dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by the Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, and C1275/A15956) and Breast Cancer Campaign 2010PR62 and 2013PR044. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). SEARCH is funded by the Cancer Research UK (C490/A10124, C490/A16561) and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.
The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. Population-based controls were from the Multi-Ethnic Cohort (MEC) funded by grants from the Ministry of Health, Singapore, National University of Singapore, and National University Health System, Singapore. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ.
The SMC is funded by the Swedish Cancer Foundation and the Swedish Research Council (VR 2017-00644) grant for the Swedish Infrastructure for Medical Population-based Life-course Environmental Research 14 Human Mutation (SIMPLER). The SZBCS was supported by a Grant PBZ_ KBN_122/P05/2004 and the program of the Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in 2019-2022 project number 002/ RID/2018/19 amount of financing 12 000 000 PLN. The TNBCC was supported by a NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. The UCIBCS component of this research was supported by the NIH (CA58860, CA92044) and the Lon V Smith Foundation (LVS39420).
The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre and MRC Core Funding (MC_UU_00004/01). The USRT Study was funded by the Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Contract grant sponsor AV was supported by the Spanish Instituto de Salud Carlos III (ISCIII) funding, an initiative of the Spanish Ministry of Economy and Innovation partially supported by the European Regional Development FEDER Funds (INT20/00071; PI19/01424); the Autonomous Government of Galicia (Consolidation and structuring program: IN607B); the Fundación Mutua Madrileña (call 2018) and the AECC (PRYES211091-VEGA).
АААА-А16-116020350032-1). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. ABCTB Investigators are Christine Clarke, Deborah Marsh, Rodney Scott, Robert Baxter, Desmond Yip, Jane Carpenter, Alison Davis, Nirmala Pathmanathan, Peter Simpson, J. Dinny Graham, and Mythily Sachchithananthan. Samples are made available to researchers on a nonexclusive basis.
Dataset 3. 2. 1. Logistic Regression Results. Using logistic regression, two BRCA2 variants (2%) (Table1) reached strong pathogenic evidence following the ACMG/AMP classification criterion (PS4 criterion, OR > 5, p value <0. 05, and CI not including 1. 0) [2]. Detailed logistic regression results for all variants are shown in Supplementary TableS4. 1. 0 N = 20,000 MAF = 0. 00003 1. 0 N = 20,000 MAF = 0. 00005 1. 0 N = 20,000 MAF = 0. 0001 0. 8 0. 8 0. 8 Power 0. 4 0. 6 Power 0. 4 0. 6 Power 0. 4 0. 6 0. 2 0. 2 0. 2 0. 0 1 2 3 4 5 6 7 8 9 10 0. 0 1 2 3 4 5 6 7 8 9 10 0. 0 1 2 3 4 5 6 7 8 9 10 Relative risk Relative risk Relative risk ccLR BRCA1 Logistic regression ccLR BRCA1 Logistic regression ccLR BRCA1 Logistic regression ccLR BRCA2 Fisher's ccLR BRCA2 Fisher's ccLR BRCA2 Fisher's (a) (b) (c) 1.
the ocean, as a vast interconnected body of water on Earth, plays an essential role in Earth's planetary dynamics, climate change, and the evolution of human society and decision-making processes. an ocean-focused global map is necessary to visually capture numerous phenomena within the world's ocean and seafloor. Here we present the power of the Spilhaus square projection with various geological and geophysical datasets, including bathymetry, teleseismicity, seafloor geography, and seafloor spreading parameters. The Spilhaus projection, compared to widely-used map projections (e. g. , Mercator and Robinson), emphasizes the seamless connection of water masses surrounded by continents. this projection has recently garnered attention for presenting ocean-oriented data, although it is not extensively used and currently supported by the arcGIS software.
Background & Summary This dynamic planet Earth has one vast ocean that covers 71% of its surface and occupies 97% of its hydrosphere  1. The ocean plays an essential role in climate, geological processes, and facilitating exchanges of mass and heat between the hydrosphere, lithosphere, atmosphere, and diverse and complex communities of lives within  2. The ocean also has historically played an influential role in humans' decision-making among their climatological environment, socio-economic settings, and geopolitics. With the modern big-data oriented geoinformatics approach, visualising the spatial distribution and connections of various datasets becomes a powerful tool for gaining knowledge, driving our understanding of Earth's complex systems. Indeed, different map projections bring different perspectives of observing phenomena arising from datasets and understanding their implications  3  (Fig.
1 ). In particular, the spatial linkage of the datasets at a global scale can be easily noticed when properly projected. In commonly used landmass-oriented global map projections (e. g. , the Robinson global projection  4  in Fig. 1b ), they often split one of two biggest oceans, i. e. , the Pacific Ocean and the Atlantic Ocean, or have incomplete or discontinuous representations of water masses. Otherwise, oceans in two polar regions are significantly distorted in their shapes and scales, giving some erroneous conceptions of their distribution and extent. Therefore, utilising geographic region-specific projections becomes indispensable to minimise local distortions and provide a more accurate representation of the data, when comparing different oceans or changing observations, e. g. , the Lambert azimuthal projection for hemispheres (Fig.
g. , the Southern Ocean with the Atlantic, Indian, and Pacific Oceans) and the seafloor, with the associated Mid-Ocean Ridge and subduction systems (Fig. 2 ). Although the Spilhaus projection was first published in 1979  5  , there has been limited use in studies that capitalize their core effort on geoinformatics  6, 7. This map projection had been successfully supported in the Data-Driven Documents (d3) and the Environmental Systems Research Institute's (ESRI) ArcGIS Pro 2. 5 + software (Methods), but not by other open-source, widely-used geoinformatics software, such as Generic Mapping Tool 8 (GMT) and Quantum Geographic Information System 9 (QGIS). Here, we present a set of geological and geophysical data, which are often used and visualised globally in landmass-oriented projections, using the Spilhaus Projection (see details in Table  1 ): 1.
Topographic data is from GEBCO  10. square shape of the Spilhaus projection enables us to display these data in a cascading view for depth sections, facilitating data interpretation, e. g. , tracing the velocity anomalies and the partial melting contents in depths at plate boundaries and hotspots. These global-scale datasets are widely accepted by scientific communities, but most are never mapped with the ocean-centric Spilhaus projection. This mapping provides a different visualization of the ocean basins and seafloor, which has the potential to benefit marine scientists working at a global scale with generating new thoughts/questions. The maps may also be exploitable by educators, communicators, and general public media to better understand our oceans. Beyond that, these maps can benefit the evaluation and assessment of ocean uses (e.
Although the Spilhaus square projection can properly connect the world's oceans, there are no detailed mathematical descriptions in his publications  5, 22, 23. Recently, this projection was implemented based on the Adams square II projection  25  (Fig. 1e ) by Bojan Šavrič, David Burrows, and Melita Kennedy within the ArcGIS software in 2020 (details in ArcGIS StoryMap: https://storymaps. arcgis. com/stories/756bcae18d304a1eac140f19f4d-5cb3d). The Adams square II projection was created in 1925  25  , which is similarly conformal and square (Fig. 1e ). Its equation is referred to Snyder  3   Fig. 4 Spilhaus square projection for heat flow (a; data from ref. 15  ), sediment thickness (b; data from ref. 16  ), and gravity (c; data from ref. 17  ) and magnetic (d; data from ref. 18  ) anomalies.
All Spilhaus maps presented in this paper are created using the ArcGIS Pro 2. 5 software. All grid datasets (source links and references are listed in Table  1 ) are formatted in netCDF4 using GMT  8  and imported into ArcGIS. The geographic coordinate system WGS 1984 Spilhaus Ocean Map (ArcGIS WKID: 54099) is chosen to map these grid datasets and other spatial information. As ArcGIS is not open-access software, we combined the work done by Torben Jansen (https://observablehq. com/@toja/spilhaus-world-ocean-map-in-a-square) to implement a quasi-Spilhaus square map in the free-access Observable Notebook with d3-geo (https://observablehq. com/d/bb73e74c1685e498). However, this implementation is not the actual Spilhaus square projection but quasi. For example, the upper right and lower left corners are not strictly at 30°N/115°E and 30°S/65°W, respectively.
The reason for this slight error may be that d3-geo does not consider transforming the ellipsoidal reference system to the spherical reference system when performing map projections. This script is customizable for various purposes, e. g. , adding spatial information, visualising grid data, and changing color patterns. It may also facilitate the implementation of the Spilhaus projection in widely-used open-source geoinformatics-related software (e. g. , GMT and QGIS) in the near future for seamless use with other projections.
Data Records All maps, datasets (grid and shape files), and an ArcGIS template package are stored in the Figshare repository  26  and shared under the CC BY 4. 0 (https://doi. org/10. 6084/m9. figshare. 21229757). Maps are editable (vector graphics in PDF format) and publicly available for users. Grid files are included and listed in Table  1. A high-resolution seafloor topographic map is also generated for general uses. Fig. 5 Spilhaus square projection for upper-mantle share wave velocity (Vs) perturbations (left panels; data from ref. 19  ) and associated partial melt content (right panels; data from ref. 20  ) in a cascading view for depth sections. Green lines show plate boundaries (data from ref. 12  ). Triangles mark the locations of hotspots. Vs varies from the average value between and +10% in depth sections of 100 and 150 km, and between -2% and +2% in depth sections of 250 and 350 km.
Code availability For reproducing the Spilhaus maps in this paper, we packaged an ArcGIS template for topographic maps (Figs. 1a, 2 ), and other maps can be easily generated by importing the grid data. The code of the quasi-Spilhaus square projection, implemented with d3-geo, is stored in the Figshare repository  26  or can be found at https:// observablehq. com/d/bb73e74c1685e498.
The NectarCAM is a camera that will be mounted on the Medium-Sized Telescopes of the Cherenkov Telescope Array (CTA) observatory. Along with the hardware integration of the camera, the scientific software, nectarchain, is being developed. The software is responsible for transforming the raw data from the camera into analysis-ready calibrated data. In this contribution, we present the structure of the software, which consists of two modules: the calibration pipeline and the data quality check pipeline. The calibration pipeline reduces the data, performs flat fielding, and determines the gain for the analysis. The data quality monitoring pipeline is used to select the data that meets the necessary standards for analysis. Additionally, we discuss the format of the downstream data and the integration of the nectarchain modules in the general software framework of CTA.
PoS(ICRC2023)862 nectarchain, the scientific software for the CTA-NectarCAM G. Grolleron software and methods that can read calibration coefficients produced by nectarchain (cf. 3) and apply them to the raw-data to fill R1 containers. This makes it possible to ingest the data from the camera prototype in the dark room in ctapipe, and thus test the entire analysis chain.
Flat-field and timing Additional information needed during the calibration of the data are the relative efficiency between pixels and the relative timing between pixels. Both are measured with a device called the flat-fielding unit which deliver short pulses of uniform blue light across the camera. The relative inhomogeneity is obtained by computing the ratio of a given pixel measured intensity to the average across the camera. The relative timing is obtained by computing the difference between the average maximum time of the waveform to the average across the camera.
PoS(ICRC2023)862 nectarchain, the scientific software for the CTA-NectarCAM G. Grolleron whole camera or adapted to each pixel for instance. Charge histograms show two main peaks : one created by the pedestal signal, and another one created by the SPE signal. The separation between the two peaks makes it possible to measure the gain, which is the charge in ADC per p. e. , with a two Gaussian modelling presented in  [3]. However, as the illumination is low, this method can only be used to reconstruct the gain in the high gain channel. Derivation of the gain in the low gain channel will then use the High-Low method described in section 3. 4.
PoS(ICRC2023)862 nectarchain, the scientific software for the CTA-NectarCAM G. Grolleron value is then the multiplication of the previously calculated high-gain amplification value by this ratio. The data quality monitoring (DQM) pipeline is a semi-automatic pipeline developed for the monitoring of the data taken by the camera and presented in Fig. 4. It is data acquisition independently of the calibration module and is still in development. It monitors the data for low and high gains and for different types of triggers such as pedestal and physical triggers. The main script reads the NectarCAM events and defines processor classes that are used to derive the quantities to be monitored. At this stage, the processor contains five classes presented below.
Continuous integration Workflows dedicated to continuous integration (CI) are being developed for the nectarchain package. For instance, the package is published on PyPI on releases, as well as a conda package on the conda-forge channel. Notably, a Singularity/Apptainer  [5]  container image is also built and deployed on the GitHub Container Registry on releases and merged push requests, which greatly eases software deployment. When processing NectarCAM data on the EGI grid through DIRAC  [6] , where the data are stored, such an image can readily be instantiated without the need of embedding the software in DIRAC jobs or of deploying it to distributed file systems prior to the job processing. Ultimately, nectarchain could be deployed on the CTA Observatory CVMFS2 instance once stabilized. Some work in progress on the CI side concerns well covering unit tests as well as an extensive code documentation.
Interface with ctapipe : ctapipe_io_nectarcam The interface between the data acquisition system and the CTA data processing pipeline for NectarCAM is ensured by a dedicated plugin ctapipe_io_nectarcam. The plugin reads the data in output from the camera server and fills the containers used by ctapipe, the base library upon which the CTA data processing pipeline is built. The ctapipe_io_nectarcam module is distributed through PyPI and as a conda package using the same kind of CI workflows as nectarchain.
Conclusion In these proceedings, the calibration pipeline for NectarCAM has been presented. The most important part is the nectarchain pipeline which is currently used to extract the calibration quatity needed to produce R1 pre-calibrated data. Moreover, nectarchain makes possible study the camera performances (pedestal estimation and evolution in time or with temperature, flatfielding, gain estimation). ctapipe_io_nectarcam is the next part of the calibration workflow and use the output of nectarchain to produce the R1 data in a ctapipe format to be CTAO 2CernVM File System, https://cvmfs. readthedocs. io/en/stable/ PoS(ICRC2023)862 nectarchain, the scientific software for the CTA-NectarCAM G. Grolleron compliant. The Monte Carlo simulations are still on the rise and will be the next piece of the calibration pipeline.
Introduction JT-60SA  [1]  will offer the possibility to study a variety of scenarios: pulsed, inductive, standard H-modes, similar to the ITER baseline scenario  [2] ; advanced inductive, high-𝛽, low magnetic shear scenarios, similar to the ITER hybrid scenario and fully non-inductive, steadystate, advanced scenarios, which could be extrapolated to a steady-state demonstration of fusion power plants. Since a poorly designed scenarios could even cause premature discharge termination, and most importantly disruption, a tool which can simulate with a sufficient degree of realism the entire pulse is of the utmost importance. Several simulators obtained coupling different numerical simulation codes have been developed in the past, and can be found in the scientific literature: in  [3]  the free boundary equilibrium code DINA was coupled with the transport code CRONOS to simulate ITER pulses; in  [4]  the coupling of JINTRAC and CREATE-NL was proposed for the same purpose.
In order to optimize ITER 15 MA DT scenario, an advanced integration  [5]  between DINA and JINTRAC  [6]  has been implemented. In  [7] , the Corsica code, with a light transport module, was used to design and simulate ITER scenarios. Also, at IPP, the Fenix flight simulator based on ASTRA transport code was developed for ASDEX-U  [8]. In this paper we propose a coupling between METIS  [9] , a numerical code combining 0D scaling-law normalized heat and particle transport with 1D current diffusion modelling, and CREATE-NL+, a free boundary equilibrium solver including magnetic control laws  [10]. One of the key aspects to run a realistic simulation is the availability of a suitable magnetic controller that is based on a combination of the following actions: vertical stabilization control, coil current control, plasma shape and plasma current control.
Coupling scheme description The link between CREATE-NL+ and METIS implies the solution of two different sets of differential equations which are coupled to each other through exchange variables 𝑦 1 and 𝑦 2 at each integration time step (strong coupling). A schematic view of this coupling is reported in Fig. 1. The dynamics of the coupled system can be described by a feedback connection that, in the state space, assumes the following mathematical expression:   { ẋ1 = 𝑓 1 (𝑦 2 (𝑡), 𝑥 1 (𝑡), 𝑢 1 (𝑡)) 𝑦 1 = 𝑔 1 (𝑦 2 (𝑡), 𝑥 1 (𝑡), 𝑢 1 (𝑡)) 𝑥 1 (𝑡 0 ) = 𝑥 10 (1) { ẋ2 = 𝑓 2 (𝑦 1 (𝑡), 𝑥 2 (𝑡), 𝑢 2 (𝑡)) 𝑦 2 = 𝑔 2 (𝑦 1 (𝑡), 𝑥 2 (𝑡), 𝑢 2 (𝑡)) 𝑥 2 (𝑡 0 ) = 𝑥 20 (2) where subscript 1 and 2 refer to CREATE-NL+ and METIS states, inputs and outputs respectively. The integration of the free boundary equilibrium equation (  1 ) requires: • the knowledge of the initial values of the currents in the active circuits and in the passive structures (𝑥 1 variables).
• The plasma boundary, the plasma current, the magnetic axis position. These quantities are part of the 𝑦 1 variable calculated by CREATE-NL+. Eqs. (  1 ) and (2) are integrated in the time interval [𝑡 0 , 𝑡 𝑓 ] as two separate processes under the same environment (co-simulation) guaranteeing a proper exchange of variables 𝑦 1 and 𝑦 2. Both METIS and CREATE-NL+ are implemented as Matlab modules. The coupling is managed via a Matlab routine implementing the following steps, where the control laws are implemented explicitly so that the control action at time step 𝑘 depends on the solution at the previous time step 𝑘 -1: • Run a first CREATE-NL equilibrium with assigned initial conditions. • Run a first METIS transport with assigned initial conditions. • For time from 𝑡 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 to 𝑡 𝑓 𝑖𝑛𝑎𝑙 (with a given time step 𝐷𝑡): -Read magnetic outputs from CREATE-NL+.
Experimental setup The algorithms Opt and Greedy, together with the MustFix heuristics, have been implemented in Python and tested on a variety of benchmarks. The implementation allows to tune the battery test under a variety of parameters that are used to randomly generate the game instances, which are introduced at each experiment description below. We run four kinds of experiments, each of them designed to test a specific feature. First, we tested the effectiveness of MustFix. In particular, we compared two versions of Opt, one with and one without the MustFix heuristics enabled, to compare their performance. The results and comments on this experiment are presented in Section 5. 1. Second, in Section 5. 2 we evaluated the performances of Opt and Greedy, both with MustFix activated. In particular, we evaluated both the running time and accuracy of Greedy, which was comparably performing well with respect to Opt, in both terms.
Acknowledgements The work of  Giuseppe Perelli  was partially funded by  MUR  under the  PRIN programme , grant  B87G22000450001  (PINPOINT), and by the  PNRR MUR  project  PE0000013-FAIR. The work of  Fabio Patrizi  was partially funded by  MUR  under the  PNRR MUR  project  PE0000013-FAIR , the  ERC Advanced Grant WhiteMech  (No. 834228 ), and the  Sapienza Project MARLeN.
A back-propagation loop of loss derivatives through the functional elements of the system allows their optimization. Source: The figure is adapted from Ref. [5]. complex future instruments, where the sheer complexity of the task induces sever non-convexities in the loss function, resulting in a non-trivial structure of the set of optimal and near-optimal parameters. Paradigms that may still be valid will be recovered automatically by this new class of optimization techniques. Computer science has undergone a fast progress in the past twenty years. The development and rapid expansion of deep neural network and gradient-optimization based software, denoted as differentiable programming, has been made possible by the concept of automatic differentiation. Such algorithms are based on nested differentiation based on the chain rule.