dataesr/scientific-paragraphs-categorization huggingface dataset:
Variability in antibiogram pictures. Examples of difficult cases for diameter reading (a). Non-circular inhibition shape (a1). Total or no inhibition (a1). Light reflections (a3). Colonies within the zone of inhibition (a4). Double inhibition zones (a5). Hazy borders (a6). Inhibition zone overlap and plate borders (a7). Low contrast (a8) defined as the difference between the inhibition and bacteria intensity value, compared to a high contrast (a9) image observed in dataset A1. The histograms in (b) show the contrast variability observed in the benchmark datasets (contrast is defined as the difference between the central gray levels of bacteria and inhibition, and normalized to the maximum available gray level). Observed variability in dominant hue (c).
Dataset A3: This dataset consists of eight Petri dishes, all circular. The antibiotic pellets were produced by Liofilchem and positioned by hand with metallic tweezers. Specifically, the Petri dishes have been inoculated with the following ATCC dried microorganisms: Pseudomonas aeruginosa (ATCC 27583) Klebsiella pneumoniae carbapenemase producer (ATCC 700603) Klebsiella pneumoniae SHV-18-ESBL-producer (ATCC 700603) Fluoroquinolone susceptible Escherichia coli reference strains (ATCC 25922) Methicillin-Resistant Staphylococcus aureus (NCTC 12493) Vancomycin-sensitive Enterococcus faecalis (ATCC 29212) Gentamicin-resistant Enterococcus faecalis (ATCC 49532).
Acknowledgements The App is supported by: the  Medecins Sans Frontieres Foundation , the  Laboratoire de Mathematiques et Modelisation d'Evry , the  Genoscope , the bacteriology laoratory of the  Universitary Hospital Henri Mondor ,  Creteil France. For  its potential impact, this project was awarded the 2019 Google AI Impact Challenge 34 , selected among over 2600 applicants worldwide. We are especially thankful to  Dominique Boissinot  and  Alain Jean  of i2a for their fundamental help in the development of the Expert System. We thank all the organizers of the  Google AI Impact Challenge and the people of Google. org,among others  Anna Achilles ,  Sebastien Floodpage , and  Mollie Javerbaum  for their invaluable help.
Code availability The image processing library described in this paper is distributed as open-source software at https://github. com/mpascucci/AST-image-processing. As of today, The App and its source code are available for research purposes upon request at https://form. typeform. com/to/qEGVBzbu. We plan to release the App as open-source software after approval of the CE authority as a clinical device. Fondation Medecins sans Frontieres sees the CE mark of its app solution (as a self-certified IVD SW) as a means to demonstrate and communicate on the quality and robustness of this digital tool for a non-profit. Waiting to comply to the IVDD Directive 98/79, they do not want to grant open access until they get this certification, as it could imply legal pursuit in France for the legal manufacturer (Fondation MSF) distributing a medical device without certification.
• Dataset A3: This dataset consists of eight Petri dishes, all circular. The antibiotic pellets were produced by Liofilchem and positioned by hand with metallic tweezers. Specifically, the Petri dishes have been inoculated with the following ATCC dried microorganisms: Pseudomonas aeruginosa (ATCC 27583) Klebsiella pneumoniae carbapenemase producer (ATCC 700603) Klebsiella pneumoniae SHV-18-ESBL-producer (ATCC 700603) Fluoroquinolone susceptible Escherichia coli reference strains (ATCC 25922) Methicillin-Resistant Staphylococcus aureus (NCTC 12493) Vancomycin-sensitive Enterococcus faecalis (ATCC 29212) Gentamicin-resistant Enterococcus faecalis (ATCC 49532).
ecRad The ecRad scheme  (Hogan & Bozzo, 2018)  has been operational in the Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) since 2017. Recent efforts have led to a notable increase in flexibility as well as in efficiency compared to previous schemes. Another important step was the development of SPARTACUS  (Hogan et al. , 2016 (Hogan et al. , , 2019;; Schäfer et al. , 2016) , a two-stream based solver that explicitly represents the 3D effects of clouds. An offline version of ecRad is freely avalaible at https://github. com/ecmwf/ecrad. The configuration used in this paper is summarized in Table  1. Three parameters need to be provided to SPARTACUS in addition to standard cloud profiles. They relate to the three main aspects of cloud geometry mentioned in the introduction: vertical overlap, horizontal heterogeneity and cloud size.
Monte Carlo Reference Computations of Solar 3D RT A MC method is used to compute solar 3D RT in 3D cloud fields, considered as the "truth" in comparisons to ecRad estimates. MC methods are widely used to accurately compute 3D RT in complex media (see for example  Marchuk et al. (1980) ,  Mayer (2009) , or  Marshak and Davis (2005) ). The model used here is based on the High-Tune library described in  Villefranque et al. (2019) , and is freely available online at https://gitlab. com/najdavlf/ scart_project. The algorithm consists in tracking a large number of virtual photon paths throughout a virtual medium, explicitly simulating all radiative processes such as emission, absorption, scattering, and surface reflection. Whenever a path hits the ground or the TOA, its weight is added to a virtual sensor.
Cloud cover ranges from 0% to 45%. All simulations were performed on small domains (6. 4 × 6. 4 × 4 km) with high spatial resolution (25 × 25 × 25 m). The horizontal boundary conditions are periodic. The four cases are standards of the literature used in LES intercomparison exercises. Detailed descriptions of the setups, initial conditions, and forcings can be found in the reference papers. From these four simulations, 35 3D fields of temperature, pressure, mixing ratio of water vapor, and liquid water are used in this study, among which eight will be used in the calibration process of Section 3 (the colored entries in Table  2 ). Using an object-identification tool (freely available at https://gitlab. com/ tropics/objects; Brient et al. (  2019 )), individual clouds are labelled in each field.
Discussion and Outlook The htexplo software enables efficient semi-automatic tuning for any aspect of a climate model. The "automatic" aspects of the tuning involve implementation of well-developed techniques from the uncertainty quantification and machine learning communities: Using Gaussian processes to quickly locate those regions of parameter space that are compatible with reference data sets. Yet htexplo is not a black box tuning software, and is designed as a tool to be harnessed by an expert physicist to assist with tuning. The physicist must define the parameters, metrics, references, and their tolerances to error (if the structural error of the model is unknown). They must interpret the results and then perhaps adapt the tuning (introducing new metrics, adjusting tolerances, recognize compensating errors etc): htexplo cannot, alone, measure the quality of a model.
Data Availability Statement The complete version of htexplo that was used for this paper is publicly available via a Subversion through "svn check out http://svn. lmd. jussieu. fr/HighTune. " Note, however, that this tool is a new research tool, and, as such, is still evolving. Version 1. 4. 0 of the ecRad package is freely available under the terms of the Apache License Version 2. 0. This paper uses a prior version (1. 3. 0) available upon request under the terms of the OpenIFS license. No major modification was made to the ecRad radiation code between versions 1. 3. 0 and 1. 4. 0. The source code of the Monte Carlo model that is used as a reference in this paper is available under the terms of the GNU General Public License. The scripts and data that were used to produce the figures and tables are available under https://doi.
ArchC  [19]  is an architecture description language (ADL) for architecture design which provides early stage system verification. In contrast, rather than jointly designing optimization methods and architecture representations, this paper concentrates on learning a model from structure hypotheses and platform measurements, with the objective to obtain an abstract, repeatable and application decorrelated model usable in a wide set of optimization contexts. In statistical system modeling, the application and the architecture are often considered together. Originally introduced for modeling of signal processing systems  [14] , the Linear System Level Architecture (LSLA)  [15] , Model of Architecture (MoA) separates the underlying architecture from the algorithmic aspects following a Y-chart approach  [8].
Our initial work was presented in  [13]  with a GPU performance model. Consequently, in this work we introduce power modeling and collect all the results for an LSLA model extended to GPU modeling. The key contributions of this work are: -An extension of the LSLA model with non-linear GPU processing modeling called GSLA (GPU-oriented System-Level Architecture). The model includes both performance and power. -Prototype tooling implemented as shell scripts and Matlab code for both execution of the application for model creation and costs prediction in exploration use. -Proof-of-Concept with three representative applications that are implemented in OpenCL and executed on two different GPU-equipped platforms for setting the model parameters and comparing the measured and predicted values for fidelity.
Later, two value sets for these parameters are defined where the S values set is inferred from the test data and the algorithm input data size and the γ values set is extracted from the platforms number of processing elements and memory sizes. In addition, the values for both S and γ sets were checked for feasibility e. g. execution of the OpenCL implementation of the parallel algorithm with a large S and a small γ values could take up to some days while multiple S and γ combinations are executed for profiling which makes it impractical. Shell scripts are used for passing the values of the S and γ parameters as command line arguments for each iteration of the execution and storing the execution time and power consumption values in files while the memory overflow errors are monitored. Linear regression is used to fit the GSLA model.
The MoC and MoA for OpenCL application on GPU is then developed for showing the application mapping on the platform. The results of this paper show the suitable parameters of the models equations for Mali and Baffin platforms i. e. α,β or a, b, c. These values in conjunction with the model equations could be used to estimate the power and performance of similar applications on Mali and Baffin GPUs. The produced models could be used with or without the presented parameters which are shown as two utilization cases in Fig. 1. In the first case, a similar algorithm to the selected three algorithms is executed on Mali or Baffin platforms while in the second case a different algorithm is executed on a platform X. The first case does not require any compilation or execution of a training code and the performance and the power estimations are done merely by simple equation calculations using the reported parameters values.
Related work There are different methodologies in performance modeling studies. One of them, which provides a general model is the statistical analysis method. For example, Moren, et al. [11]  present a statistical approach for work load scheduling on heterogeneous platforms consisting of CPU and GPU. Authors have modified the OpenCL API code for dynamic code feature collection which is used for performance prediction. In modeling methods, it is common to use a graph to present a software or a hardware system, or a system of systems. These methods are divided into two different categories: data flow graphs and non data flow graphs. In a data flow graph, a vertex is used to model a run-to-completion block of computation called an actor. Edges are used to model data token communications between actors, realized by FIFO queues (First In First Out).
BSP (Bulk Synchronous Parallel)  [22] , unlike SDF or CFDF, is a system modeling method rather than an application modeling method, and it has its own graph representation. In BSP, there are processing units with local memories connected over a router. Processing elements access each other's memories by remote access messages. DAL (Distributed Application Layer)  [20]  has a dynamic mapping methodology. It employs Kahn process networks to explore application mappings and a finite state machine to represent execution scenarios. Multiple scenarios are precomputed at design-time and the suitable one is selected at run-time. Bezati et al. [4]  present a data flow modeling method according to the CAL language  [5]. Their method has six steps. First, two different models for application and architecture are designed.
Second, simulation and profiling results are collected. Third, the application is mapped to the architecture. Fourth, C++ and HDL codes are generated from CAL. Fifth, the code is compiled and synthesized. Finally, compiled code is executed. LSLA  [15]  is a MoA, modeling hardware architecture separately from the MoC. The LSLA MoA includes Processing Elements (PE) and Communication Nodes (CN). PEs and CNs of the LSLA MoA have cost functions including parameters that may be retrieved from 123 representative platform benchmarking. In that case, the calculated cost functions are obtained from measured application executions and the cost function parameters can be used to predict system efficiency for a set of comparable applications. Holmbacka et al. [7]  studied the energy consumption of different phases of the applications on multi-core CPUs.
Usage of the methodology Figure  4  depicts the tool flow steps for creating the cost prediction using the models either for performance or power. Applications are characterized with the S and γ sets, written in OpenCL source code and compiled for execution and measurements. The employment of the models is provided by a shell script and a Matlab script which is depicted in Fig. 4. First, the S and γ sets are selected and the shell script is edited accordingly. Then, the scripts perform the mapping, compilation and execution of the OpenCL application. The application receives S and γ as command line arguments. Pseudocode 1 presents an example of S and γ implementation in the matrix multiplication. Later, the Matlab script is used to extract the parameters α and β. Finally, the parameters are used to estimate the appropriate workload.
Experiments: execution time modeling The experiments presented below serve to illustrate the suitability of the proposed model and Eqs. 2-5 for real-life GPU-equipped platforms. Typical signal processing applications were used as case studies: matrix multiplication, digital predistortion and Gaussian image filtering. The applications were written in OpenCL and were executed on two GPU-equipped platforms: the Odroid XU3 containing a Mali T628 GPU and a desktop workstation with the AMD RX 460 (Baffin) GPU. The test input data was randomly generated for matrix multiplication and predistortion, and private data for Gaussian filtering. The α and β parameters of the cost functions were obtained with a Matlab script that invoked a least squares fitting algorithm (see Section 2 of  [2] ). In the Matlab script, the lsqlin function was used with a positive solution constraint as a standard method to perform linear regression.
The β value represents the constant time offset of the relevant GSLA element and is the t w intercept of the mesh graph. Due to technical difficulties, values for the digital predistortion application were not acquired on the Mali platform. App 1 is matrix multiplication, App 2 is digital predistortion and App 3 is Gaussian filtering. M stands for Mali and B for Baffin platforms. Table  3  demonstrates the fitting error between the model and measured samples for each application/platform combination as fidelity values. To highlight the improvement of the proposed GSLA model over conventional LSLA for GPU targets, Table  3  also shows the fidelity value for LSLA. Fidelity is computed similarly to  [3]  with the Kendall Tau Coefficient value, as calculated by the corr function of Matlab when configured for it.
p t (γ , S) = a G PU + b G PU S + c G PU /γ + d G PU S/γ (7) The S and γ variables depicted in Eq. 6 show the input data quantity and parallelism factor. From the observations we noticed similar variable relations like the performance cost for the measured power samples. We observed that, logically, the input data quantity increases the power consumption while an increase of the parallelism reduces the power consumption. In addition, the Pseudocode 3 depicts the Matlab script of the power modeling according to the proposed Eq. 6. The values of the S and the γ sets are considered according to the memory capacity of the targeted hardware and the input data size of the applications.
Fitness of the models The proposed LSLA extension aims to model parallel execution on the GPU of the considered platform. With the following fitness study, we show that the created GSLA model is capable of fitting both GPU performance and GPU power consumption. The performance model shown in Eq. 8 and the power model presented in Eq. 6 have the same terms with exception of an extra constant term for Eq. 6. This constant logically models the static power of the platform while timing is null when no computation is requested. The fitness of the performance model and the presented power model are compared using the R-squared for the power measurements calculated with Matlab regress function. Table  6  depicts the fitness comparison of the models. The R-squared values of 0. 850 and 0. 916 for Eq.
g. CYGD  (2)  for yeast, or entries were curated based on individual papers rather than amalgamated knowledge, e. g. CORUM (3) for mammalian species. The Complex Portal (www. ebi. ac. uk/complexportal) was created to meet this unmet need: It is a manually curated, encyclopaedic resource that provides stable identifiers and summarizes compositional, topological and functional aspects of stable macromolecular complexes from a selection of model organisms and organisms of special interest. It enables protein complex identification in large-scale data analyses, contributing to the study of complex evolution and increasing our understanding of cell biology  (4) (5) (6) (7) (8). Since the last update  (9) , the coverage of model organism complexomes, the compendium of known complexes for a given species, has increased significantly, with that of Saccharomyces cerevisiae being maintained and added to as more experimental data leads to the identification of new assemblies  (1)  and the completion of a first draft of the Escherichia coli complexome.
Work is now focused on annotating human complexes, wherever possible in collaboration with other data resources or scientific groups. We have also responded to the ongoing SARS-CoV-2 pandemic by creating the complexome of this organism and also of related viruses, in order to contribute to global efforts to respond to this threat. We have improved ComplexViewer so that it can display multiple features simultaneously and show links between participants of sub-complexes and other complex participants. We updated the participant table so that all participants are now colour-coordinated between ComplexViewer and the table (Figure  1 ). We have made updates to the Com-plexTab format, adding a 'UniProt ID-only' column that lists the UniProt accession numbers (and their stoichiometry) for the protein participants of complexes, including those that are part of subcomplexes and molecule sets.
Curation update A protein complex is a functional, biological entity that contains two or more macromolecules (proteins, nucleic acids or small molecules) for which there is experimental or inferred evidence that these molecules stably interact with each other. As of release 241 (18 October 2021), 3572 complexes from 26 species have been curated and released. Each entry is species-specific, describing the complex composition, topology and function and linking out to external databases that provide further domain-specific information, such as structural details from wwPDB  (10)  or EMDB  (11)  or the role of the complex in metabolic reactions or signalling pathways in Reactome  (12). Complex components are linked to primary reference resources; UniProt  (13, 14)  for proteins, ChEBI  (14)  for small molecules and RNAcentral  (15)  for noncoding RNAs.
This work was based on extensive literature mining and subsequent comparison with existing resources, in this case primarily UniProt KB and EcoCyc  (16). This systematically annotated set of E. coli complexes includes, at the time of writing, 786 unique proteins (18% of the proteome). 95% (305) of complexes contain 5 or fewer proteins (median = 3) (Figure  2 ). 87% unique proteins (681/786) are found in only one complex and 9% protein (74/786) in two complexes with the remaining 31 proteins found in more than two complexes. This distribution is similar to what we saw for yeast (1) except that fewer proteins in general were found in heteromeric complexes (18% versus 32%). As with the recently completed S. cerevisiae complexome, a watchlist of additional potential candidate complexes exists and these are being added to the dataset on an ongoing basis, if and when they are experimentally verified.
We continue to add new interaction evidence from IntAct  (19)  and structural evidence from ww-PDB  (20)  and EMDB (21) at each release. These complexes are already used as cross-references by several external databases such as the IMEx resources  (22) , Gene Ontology  (23, 24) , MatrixDB  (25) , Reactome, SIGNOR  (26) , UniProtKB and WikiPathways  (27)  thus allowing for a more integratable set of coronavirus-related information to be freely available to the research community. Human complexes. Efforts are now focused on expanding the collection of manually curated human complexes. Exactly how many assemblies comprise the human complexome is a question still very much open to debate. We anticipate that most, but not all, of the intracellular complexes we have identified in S. cerevisiae are conserved in multicellular organisms, but human complexes often contain additional protein components or the existence of paralogous proteins lead to increased numbers of complex variants.
One simple example of the latter case is the replication protein A complex, a single heterotrimer in yeast (CPX-21), but in humans one protein (P15927/Q13156) has been duplicated resulting in two heterotrimeric complex variants (CPX-1878/CPX-1879). Additionally, there will be an appreciable number of transmembrane and extra-cellular complexes with a role in the immune response, inter-and intracellular communication and signalling. A data mining exercise, undertaken in collaboration with the UniProt group, of information embedded in UniProtKB records has suggested that there may be at least 4000 different assemblies in the human complexome. To date, we have released 1255 human complexes including almost 200 that are either targets of viral proteins or play a role in the immune system, including the B-cell and T-cell receptor complexes, the interferon-receptor family of complexes and the complete complement cascade of the innate immune system.