dataesr/scientific-paragraphs-categorization huggingface dataset:
X-ray propagation-based imaging techniques are wellestablished at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and spreading of the use of such techniques difficult. Here, we present PyPhase, a free and opensource package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license. PyPhase implements some of the most popular phase-retrieval algorithms in a highly-modular framework supporting the deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straight-forward.
Although there is a plethora of literature regarding phasereconstruction algorithms, access to those algorithms is relatively closed, with a few exceptions  [15, 16]. To address this limitation, we present a complete Python phase-retrieval package for Xray phase-contrast imaging, named PyPhase. PyPhase is a fully open-source and modular package that relies solely on free and open-source tools. With PyPhase, we aim to provide: • A flexible phase-retrieval toolbox for expert users • An interface, currently a command-line interface and in the future a graphical user interface, for non-expert users • Tools for deployment on computer clusters and heterogeneous computing infrastructures • Tools for implementation and development of phase retrieval algorithms • High level of modularity to facilitate the integration of different packages, e.
g. , registration, tomography, Fast Fourier Transform, reading and writing data, and visualization. The package consists of a number of modules, namely: • Phaseretrieval contains functionality for phase retrieval algorithms. Table  1  lists the currently implemented algorithms. • Propagator contains functionality for the propagation of a wave-field and generation of intensities. The propagators are typically used in iterative phase-retrieval algorithms. Currently, a Fresnel operator is implemented, as well as linearized versions based on transport of intensity  [17]  and contrast transfer equations  [18]. • Tomography contains functionality for tomographic operations: forward projection, back-projection, as well as 3D image processing operators. The main part of this module wraps other codes for tomographic reconstruction, currently PyHST  [19]  and TomoPy  [20].
• Dataset contains functionality to read and write images in different formats and from different data sources. Current data sources are ESRF style EDF data sets, NanoMAX style HDF5 data sets, and TOMCAT style TIF data sets. Compatibility with other data sources is planned, e. g. , by interfacing DXChange  [21]. • Utilities contains supporting functionality, such as image registration and visualisation. Registration is currently implemented as a wrapper of Elastix  [22, 23]  via pyElastix  [24]. • Parallelizer contains functionality for parallelization. This is implemented as decorators decorating functions that take a range of projections as input. Currently, the supported infrastructures are OAR [25], SLURM  [26] , and serial processing. Switching between infrastructures requires the modification of one line in the configuration file.
The algorithms currently implemented in pyPhase, suitable for both edge-enhancement and holographic regimes, are listed in Table  1. There are many more algorithms presented the literature than currently by PyPhase. The aim, however, is to progressively include as many algorithms as possible. This would permit a fair evaluation of algorithms in practice. Furthermore, it would help the practitioner choose the most suitable algorithm, since different algorithms work well in different circumstances. The package contains classes representing the building blocks of phase retrieval algorithms. This modularity makes it simple to swap between different implementations, e. g. , one optimized for GPU or CPU processing, without substantial changes to the code. The most basic operation for phase retrieval is arguably the FFT.
For flexibility, the package contains a wrapper for the FFT functions (FFT, IFFT, fftshift, ifftshift, frequency variables). In the current version, the numpy FFT is used. Interfaces to other FFT packages, such as FFTW, can then be easily implemented and used with minimal modification of the code. Iterative phase retrieval algorithms require the implementation of propagators. An implementation of the Fresnel transform as well as a linearized propagator based on the CTF are provided. The modular implementation makes it straight-forward to interface with other codes and algorithms. For the implementation of 3D phase-retrieval algorithms, it is necessary to implement the Radon transform and its inverse transformation. These tomographic operations are provided as wrappers of specialized tomographic-reconstruction packages.
Currently, Tomopy and PyHST2 packages can be used. However, the modularity of PyPhase makes interfacing other packages straight-forward. A limiting factor for the practical application of phase retrieval in tomography is efficient data handling. We implement this as a dataset class, with subclasses that interface different sources, which provides the basic reading and writing of images in a tomographic or near-field imaging setup. Currently, classes for ESRF style EDF data, Tomcat style TIF data, and NanoMAX style HDF5 data are implemented. The modularity of the code makes it simple to add new data sources, e. g. , by interfacing DXchange  [21]. This facilitates the treatment of data from different sources using the same reconstruction parameters. Another limiting factor for phase retrieval in tomography is the computation time.
Parallelization efforts are often centered on GPU processing, neglecting the large availability of computing clusters and heterogeneous computing architectures. Since the projections in a tomographic data set are usually considered independent, parallelization over the projections is "embarrassingly parallel". To leverage this, a function decorator @Parallelize is provided, which can be applied to all functions in the package that take a range of projections as an argument. It allows to automatically split the implicit for loop into appropriate chunks and farm them out on the desired number of CPUs. The infrastructure used can be switched with a centralized parameter in a configuration file, thus requiring no modification of the code to change from serial to parallel computing. Currently, supported infrastructures are serial, for workstation use, SLURM and OAR resource and task managers, as well as serial execution.
The modular implementation makes it straight-forward to include other infrastructures. A certain number of auxiliary functionality important for phase retrieval is implemented. Most important is image registration, which is used to align the images at one projection angle. Phase-contrast images at different propagation distances and magnifications do not contain identical contrast. This effect precludes simple correlation-based approaches in all but the most trivial cases. For this case, registration algorithms based on mutual information  [32]  seem to be more appropriate. Image registration is implemented as a class Registrator. Currently, this is implemented as a wrapper for the Elastix software through the PyElastix interface. Elastix provides all the necessary functionality, but it might not be available on all platforms.
In that case, the modularity makes it straight-forward to integrate other image registration codes. A simple interface for the visualization of the different images is provided. This is to simplify the access to images, e. g. , corrected and non-corrected projections, for verification purposes. Furthermore, it enables plotting curves related to the reconstruction process, e. g. , the image alignment and regularisation parameter choice. The current implementation relies on the Matplotlib package, but more advanced visualization could be used exploiting PyPhase modularity. The number of dependencies is kept as low as possible. All arrays and matrix-vector operations are implemented using NumPy  [33]. Other dependencies are currently i) Elastix for image registration, since no adequate pure Python implementation is available, and ii) PyHST2 or TomoPy for tomographic operations.
The implementation choices yields extremely compact code. For example, the code for the reconstruction of the image in Fig. 2  (a) using the CTF Pure Phase algorithm is: Reconstructions obtained with PyPhase using a similar code for three different algorithms are depicted in Fig. 2. A documentation and API is available  [34]. To conclude, we have presented an open-source and modular Python package for phase retrieval, christened PyPhase. PyPhase has the potential to lower the entry barrier to phase retrieval in the Fresnel regime and thus make the technique more accessible to non-expert users. The package covers the most popular phase-retrieval algorithms presented in the literature. Furthermore, it provides tools and an interface that facilitate the implementation and deployment of other phase-retrieval algorithms.
ACKNOWLEDGMENTS We are grateful to  S. Kalbfleish  and  M. Kahnt  for their support acquiring and implementing propagation-based phase contrast imaging at NanoMAX, MAX IV, Lund,  Sweden. Jean-Baptiste Forien  acknowledges support by the  US Dept. of Energy  (contract No. DE-AC52-07NA27344 ) and by the  Office of Laboratory Directed Research and Development (LDRD) , tracking numbers  19-ERD-022.
Hybrid Models Extending Dynamical Systems by Incorporating Regulations and Enzyme Cost Production In theory, adding specific information into a dynamical model such as (1) is relatively straightforward: new variables can be inserted in the state vector x(t) and the mechanisms involving these variables are then included in the vector field F(k, x(t)). In practice, this modelling step is delicate as it is strongly dependent on the level of information available for those mechanisms. In biology, those mechanisms may be unknown or partially known. Even when they are fully known, they are dependent on numerous parameters that generally need large datasets to be estimated. The construction and analysis of differential models in biology is obviously a very rich field, that goes beyond the present paper.
Methods Preregistration and open science practices. The study design, data collection, and analysis plan were preregistered at https:// osf. io/ ycnp7. Our R code and de-identified data are available at https:// osf. io/ qtkzm/. Note that we had three minor deviations from our preregistration. First, we included more participants than described in the preregistration. Whereas our target sample size was 650 participants, 1764 participants completed the survey. We decided to include all participants rather than selecting a subsample of our dataset. Second, to align with prior research, we also deviated by using the sum score of the facets instead of the mean score (i. e. , sum score divided by the number of items, as described in the preregistration). However, after running the analyses, we recognized that both computations generated the exact same network models.
The Hittner method was implemented via the goldbricker function of the R package networktools. This method did not identify any redundancy between our five variables. Estimation of the graphical Gaussian model. We present a GGM that was regularized through the graphical LASSO algorithm, which has two main goals  33, 43. First, it estimated regularized partial correlations between pairs of nodes, thereby excluding spurious associations (or edges) resulting from the influence of other nodes in the network. Second, it shrunk trivially small associations to zero, thus eliminating possibly "false positive" edges from the model and returning a sparser network including only the strongest edges. We did so via the R package qgraph, which automatically implements such a regularization along with model selection based on the Extended Bayesian Information Criterion (EBIC).
Directed acyclic graph (DAG). Following prior research (e. g. 36, 39, 44 ), we estimated the DAGs via the implementation of a Bayesian hill-climbing algorithm  46. To do so, we relied on the R package bnlearn. As implemented in this package, this approach relies on a bootstrap function that estimates the structural features of the model by adding edges, removing them, and reversing their direction to ultimately optimize the goodness-of-fit target score, i. e. , the Bayesian Information Criterion (BIC; a relative measure of a model's goodness-of-fit). This bootstrap function requires an iterative procedure of randomly restarting this process with various possible edges connecting various node pairs, disturbing the network system, and applying 50 different random restarts to circumvent local maxima.
Acknowledgements AH was supported by the  F. R. S. -FNRS  (Grant " 1. C. 059. 18F "), a  "Special Research Funds" -FSR Research Grant  from the  Université catholique de Louvain" , and a  Coordinated Research Grant  from the  French Community of Belgium  ( ARC Grant   19/24-100 ). MAB was supported by a research fellowship from the  F. R. S. -FNRS  and a  Coordinated Research Grant  from the  French Community of Belgium  ( ARC Grant   19/24-100 ). CC was supported by a research fellowship from the  "Beau Vallon Psychiatric Hospital" (Grant  " G01180016 "). YH and AV was supported by a  "Special Research Funds"-FSR   Research Grant  from the  Université catholique de Louvain ". SL was supported by a postdoctoral fellowship from the  Belgian American Educational Foundation. The open-access nature of this publication has been made possible by the financial support ( AS-0351 ) of the  Belgian University Foundation  (awarded to AH).
All the units between these two layers are called hidden units, and they are not directly connected to the outside environment. Neural networks which have in their structure at least one hidden layer are called multilayer networks and are different from perceptrons from this perspective. Together with the regional and floral information about the samples, the isotopic and elemental profiles were converted into a Comma Separated Values (CSV) file, from which the developed program transformed each line into a honey sample object having as attributes: a unique id, the country of provenance, the specific region where the honey was produced by the bees and a list of 34 real values consisting of the elemental (e. g. , Li, Na, Mg, Al, K) and the isotopic (e. g. , δ 18 O, δ 2 H) analyses. For developing the prediction models, the Keras Application Programming Interface (API) was utilized at the backend level of the application.
Keras serves as an approachable Python-based interface for constructing machine-learning algorithms, established on the top of the end-to-end machine learning platform TensorFlow. As it is thoroughly integrated with the TensorFlow usability, Keras API allows users to customize the provided functionalities, and therefore offers a high degree of flexibility. For common use cases, the Keras API reduces the number of necessary steps; it also displays clear feedback when errors occur. Moreover, TensorFlow 2 and Keras represent the top mentioned options for deep learning in research papers on Google Scholar  [16]. The previously mentioned aspects count as the main advantages of choosing and using Keras API in deep learning solutions.
Umkehr Averaging kernels (AKs) are applied to vertically smooth all overpass satellite profiles prior to comparisons. The station Umkehr record consists of several instrumental records, which have different optical characterizations, and thus instrument-specific stray light contributes to the data processing errors and creates step changes in the record. This work evaluates the overall quality of Umkehr long-term measurements at NOAA ground-based stations and assesses the impact of the instrumental changes on the stability of the Umkehr ozone profile record. This paper describes a method designed to correct biases and discontinuities in the retrieved Umkehr profile that originate from the Dobson calibration process, repair, or optical realignment of the instrument. The Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) Global Modeling Initiative (M2GMI) and NASA Global Modeling Initiative chemistry transport model (GMI CTM) ozone profile model output matched to station location and date of observation is used to evaluate instrumental step changes in the Umkehr record.
DeLuisi (1979)  and  DeLuisi et al. (1989)  studied the effects of volcanic aerosol interferences and found that stratospheric aerosols from the Agung and El Chichón eruptions produced large errors in Umkehr-retrieved ozone profiles. Despite short-term impacts from stratospheric aerosols on Umkehr ozone retrievals, the length and stability of the record were considered as an advantage for satellite validation. Newchurch et al. (1987)  provided reference to the Stratospheric Aerosol and Gas Experiment (SAGE) I ozone data processing to assist with the correction of its altitude registration. The analysis helped to homogenize SAGE I and SAGE II records for trend analyses. Comparisons of Umkehr profiles with multiple Solar Backscatter Ultravi-olet Radiometer 2 (SBUV(/2)) ozone records  (Bhartia et al.
In addition to the six NOAA Dobson stations (Table  1 ) and four NOAA Brewer stations, Umkehr observations are regularly performed by several  Dobson (three)  and Brewer spectrometers (six) that are distributed globally. Stratospheric ozone recovery rates will differ between tropics, middle latitudes, and high latitudes  (WMO, 2018). Umkehr stations are located at multiple locations around the world and will hence provide important information for tracking ozone recovery. The current operational Umkehr profile algorithm produces data that have relatively large uncertainty (∼ 5 % in the stratosphere,  Hassler et al. , 2014 ; also see Fig. 2  in  Petropavlovskikh et al. , 2005b) , which precludes our ability to detect small changes in stratospheric ozone. The refinement of the processing software is required to resolve the instrument-related offsets in ozone profile retrievals.
NOAA Dobson total ozone and Umkehr ozone profiles Dobson total column ozone records are regularly used in satellite record validation  (Bai et al. , 2015; Koukoulil et al. , 2016; Boynard et al. , 2018)  and development of the global combined ozone data records  (Fioletov, 2008; Hassler et al. , 2008). In 2017, NOAA long-term Dobson total column ozone records at 15 stations were homogenized to account for inconsistencies in the past calibration records, data pro-   1 ). Observations at all Umkehr stations are ongoing except at Perth where the Dobson stopped collecting data in 2016. In this paper, we focus our discussion of changes in Umkehr record at Boulder, CO. Section S1 in the Supplement shows summary results for the OHP (middle northern latitude), the Mauna Loa Observatory (MLO) (tropical latitude) and Lauder (Southern Hemisphere middle latitude) stations, while results for Fairbanks (high northern latitudes) and Perth (middle southern latitudes) are similar to other stations and therefore are not shown.
The quality check of the retrieved ozone profile includes assessment of the number of iterations (fewer than four is considered a good profile) and the condition that the difference between observed and retrieved Umkehr observations at all SZAs remains within measurement uncertainty  (Petropavlovskikh et al. , 2005b). The number of accepted Umkehr profiles per month depends on the station geoloca-tion and season and can vary between a few (e. g. , at Boulder in spring due to seasonal increase in clouds) and up to 60 (e. g. , at MLO when counting both morning and afternoon retrievals in winter), but on average Umkehr stations observe 15 profiles per month or more (∼ 30 profiles at MLO). NOAA Dobson Umkehr operational ozone profile data are posted on the GML archive https://gml. noaa. gov/aftp/data/ ozwv/Dobson/AC4/Umkehr/ (last access: 21 May 2021).
Ozonesonde data The ozonesonde instrument has been launched on the meteorological balloons since the 1980s at 10 NOAA stations. Evolving instrumentation has created discontinuities and gaps leading to inhomogeneous data records. NOAA and the international community developed homogenization methods for ozonesondes that were applied to NOAA and Southern Hemisphere Additional Ozonesondes (SHADOZ) networks  (Sterling et al. , 2018; Witte et al. , 2018). The error budget for each profile is calculated and included in the archived files  (Sterling et al. , 2018). Modern ozonesonde instruments sample ozone at the high vertical resolution, on the order of 100-200 m. The sondes constitute an essential component of satellite calibration and cross calibration  (Hubert et al. , 2016)  and are used for verification and improvement of climate chemistry, chemistry transport models, and reanalyses  (Stone et al.
, 2016; Miyazaki and Bowman, 2017; Wargan et al. , 2018; Stauffer et al. , 2019). The ozonesonde profile records provide key measurements for the middle and lower stratospheric, and tropospheric ozone trend calculations and are a benchmark network for stratospheric ozone profile observations  (Steinbrecht et al. , 2017; Petropavlovskikh et al. , 2019; WMO, 2018). Data for ozonesonde records are publicly available from the NOAA Global Monitoring Lab (GML) at https://gml. noaa. gov/aftp/ozwv/Ozonesonde/ (last access: 10 March 2021), from the WOUDC at https://www. woudc. org (last access: 18 October 2021), from the Network for the Detection of Atmospheric Composition Change (NDACC) at https://www. ndacc. org (last access: 8 July 2021), and from the NOAA National Centre for Environmental Information (NCEI) archive at https://data.
Introduction Ku-band power amplifiers are used for many satellite communication systems, military radar systems, etc. Typically, these amplifiers are created using commercial software packages for analysis and design of RF and microwave devices. One of such user-friendly packages is Microwave Office, which we use to G. Z. Garber is with Pulsar R&D Manufacturing Company, Moscow, 105187 Russia, gzgarber@gmail. com, www. gzgarber. narod. ru. study the simplified equivalent circuit and, in particular, the Curtice-Ettenberg model  [1]  implemented in the package.
Conclusion The complete large-signal equivalent circuit  [3]  is used in the non-commercial program HIFETA (Harmonics In FET Amplifier). Its use in commercial packages for analysis and design of RF and microwave devices is currently not possible. Therefore, we took the path of its approximation by the simplified equivalent circuit on the base of the Curtice-Ettenberg model  [1]  used in these packages. The article describes the approximation method. The important thing is that our method is implemented in Microsoft Office Excel, by developing appropriate macros  [6]. Due to this, when we change the parameters of the approximating equivalent circuit, its current-voltage relationship instantly changes on the screen. Comparing the new current-voltage relationship with the unchanged etalonic current-voltage characteristics of the complete equivalent circuit, we can judge how good the parameters of the Curtice-Ettenberg model are, and, if necessary, change them.
Moreover, in order to specify the variation trend of Lyapunov coefficients, further stability analysis contributes to demonstrating the effects of Lyapunov coefficients on the MMCs state variable errors and its dynamic. As another main contribution of this paper, two independent capability curves based on the power injection capability of the MMCs upper and lower arms, are obtained which will be assessed through changing the input and output voltages as well as MMC parameters. Finally, simulation results in MATLAB software are utilized to verify the validity of proposed control strategy.