dataesr/scientific-paragraphs-categorization huggingface dataset:
A pile-up was generated with samtools (113) taking into account only bases with quality of at least 30. The pileups were then quantified with a custom Python script and the resulting mutation counts were processed and visualized with custom scripts in R (v3. 5. 1), with the help of additional R packages (data. ). Background mutation profiles were controlled for by subtracting the corresponding mutation frequencies in control samples from the frequencies in the samples of interest, at each position and for each substitution type. Annotation of hot and cold spots was created by means of regex search for the corresponding patterns in the reference sequence. manuscript. ITS performed bioinformatic analysis, provided resources and analyzed data. RP conceived the project, analyzed data and wrote the manuscript with critical inputs from UES and JF.
Acknowledgements We thank  Almudena Ramiro  and  Angel Alvarez-Prado  ( CNIC, Madrid, Spain ) for sharing the mutational datasets from Ung -/-Msh2 -/-GCBs. We gratefully acknowledge the  Vienna Biocenter Core Facilities (VBCF)  for assistance in generating Eµ -/-lines and for next generation sequencing, the  IMP/IMBA BioOptics facility  for flow cytometry usage, the  IMP/IMBA Molecular biology  services for Sanger sequencing and reagents, and the  IMP/IMBA  animal house. We thank  Maximilian von der Linde  for uploading NGS tracks to GEO. We thank  Carrie Bernecky  ( IST, Vienna ) and  Clemens Plaschka  ( IMP ) for generating the 3D Pol II elongation complex structural visualizations. This work was funded by  Boehringer Ingelheim , The  Austrian Industrial Research Promotion Agency (Headquarter  Grant  FFG-834223 ), and grants from the  Austrian Science Fund to UES  ( FWF T 795-B30 ) and RP ( FWF P 32043-B ).
If |Θ k -IGM(X k )| < ɛ where ɛ is a fixed threshold, then the iterative scheme stops. Otherwise, the current end-effector pose is updated using Equation (5). � Return to the first step. To verify the results, an algorithm with the calculation described above was established and verified with MATLAB. This algorithm considers the dimensions of the robot presented in Table  2  and the different positioning configurations of the needle introduced in Figure  3. Table  1  presents the error between the real position and the position obtained after the resolution of the forward robot geometric model by the Newton-Raphson method. The average error, for all the points studied, is 0. 86 mm which is sufficient to validate this method of calculation given the complexity of resolution of the forward geometric model for the 6-RUS manipulators.
This function gives a score adaptation between V workspace and V r. fðδÞ ¼ � � V workspace ðδ; XÞ -V r � � ð20Þ To determine V workspace , we implemented in MATLAB a recursive algorithm that determines the size and bounds of the robot's translation workspace for constant orientations (α = β = γ = 0°). This computational algorithm integrates the discretisation method, which determines for specific values of the geometric parameters vector δ the inverse geometric model at each set of Cartesian coordinates. Once the inverse geometric model is solved and, the values of the rotary joints angles are determined, we check if these angles are within the range of rotary actuators constraints defined by Equation (19). In a case where the values of these angles respect these constraints, the Cartesian coordinates of the corresponding points will be saved, seeing that they define the operational workspace achievable by the robot's end-effector.
25 It is inspired by the process of natural selection  26  and its operation principle is illustrated as follows. By analogy with our optimisation, the algorithm selects random values of the geometric parameters, defined by vector δ, from Table  2   � Mutation: random alteration of the characteristics of an individual. Thus, over the generations, the population evolves towards optimal solutions whose values are included in Table  2. We have created a code that involves the definition of the design parameters, the formulation of the problem to be solved, and the integration of a predefined module of the geometric algorithm in MATLAB. 27 This code allows the implementation of the optimisation algorithm and to search for the optimal values of the geometric parameters. The configuration of the parameters of the genetic algorithm adopted for our optimisation is presented in Table  3.
1 to B. 4  are also available in FITS format at the CDS via anonymous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/ cat/J/A+A/664/A97 The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. elucidate the evolutionary path of the CND by understanding its structure and dynamics in as much detail as possible. Furthermore, because many galaxies with gas-rich nuclei apparently have nuclear disks surrounding their central supermassive black holes (e. g. ,  Netzer 2015; Gravity Collaboration 2020; Vermot et al.
2232 µm) and the Brγ line of the H I spectrum (2. 1667 µm), tracing H II. The two H 2 lines are several orders of magnitude stronger than any other H 2 line in the band according to the high-resolution transmission molecular absorption (HITRAN) database  (Gordon et al. 2017; Komasa et al. 2011; Wolniewicz et al. 1998). The data are dominated by continuum flux from stars. We estimated this continuum emission for each spatial pixel as a spline function going through the average flux in 12-13 featureless regions (Tables  1  and 2 ) of the spectrum and subtracted it from the data. Unfortunately the H 2 line at 2. 12 µm is at the edge of the spectral bandpass so that we have no continuum estimate on the short wavelength side of this line and must resort to extrapolation there. The presence of the second line at 2.
The µ i allows the various regularization terms in E to be weighted. We developed CubeFit 1  , a code that implements this method using the Yorick interpreted language 2. We borrowed the L2-L1 regularization function from Yoda 3  by Damien Gratadour, a Yorick port of the MISTRAL deconvolution software by  Mugnier et al. (2004). The model-fitting engine is the conjugate gradient algorithm implemented in the Yorick package OptimPack 4  , version 1. 3. 2. A Python port of CubeFit is under development.
The ten parameters are the intensities of the eight lines {I j }, the common Gaussian width σ (in the radial velocity domain) and a common radial velocity v. We fitted the data with this model using two methods: individual 1D fits on each spaxel (the maps have been σ-filtered to remove some bad fits) and with CubeFit. Figure  2  shows the result of the 1D and 3D fits for the SW mosaic and their differences. The linewidth (which traces spectral resolution for those intrinsically very thin telluric lines) varies considerably across the mosaic. In each subfield, the resolution varies from 35 km s -1 in the southeastern corner to 60 km s -1 in the northwestern corner. These strong variations create sharp edges at the transition between subfields. In addition, the linewidth map shows some striping at a much smaller level than this overall gradient.
Application to the CND data To apply CubeFit to the (sky-subtracted) CND data, we chose to use line flux as a parameter for the Gaussian profile rather than line amplitude. The amplitude I(l, m) at any point is linked to the a color: red shows H2 (2. 12 µm), green H2 (2. 22 µm), and blue H II (traced by Brγ, 2. 17 µm). Line flux is estimated by our fitting procedure. Regularization ensures that the maps are smooth and provides for extrapolation (in particular, there is no gap anymore between the two fields). It actually also provides some extrapolation, but we cut the field-of-view to the initial one (except for the gap). flux F(l, m) and the total linewidth σ tot (l, m) (Eq. (  9 )) by I(l, m) = F(l, m) √ 2πσ tot (l, m). ( 11 ) The reason for this choice is that line amplitude depends on the instrumental spectral resolution, which in our case varies across the field with sharp edges (Sect.
As a comparison, the median uncertainties using a traditional 1D fitting method (directly estimated by the Yorick 6  Levenberg-Marquardt-based fitting engine lmfit 7  ) for the NE H 2 data are σ F ≈ 3. 74 × 10 -27 W m -2 arcsec -2 , σ r ≈ 10%, σ v ≈ 9. 4 km s -1 , σ σ ≈ 9. 2 km s -1. The 1D-fit uncertainties can also be estimated by taking the RMS of each parameter map over four neighboring points, yielding median values of σ F ≈ 1. 24 × 10 -26 W m -2 arcsec -2 , σ r ≈ 15%, σ v ≈ 13. 9 km s -1 and σ σ ≈ 23. 7 km s -1. The CubeFit uncertainties are therefore about ten times smaller than their 1D counterparts.
4. We provide another validation in Appendix A, where we compare the results of CubeFit with a more classical 1D fit at a few locations. With this comparison, we also demonstrate that variations in linewidth across the field, as estimated by the 3D method, are robust. This method has also been applied to another data set in a companion paper,  Ciurlo et al. (2016) , where we detected H 2 throughout the central cavity using the Spectrometer for Infrared Faint Field Imaging (SPIFFI) integral-field spectrometer at the European Southern Observatory (ESO) Very Large Telescope (VLT). Furthermore, in the application presented here we find small-scale features that corresponds to filaments and clumps observed in the radio and in other molecules. This provides a strong independent confirmation of the robustness of our findings with CubeFit.
We apply CubeFit to the Keck/OSIRIS data of the Galactic Center CND in Sect. 4. The linewidth map is a useful tool for identifying denser knots in the ISM, which appear as sharp local minima in the velocity dispersion. The ISM in the SW field is organized into two components: 1) a diffuse component near -30 km s -1 that contains only a few features, including two   6 ). The anticorrelation between line flux and width is quite remarkable in the lower panel (Brγ), where it takes the form of several narrow, densely populated streaks on the density plot. In the top panel (H 2 ), the anticorrelation is weaker than in the bottom panel and the density distribution is more diffuse, but one can still identify linear features, showing that such a correlation exists locally. filaments oriented roughly in the north-south direction, and 2) a number of thin, compact, tidally sheared filaments, aligned along the orthoradial direction, with radial velocities near -80 km s -1.
We also performed the 1D fit over the entire SW cube, once on the original non-smoothed cube and once on a smoothed version of the cube where every spaxel contains the average spectrum over a 0. 5 radius as above. We again subtracted the OH linewidth in quadrature. A close-up of the width map derived from these two fits and from CubeFit is displayed Fig. A. 2. Results from the 1D fit on the non-smoothed data contain high-resolution features but are very noisy. Smoothing the cube enhances the S/N significantly, at the cost of erasing the smallscale features. CubeFit brings the best of the two worlds together, keeping the high-resolution features while removing most of the noise, thus enhancing those small-scale features. The sharp astrophysical features that we discuss in Sect. 5, very clear with our method, are difficult to make out from the non-smoothed 1D fit and are smoothed out in the smoothed 1D fit.
Acknowledgements. T. P. thanks  Damien Gratadour  for sharing the Yoda code and fruitful discussions concerning regularization. A. C. , M. R. M. , T. D. and A. M. G. acknowledge the support provided by the  U. S. National Science Foundation  (grants  AST-1412615 ,  AST-1518273 ),  Jim  and  Lori Keir , the  Gordon and Betty Moore Foundation , the  Heising-Simons Foundation , the  W. M. Keck Foundation , and  Howard  and  Astrid Preston. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.
Treering3  (Fritts et al. , 1999)  can even automatically interface (code written by Geoffrey M. Downes) with the SilviScan digital output for model verification. SilviScan is a semi-automated device to rapidly obtain wood density observations (amongst others) using X-ray technology and image analysis. It was originally developed in 1992 for commercial forestry  (Evans et al. , 1994)  and, after several upgrades, is still widely used in the scientific community. Nevertheless, while at a higher resolution, these static observations do not allow for the exact inference on the timing of the inception of these high and low-density features. Therefore, care is needed when applying a distance-to-time conversion approach across tree ring anatomical features (i. e. , equally-sized sections of the ring have not emerged during an equally-long period  (Pérez-de-Lis et al.
Radial diameter can refer to either cell or lumen radial diameter. Tree ring width is equivalent to the end-of season value of cumulative radial growth, measured as cumulative cell anatomy properties or directly as ring width. Cell numbers is equivalent to end of season cumulative tracheid production. Xylogenesis refers to cell numbers or cell production rates derived from xylogenesis observations. TRWi, Tree ring width index. be done with care. To our knowledge, dendrometer data has so far not been used for wood formation model validation, rather for radial growth or stem increment models. Therefore, we see scope for involvement of this type of observation to help fill the gap between cell counts at weekly time-scales with daily-resolved "anatomical" information on radial increment. Nevertheless, challenges remain to attribute observed increments to irreversible growth due to diurnal shrinking and swelling of the stem (but see  Mencuccini et al.
Model Overall, the use of intra-ring (especially cell anatomical) and xylogenesis data in tandem will likely provide the best way to challenge individual model process hypotheses around each cell developmental phase, its drivers, and the resulting anatomical features. The only model which to our knowledge has formally compared output against both dynamic (xylogenesis) and static (cell anatomical) data is XyDys1 and 2  (Hartmann et al. , 2017 (Hartmann et al. , , 2021)). The challenge remains to integrate dynamic and static data in the light of a lot of sample variability. While significant progress has been made in this from the data analysis side (e. g. ,  Cuny et al. , 2013) , wood formation models will be a useful bridge between the datasets. Not discussed in any detail in this review are molecularlevel and gene expression observations, which remain unused in wood formation model verification or hypothesis construction, with the exception of auxin and sugar.
Schuldt et al. , 2013  for user-example) can be used to study tree ring anatomical structure including cell diameter, lumen area, or wall thickness amongst others. These tools provide valuable smaller-scale data which can be used to confront models. Their semi-automated nature can provide large datasets to verify models against. When used together with dynamic xylogenesis observations they have great potential to constrain wood formation models and shed more light onto their plausible structures. Overall, the use of data should help verify wood formation models further. The utility of the data depends on the model, the processes it resolves and the purposes it serves. However, in general the most useful combination of datasets for model validation are a combination of both dynamic (xylogenesis) and static (anatomical) data.
The IoTwins project leverages edge-enabled and cloud-based big data services to support the design and development of digital twins (DTs) to be deployed in the IoT-edgecloud continuum in order to achieve the aforementioned goals. DTs are virtual copies of a company's assets that can interact with each other and, by leveraging the knowledge extracted by large amounts of data, implement corrective actions, optimize efficiency and diagnose anomalies before they occur. The project focuses on hybrid DTs, i. e. , DTs that mix a data-driven and a model-driven approach. The data-driven component of the hybrid DT is a machine learning (ML) model that is trained in the cloud with historical and real data coming from the field, and is then executed on the edge (i. e. , close to the field) to ensure prompt reactions to anomalous situations.
The model-driven component is a software simulator that mimics the behaviour of physical assets and can also provide useful information to the data-driven model. In the course of the project, a distributed platform and a number of cloud-and edgeenabled DTs have been designed, developed and made available to the project stakeholders. The mentioned project's output will be released to the research and industrial community after the end of the project. In this paper, we introduce the software platform developed in the context of the IoTwins project and provide some technical details of its implementation. We also present and discuss a manufacturing use case, developed by one of the project partners, that demonstrates the practical benefits of the proposed solution. This work is grounded on the research results published in  [6] , and advances them by (a) proposing further technical details of the platform and (b) showcasing the platform potential in a real manufacturing scenario.
0  [23] , a European initiative that applies big data in manufacturing applications through fog/edge technology. Cloud-edge architectures can be created by using AWS' IoT Greengrass  [24]. Here, smaller devices may not connect directly to the cloud, but to a dedicated Greengrass core (GGC) device located on the edge that is able to act locally on the sensed data  [25]. In this architecture, the GGC is the only device that needs a connection to the cloud. AWS' IoT SiteWise  [26]  is a managed service provided to collect, store, organize, and monitor data from industrial facilities at a large scale. Models of the physical assets, processes and facilities are used to provide metrics, e. g. , for the prediction of maintenance issues. The IoTwins project proposes a highly distributed (cloud-to-things) and hybrid digital twin model, which provides for an integration of simulative and data-driven models to feed AI services.
To the best of our knowledge, this approach has not yet been explored extensively in the literature. The project can rely on the outcomes and the experience of past EU-funded projects such as: (i) the DEEP Hybrid DataCloud (DEEP-HDC) project  [27] , which aimed to bridge together cloud and intensive computing resources in order to explore different datasets for artificial intelligence and deep and machine learning; (ii) the eXtreme-DataCloud (XDC) project  [28] , which focused on developing scalable technologies aimed at federating storage resources and managing data in highly distributed computing environments; (iii) the INDIGO-DataCloud project  [29] , which centered on developing a computing platform that can be deployed on different hardware and provisioned over hybrid infrastructures.
Design of an Open Platform for Digital Twins The IoTwins platform has been proposed as an open framework that enable Users to develop, configure and run DTs in the cloud-to-things continuum. The platform is built making use of open-source software and tools. Moreover, third-party software have been integrated by using open APIs. The proposed platform's architecture is drafted in Figure  1. The figure shows the different computing layers targeted by the platform and the related interactions. Openness and software re-use are the base concepts that drove the design of the IoTwins platform. As already mentioned, the technologies adopted to design and implement the IoTwins platform come from the requirements and the experiences collected in the previous cloud-related projects. The different components and tools are extensively presented in the next section, where the interactions among them are also described.
By harnessing and integrating the functionalities of existing technologies and tools, the platform provides users with different services such as data handling, computation, encryption and anonymization: • Data handling: this refers to the process of gathering, recording and presenting information in a way that is helpful to the final user. It includes three different consecutive steps: data gathering, data transportation and data storage. Data gathering is performed by the sensing devices from the field. Data transportation provides the protocols to implement communication among the three layers (IoT, edge, cloud). Currently, data transportation leverages standard (or de facto standard) technologies to maintain compatibility among the services. Data storage solutions (data models and databases) are used either as preliminary storage or as long-term storage for the testbeds; • Computation: this is requested at all layers.
In the same way, the INDIGO-IAM dashboard can also be used for the same purposes. As soon as the client has been registered, it can obtain a token using both APIs or the available scripts and GUIs. The INDIGO-IAM service has been successfully integrated with many off-the-shelf components such as Openstack, Kubernetes, Atlassian JIRA and Confluence and Grafana, and several middleware services that can be used in the IoTwins architecture.
The engine uses the list of IaaS instances and their properties, provided by the orchestrator, to choose the best provider that can support the user requirements; • The Cloud Information Provider  [52]  generates a representation of a site's cloud resources to be published inside the INDIGO Configuration Management Database (CMDB); • The Configuration Management Database  [53]  is a REST service storing all the information about the cloud sites that is available and providing details such as the images and containers they support. It is used as an authoritative source of information for matchmaking and the orchestration of VM and containers; • The QoS/SLA Management Service  [54]  allows for the handshake between users and resource providers on a given service-level agreement (SLA). The SLA describes the quality of service (QoS) for a group or a specific user, both in the PaaS as a whole or over a given provider.
Edge Service Components The edge-layer software is aimed at providing services that are able to establish communication with both the IoT layer and the cloud layer. The applications in the IoTwins architecture are packed in OCI  [55]  containers. The edge service orchestration, thus, has to provide functionality to download, start, stop and configure the OCI containers. In the IoTwins infrastructure, Apache Mesos  [56]  has been used as the service orchestrator in the edge. Apache Mesos is an open-source project to manage computer clusters and has two frameworks: Marathon  [57] , a production-grade container orchestration platform that can launch applications and provide scaling and self-healing for containerized workloads, and Chronos  [58] , a fault-tolerant scheduler that runs on top of Apache Mesos and that is used for job orchestration.