the real movement of the imaging system, creating artifacts on the reconstructed hyperspectral image.
In addition, the flight plan should be as simple as possible, avoiding turns and curves, as well as an excess number of waypoints, and best adapted to meteorological constraints, mainly so as to avoid crosswind. Despite the aforementioned measures, some degree of georeferencing error persists, particularly scan-line to scan-line errors. As Hruska et al. [16]  also point out, such errors most likely result from differential movement or vibration of the platform. For Hyper-DRELIO, the speed of the engines is 7640 rpm at maximum output, that is, 127 Hz. As the navigation data is recorded at a frequency of 200 Hz, the IMU does not capture all the vibrations induced by the rotors. The Kalman filter implemented in the Ekinox-D IMU, designed after an aircraft-type sensor model, may also be not optimally parametrized for the Hyper-DRELIO UAV.
Conclusions For UAV surveys, payload restrictions are critical, particularly when the data collected require direct georeferencing. Indeed, the quality of the direct georeferencing is limited by the resolution and the accuracy of the proprioceptive sensors. Hyper-DRELIO allows hyperspectral data to be collected above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1. 85 nm) in VNIR domain, and with georectification accuracy on the order of 1 to 2 m. The pushbroom image formation process is a major source of complexity in the geometrical correction step. While this problem should be overcome in the future with the development of light full-frame hyperspectral cameras, currently such sensors require a compromise between spatial coverage, spatial resolution, and spectral resolution.
ACKNOWLEDGMENTS We thank  Elian Martin  for help with the syringe pump and the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , a member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). This work was supported by  Canceropôle Ile-de-France  (  2015-1-EMERG-10-ICR-1  to M. C. P. and V. S. ; no. 2012-2-EML-04-IC-1 ),  Ligue National Contre le Cancer-Comitede Paris  ( RS14/75-54  to M. C. P. ),  Fondation pour la Recherche Medicale  ( ING20160435436  to M. C. P. ;  DGE20111123020 ),  Institut National de la Santeet de la Recherche Medicale ([INSERM] ITMO Plan Cancer   2014-2018 ,  EQ2016-01 , to M. C. P. ),  INCA (Cancer National Institute , no. 2011-1-LABEL-IC-4 ), and  Association Christelle Bouillot  (to J. C. ). A. D. N. was supported by the  Scientific Independence of Young Researchers Program  ( SIR 2014 ) under grant no.
Sentiment analysis, which is also called opinion mining, aims to determine people's sentiment about a topic by analyzing their posts and different actions on social media. Then, it consists of classifying the posts polarity into different opposite feelings such as positive, negative and so on. Sentiment analysis could be divided into two main categories: • Lexicon analysis aims to calculate the polarity of a document from the semantic orientation of words or phrases in the document. However, applications based on lexicon analysis do not consider the studied context. • Machine learning (ML) involves building models from labeled training dataset (instances of texts or sentences) in order to determine the orientation of a document. Studies that used this type of methods have been carried out on a specific topic.
Since our approach aim to compare Y i , we identify hashtags that have a high frequency as the most popu- lar hashtags for each Y i. Then, we classify a very small set (between two and three for each one) of them manually into positive and negative classes and collect related data for each class separately. In other words, collected tweets will be classified into a positive or negative class basing on the upper defined polarity of hashtags. • Step2 consists of preprocessing classified data from hashtags. Social data is informal and could contain misspelling and non-textual information, hence the need of a preprocessing step. For this, we apply various filters on tweets as following: -Tokenization this sub-step consists of identifying nouns, verbs, adverbs, adjectives, URLs, common emoticons, phone numbers, HTML tags, Twitter mentions hashtags, and repetition of symbols and Unicode characters.
Implementation Big data analytics systems should have enough memory, bandwidth and the capacity to perform parallel processing of tasks in real-time. Therefore, we implemented the prototype system over a cluster of 3 servers. Each of these 3 servers has two Intel Xeon E5530 quad-core CPU 2. 4 GHz processors that run on 64-bit Linux Ubuntu. Also, the servers are equipped with 24 Go DDR3 RAM and 1 TB hard disk. For data gathering, we used apache Kafka which is a distributed streaming platform. Kafka uses publish-subscribe messaging and offer a distributed and replicated service. More specifically, we used the integrated Streams API library that allows building applications for stream processing. Then, the large volume of collected data is stored in HDFS (Hadoop Distributed File System). For data processing (building dictionaries and data classification), Spark is employed.
Spark allows scalable and fault-tolerant processing of data streams in near real time. In our case, Spark is used on Hadoop, managed by YARN and distribute processing across 3 nodes. Also, in order to compare the proposed method with other methods, we use Spark MLlib's implementation of Naïve Bayes classifier? for classifying the tweets in realtime. The model created by Naïve Bayes is applied in real-time to retrieved tweets. All the algorithm for data gathering and processing were implemented using Python.
Data gathering • First stage To focus on 2016 US presidential election, we only used two candidates' names: Hillary and Trump as keywords to retrieve tweets (i. e D = { Y 1 , Y 2 } with Y 1 = Trump and Y 2 = Hillary). Data is collected from Twitter REST API in real time and tweets should be written in English. In order to identify the most popular hashtags, we generated a hashtag frequency list for the entire collected data. We selected most relevant hashtags from this word frequency list, then, we classified them manually as following: Positive Trump: #MAGA, #makeamerikagreatagain and #voteTrump Negative Trump: #NeverTrump #dumptrump Positive Hillary: #imwithher, #strongertogether and #voteHillary Negative Hillary: #podestaemails, #NeverHillary and #crokeedHillary. In order to validate the construction method of dynamic dictionary, we collected data related to chosen hashtags over 5 and 6 October 2016.
Classification accuracy evaluation To assess the ability of classifying tweets basing on the automatically constructing dynamic dictionary, we have randomly selected a subset of 600 tweets from the political Twitter corpora: 50 for each class. The tweets were carefully inspected and manually labeled as positive, moderately positive, highly positive, lightly negative, moderately negative, strongly negative or neutral for each candidate. Then, the same data was processed, as mentioned above, by removing stop words, applying tokenization, stemming and various filters. This step was done by TreeTagger, which is a tool for annotating text with part-of-speech and lemma information. TreeTagger was modified to handle negation, URLs, usernames, Twitter mentions and hashtags and intensifiers. In order to test the proposed approach, we compared it with two following classifiers approaches: • Google cloud prediction API that provides a RESTful API as a black box to build ML models basing on a training dataset.
Doing Visual Research with Farmers in Rural Switzerland Data collection for this study was performed in summer 2012 and spring 2013. Decisive for the timing was the research process itself, but summer was chosen for data collection as there is much 'to see' in the landscape then, even though summer is a highly demanding season for farmers. In every municipality, a key person in relation to agriculture helped obtain access to the farmers. The farmers were asked for their consent by phone while at the same time being informed that the project is locally advocated. Motivation among farmers to participate was generally high. Taking photos already started a process of thinking and awareness-raising concerning the research topic among the participating farmers. Afterwards, the photographs served as a stimulus for the in-depth interview, which focused on experiences and reflections on particular topics shown in the pictures (see  Collier and Collier 2004; Pauwels 2010).
A letter sent in September 2012 informed him that the data collection had been terminated. The films in the disposable cameras were successively developed and photo negatives were digitalised by a local photoshop. DIN A4 colour print-outs were made for the interviews. This process normally took three working days and interviews were held promptly. Farmers also appreciated that, because they could take the weather forecast into consideration for the appointment. Interviews were held on each farmer's farm and it was up to the farmers to include his partner and family or not, because even if it was the farmer himself who took the photos, the 'camera' was often discussed with the whole family. A total of 28 interviews lasting 50-80 min were conducted with farmers on their farms between July and September 2012.