dataesr/scientific-paragraphs-categorization huggingface dataset:
We store randomly-rotated relative location coordinates, where the origin was the location that was most common during the first three days after installation (typically the patient's home). Our apps are available via app stores, which allows us to continuously update and improve the app based on newly discovered bugs and also user feedback. However, the access to the apps contents and features is permitted only for patients accepting to participate to the study. All online self-report data are de-identified (identified by a random code, unrelated to any data protected by HIPAA). Only the PI holds the code, stored in a secure location separate from data. Personal information about potential and enrolled participants will be collected, and maintained in order to protect confidentiality before, during, and after the trial according to the terms described in the ethic committee report.
A paradigm shift in suicide risk prevention This study stands out from randomized controlled trials or observational studies ailing to identify suicide risk factors. We conducted a preliminary study showing that the eB2 system was capable of identifying mobility pattern changes, which may be used as proxies for behavioral changes and relapses in mental health  [6]. We will apply the two key type of models previously tested in suicidal prediction: 1) predictive models or discriminative models and 2) explanatory or generative models. Both predictive and explanatory models use patient features to provide information on future events, such as the likelihood that a patient will attempt suicide in a given time interval. Applying these models to our database, will generation of tools to improve medical decision-making, patient outcomes, and to identify mediators and moderators within causal pathways.
Funding This study was partly funded by  Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III  ( PI16/01852 ),  Delegación del Gobierno para el Plan Nacional de Drogas  ( 20151073 ),  American Foundation for Suicide Prevention (AFSP)  ( LSRG-1-005-16 ), the  Madrid Regional Government  ( B2017/BMD-3740 AGES-CM 2CM ;  Y2018/TCS-4705 PRACTICO-CM ) and  Structural Funds  of the  European Union. MINECO/FEDER  ('ADVENTURE', id. TEC2015-69868-C2-1-R ) and  MCIU Explora Grant  'aMBITION' (id. TEC2017-92552-EXP ), the  French Embassy in Madrid, Spain , The foundation de l'avenir, and the  Fondation de France. The work of  D. Ramírez  and  A. Artés-Rodríguez  has been partly supported by  Ministerio de Economía of Spain  under projects: OTOSIS ( TEC2013-41718-R ),  AID  ( TEC2014-62194-EXP ) and the  COMONSENS Network  ( TEC2015-69648-REDC ), by the  Ministerio de Economía of Spain  jointly with the  European Commission (ERDF)  under projects ADVENTURE ( TEC2015-69868-C2-1-R ) and CAIMAN ( TEC2017-86921-C2-2-R ), and by the  Comunidad de Madrid  under project  CASI-CAM-CM  ( S2013/ICE-2845 ).
RELATED WORK Many tools have been presented in literature to address some of the issues of automating experiments set-up generation. However, most of them only focus on generating or monitoring network topologies and do not address network activity generation nor experiments reproducibility. CAIDA 1  , RocketFuel 2  or the Oregon Route Server 3  provide datasets from AS relationship or BGP tables. These datasets represent a part of the Internet topology monitored at a given time. Though these topologies can be easy reproduced (just parse the datasets again), these datasets only describe how nodes are interconnected. No information, about topologies properties (bandwidth, nodes configuration,. ), about topologies activities (workflows, dataflows,. ), is provided. There are many tools generating large scale synthetic network topologies using literature models (Section 3.
SeeDep Architecture SeeDep consists of 4 modules: the network topology generator module; the application topology module; the reproducibility module; and the deployment module. As seen in Figure  1 , network topologies are represented by a graph and application topologies are the mapping of a set of applications onto a network topology. Reproducibility is represented by an application topologies catalog on which each application topology is identified by a seed. Finally, deployment consists in mapping an application topology on a real infrastructure. The following sections give a detailed explanation of each module.
, 2001)  remains the most popular generator. It is implemented under the GPL license, both in Java and C++. Brite allows users to create large graphs (more than 500K nodes) from all classes (random, hierarchical, power law) using a graphical user interface or the command line API. More recently, in 2013, FNSS  (Saino et al. , 2013)  brings users with a python library, allowing them to generate graphs of thousands of nodes from the three classes. We can also cite aSHIIP  (Tomasik Joanna, 2012)  providing a graphical interface to quickly generate graphs (less than 2s for 5K nodes) from the three classes in a simple way. SeeDep is built in a modular way so that many network topology generators can be used. Thanks to its popularity and simplicity, Brite is the network topology generator used by default in SeeDep.
Application Mapping To get an application topology, the combination of the two previous components (network topology and applications) is achieved by mapping the applications to the nodes of the network topology. This mapping is done following some user constraints (one application per node,. ) called rules. Each rule consists of a set of formulas in Conjunctive Normal Form (CNF). All rules are processed using a SAT solver in order to provide an application mapping. A CNF formula consists of a set of literals set to false or true. In SeeDep, a literal represents: "this application hosted on this node". SeeDep, being written in Python, relies on pycosat 4 to solve the rules. In pycosat, a literal is represented by an integer. Consequently, SeeDep's Literal Representation (SLR) must be transformed into Pycosat's Literal Representation (PLR).
To explain how to do that, let's have an example. We want to map the 10 previous applications on the 10 nodes of the network topology shown in Figure  3. Each application is assigned with an id from 1 to 10, as seen in Table  1. Each node is also assigned with an id from 0 to 9. Using the formulas (1) and (  2 ) we are able to transform the SLR into PLR and vice versa. For example, "application with id 3 hosted on node with id 1" is transformed into 14, while 10 means "application with id 10 hosted on node with id 0". 4 https://pypi. org/project/pycosat/ (2) app id = PLR%11 More generally, let's assume A the set of applications to be mapped and N the set of nodes of the network topology, PLR and SLR are processed using formulas (3) and (  4 ) ∀n ∈ N, a ∈ A, PLR(n, a) = (|A| + 1) × n + a (3) n = PLR/(|A| + 1) (4) a = PLR%(|A| + 1) Finally, SeeDep rules are represented by CNF formulas composed of PLR literals.
, 2011). In addition to make code and data available to others,  (Gavish and Donoho, 2011; Koop et al. , 2011)  allow users to run the code in the Cloud, while  (Stodden et al. , 2012; Gorp and Mazanek, 2011)  allow authors to share remote Virtual Machines containing the experiments set-up. By clicking on a link users can connect to these VMs. On its side, SeeDep belongs to the practical approach. It avoids the lack of information by: associating a generation key, called a seed, to an application topology; and storing this application topology into a mongodb NoSQL database 5. To one seed corresponds one and only one application topology. Thus, users can share their results along with the seed used in their experiment, enabling other researchers to easily reproduce the experiment set-up. Since our application topology example composed of 10 nodes is the first one we have created, we use the seed 1.
The plugin blueprint acts as a "super" blueprint. It allows to describe the lifecycle of nodes type, that can be used in classics blueprints. For example, Figure  6  describes {(0, 1), (1, 2),. , (9, 10)} the lifecycle of the cloudify. nodes. Compute node type (Figure  5 , lines 27 and 34). The plugin source code, written in Python, implements the lifecycle of nodes type. For example, the following code is a part of the lifecycle start action of the cloudify. nodes. Compute node type (Figure  6 , lines 6-7). SeeDep is responsible for writing blueprints. Each blueprint created by SeeDep fits to the target cloud platform by importing the right plugin into the blueprint import section (Figure  5 , lines 3-4), and by using the right node type (provided by the plugin) for each application and virtual machine described in the blueprint.
CONCLUSIONS As computer networks become more accessible and pervasive, more and more research areas rely on computer networks experiments. Along with this diversity, reproducibility issues arise. As part of the scientific method, any researcher in the world should be able to reproduce the experimentation in order to verify the result. Unfortunately, experiments results shared in a paper conference are not often submitted along with information related to the experiment setup. In this paper, we have proposed SeeDep a framework aiming at being a standard tool for generating, reproducing and deploying experiments set-up called application topology. SeeDep relies on an algorithm that generates experimentation networks, and a seed that can be shared which specifies the said network. It is composed of 4 modules and 7 commands.
The network topology generator and the application topology modules, associated with seedep edit apps, seedep edit rules and seedep create deploy commands, allow for creating a catalog of application topologies. The reproducibility module, associated with seedep gen deploy, allow for regenerating any application topology belonging to the catalog. Finally, the deployment module associated with seedep gen env, seedep deploy and seedep gen info, allow for deploying an application topology on different Cloud platforms. Seedep likes to be modular. Different network topology generators or different SAT solvers can be used. Similarly, different Cloud platforms can be used for the deployment. For these reasons, achieving a SeeDep performance evaluation makes no sense and is out of scope. For future works, we plan to implement consistency rules for applications mapping.
The main purpose of this paper is to show that photogrammetric bundle adjustment computations can be sequentially organized into modules. Furthermore, the chain rule can be used to simplify the computation of the analytical Jacobians needed by the adjustment. Novel projection models can be flexibly evaluated by inserting, modifying, or swapping the order of selected modules. As a proof of concept, two variants of the pin-hole projection model with Brown lens distortion were implemented in the open-source Damped Bundle Adjustment Toolbox (DBAT) and applied to simulated and calibration data for a non-conventional lens system. The results show a significant difference for the simulated, error-free, data but not for the real calibration data. The current flexible implementation incurs a performance loss.
Related work The Damped Bundle Adjustment Toolbox (DBAT) was originally developed to illustrate the behaviour of different damping schemes known from non-linear optimization  (Börlin and Grussenmeyer, 2013; 2014). Later work has focused on the use to validate commercial black-box photogrammetric software, e. g. , PhotoModeler and Photoscan  (Börlin and Grussenmeyer, 2016) , especially to provide detailed diagnostics of the photogrammetric network  (Dall'Asta et al. , 2015; Murtiyoso et al. , 2017; Börlin et al. , 2018). Recently, the differential observation weighting capabilities of DBAT was used by  Menna et al. (2018)  to mitigate peripheral image quality degradation in underwater photogrammetry. The chain rule has been used to simplify geometrical computations in e. g. , computer vision  (Brown and Lowe, 2003; Fusiello, 2013) , computer graphics  (Barr, 1984; Piponi, 2004) , imaging  (Gallego and Yezzi, 2015) , robotics  (Koditschek and Rimon, 1990) , and registration of TLS point clouds  (Rabbani et al.
The Damped Bundle Adjustment Toolbox (DBAT) The modular technique has been used in the open-source Damped Bundle Adjustment Toolbox (DBAT) 1  package for Matlab  (Börlin and Grussenmeyer, 2013; 2016). For instance, the modules presented in equation (  4 ) and (  7 )-(  9 ) were combined differently to implement two bundle pipelines that used different adaptations of the  Brown (1971)  lens distortion model. In the Photogrammetric formulation, the Brown polynomials are used to "correct" for the effect of lens distortion on the measured image coordinates. In contrast, the formulation largely adopted by the Computer Vision community uses the same polynomials to "add" lens distortion to the ideal projection of object points  (Tsai, 1987; Heikkilä and Silvén, 1997; Zhang, 2000; Drap and Lefévre, 2016).
Test case: The tilt-shift lens A tilt-shift lens is a non-conventional optical system that allows the following lens movements  (Ray, 2002)  (Figure  5 ):  a tilt, i. e. , a rotation of the optical axis about either the exit pupil or the centre of the sensor plane,  a shift, i. e. , a translation of the optical axis, and  a rotation, i. e. , a rotation about the optical axis. The projection of a tilt-shift camera lens system is not completely modelled by the classic photogrammetric projection model (pin-hole with Brown lens distortion) available in regular photogrammetric software, including DBAT. In order to illustrate the modularity of DBAT, we chose to investigate whether the ordering of the affine and lens distortion steps, described in Section 3. 2, could improve the projection model.
Simulation experiment The first experiment was constructed to investigate the effect, if any, of the difference in the affinelens distortion ordering. Two sets of synthetic data were generated, where simulated error-free image observations were generated by back-projection of known 3D object coordinates and with known exterior orientation parameters (EO), and camera calibration parameters (IO) of a strong self-calibration network. The network consisted of 24 cameras at varying roll angles. About 100 targets were simulated in a 3D configuration with a largest dimension of 1000 mm (Figure  6 ).
Camera calibration In the second experiment, models 2, 3 and 4 were used to analyse calibration data for a camera with a tilt-shift lens. The data set from  (Nocerino et al. (2016)  was used for the calibration. The dataset was acquired in a controlled environment, with stable environmental conditions, consequently reducing the influences of uncontrolled factors on the calibration. Furthermore, high accuracy reference coordinates were available: A point precision vector length of 2. 4 μm and an RMSE with respect to another photogrammetric system of 6 μm. The calibration target was a 3D photogrammetric calibration test object (Figure  7 ) with about 170 coded targets with a largest dimension of 900 mm. The used camera was a Nikon D750 full-frame DSLR camera with a PC-E Micro NIKKOR 45mm f/2.
8D ED tilt-shift lens (Figure  5 ) in two different configurations:  NORMAL: The normal configuration where neither tilt nor shift was applied.  TILTED: The lens was tilted in the vertical plane by 4 degrees. A high redundancy photogrammetric camera network was realized, consisting of up to 48 convergent images with a diversity of camera roll angles to enhance the determinability of the IO parameters  (Fraser, 2001). The ground sample distance (GSD) was about 0. 13 mm. The image measurements were performed by PhotoModeler Scanner v. 6 and exported as a text file that was imported to DBAT. The NORMAL data set was analysed and used as a reference for the processing of the TILTED data set. The TILTED data set was analysed by a self-calibration bundle adjustment in DBAT using models 2, 3 or 4.
The datum and prior weights were the same as in the synthetic experiment. Furthermore, the parameters  1 ,  2 ,  1 , and  2 were individually included or excluded from the estimation ( 1 and  2 were always estimated together). The quality of the estimation was evaluated as in the synthetic experiment with the results of the NORMAL data set used as the reference. The results are given in  Table 2. From Table  2 , we see that the difference between models 3 and 4 on this data is small. The internal and external residuals were small when the aspect parameter  1 and the decentering distortion parameters  1 - 2 were included in the estimation. In those cases, the 3D RMSE was about 0. 09 GSD and the estimated  1 value was about 0. 0019. This value is equal to the value in Nocerino et al. (2016, Table  3 , column T-4S0R90) that was estimated by other software.
Performance To investigate the relative performance between the models, the St-Pierre-le-Jeune data set from  Murtiyoso et al. (2017)  was used. The data set consisted of 239 images and about 18000 object points. DBAT model 1, a previous, more optimized implementation of Model 2, was used as a performance reference. A self-calibration bundle adjustment process was applied to the St-Pierre data set. The timings were performed using an HP EliteDesk 800 with Quad Intel Core i7-4790 CPU at 3. 60GHz with 32 GB RAM running Matlab R2018b under Ubuntu Linux 14. 04. 5. In all cases, the models required the same number of iterations and produced the same results to the reported number of digits. The average bundle execution time over five repeated runs was about 13 seconds for Model 1. The corresponding times for models 2-4 were 53-56% higher.
The tilt-shift lens is a complex design whose projection model is not yet rigorously supported by regular photogrammetric software, including DBAT. In a previous paper, it was found that some of the deviation of the tilt-shift lens from the classic photogrammetric projection model (pin-hole with Brown lens distortion) could be mitigated by including the affine distortion parameter  1 in the estimation  (Nocerino et al. , 2016). The modularity of DBAT presented an opportunity to investigate further modifications of the projection model. As such, two models with different placement of the affine transformation compared to lens distortion were implemented with minimal effort. An experiment on synthetic image observations verified that the relative placement of the affine transformation and lens distortion can be detected by DBAT on synthetic, error-free data.
From the results, we may conclude that the standard pinhole camera model with Brown lens distortion cannot fit the tilted data better, even with the image-variant approach. However, if an RMSE of 0. 09 times the GSD is considered satisfactory, standard photogrammetric formulations and software can be used in 3D modelling application that require the tilt-shift lens system. We believe that to improve on those results, a proper mathematical projection model of the tilt-shift lens is required. In relation to the main goals of this paper, we conclude that the modularity of DBAT can indeed be used to test novel projection models with minimal implementation effort. The performance results in this paper show an increase of about 50% in computation time on a medium-sized problem for the flexible implementation to the original implementation in DBAT.
A potential explanation is if the computation of the composed Jacobian as a chained multiplication of simpler Jacobians is inefficient. Another possible explanation is if some of the computations that depend on the same parameters are distributed over multiple sub-expressions. In that case, the expressions may need to be jointly considered in order to be fully optimized. If true, either case would highlight a limitation of the chain rule approach. The reported increase in execution time would hardly be acceptable for production code, where speed is essential. However, the driving force behind the development of DBAT has been to provide an open-source, statistically rigorous, reference implementation of Bundle Adjustment. Performance has been important, but never the primary focus. Furthermore, the code for the original projection model has gone through several optimization iterations whereas the flexible chain rule code has not.
In addition, they estimated the H i column density of each component from the observed Ca ii column density pattern, assuming a constant Ca ii/H i ratio. Interestingly, the thus estimated H i column density for each component, on its own, exceeds the canonical DLA definition (log [N (HI)/cm -2 ] >20. 3). We obtained the VLT/UVES reduced quasar spectra from the ESO advanced data products archives 1. Fig. A1  of the appendix A presents our independent fit to the metal absorption profiles, which is consistent with findings from  Guber, Richter & Wendt (2018) , adding lines from Fe ii, Mg i and the saturated Mg ii doublet. The zero velocity in the figure is set to the systemic redshift of galaxy G0, zG0 =0. 31305 (see later section). Most of the absorption lies blueward of this systemic redshift.
313. Some objects are bright in the continuum but undetected in pseudo narrow-band images because of weak emission lines (i. e. G4, G20 and G21). R=1770 at 4800 Å and R=3590 at 9300 Å resampling the whole spectrum to a spectral sampling of 1. 25 Å/pixel. The ESO MUSE pipeline  (Weilbacher 2015)  version v1. 6 was used to reduced the data together with additional external routines for sky subtraction and extraction of the 1D spectra. We used master bias, flat field images and arc lamp exposures from observations taken closest in time to the science frames to correct each raw cube. We processed the raw science data with the scibasic and scipost recipes, correcting the wavelength calibration to a heliocentric reference. The wavelength solutions were checked using the known wavelengths of the night-sky OH lines and were found to be accurate within 15 km s -1.
We centered the individual exposures with the exp align recipe using the point sources in the field to ensure accurate relative astrometry. We combined the individual exposures into a single data cube using the exp combine recipe. We measured the seeing of the final combined data from the quasar and other bright point sources in the cube. We calculated that the point spread function (PSF) has a full width at half maximum of 0. 76 arcsec. Finally, to estimate the flux uncertainty, we compared the flux calibration with the R magnitude of the quasar and a bright star in the field. We computed the MUSE broadband fluxes in the R-band filter. We determined the flux error to be ±25%. The removal of OH emission lines from the night sky was accomplished with an additional purpose-developed code. The scipost recipe was performed with sky-removal method turned off.
We started the data reduction with the pipelinecalibrated uv-data sets, as delivered by ALMA. Additional data reduction steps were carried out with the Common Astronomy Software Applications (CASA) software package version 4. 7. 0. Minor manual flags were added to remove some uv-data points with strongly outlying amplitudes. Next, we applied two cycles of self-calibration on the data. The quasar in the centre of our science field is very bright at mm frequencies (∼800 mJy), which makes the field very suitable for self-calibration of both the phases and amplitudes. First, we used the tclean recipe to Fourier-transform the uv-data into a continuum image. The self-calibration procedure was performed using the tasks gaincal and applycal on individual measurement sets to create corrected uv-data, after which the data were re-imaged to create an improved continuum map.