dataesr/scientific-paragraphs-categorization huggingface dataset:
MODELS AND IMPLEMENTATION We train and test two different RF models, with redshift and stellar mass as the target variables and the following as input features: •  luptitudes •  luptitude errors •  -,  -, and  - lupticolours, and their associated errors We build the first model using the DF data set and refer to it as DES-DF from here onwards. The high-precision photometry of DF galaxies combined with the accurate redshifts and stellar masses allows us to establish the baseline performance. We build the second model to produce valid posterior PDFs of galaxies in our target data set (the DES Y3 GOLD) by training on the WF data set. We refer to this model as DES-WF. To train and test our RF models, we use the implementation of the algorithm in the Python ML package scikit-learn. In particular, we use the RandomForestRegressor module from the package, which allows us to do regression.
Marginal probability distributions The point estimates we extracted are not perfect. In general, inaccuracies can arise from • Incomplete and incorrect information -The information provided to an ML algorithm may not be sufficient to learn the perfect mapping between the input features and target variables. For example, to estimate redshifts to a high degree of accuracy, spectroscopic data are required. However, we use photometric data that only provides a rough sampling of the underlying SED. Furthermore, the data used for training and testing have to be accurate. In our case, the redshifts and stellar masses we use to train our RF models may contain some errors. They have been estimated using the templatefitting code LEPHARE, which utilizes template SEDs and they may not be a perfect representation of the true SED.
We believe that DES-WF is better capturing the redshift-stellar mass dependence structure as it is trained using the WF data set that contains multiple scattered copies of the same DF galaxies, resulting in better incorporation of photometric errors present in the data into the model. Collectively, the joint PDFs are less marginal/Kendall calibrated compared to the marginal PDFs as the deviations are larger in magnitude. However, we hypothesize that the deviations in the Kendall calibration are not significant given the complex nature of joint PDFs, and to prove this, we compare our results to those achieved by the template-fitting code BAGPIPES in the next section.
TEMPLATE-FITTING COMPARISON The different diagnostic plots and the metrics we utilise to validate the marginal and joint PDFs produced by our RF models are difficult to fully appreciate without familiar context. Consequently, we utilise Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation, or BAGPIPES  (Carnall et al. 2018 ) to benchmark our results. BAGPIPES is a Python package that uses MultiNest  (Feroz & Hobson 2008 , Feroz et al. 2009 , 2019)  nested sampling algorithm to model the emission from galaxies and to fit these models to any combinations of spectroscopic and photometric data in order to output multivariate posteriors distributions of parameters such as redshift and stellar mass, hence making it ideal for comparison. The photometry in the COSMOS2015 and DES Y3 DF catalogues have been calibrated independently of one another.
Importantly, however, we avoid introducing any possible systematic effects that could arise from inter-dataset calibration differences. The model templates used by Laigle et al. (  2016 ) cannot be exactly reproduced in BAGPIPES. It is important for the validity of our comparison that the four-band PDFs and the truth values are constructed under the same set of model assumptions. Therefore, we produce a new set of truth values using the 22 COSMOS bands (including the four aforementioned) listed in Tab. 1. In both the 4-band and 22-band runs, we employ the same physical information about the model as outlined in Tab. 2. These choices were made to closely mimic the set-up adopted by Laigle et al. (  2016 ) to compute the redshifts and stellar masses in the COSMOS2015 catalogue, so that we can make a fair comparison.
Nevertheless, the marginal redshift PIT distribution is competitive with template-fitting approaches used in code comparison works, e. g. Schmidt et al. (2020, Fig. 2 ) and Euclid Collaboration:  Desprez et al. (2020, Fig. 7 ). However, these studies use deeper data than in this work. Unsurprisingly, a small number of joint PDFs are also biased as reflected by the non-uniform copPIT distribution. Despite the biased PDFs, BAGPIPES does manage to capture the dependence structure between redshift and stellar mass on a similar level to that achieved by the RF. On the whole, RF outperforms BAGPIPES on the metrics we have considered in our analysis. Having said that, it should be possible for BAGPIPES to match the performance of the RF through judicious use of priors and great care in photometric calibration.
CONCLUSIONS The emergence of template-fitting methods with the capability of generating multivariate PDFs of redshift and physical properties of galaxies represents a paradigm shift. These PDFs account for potential correlations between different galaxy properties and fully characterize uncertainties associated with point estimates of the quantities. However, with their potential benefits, comes the task of generating them quickly, which is difficult given their complexity. For example, the template-fitting code BAGPIPES takes a few minutes to fit each galaxy. While this may not seem significant, the amount of time required to generate them for hundreds of thousands of galaxies, let alone the billions that will be observed with the upcoming photometric surveys such as the LSST and Euclid, quickly becomes impractical.
Despite the success of our method, template-fitting approaches such as BAGPIPES undoubtedly still have a vital role to play in building the training samples for ML-based codes. To conclude, joint redshift-stellar mass PDFs have many potential science applications from determining the evolution of the SMF, to constraining the SHMR. Consequently, we have developed GALPRO, a highly intuitive and efficient Python package for rapidly generating n-dimensional PDFs on-the-fly, thus solving the potential issue of storage. We have trained and tested our RF models using GALPRO on a 13" Macbook Pro (2. 4 GHz Intel Core i5, 16GB LPDDR3) and found that, at best, it takes on average a few milliseconds to generate a PDF. Thus, GALPRO can potentially offer a 100, 000 reduction in run time compared to packages based on template-fitting methods, making it ideal for the impending era of 'big data'.
ACKNOWLEDGEMENTS SM was supported by the  STFC UCL   Centre for Doctoral Training in Data Intensive Science (grant   ST/P006736/1 ). OL acknowledges support from a  European Research Council Advanced  grant  TESTDE FP7/291329  and an  STFC  Consolidated grants  ST/M001334/1  and  ST/R000476/1. AFLB acknowledges  ERC Advanced  grant  695671  'QUENCH' and support from the  UK STFC. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the Collaborating Institutions in the DES.
DATA AVAILABILITY The data underlying this article were produced by the DES and the COSMOS. The DES Y3 DF and DES Y3 Balrog catalogues are expected to be available to the public in 2021 and will be hosted at https://des. ncsa. illinois. edu/releases/dr2. The COS-MOS2015 catalogue can be accessed at https://ftp. iap. fr/ pub/from_users/hjmcc/COSMOS2015/. The derived DF and WF data sets will be shared on reasonable request to the corresponding author. The code used to perform all the analysis in this paper and an example data set is available at https://github. com/smucesh/ galpro/.
More and more homeowners joined the project thanks to a very wide communication. City officials were fierce communicators of the process -mayor first -and important facilitators since most building authorizations were eased when approved by BIMBY officers. 60% of real estate on sale within the city boundaries had been through the process. Lab InVivo had contributed to matching homeowners with buyers but above all with their new neighbours, which made several local newspaper front pages. Before the end of the year, when the City Council faced the end of Lab InVivo's mission, the decision was taken to reconduct Lab InVivo and widen the scope of the research project to abandoned houses. The limits of the BIMBY proces were pointed out during the national research program and the first experiments (Miet 2013) which took place between 2009 and 2012.
Automated Image Analysis Protocol for Quantifying Endothelial Wound Healing Rates To quantify the time evolution of the area of the wound and thus to calculate the wound healing rate over time, we developed an automated image analysis protocol. In each experiment, 12 regions of interest (ROIs) were selected from the TRITC (endothelial) channel, each containing a well-defined section of the wound: four ROIs upstream of the stent, and eight ROIs within the stented zone (Fig. 4 ). Areas of the artery containing collagen imperfections were discarded as to not interfere with the analysis (see green box in Fig. 4  for an example). The wounded area of the artery in each ROI was analyzed independently using a Matlab-based multistep process as depicted in Fig. 5a. A first step of preprocessing was performed to enhance input images by mapping intensity values, expanding histograms, and improving contrast.
5b ) was chosen as the threshold and the image binarized. This method proved highly robust and effective in delineating wounded and healed areas. Further optimization was performed by filtering small elements by size, including isolated cells in the wounded area and small gaps between cells. This optimization involved the following step-by-step process: beginning with the binary image, a filter to remove small objects in the wounded area corresponding to noise or isolated cells was first applied in Matlab using the function bwareafilt. A second filter step was then applied to the previous image to remove small objects in the endothelium. A final step involved removing small objects (gaps) surrounded by cells very close to the front line of the wound. This was accomplished by calculating the Euclidean transform of the previous binary image and filtering out all objects whose dimensions were smaller than half of a typical cell size.
Demonstration of the Validity of the Automated Image Analysis Algorithm The accuracy of the method was investigated by comparing the wounded area obtained with the Matlab routine to that obtained by manual interactive selection of the wound in Fiji for several ROIs. Several time points were selected to perform the comparison as shown in Fig. 6. The results of this comparison indicated that the automated Matlab routine is capable of accurately capturing the wounded area as depicted by the overlay of the computed contours (in red) over the original ROIs.
System boundaries-The functional unit for this study is 1 m 2 of the constructed floor area with a life span of 30 years for the houses studied. The environmental impact assessment was carried out on the structural, functional, and end-of-life aspects of the houses. This study, therefore, presents a cradle-to-grave LCA. Life Cycle Inventory-In this article, LCA based on the whole life cycle was used to first evaluate the single-family houses. It is then used to evaluate the evolution of the production of the Reunionese electricity mix. The environmental impacts were calculated from the processes modified and adapted to the case of Reunion Island, using generic data from the GEMIS (Global Emissions Model for Integrated Systems) tool. The materials and products were imported from several countries (France, Europe, Asia, South Africa, etc.
Clustering Principal component analysis-The principal component is a widely used statistical method for dimension reduction of a set of data  [44]. Thus, PCA is based on the assumption that only some variables are independent; the others can be inferred from each other  [45]. For this reason, the first steps of the analysis consist of standardizing all the data to make them comparable, then calculating the correlation matrix to observe possible links. Hence, PCA is applied to the 7 indicators, defined in Section 3. 1, in the three phases of LCA assessment counting, a total of 21 variables. K-means clustering-The HCPC function from the FactoMineR package was used to perform hierarchical clustering. Inertia is used as a measure of inter and intra-class variability in this function. The K-means method, one of the most widely used clustering algorithms, is used to partition the data.
The cluster is represented by its center of gravity in this method. The principal goal of this technique to reduce total within-cluster variation  [46]  according to Equation (1). I a + I b = I a∪b - m a m b m a + m b d 2 (a, b), ( 1 ) where I is the inertia; m a and m b are the numbers of SFHs (called individuals) in clusters a and b, respectively, and d 2 (a, b) is the squared distance between their centers of gravity. Inertia is reduced when the two clusters are grouped by a value proportional to the clusters' distance. The number of individuals in each class is the weight. All the statistical analysis and clustering were carried out using the FactomineR package  [47]. Optimization of the number of clusters was carried out using the gap statistical method, which uses a statistical solution rather than a global metric, such as the elbow or the average silhouette method  [48].
OSeMOSYS is a highly adaptable and scalable architecture for optimizing energy systems. It is designed as a linear program that determines the optimum technology power investments and dispatch schedules to optimize costs. In contrast to many other energy modeling platforms, OSeMOSYS is open source to promote its use. An OSeMOSYS implementation is composed of distinct "blocks" of functionality that correspond to the various components of the modeled structure. Before the prospective exercise, it is first necessary to define the reference energy system. The objective is to represent the different sources of production for the reference year 2018. Figure  3    Scenario setting-The model structure was developed considering the demand pattern, the power generation technologies, and their associated economic, technical, and environmental aspects.
Typology of the Reunion Houses A PCA is performed on a matrix of 34 SFHs defined by 21 LCA indicators, using the methodology outlined in Section 3. 2. The factorial axes inertia suggests that the PCA's first two axes express 46. 3% of the data set's total inertia. This value is higher than the reference value of 26. 81%, indicating that this first projection plan explains a large amount of variability. This reference inertia is the quantile 0. 95 of the distribution of inertia percentages obtained by simulating a random data set of comparable dimensions of the normal distribution. Clustering is performed on either the raw data table or on selected objects. Our process begins by performing a PCA and then clustering using the individuals' coordinates (SFH). The PCA can hold only the most significant dimensions for study.
The categorical variables associated with each cluster characterize them. A relevant p-value of 0. 05 is used to classify these variables. Our classification's objective was not to characterize each according to the variables but to define construction typologies according to environmental quality. The process of clustering entails the identification of paragons. The individual whose coordinates are nearest to the barycenter for each group is known as the parangon, and its profile better characterizes the cluster to which the individual belongs. These parangons, therefore, avoid treating all 34 houses in our analysis but focus on three of them that are representative of the entire database. The parangons are SFH16 (cluster 1), SFH12 (cluster 2), and SFH13 (cluster 3). The SFH16 house represents a cluster with low environmental impact in all phases of the life cycle, while SFH12 has the characteristics of a low impact group in terms of ozone depletion but high values on secondary raw materials and ADPE in the structural and end-of-life phases.
Introduction Many synchrotrons make use of some sort of graphical user interface (GUI) to control their beamlines and acquire data. Historically, macromolecular crystallography (MX) has been at the forefront of these developments due to the rigid nature of the experiments involved, many years experience in software development, and the demand for efficiency and automation. This has resulted in the development of software packages such as MXCuBE(1-3)  (Mueller et al. , 2017; Oscarsson et al. , 2019) , co-developed and available at many of the European synchrotrons, as well as GDA (and MXGDA)  (Enderby & Pulford, 2004)  at Diamond Light Source, and BlueIce  (Stepanov et al. , 2011)  at NSLS-II. Synoptic overviews of beamline layouts and control of hardware elements have a long history and include user interfaces (UIs) such as EDM and MEDM for EPICS  (Dalesio et al.
Concepts Daiquiri does not provide a scan engine or a controls system, it provides only the UI layer. Interaction with the scan engine is conducted via actors, scan data are accessible via an interface, and hardware element control and notification makes use of a very thin abstraction layer. These can technically connect to any control system and integration is relatively straightforward. At the present time, adapters are available for BLISS and Tango objects, and scans are integrated with the BLISS scan engine. Connecting to a controls system requires imple-menting an abstract device, for example a motor, and creating a local implementation for the controls system of interest which tells daiquiri how to interact with this object. The scan data interface has functions to retrieve 0, 1 and 2D data and hooks for the underlying controls system to send events to daiquiri.
Only a single user can be in control of the beamline at a time with other users requesting control as needed. Staff can take control of the beamline at any time. Actors. Daiquiri decouples itself from any action it executes via actors; these are a simple Python class with a method that can execute any Python code. These are discussed in more detail later. Action queue. Daiquiri implements a basic queue system. Actors can be placed into this queue to run sequentially or be executed immediately. The status of the queue is monitored and reported, and items can be promoted and demoted as needed This allows daiquiri to perform overnight and unattended data collection. Metadata. Internally daiquiri makes use of the ISPyB database  (Delageniere et al. , 2011)  to store information about who can login, their associated beamline session information from a User Office system, and what privileges they have, as well as store metadata associated with data collections.
Implementation Daiquiri is implemented with a traditional client server methodology to provide clear separation between the UI and associated application programming interface (API). Much inspiration was taken from the MXCuBE3 project with the intention of producing a more generic framework for acquisition. Many core ideas were also inspired by features and concepts in the GDA application. The general application architecture is shown in Fig. 1  and is implemented in three distinct projects: daiquiri (server), daiquiri-local (local beamline code and configuration) and daiquiri-ui (client).
Server The server (daiquiri) is implemented in Python 3 making use of Flask, Flask-RESTful, Flask-SocketIO and Marshmallow to provide a REST API and SocketIO service for websocket support and event-driven feedback. Marshmallow and apispec allow for validation of parameters passed to the API and automatic generation of associated documentation. This includes a full description of the REST API comprising detailed information about payloads and response types in Swagger /OpenAPI format. The schemas generated by Marshmallow are also available to the UI allowing for a single point definition of validation. This avoids mismatched validation between server and client. A full test suite is provided using pytest and a comprehensive continuous integration pipeline provides code style validation, testing, coverage reports and up-to-date API documentation.
(v) A visual light microscope (VLM) component to align samples via a video camera, discussed in more detail later. (vi) A simple component that executes actors in the context of a defined sample. (vii) A chat component so users and beamline staff can communicate with each other. The hardware abstraction layer defines how daiquiri should map a particular object from the control system type to its internally defined abstract object model. In the case of BLISS, this simply maps the Python attributes. The abstraction layer currently includes objects for basic types such as motors, shutters, cameras and a few other devices. New objects can be easily added and a tutorial is provided in the documentation on how to do this. For example, the daiquiri motor object contains properties such as position, velocity and acceleration, and functions such as move, and rmove (for a relative move).
Client The client (daiquiri-ui) is implemented in Javascript es6 making use of the popular front-end framework React (React, 2013) with state managed using Redux  (Redux, 2015) ; the application was bootstrapped using create-react-app (Create React App, 2016). Layout is controlled using react-bootstrap  (React Bootstrap, 2014; Bootstrap, 2011)  and styled with CSS preprocessor SASS  (SASS, 2015). A variety of libraries and helpers allow for rapid development and abstract away some of the complexities of Redux and management of asynchro- nous resources. Testing uses jest and react-testing-library. A comprehensive continuous integration pipeline provides code style validation, testing, coverage reports and analysis. The client requires no client side configuration and is passed all configuration automatically from the server.
Actors Actors decouple daiquiri from other systems. They are simply a class with a method function and an associated schema to define and validate the required parameters. In order to aid rapid development and testing, actors are automatically reloaded both in the server and client on each instantiation. Actors can provide extended validation, for example, when parameters depend on each other, asynchronous validation, for example, revalidation on each keystroke, calculate parameters and report these back to the client, and provide warnings. Code executed in these classes is isolated from the core daiquiri code and output from these calls are logged and captured for review to aid debugging.