dataesr/scientific-paragraphs-categorization huggingface dataset:
Typical acquisition parameters are ~100,000 frames, a frame rate of 100 ms, and the laser intensity of 6 kW/cm 2 as a good compromise between localization precision and imaging time  26. Samples were mounted and imaged until almost all fluorophores were bleached and no further localizations were detected under continuous ultraviolet irradiation. The yeast dual-color data were acquired on a microscope with a commercial laser box (iChrome MLE, Toptica, Gräfelfing) with 405 nm, 561 nm, and 640 nm lasers and a 640 nm booster laser (Toptica) which were coupled via single-mode. The output of the fiber was collimated, focused on the BFP of the TIRF objective (60× NA 1. 49, Nikon), and adjusted for epi illumination. The emitted fluorescence was laterally constricted by a slit, split by a dichroic mirror (640LP, ZT640rdc, Chroma), filtered by the respective bandpass filters (transmitted/AF647: 676/37, FF01-676/37-25, Semrock; reflected/mMaple: 600/60, NC458462, Chroma), and imaged on two parts of the EMCCD camera (iXON Ultra, Andor).
Astigmatic 3D SMLM The 3D SMLM data was acquired using a cylindrical lens (f = 1,000 mm; catalog no. LJ1516L1-A, Thorlabs) to introduce astigmatism. The data were fitted and analyzed as described previously  39. First, z stacks with known displacement of several (15-20) fields of view of TetraSpeck beads on a coverslip were acquired to generate a model of the experimental point spread function. This model was then used to determine the z position of the individual localizations.
Data was then drift corrected in x, y and z by a custom algorithm based on redundant cross-correlation. From the spread of the redundant displacements we estimated the accuracy of the drift correction to be better than 1. 5 nm in x and y and 2 nm in z. To exclude bad fits and z position to reject molecules far away from the focal plane, the filtering was applied as follows. 3D data of Nup96 were filtered based on lateral localization precision ([0, 5] nm), z position (boundaries defined to exclude localizations away from the nuclear envelope), log-likelihood (lower boundary defined to exclude the left tail of the distribution), and frames (boundaries defined to exclude ~1,000 very first and last frames). 3D data of Microtubules were filtered based on lateral localization precision: ([0, 5] nm) and frames ([30,000, 90,000], also for efficiency).
3D dual-color data of NPCs were filtered based on lateral localization precision ([0, 10] nm for Nup96 and [0, 5] nm for target proteins), log-likelihood (lower boundary defined to exclude the left tail of the distribution), and frames (boundaries defined to exclude very first and last thousands of frames). 2D dual-color data of endocytic sites in yeast were filtered based on localization precision ([0, 25] nm), PSF size ([0, 175] nm), frames (boundaries defined to exclude ~20,000 very first and last frames).
Correction of depth-dependent distortion We observed a depth-dependent distortion along the z-axis, as reported previously  27. The distortion is reflected by the depth-dependent ring separations s of NPCs (Extended Data Figure  4a ). As the expected ring separation for Nup96 is known, we used it as the standard to correct the distortion. By definition the ring separation is the distance between the two rings of one NPC so that  =  ⃑ ]1 - ⃑ ]0 , where  ⃑ ]1 and  ⃑ ]0 are the center positions of the two rings. As the orientation of an NPC is not necessary perpendicular to the x-y plane, we measured the tilt angle radian ψ of an NPC from the z-axis, and used the angle to derive the vertical component of the separation as  $ =  cos  =  ]1 - ]0. We also calculated the expected  $ as E( $ ) = E() cos , with E() defined as 49.
3 nm, the known average NPC ring separation  17. With these values, we can calculate for each NPC a scaling factor  & () = E( $ )  $ ⁄. We found that the moving median of  $ along z-axis appeared as a quadradic-like curve. We then fitted a quadradic function  $ =  1  0 +  0  +  / to the data. Given that the correction factor represents the change of the expected z position over the change of measured z position,  & () ≈ ()/. We then defined  7 , which makes  & () = 1, as the origin of distortion. The expected or undistorted z position can then be acquired as () = ∫  & ()  with ( 7 ) =  7. The corrected z position of each localization k was then defined as  ! E = ( ! ) -(0) for keeping the focal point zero. This correction was applied to all the NPC data sets before further quantifications.
Example sites simulated across various conditions. a, the NPC model used for simulations. b-e, example simulated NPCs across different localization precision (b), labeling efficiency (c), background (d), and re-blinks (e). In b, corresponding photon counts  -. with zero fluorescence background  were used to derive the specified median localization precisions. In c-e, all sites were simulated with  -. = 5,000 and  = 100 photons/pixel/localization and yielded a median lateral localization precision of 3. 6 nm. STORM mode and the condition as for b except for the parameters specified in the headers. Detailed simulation parameters are shown in Supplementary Table 1. Scale bars: 100 nm. Extended Data Figure 2. Errors of parameter estimations against factors contributing to data quality in the simulated datasets.
, respectively. Sample size: ns = 1,000 for each dot. Extended Data Figure 3. Distributions of the fitted ring separation at different labeling efficiencies in simulated data. Three side-view examples of pores at different fitted ring separations, indicated by red crosses. All examples are from the dataset simulated with 30% labeling efficiency. Scale bar: 100 nm. Extended Data Figure 4. Correction of z-dependent distortion. a. A scatter plot showing the z-dependent spatial distortion along the z axis, as the NPC ring separation appears dependent on the z position. b. The median correction factor over z positions can be approximated by a quadratic fit. The correction factor is defined as the expected ring separation divided by a fitted ring separation before the correction. c. Fitted ring separations over the z positions after the correction based on the correction factor.
5 ± 1. 5 nm (c), separation s = 50. 3 ± 3. 8 nm (d), and twist θ = 8. 6 ± 5. 9° (e). Simulation parameters are summarized in Supplementary Table 1. Sample size: ns = 4,000. Scale bars: 100 nm. Extended Data Figure 6. Validation of the model selection using simulations. a, Normalized AICC of fitting simulated NPCs with different rotationally symmetric models. Each panel corresponds to a simulated NPC dataset generated from the model indicated in the insets. Sample size: ns = 3,000 for each panel. b, Scatter plot showing normalized AICC values of fitting the simulated 8-fold symmetric sites with models having 6-fold and 8-fold rotational symmetries. c, Scatter plot showing normalized AICC values of fitting both the simulated six-fold and eight-fold symmetric sites with models having six-fold and eight-fold rotational symmetries.
Acknowledgements We thank  I. Schoen  and  J. Hériché  for inputs on the manuscript. This work was supported by the  European Research Council  (grant no. ERC CoG-724489  to J. R. ), the  National Institutes of Health Common Fund 4D Nucleome Program  (grant no. U01 EB021223  to J. R. ), the  Human Frontier Science Program  (grant no. RGY0065/2017  to J. R. ), and the  European Molecular Biology Laboratory.
1. 1 Example sites simulated across various conditions. a, the NPC model used for simulations. b-e, example simulated NPCs across different localization precision (b), labeling efficiency (c), background (d), and re-blinks (e). In b, corresponding photon counts  -. with zero fluorescence background  were used to derive the specified median localization precisions. In c-e, all sites were simulated with  -. = 5,000 and  = 100 photons/pixel/localization and yielded a median lateral localization precision of 3. 6 nm. STORM mode and the condition as for b except for the parameters specified in the headers. Detailed simulation parameters are shown in Supplementary Table 1. Scale bars: 100 nm. Extended Data Figure 2. Errors of parameter estimations against factors contributing to data quality in the simulated datasets.
Implementation of both MCHM models into the Event Generator We have implemented both models in FeynRules (v2. 3)  [47]  and produced an associated UFO file for each model, that can be interfaced with MadGraph 5 (v2. 6. 2)  [48]. The numerical input from the diagonalization of the mass matrices is then fed via a customwritten Python script into the param card. dat for processing within MG5. We simulate the tth and tthh processes in MG5. We have also checked that the deviations from the SM in the top quark properties are negligible, since the new physics is rather heavy. It is important to stress here that the generation framework we fully developed at the parton and LO level can be connected directly to detector simulations such as DELPHES (fast simulation) or detailed experiment full simulations such as the ones of ATLAS or CMS.
Points P 4 and P 5 , instead, have a high f value which translates into a µ(tthh) very close to 1. Point P 3 has still a rather low f value with, as striking features, the strong increase in µ(tthh) at the expense of the low NR-tthh contribution (see dominant decay of the lightest resonance into th) and µ(tth) getting close to 1. All the expected resonances, in this case, have relatively low mass well reachable at the HL-LHC (and even may be before, i. e. by end of the forthcoming Run 3). The HL-LHC increased luminosity will allow to measure the branching decay especially into th, predicted to be dominant with respect to tZ. Besides, the full HL-LHC dataset could indicate a possible excess in µ(tthh). Points P 4 and P 5 are rather similar in terms of all the measurable quantities listed in this Table.
Conclusions and Outlook The Minimal Composite Higgs Models MCHM 5 and MCHM 14 are studied here as an important show-case for the exploration of the beyond standard Model world, in the top-Higgs sector at high energy hadron colliders. The full generation and simulation framework is developed which allows a phenomenological analysis over the whole parameter space of these two models. Besides, the developed generation and simulation software defines a preliminary experimental framework; they are ready for use by more detailed data analysis at the LHC and HL-LHC experiments and by advanced detector simulations developed for the 100 TeV machines in project. The focus is on the analysis of the tth and tthh production processes, covering in the later both the production of heavy fermions and the non-resonant contributions.
These points were then complemented by exceptional points where needed, and the most important phenomenological data is summarized in tables 1, 5 and 7 for the MCHM 5 , and tables 2, 6 and 8 for the MCHM 14. This exploration was done in two steps, first covering physics at "Low Scale", where we focus on the part of the parameter space that is within reach of the HL-LHC. On a second step we extend the analysis to the "High Scale" region, with mass parameters reaching up to 30 TeV, which will be of interest for planned future colliders such as the 100 TeV FCC-hh and the SppC. The first observations from the phenomenological and preliminary experimental analysis are that a deviation from the SM in the tth production is also an essential measurement for MCHM. An increase will reject the MCHM 5 scenario and greatly refine the areas of the parameter space where MCHM 14 would be valid.
Acknowledgments The authors thank  Geum Bong Yu  for useful discussions and references. One of us L. A. F. do Prado thanks  IRFU-CEA, University Paris Saclay  for hospitality. This work was supported by the  São Paulo Research Foundation (FAPESP)  under grants # 2016/01343-7 , # 2013/01907-0 , # 2015/26624-6  and # 2018/11505-0 , by  Science Without Borders / CAPES  for  UNESP-SPRACE  under the Grant No. 88887. 116917/2016-00and  in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES) -Finance Code 001.
Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission.
Introduction The coronavirus disease 2019 (COVID-19) outbreak represents one of the most critical global health emergencies in modern times, with >4. 7 million deaths reported worldwide as of the end of September 2021. [1] The COVID-19 pandemic has posed an unprecedented healthcare challenge, with intensive care unit (ICU) capacity rapidly exceeded around the world in the first weeks of the outbreak and subsequent resurgences. [2] The ability to predict patient outcomes by analyzing ICU medical records is hampered by numerous challenges such as a lack of structured clinical data, missing values, and datasets with a limited number of patients. Under these conditions, predicting the probability that a patient will either develop complications associated with COVID-19 or improve is important as it may help to define a personalized risk profile that will optimize clinical management.
[3] The analytical capability of machine learning (ML) methods has proven extremely accurate and in some cases, superior to classical statistical approaches. [4] [7]  The use of ML to design a Clinical Decision Support System (CDSS) also has practical value beyond clinical diagnosis and disease modeling. [8] al-world datasets from the ICU usually consist of data with high dimensionality in terms of both an absolute number of monitored parameters and sampling frequency. On the other hand, the collected features are usually characterized by noise and/or redundancy. While managing and modeling this amount of data, there are several challenges such as overfitting, low interpretability, data heterogeneity, and missing values. Data-driven techniques may be useful for analyzing stored multifactorial temporal ICU data to construct advanced ML models that can predict clinical outcomes and reveal complex patterns that may not be obvious to physicians.
The model was constructed using the entire RISC-19-ICU dataset after excluding patients with a change in SOFA score ≤1 and those with a missing SOFA score at admission or on day 5 after admission. The model was tested using a 10-fold cross-validation (CV-10) procedure that divided the entire study cohort into 10 non-overlapping folds for each cross-validation cycle by selecting 9 folds for training and 1 for testing. To improve generalizability, ML model complexity was modulated by optimizing the parameters controlling the training process (i. e. , hyperparameters) using nested cross-validation within the training set. XGBoost hyperparameters included the number of iterations of the boosting procedure, learning rate, maximum depth of a tree, and subsample ratio of the training features. The optimal hyperparameters were determined by implementing a grid search and optimizing the macrorecall in a nested 5-fold CV.
Hence, each split of the outer loop was trained with the optimal hyperparameters tuned in the inner loop. Although this procedure is computationally costly, it allows an unbiased and robust performance evaluation. [19] We compared the predictive performance of the XGBoost model to that of a standard logistic regression-based prediction model based on a confusion matrix and area under the receiving operating characteristic (ROC) curve with a 95% confidence interval (CI). All experiments were reproducible and were performed using Python 3. 7 with a 2. 3 GHz Intel Core i7 quad-core processor and 16 GB RAM.
Compared with traditional methods that use univariate and multivariate statistics for pattern discrimination, an ML approach based on the XGBoost algorithm has superior detection sensitivity and generalizability because it combines multiple types of information across several variables (i. e. , a high-dimensional problem) based on a relatively small dataset. XGBoost also has advantages over other ML methods: it makes no assumptions regarding data distribution and uses individual decision trees, and may thus be unaffected by multicollinearity. Another benefit of ensemble methods such as XGBoost is that they automatically estimate feature importance from a trained predictive model, yielding a score for the utility or value of each feature in the construction of boosted decision trees within the model.
Acknowledgments We also want to thank all of the physicians and nurses in our collaborating centers for their tireless and brave efforts in patient treatment and care, as without them it would have been impossible to contain this ongoing health care emergency. The authors wish to acknowledge all of our collaborators from the RISC-19-ICU registry as collaborating authors. Nottwil, Nottwil, Switzerland (Hermann Redecker, MD); Soins intensifs,  Groupement Hospitalier de l'Ouest Lémanique ,  Hôpital de Nyon, Nyon (Thierry Fumeaux, MD; Mallory Moret-Bochatay, MD );  Klinik für Anaesthesie und Intensivmedizin, Spitalzentrum Oberwallis, Visp, Switzerland (Friederike Meyer zu Bentrup, MD, MBA) Intensivmedizin & Intermediate Care, Kantonsspital Olten, Olten, Switzerland (Michael Studhalter, MD) ;  Intensivmedizin, Spital Oberengadin, Samedan  ( Michael Stephan, MD; Jan Brem, MD); Intensivstation, Kantonsspital Schaffhausen, Schaffhausen, Switzerland (Nadine Gehring, MD );  Anaesthesie Intensivmedizin Schmerzmedizin ,  Spital Schwyz ,  Schwyz  ( Daniela Selz, MD ;  Didier Naon, MD );  Medizinische Intensivstation, Kantonsspital St.
Funding The present study has been supported by the  "Microsoft Grant  Award: AI for Health COVID-19". The RISC-19-ICU registry is supported by the  Swiss Society of Intensive Care Medicine  and funded by internal resources of the  Institute of Intensive Care Medicine , of the  University Hospital Zurich  and by unrestricted grants from  CytoSorbents Europe GmbH (Berlin, Germany ) and  Union Bancaire Privée (Zurich, Switzerland ). The sponsors had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.
Availability of data and materials Any intensive care unit or other center treating critically ill COVID-19 patients is invited to join the RISC-19-ICU registry at https://www. risc-19-icu. net. While the registry protocol prevents the deposition of the full registry dataset in a third-party repository, analyses on the full dataset may be requested by any collaborating center after approval of the study protocol by the registry board. Reproducibility of the results in the present study was ensured by providing code for registry-specific data transformation and statistical analysis for collaborative development on the GitHub and Zenodo repositories. The registry protocol and data dictionary are publicly accessible at https://www. risc-19-icu. net.
1) TYPE A -POWER SYSTEM AND MULTI-TERMINAL NETWORK STUDIES (PS&MTN) In this system the primary focus is converter performance and operation within a wider network. The study is of the operation of known architectures -new SM designs are of less interest. A variety of different control studies and applications (scenarios) of the converter are typically the target. The ability to quickly reconfigure control, a fast learning curve, good documentation, and electrically well protected hardware are desirable. Ideally control system programs would be developed using well-known tools such as MATLAB or LabVIEW; internal dynamics are abstracted away from the user, to simplify development, so this system should require minimal converter expertise. This classification typically represents commercial products for laboratory hardware and software development.
V. CONTROLLER SELECTION AND SOFTWARE DEVELOPMENT A. CENTRALIZED CONTROLLER ARCHITECTURE The control and operation of a converter is handled by a device or set of devices which form the control architecture. The simplest method of control is referred to as a centralized control structure where one powerful device is used to control the whole system. In the case of the ICL converter this is a real-time simulator from Opal-RT (OP5600), programmed using MATLAB/Simulink models, and formed of a CPU and an FPGA, as shown in Fig. 9. A fully centralized control unit (FCCU) requires a large amount of analogue and digital I/O to communicate to the SMs and any auxiliary devices. Using an FCCU enables very fast control system prototyping and development and offers system omniscience for easy access to I/O data for research.
• High speed 2. 4 Gbps downstream/1. 2 Gbps upstream. • Simple distribution network with passive components. • High reliability. If one of the secondary nodes crashes the network is not affected. • All the information send by the primary node is received simultaneously by all the secondary nodes. • Provides local intelligence at SM level which facilitates the monitoring and feedback from each SM. Additionally, it is possible to communicate between the CCB and real time fast prototyping hardware such as those supplied by dSPACE, Opal-RT, or National Instruments. The execution of the control algorithms can be split between the CCB and the real time hardware. This allows development of control algorithms using MATLAB/Simulink, which is very convenient for fast prototyping and testing of the developed controllers.
In the distributed architecture, synchronization of reference and measurement signals in different arm secondary controllers should be considered. In the UoL prototype an optimization process provided by the Texas Instruments Code Composer Studio environment has been applied to the three basic functions (i. e. sorting, limiting and regulating functions) of CBC based in each TMS320F28377D DSP secondary node. The global calculation time is around 5. 8 µs, which allows the low-level control to be implemented on a secondary DSP, running properly, over a period of 10 µs.
This work was supported in part by  The University of Manchester  supported by the  National Innovation Allowance  project '' VSC-HVDC Model Validation and Improvement '' and  Dr. Heath's iCASE Ph. D. studentship supported through  Engineering and Physical Sciences Research Council (EPSRC)  and  National Grid , in part by the  Imperial College London  supported by  EPSRC  through the  HubNet Extension under Grant   EP/N030028/1 , in part by an  iCASE Ph. D. Studentship  supported by  EPSRC  and  EDF Energy  and the  CDT in Future Power Networks  under Grant  EP/L015471/1 , in part by  University of New South Wales (UNSW)  supported by the  Solar Flagships Program through the Education Infrastructure Fund (EIF) , in part by the  Australian Research Council  through the  Discovery Early Career Research Award  under Grant  DECRA-DE170100370 , in part by the  Basque Government  through the project  HVDC-LINK3  under Grant  ELKARTEK KK-2017/00083 , in part by the  L2EP  research group at the  University of Lille  supported by the  French TSO (RTE) , and in part by the  Hauts-de-France region of France  with the  European Regional Development Fund  under Grant  FEDER 17007725.