dataesr/scientific-paragraphs-categorization huggingface dataset:
To cope with these errors without negatively impacting the SSA estimation, another model including a free scaling parameter A is also used: ↵ 2-param ( , ✓) = A ⇥ r diff ( , ✓)↵ diff ( ) + 1 r diff ( , ✓) ↵ dir ( ) ⇤ , (10) Nevertheless, considering that a value of A significantly different from 1 reveals a serious issue with the observations, we reject any fit that gives A outside the range 0. 9 -1. 1. The fit is performed with a non-linear least square method (provided by the Python scipy. optimize. leastsq function) to minimize the squared difference between the model and observed spectrum. Smoothing is done beforehand to reduce the noise using a first order Butterworth low-pass provided by the Python scipy. signal. butter function with cut-off of 0. 1. Given the stronger sensitivity of the albedo to SSA in the near-infrared with respect to the visible, only the data for wavelengths in the range 700 -1050 nm are used for the fit.
Acknowledgements. This study was supported by the  ANR  program  1-JS56-005-01  MONISNOW. The authors acknowledge the  French Polar Institute (IPEV)  for the financial and logistic support at Concordia station in Antarctica through the  NIVO program. This work has also been supported by a grant from  OSUG@2020  (investissement d'avenir - ANR10 LABX56 ). We thank the winter-over staff at Concordia for the maintenance of Autosolexs.
We know that, Partial Differential Equations (PDEs) are generally used to model many physical phenomena such as fluid dynamics and electromagnetism, etc. In complex media, solutions to the governing equations can be difficult to derive in closed-form by traditional analytical routes -Fourier or Laplace transform methods, power series expansion and so on, hence one often has to resort to numerical approximation of the solutions. As a particular class of numerical techniques, FEM is efficient to solve problems in heterogeneous anisotropic media  [11, 12] , and it has been widely applied in engineering design and analysis in mechanics starting from the late seventies, while researchers in photonics started to use it in the nineties. Notably, the COMSOL Multiphysics package is a finite element analysis software much used nowadays to solve various physics and engineering applications in the metamaterials' community, as it allows entering coupled systems of PDEs, such as in opto-mechanics, thermal elasticity etc.
Problem setup with open boundary conditions In the last section, we have discussed the general PDEs in bianisotropic media, which allows us to introduce the PDEs model in COMSOL Multiphysics; however, in order to mimic the scattering and propagation properties of bianisotropic media in an open space, we need to consider the boundary conditions. We usually introduce a transformation of an infinite domain into a finite one, which should enforce that the wavelength is contracted to infinitely small values as it approaches the outer boundary of the transformed domain within a perfectly matched layer (PML). At this outer boundary, Dirichlet data could be set, thereby enforcing a vanishing field  [14, 15]. The PML is shown to be equivalent to an analytic continuation of Maxwell's equations to complex variables's spatial domain  [16].
This wider project collected both qualitative (Du & Moore ) and quantitative/epidemiologic data that were used to develop and parameterise the key features of the model. In the first section we describe the conceptual model and its principal components; the second section presents the calibration of the model by testing key model parameters and shows the evolution of our population of methamphetamine users; lastly, the third section discusses the outcomes and limitations of TreatMethHarm in its final form, and the potential for its future use in modelling policy scenarios.
Stages of methamphetamine use. The trajectory of a given methamphetamine user was modelled within a hierarchy of four use 'stages' defined by frequency of use and a related set of practices  (Becker ; Abbott ). The data collected during the epidemiological survey suggested three main stages of methamphetamine use within the cohort: • Occasional users (stage or S ) -users who consume methamphetamine less than twice a week and smoke or snort it; • Frequent users (stage or S ) -users who consume methamphetamine at least three times a week; • Problematic users (stage or S ) -individuals with daily or almost daily methamphetamine consumption and inject drugs. These three stages and their related frequency of use are implemented in the model as attributes. TreatMeth-Harm also includes users at S , who are potential 'experimenters' or previous users (who can return to use).
Implementing and Calibrating TreatMethHarm. The conceptual model was implemented in the multi-agent platform NetLogo (Wilensky ) using a variation of the StarLogo programming language (the global architecture of the model and attributes of the agents are presented in Figure. This platform is widely used by social scientists aiming to reproduce socially complex systems because of the simplicity of the programming language, the user-friendly interface that includes a spatial environment visualisation, the possibility to directly act on the course of a simulation, and the capacity to observe outputs by plotting key parameters. The simulation runs on a daily time-step system to acknowledge the frequency of consumption of high-stage methamphetamine users. TreatMethHarm o ers the possibility to run simulations with a 'closed' or 'open' system population: the modeller can choose to observe either a specific population of agents, or to run the simulation with the addition of newly generated agents at programmed intervals.
Acknowledgements The research reported in this paper was funded by  NHMRC  Project Grant. The  National Drug Research Institute at Curtin University  is supported by funding from the  Australian Government  under the  Substance Misuse Prevention and Service Improvement Grants Fund. The authors gratefully acknowledge the contribution to this work of the  Victorian Operational Infrastructure Support Program , the sta from  Burnet Institute  who assisted with the recruitment of the participants (especially,  Cerissa Papanastasiou ),  Professor Pascal Perez  and  Georgiy Bobashev  for their advices, as well as all the participants of this survey.
Introducing new mutations Plant breeding has been done for the most part of its 10,000 years of history in an unconscious way. However, the progress of the scientific knowledge has an immediate impact in these practices throughout history. Understanding the key role of genetic variability and selection had a profound impact on plant breeders and the discovery that physical and chemical agents induce mutations in the DNA  (Muller 1928; Stadler 1928)  prompted the breeders to start mutagenesis programs to increase genetic variability. Over the past 70 years, more than 3200 new crop varieties in over 200 plant species have been obtained through mutagenesis (IAEA/FAO mutant variety database, http://wwwinfocris. iaea. org/MVD/). This approach has proved to be extremely useful despite its limitations  (Parry et al.
Ultrasonic vocalisation is a broadly used proxy to evaluate social communication in mouse models of neuropsychiatric disorders. The efficacy and robustness of testing these models suffer from limited knowledge of the structure and functions of these vocalisations as well as of the way to analyse the data. We created , an open database with a web interface, to mouseTube facilitate sharing and comparison of ultrasonic vocalisations data and metadata attached to a recording file. Metadata describe 1) the acquisition procedure,. , hardware, software, sampling frequency, bit depth; 2) the biological e. g protocol used to elicit ultrasonic vocalisations; 3) the characteristics of the individual emitting ultrasonic vocalisations ( , strain, sex, age). To promote e. g. open science and enable reproducibility, data are made freely available.
Some laboratories have developed their own detection and/or analysis methods (e. g. ,  Chabout et al. , 2015; Hammerschmidt et al. , 2012; Holy & Guo, 2005; Neunuebel et al. , 2015; Seagraves et al. , 2016; von Merten et al. , 2014)  or use commercial and/or manual solutions for detection and/or analysis  (Chabout et al. , 2012; Ey et al. , 2013; Wöhr et al. , 2011b; Yang et al. , 2013). Nevertheless, little is known about the advantages and disadvantages of each of these methods, and comparisons of these methods on the same files would be highly valuable for the field. To counteract these current weaknesses of the domain, we developed mouseTube. This database is designed to share and exchange recording files from mouse ultrasonic vocalisations, along with all the corresponding metadata. It aims at increasing knowledge on mouse vocal communication, improving reproducibility of the experiments and stimulating the development of robust analysis tools.
The web interface is available at http://mousetube. pasteur. fr. Data uploaded on mouseTube are shared between all members of the community connected to mouseTube. Members uploading data on mouseTube are fully responsible for the content of uploaded files and the accuracy of the metadata provided. Data uploaded still belong to the laboratory that recorded them, but the owner gives the right to the members of mouseTube to use them for analyses and publications. Any member of the community can download data. mouseTube data are freely available upon online registration and can be used for subsequent publications. Any publications derived from the data should state the contributors (user) of the data and mousetube. pasteur. fr as being the data source and, whenever possible, cite the original paper(s) in which data have been first described.
Database design The mouseTube database stores the links toward each audio recording file and all the corresponding metadata. The audio recording files themselves are stored on external servers, owned by each laboratory and accessible with the login and password given when registration is confirmed (these servers should be configured with these login and password). This allows the owner to control his or her own data. For a video tutorial on how to upload data on mouseTube, please see  (Ferhat et al. , 2016). mouseTube is a web interface coded in php and a MySQL relational database hosted by an Apache server. The web interface allows users (i. e. , data contributors and data downloaders) to manage the data in the database. In relational databases, each table has its own unique key to connect tables together (it is possible to combine several keys but in the case of mouseTube, we identify a simple unique key for each table).
In this way, the data are well organised and it is very fast to find all the specificities of an element, looking through the links. mouseTube is organised as seven tables connected by unique keys within the database (Figure  1 ): -"strain": gathers all mouse strains already entered in the database. This table contains information about the name of the strain, the background on which it has been generated, and the bibliographic reference where it has been first described. If a new strain is needed, every user can send an email with all requested information for the administrators to add it. This procedure will avoid any inconsistencies between the strains in the database. -"subject": lists all the individual mice entered by contributing users. New subjects are created by each contributing user.
The table "subject" is connected to the table "user" (unique key "id_user"), meaning that one individual belongs to only one contributing user. The "subject" table is also connected to the table "strain" by the unique key "id_strain", meaning that one subject can only be characterised by one strain. This table stores all information relative to each subject such as its origin, name, sex, genotype, treatment, and subgroup. -"user": stores the information about all contributing and non-contributing users having access to the mouseTube database. This table is connected to the table "subject" and "protocol" through the unique key "id_user", identifying the owner of a subject and the protocols that have been used by this person (a subject or a protocol belongs to only one user). The table "user" stores contact information and the encrypted version of the password and login.
Data organisation A help section is available on the home page of each user's session. This part provides all details about the information stored in each section of mouseTube. It can also be accessed at any time through the "Help" tab available on each page. For each protocol, a name is requested. Users should also specify how many recording files are generated for each mouse in this protocol. Users should then provide a very precise description of the exact protocol. If another user has already created the protocol, the owner of the vocalisation files needs to create it again with a similar name and same information. He/she will then be able to select it when he/she creates an experiment. The database is built in this way to track changes in each protocol. For each experiment embedding several audio recording files, the following metadata are stored: -name of the experiment -protocol -mouse group -date of the beginning of the experiment -temperature -light phase for testing (light or dark period) -hardware used (microphone & sound card; auto-completion of this field to ease the identification of the equipment) -acquisition software -sampling frequency -bit depth -laboratory These metadata will permit useful comparisons of the software, hardware and environmental conditions of each audio recording.
Data retrieval The mouseTube web interface allows users to search different types of data into the database without coding a MySQL request. Data can be searched according to several criteria, such as protocols, owner of the files, mouse strain, and experiment. Users can select all recording files from a specific mouse strain or user/laboratory. mouseTube also aims at providing the users with different protocols to record mouse ultrasonic vocalisations. mouseTube even provides the possibility to search for individual mice. The more information is entered, the easier it is to find a subject. To download any vocalisation file, users need the login and password provided at the confirmation of their registration and common to all servers hosting vocalisation files linked in mouseTube. These are different from their personal login and password.
Use cases Example 1: searching for protocols and control strains In this example, we use the search function of mouseTube in the protocol section to gather information on potential protocols to record ultrasonic vocalisations in adult male mice and to collect data on control animals in this protocol. By going through all protocols (Figure  2 ), we found several protocols used to record male ultrasonic vocalisations. Elodie Ey provided protocols to record male-male interactions in different cages after 3 weeks of isolation or male-female interactions. Jonathan Chabout also provided a protocol to record male vocalisations in response to urine (male or female), an anesthetised mouse (male or female), or an active female. With these selected protocols, at the time of writing, we managed to find 16 individuals from the C57BL/6J strain recorded in the male-male interaction protocol from E.
Ey, 48 individuals from the ProSAP1/Shank2 strain recorded in the male-oestrus female protocol from E. Ey, and 12 individuals from the B6D2F1/J strain recorded with the protocol of J. Chabout. Altogether, we now have an important set of reference files for each protocol to compare to our and soon we will try the different protocols in our laboratory. The output file (Supplementary file 1) provides a sample of the metadata and links to the files recorded in the male -oestrus female interactions by E. Ey. This file is automatically generated for each vocalisation search request and can be downloaded to save the metadata related to each file. A sample of the metadata and links to the files recorded in the male-oestrus female interactions by E. Ey. This file is automatically generated for each vocalisation search request and can be downloaded to save the metadata related to each file.
Example 2: testing a detection algorithm In this example, we plan to test a new detection algorithm under various recording conditions. To do so, we need to gather files recorded with different equipment under various levels of background noise conditions. We use the search function of mouseTube in the vocalisation section to select three vocalisation files in each of the protocols available in mouseTube. The diversity of recording conditions allows us to investigate the limit of our detection algorithm to extract ultrasonic vocalisations from background noise. We end up with a set of many vocalisation files to include in our test set. For each vocalisation search, a table recapitulates the corresponding metadata of each vocalisation file, specifying the protocol and hardware and software used.
Conclusion We present mouseTube, a database with a web application to boost knowledge on mouse ultrasonic communication. This database stores recording files of mouse ultrasonic vocalisations as well as the corresponding metadata. It provides a source of information on the protocols to record mouse ultrasonic vocalisations and on the availability of recording files for different mouse strains. At the time of writing this paper, mouseTube provides a platform to up-or download mouse recording files and the corresponding metadata. The aim is to constantly develop mouseTube, and we are currently exploring ways of enabling users to analyse their data online, where the owner of the data will be notified of each analysis performed with their data. The database will also offer researchers the option of keeping a portion of their data on a private part of mouseTube until they have been analysed and published, after which the data will be made publicly available.
Acknowledgements We thank  Eliza Jones  for lending her voice for the video presenting mouseTube. We thank the reviewer for his comment. We agree that it would be helpful to broaden the distribution of the system. We are working on it. We are working on a way to couple the analysis of the data files with the database. This will for sure attract many users. More and more journals ask for the availability of the raw data, and we should make sure that mouseTube will be accepted as a way to publish the raw data. Some journals accept it; others are still reticent. We are also working on it. No competing interests were disclosed.
by the  ANR [ANR -Grant information:  08-MNPS-037-01-SynGen ]; by  Neuron-ERANET (EUHF-AUTISM) ; by the  Fondation Orange ; by the  Fondation FondaMentale ; by the  Fondation de France ; by the  Fondation Bettencourt-Schueller. The research leading to this article has also received support from the  Innovative Medicine Initiative Joint Undertaking  under grant agreement no. 115300 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data and software availability Audio recording files are available for all mouseTube users via the web application http://mousetube. pasteur. fr. To download the vocalisation files from the different storage servers, users need to enter the login and password common to all servers hosting mouseTube data files. These are provided upon registration online. F1000Research: Dataset 1. Output file automatically generated for each request of vocalisation file, 10. 5256/f1000research. 9439. d135667 (Torquet et al. , 2016).
The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (http://volbrain. upv. es), which is publically and freely accessible to the scientific community.
The BET (Brain Extraction Tool) software from the FSL image processing library  (Smith, 2002)  is one a well-known and widely used brain extraction techniques. Other techniques such as Brain Surface Extractor (BSE) have been also used successfully  (Sandor and Leahy, 1997). More recently, multi-atlas label fusion methods have been shown to be competitive  (Leung et al. , 2011; Eskildsen et al. , 2012). Intracranial cavity extraction can also be obtained indirectly as part of the full modeling of brain intensities using a parametric models as done in Statistical Parametric Mapping (SPM)  (Ashburner and Friston, 2005)  or VBM8  (Nenadic et al. , 2010)  software packages. Recently, we presented a novel approach for intracranial cavity extraction called NICE  (Manjón et al. , 2014)  which is an evolution of the BEaST technique enabling faster and more accurate results.
Another set of methods aim at classifying the main intracranial tissues such as white matter (WM), gray matter (GM), and CSF. A usual approach is to model the histogram of the ICC area using a mixture of Gaussians estimated with the EM algorithm  (Wells et al. , 1996)  or with fuzzy C-means clustering  (Ahmed et al. , 2002). A common feature of those methods is the use of a priori information in the form of spatial probability maps (e. g. , SPM software  Ashburner and Friston, 2005). All these methods assign a membership degree or probability to belong to specific tissue to every voxel rather than calculate the actual amount of each tissue within each voxel. For this reason some authors used the concept of partial volume coefficients (PVC) to represent the actual amount of every tissue within each voxel  (Tohka et al.
Several recent software tools have been developed to automatically obtain some or all of these volumetric measures using different strategies. For example, the SPM software is a widely used tool to analyze global GM or WM alterations. Voxel-Based Morphometry (VBM) toolbox (an extension of SPM) has also been used to measure local GM atrophy. To perform more specific volume measurements tools like the FSL package  (Jenkinson et al. , 2012)  or Freesurfer  (Fischl et al. , 2002)  are freely available. FSL is a comprehensive library of analysis tools for functional MRI, anatomical MRI and DTI brain imaging data. One of these tools, called FIRST  (Patenaude et al. , 2011) , is able to automatically segment subcortical brain structures. Similarly, the Freesurfer pipeline can be used for volumetric segmentation, cortical surface reconstruction and cortical parcellation; it has been used in numerous studies despite its high computational burden due to its ease of use.
The great success of these tools is due to their success in obtaining volumetric information from MRI data, but also because of their free public availability. The aim of this paper is to present volBrain, a new software pipeline for volumetric brain analysis. This pipeline provides automatically volumetric brain information at different scales in a very simple web-based interface not requiring any installation or advanced computational requirements. In the following sections, the different parts of the volBrain platform are described and some performance evaluation presented by comparing results to existing methods.