dataesr/scientific-paragraphs-categorization huggingface dataset:
Segments under analysis Each of the 5 code segments is meant to test one different feature of AutoJMH. SUA 1 in listing 10: participants were requested to evaluate the execution time of the for loop. Here we evaluate a segment which execution time depends on the different input's types. The parameter c of addFunction is of type MyFunction, which is inherited by two subclasses, both overriding the calc method. The calculations performed by both subclass are different, which required several microbenchmarks to evaluate all possibilities. SUA 2 and 3 in listing 11: participants were requested to evaluate the time it takes to add one element into an array list, and the time it takes to sort a list of 10 elements. Here we wanted to test the participant's ability at using different reset strategies to force the microbenchmark reach stable state measuring the desired case.
Second, we observe that Java developers build microbenchmarks that measure times that are very different from the baseline. In order to understand the root cause of these dif-9 https://github. com/autojmh/autojmh-validation-data/ blob/master/eng6/src/main/java/fr/inria/diverse/ autojmh/validation/eng6/TransientStateListSortEng6. java ferences, we manually review all the microbenchmark. Here we observe that the participants did encounter the pitfalls we expected for each kind of segment: 3 participants fail to distinguish 2 performance behaviors in MB1; 3 participants made mistakes when initializing MB2 and MB3; we found multiple issues in MB4 and MB5, where 3 engineers did not realize that their microbenchmark was optimized by DCE, Engineer 6 allowed parts of its microbenchmark to be constant folded and 3 participants bloated to some extend their microbenchmark with overhead.
Threats to validity The first threat is related to the generalizablity of observations. Our qualitative evaluation was performed only with 5 segments and 6 participants. Yet, segments were designed to be as different as possible and to cover different kinds of potential pitfalls. The quantitative experiment also allowed us to test AutoJMH on a realistic code base, representative of a large number of situations that can be encountered in Java applications. AutoJMH is a complex tool chain, which combines code instrumentation, static and dynamic analysis and code generation. We did extensive testing of our the whole infrastructure and used it to generate a large number of microbenchmarks for a significant number of different applications. However, as for any large scale experimental infrastructure, there are surely bugs in this software.
RELATED WORK We are not aware of any other tool that automatically Listing 12: SUAs 4 and 5 generates the payload of a microbenchmark. However, there are works related to many aspect of AutoJMH. Performance Analysis. The proper evaluation of performance is the subject of a large number of papers  [13, 24, 2, 18, 9]. They all point out non-determinism as the main barrier to obtain repeatable measurements. Sources of non-determinism arise in the data, the code  [20] , the compiler  [24] , the virtual machine  [14]  the operating system  [24]  and even in the hardware  [10]. Various tools and techniques aim at minimizing the effect of non-determinism at each level of abstraction  [24, 10, 14]. JMH stands at the frontier between code and the JVM by carefully studying how code triggers JVM optimizations  [1].
Performance testing in isolation. Specially close to our work are the approaches of Horkỳ  [18, 17] , Kuperberg  [22]  and Pradel  [28]. Microbenchmarking, and therefore AutoJMH, evaluate performance by executing one segment of code in isolation. A simpler alternative favored by industry are performance unit tests  [10, 11] , which consist in measuring the time a unit test takes to run. Horkỳ et. al. proposes methodologies and tools to improve the measurements that can be obtained using performance unit tests uses, unlike AutoJMH, which uses unit tests only to collect initialization data. Kuperberg creates microbenchmarks for Java APIs using the compiled bytecode. Finally, Pradel proposes a test generator tailored for classes with high level of concurrency, while AutoJMH uses the JMH built-in support for concurrency.
All these approaches warm-up the code and recognize the intrinsic non-determinism of the executions. The main distinctive feature of AutoJMH over these similar approaches is its unique capability to measure at the statement level. These other approaches generate test execution for whole methods at once. Baudry  [6]  shows that some methods use code living as far as 13 levels deep in the call stack, which gives us an idea of how coarse can be executing a whole test method. AutoJMH is able to measure both complete methods and statements as atomic as a single assignment. During the warm-up phase the generated JHM payload wrapper method gets in-lined and therefore, the microbenchmark loop do actually execute statements. Another important distinction if that AutoJMH uses data extracted from an expected usage of the code, (i.
CONCLUSION AND FUTURE WORK In this paper, we propose a combination of static and dynamic analysis, along with code generation to automatically build JMH microbenchmarks. We present a set of code generation strategies to prevent runtime optimizations on the payload, and instrumentation to record relevant input values for the SUA. The main goal of this work is to support Java developers who want to develop microbenchmarks. Our experiments show that AutoJMH does generate microbenchmarks as accurate as those handwritten by performance engineers and better than the ones built by professional Java developers without experience in performance assessment. We also show that AutoJMH is able to analyze and extract thousands of loops present mature Java applications in order to generate correct microbenchmarks.
Due to the widespread use and high value to researchers of global models, a number of well known codes are available that will solve global models for a user defined set of conditions. One such program is the GlobalKin code,  [1, 2]  which solves a global model using given rate coefficients, and also contains a module for finding the electron energy distribution function (EEDF) using a common two term Boltzmann approximation. A commercial application, Quantemol-P, is built around GlobalKin to provide a graphical user interface for increased usability. [3] Another powerful tool is ZDPlasKin, which is able to solve for the time evolution of species densities in a system with a user defined reaction scheme. [4] As with GlobalKin, it includes a solver for the EEDF, and is able to calculate gas temperatures.
The outputs of both ZDPlasKin and GlobalKin are compatible with the PumpKin pathway analysis software. [5] The plasma modelling toolkit PLASIMO also provides users with a framework to create a global model. [6] espite these tools existing, and usually being easy to use, it is still sometimes desirable to create a global model of a particular system. This may be for reasons of furthering the understanding of the underlying physics, or because the system to be described is unusual in some way. In the first part of this tutorial review, an introduction on global models is given, along with a tutorial on the development of a simple model. Emphasis is placed on the key issues that need to be considered when constructing such a model, and generality is maintained so that the framework presented can be easily built upon.
Chemical Reaction Schemes Global models containing detailed chemical kinetics are used by several communities to describe a variety of systems. Typical examples where complex chemistries are important are in atmospheric, astrochemical, and combustion modeling. Many species of interest in these fields overlap with those that are important in plasma modeling as well. There exist a number of databases from these communities,  [55] [56] [57] [58] [59]  providing a base for building complex chemical models. Typical species where significant overlap of interest exists between the discussed communities are those containing nitrogen (N), oxygen (O), hydrogen (H), or carbon (C). These species are important in many plasma assisted processes, such as the production of reactive species for biomedical applications, plasma etching/ deposition processes, or plasma assisted combustion.
Reduction of Reaction Schemes It has been discussed that some plasma systems require large chemical reaction schemes to be described. Although a number of databases for reaction rates are available to construct extensive schemes, many rates are not known, or only poorly known, as previously mentioned. When constructing a model, it is tempting to include all reactions possible to describe a physical system. However, it can be difficult to realistically assess the accuracy of each reaction rate coefficient in schemes comprised of 1000's of reactions, and as such the uncertainty in the solution of the model is hard to quantify. This has been emphasized in recent investigations by Turner  [69, 105]  and is demonstrated concisely in Figure  6. This shows a number of possible solutions to the time evolution of the density of helium metastables in a helium APP containing 0.
In addition to the Monte-Carlo approach of Turner,  [69]  other methods for reaction scheme reduction have been proposed. An algorithm was developed by Lehmann  [106]  to Figure  6. Possible solutions to the trajectories of the helium metastables in an APP, calculated with different values for reaction rate coefficients taken from within the uncertainty of each. ß IOP Publishing. Reproduced with permission. All rights reserved. [69] ncepts, Capabilities, and Limitations of Global Models. Early View Publication; these are NOT the final page numbers, use DOI for citation !! R identify the significant pathways in a reaction scheme by performing an analytical investigation of the possible reactions and their relative rate coefficients. This algorithm has since been used to create the software tool PumpKin,  [5]  which is able to perform this analysis for user defined reaction schemes, and can identify which processes are important over different time scales.
Acknowledgements: The authors would like to thank the editorial team of Plasma Processes and Polymers for the invitation to present a review tutorial on global models. This work was part funded by financial support from the  UK EPSRC  ( EP/K018388/1 ,  EP/M056680/1 ) and the  York-Paris Low Temperature Plasma Collaborative Research Center. This work has been done within the  LABEX Plas@Par project , and received financial state aid managed by the '' Agence Nationale de la Recherche ,'' as part of the '' Programme d'Investissements d'Avenir''  under the reference  ANR-11-IDEX-0004-02.
Of course, attempts to enrich the libraries are continuing, but the situation is not improving rapidly. In addition, ionization and fragmentation are sometimes inherent to instrumental conditions particularly in liquid chromatography/mass spectrometry, which is o en applied to polar compounds. erefore, manual interpretation of the resulting data continues to be time consuming and cumbersome. During the last ve years, several promising automated methods for interpreting accurate mass spectral data have appeared on the scene. One of the methods is the utilization of public molecular structure databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes), PubChem, and ChemSpider, which are more comprehensive than are mass spectral libraries. Insu ciency in mass spectral libraries is considered serious even in the eld of environmental research because the number of unregulated emerging micropollutants has increased over the last few decades.
Data were acquired both under MS mode and under AIF mode. Determination of elemental composition from accurate mass measurement was carried out with ermo Fisher Xcalibur So ware. e LC column was an ODS-100S column (2. 0 mm×150 mm, 5 µm; Tosoh Corp. , Tokyo, Japan). A gradient condition consisting of 2 mM ammonium bicarbonate in ultrapure water (A) and methanol (B) was adopted. e gradient, expressed as changes in mobile phase B, was as follows: 0-2 min, hold at 30% B, 2-15 min, a linear increase from 30% to 100% B; 15-20 min, hold at 100% B; 20-25 min, equilibration at 30% B. Another ODS-100S column was installed before the autosampler to delay elution of substances from the LC system. Con rmation of substances with reference reagents was conducted using a triple quadrupole mass spectrometer.
MAGMa Ridder et al. introduced an automated method for the interpretation of accurate mass spectra in 2012. 8) e method is based on an algorithm for candidate substructure annotation of multistage accurate mass spectral trees without relying on a spectral library. First, candidate structures are retrieved from a compound database by querying on monoisotopic mass. Next, fragmentation of structural skeletons (i. e. , non-hydrogen atoms) is performed by removing each non-hydrogen atom sequentially and collecting substructures. During the fragmentation, a simple penalty score that depends on the type of bond is taken into account. en in silico generated substructures are assigned to accurate m/z values in a mass spectrum. Referred molecular structures are retrieved from chemical compound databases such as PubChem, Human Metabolite Database (HMDB), and KEGG.
MetFrag/MetFusion MetFrag was developed in 2010 by Wolf et al. MetFrag also simulates fragmentation to explain ions in a mass spectrum. 9) Before bond disconnection of candidate structures, a small set of rules for molecular rearrangements are applied. en all bonds to be disconnected are labeled linear or ring. A redundancy check is performed at every disconnection process. e in silico fragments are matched against the query peak list. Referred molecular structures are retrieved from a chemical compound database such as PubChem, KEGG, or ChemSpider. A web application is available at the following URL: http://msbi. ipb-halle. de/MetFrag/. MetFusion is the successor of MetFrag and was released by Gerlich et al. in 2013. 10) MetFusion incorporates the similarity using a mass spectral library in MetFrag scoring.
erefore, subtle di erences in scoring resulted in large di erences in rank. Around the peak of 1,3-di-o-tolylguanidine, another peak was observed with an m/z di erence of 28. 0312 corresponding to C 2 H 4 and the second highest intensity. e m/z value was 212. 1185. e ion closely resembled 1,3-di-o-tolylguanidine in its fragmentation pattern. erefore, diphenylguani-    2. dine could easily be inferred. It was purchased from Tokyo Chemical Industry (Tokyo, Japan) to con rm the identi cation. A er submitting queries of diphenylguanidine to both MAGMa and MetFrag, the correct answer ranked 3rd in 1,442 candidates and 88th in 1,499 candidates, respectively. In a similar way, fragmentation data were sequentially interpreted to determine compounds. All identi cation processes were completed by using authentic standard reagents.
CONCLUSION In this study, nontarget analysis was conducted to identify anthropogenic compounds in an urban aquatic environment and two automated methods were evaluated using the results. Both MAGMa and MetFrag retrieved correct answers in most queries. ey also usually retrieved many incorrect answers with the same score as the correct answer. Increasing the number of submitted fragment ions has not always improved results. Both tools appeared to be equal in their results in the present sample, but MAGMa o ered a great advantage in terms of processing time. To explain the fragment ions in a query, MAGMa and MetFrag use disconnecting bonds of numerous compounds in molecular structure databases. is procedure is impossible to conduct manually, and considerably reduces the burden on humans in reading fragmentation.
Background: small Heat Shock Proteins (sHSP) is a wide proteins family. SHSP are found in all kingdoms and they play critical roles in plant stress tolerance mechanisms (as well as in pathogenic microorganisms and are implicated in human diseases). Results: sHSPdb (small Heat Shock Proteins database) is an integrated resource containing non-redundant, full-length and curated sequences of sHSP, classified on the basis of amino acids motifs and physico-chemical properties. sHSPdb gathers data about sHSP defined by various databases (Uniprot, PFAM, CDD, InterPro). It provides a browser interface for retrieving information from the whole database and a search interface using various criteria for retrieving a refined subset of entries. Physicochemical properties, amino acid composition and combinations are calculated for each entry.
sHSPdb provides automatic statistical analysis of all sHSP properties. Among various possibilities, sHSPdb allows BLAST searches, alignment of selected sequences and submission of sequences. Conclusions: sHSPdb is a new database containing information about sHSP from all kingdoms. sHSPdb provides a classification of sHSP, as well as tools and data for the analysis of the structure -function relationships of sHSP. Data are mainly related to various physico-chemical properties of the amino acids sequences of sHSP.
sHSPdb has been conceived with multiple functions to search and describe the different sHSP. sHSPdb provides a global vision of sHSP physicochemical properties, amino acid usage, literature as well as statistical analysis on any sHSP dataset built by the user. Such a resource will be invaluable for computational analyses of sHSP structure -function relationships, especially regarding the specific roles of each of the three domains. sHSPdb is useful to gather information about sHSPs from all kingdom. sHSPdb provides the largest set of sHSP sequences, a new classification and a precise description of each class of sHSP. Physico-chemical properties and statistical analysis provided by sHSPdb represent an actual plus-value.
Data sources and characteristics of the dataset To fill sHSPdb, we used a two-stage process. The first step filled automatically the fields of the tables of the database using PHP and perl scripts, getting the information from text and XML files from the following databases: NCBI/Proteins, NCBI/CDD, NCBI/Taxonomy, EBI/Picr, UNIPROT, Interpro, SANGER/Pfam, AMIGO. Starting with the NCBI accession number, we retrieved the « GenPept » file to derive the GI number, the eventual PFAM, CDD and Interpro  [8, 20, 21]  identifiers and the textual information. Then, using the crossreferences of EBI/Picr we obtained the Uniprot accession number and name to complete and double-check the data. The main sources to fill sHSPdb are information contained in « GenPept » files and in the corresponding Uniprot files.
The primary request to get « GenPept » files from the public database NCBI contained a construction of Boolean and keywords. This allowed the retrieving of ≈ 17,300 « GenPept » files stored in sHSPdb. Nevertheless, there was a very high redundancy of sequences for many organisms. The second step was a manual check to ensure that all information and links were pertinent and relevant to the sHSP issue. The pertinence of the files annotation and that of the sequences were checked using: (i) the sHSPdb « Search » option (see below); (ii) BLAST homology analysis and multiple alignments software; (iii) MOCAR (http:// forge. info. univ-angers. fr/~gh/wstat/Mocar/), a program developed specifically for our sHSP classification based on pattern motif recognition in any Fasta sequences dataset; (iv) the « Analyze » and the « Statistical analysis » tools implemented in sHSPdb; (v) Finally, the motifs determined for each class of sHSP were further used to perform PHI-Blast allowing to get additional sequences.
Sequence conflict (in particular in the case of AGI entries for Arabidopsis thaliana) was checked using annotations from the Uniprot files. Identical sequences were removed using a program developed for sHSPdb feeding. This allowed detecting truncated, ambiguous (e. g. , annotated « unnamed protein », « hypothetical », « unknown product »…, although some few populated sHSP classes may contain such annotated files) or false sHSP sequences. Sequences containing the undefined amino acid symbol (X) were removed. All files are stored in sHSPdb but only non-redundant, full-length and annotated ones are accessible. The two holding chaperones (holdases) families, i. e. , HSP31 and HSP33, cannot be considered formally as sHSP since they do not harbor the ACD domain, and interact with client proteins through Cys and disulfide bonds formation  [22].
Partials results have already been published  [23, 24]. Sequences are mainly issued from ≈ 2355 Bacteria, ≈ 2259 Eukaryota (among which ≈ 1050 Viridiplantae) and ≈ 129 Archaea (Table  1 ). sHSPdb contains also ≈ 14,240 non-accessible entries (i. e. , not retained after our curation). This dataset is important when the database is updated and for further analyses of sHSPdb since it may serve for statistical studies and forbid wrong entries in the curated set.
Organization and main features of sHSPdb A link to a guided tour is proposed in the homepage to learn how to use the interface of sHSPdb. This guided tour proposes various scenarii of search. The user can select any entries through various parameters and conduct further analyses using the implemented tools. For this purpose, sHSPdb has three main features: (i) the browse mode that allows the user to consult all or part of the database; (ii) the search mode based upon multiple search criteria; (iii) the export mode to retrieve sequences in different formats.
The browse mode It allows consulting the whole database (Fig. 1 ). The « Summary » option provides the NCBI-GenPept accession number and the Uniprot accession number, the name of the sequence and of the organism, the putative function of the sHSP (if any). The « Details » option provides more information (up to 20 fields from the GenPept or the Uniprot files). The accession numbers, the name of the organism, the PFAM, the CDD and the InterPro numbers provide a link to their relevant website. A series of physicochemical properties (length/pI/ MW/FoldIndex/GRAVY/mean net charge at pH7/ mean hydrophilicity/mean hydrophobicity < H>/flexibility/bulkiness/accessibility of residues/transmembrane tendancy of residues) are given by selecting the « Physicochemical properties » option. The « AA comp » option displays the amino acid composition of sHSP.
The search mode (multiple search capabilities) It corresponds to an advanced search with multiple parameters and permits retrieval of very fine subsets of data (example of search: « sHSP of the PF00011 family from Arabidopsis thaliana, containing the motif of Class 1, associated to the key word « chromosome », published by an author called Walker in 2012, with a length comprised between 150 and 200 amino acids »). The search can be made by using a sophisticated text expression accepting wildcards and Booleans -this search applies to all fields of the whole database; by accession number (accepting wildcards) -one or multiple accession number(s) separated by a space can be searched at one time; by organism; by PFAM or CDD numbers; by date; by range of length of amino acids sequence. The user can also retrieve sequences by entering amino acids motif either exact or degenerated using regular expressions with sophisticated syntax, allowing to specify complex motifs.