dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, two new tools were introduced, FLASHIda  12  for instrument acquisition control in top-down proteomics and FLASHQuant  13  for label-free quanti cation of top-down MS data. OpenMS 3 offers signi cant improvements and innovations for metabolomics, where identi cation and quanti cation from complex samples continues to pose challenges. For feature identi cation, OpenMS includes tools that use mass-to-charge and retention time information (FeatureFinderMetaboIdent) as well as formula and structural predictions using isotope patterns and fragmentation spectra of detected features (via SIRIUS and CSI:FingerID  14  ). One of the most widely used tools for untargeted metabolomics data exploration after feature detection is Feature-Based Molecular Networking  7, 15  (FBMN) by GNPS. All necessary les can be generated using GNPSExport, a new OpenMS tool that creates a spectral library, a feature quanti cation table, and a meta value table.
Furthermore, Ion-Identity Molecular Networking 16 (IIMN) is supported by providing the required table with adduct information. Following the identi cation of unknowns, OpenMS provides methods for accurate quanti cation of known components in complex challenges. Methods include peak integration, retention time alignment, quanti cation and calibration curve tting, and quality control. The combination of targeted and untargeted metabolomics methods offers a complete package for qualitative discovery and quantitative validation experiments  32. OpenMS 3 provides several tools for quality control (QC) and QC metrics can be calculated for each step of an experiment, from data acquisition (e. g. , retention time shifts, mass calibration) to proteomics and metabolomics-speci c analyses (e. g. , MS² identi cation rate, contamination and missed cleavages).
These metrics can be exported to the HUPO-PSI mzQC format to enable sharing and comparability. OpenMS added the NucleicAcidSearchEngine 18 (NASE) as the rst integration of an oligonucleotide mass spectrometry-focused tool into anycomputational MS library. NASE implements sequence database search for MS² spectra of RNA oligonucleotides, enabling the identi cation of RNA sequences and modi cations. We demonstrated the utility of NASE in identifying post-transcriptional modi cations of mRNAs, tRNAs, and rRNAs. Recently we included more digestion enzymes to enable multi-site-speci c cleavage, added support for DNA in addition to RNA, and improved integration between NASE and other OpenMS tools.
The wider OpenMS ecosystem contains applications (work ows or tools) that are either based on OpenMS or interact closely with results produced by OpenMS. For example, the R package MSstats  7  enables statistical analysis and signi cance testing for many experimental designs. Triqler  19  is another compatible package for statistical analysis of fold changes and error probabilities. The Python package pmultiqc and the R package PTXQC  20  for computing and visualizing quality control (QC) metrics are also compatible with OpenMS. pyOpenMS seamlessly integrates with the mass spectrometry query language (MassQL 21 ), a Python package to capture complex mass spectrometry patterns. Furthermore, peak and feature maps can be exported to Pandas DataFrames for convenient downstream processing. In another example, the SmartPeak application wraps OpenMS methods for targeted and untargeted metabolomics and lipidomics work ows into a native C++ GUI and CLI application suitable for non-bioinformaticians and industry settings where automated work ows for Big Data are needed.
OpenMS is optimally suited for use in high-throughput environments such as core facilities or industry. The OpenMS tools can be assembled into pipelines by work ow managerssuch as KNIME  22  , Galaxy  23  , Snakemake  24  , and Next ow  25. Many tools are optimized for parallel execution on clusters or multi-core processors, making them highly scalable, both on local machines and in a cloud environment. OpenMS is easily deployable on high-performance clusters with containerized builds (e. g. , Docker or Singularity). Examples of such work ows include quantMS, an nf-core  31  work ow that combines identi cation and quanti cation of proteins in DDA and DIA mass spectrometry data and includes statistical analysis and quality control reports (Figure  S4 , middle panel). MHCquant 26 is another nf-core pipeline for the identi cation, quanti cation, and binding a nity prediction of MHC-bound peptides.
bSLIM labeling  27  is an example of a novel analysis developed as a KNIME work ow using OpenMS nodes for MS signal processing. The latter two pipelines signi cantly extend the capabilities of OpenMS into the domains of immunopeptidomics and structural proteomics. For untargeted metabolomics, the UmetaFlow 28 pipeline combines all the major metabolomics tools with the powerful feature detection and quanti cation capabilities of OpenMS and linkage with SIRIUS and GNPS, as well as data integration. It was developed in Jupyter Notebooks using pyOpenMS and independently in Snakemake, making it compatible for big data, easy to use, and reproducible. The DIAMetAlyzer 29 KNIME work ow takes a different approach by integrating DDA and targeted DIA analysis that allows for false-discovery rate estimation based on a target-decoy approach.
Online Methods Improvements in the C++ library has traditionally integrated external tools into its ecosystem through wrappers that make their inputs, outputs, and parameters compatible with the rest of the software suite. A prime example of this is the integration of external peptide search engines. OpenMS 3 adds support for newer tools such as Comet and MSFragger and at the same time has deprecated support for tools that are not supported anymore like OMSSA. In other elds where adequate solutions were not available, new native OpenMS tools were implemented. These areas include top-down proteomics, protein-protein and protein-nucleotide crosslinking, and RNA oligonucleotide identi cation. This required the development of e cient algorithms for generating theoretical spectra, for matching and scoring, as well as the implementation of novel data structures with support for molecules beyond linear peptides, all of which are now available to developers (see Fig  S3  bottom for visualization of a cross-linked peptide pair).
Another addition to the library is the support for hyper ne isotopic distributions through the integration of IsoSpec 30 algorithms. The library now also makes use of modern features of C++17, in contrast to C++03 in OpenMS 2. 0. Several additional algorithms were moved from the source code of speci c tools into the library to allow access to complete tool functionality from Python bindings or other C++ tools. pyOpenMS pyOpenMS relies on Cython and autowrap to create the interface between C++ and Python. Since OpenMS 2. 0, the development of autowrap 34 has continued as part of OpenMS. Additions include support for static functions, autogeneration of type stubs, and improvement of autogenerated docs for display with Sphinx. The pyOpenMS bindings have been expanded to support the majority of the OpenMS library.
Acknowledgements C. B. was in part supported by the  Chan Zuckerberg EOSS program  ( 179 ). J. P. was funded by  Forschungscampus MODAL  (project grant  3FO18501 ). K. J. , E. N. and T. S. were supported by the  Ministry of Science, Research and Arts Baden-Württemberg. A. S. is part of the  MSCA-ITN-2020 PROTrEIN  project, which received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No:  956148. J. C. was funded by the  Government of Canada  through  Genome Canada  and the  Ontario Genomics Institute  ( OGI-164 ), and supported by the  Ontario Graduate Scholarship. J. C. S. was funded by the  European Research Area Network Personalized Medicine Cofund (PerProGlio). A. W. was supported by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation   TRR 261/1 ,Z03).
S. W. was supported by  de. NBI  ( 031A535A ),  Analytics for Biologics  ( H2020-MSCA-ITN-2017 ),  PROTrEIN  ( 956148 ),  EPIC-XS ( H2020-INFRAIA ), TRR ( TP Z03 ), and TUE. AI. S. K. was funded by  Horizon 2020  under grant agreement No  686070  M. P. S. was funded by  Polish National Science Centre  grant no. 2017/26/D/ST6/00304. Y. P. -R. acknowledges funding from  EMBL  core funding,  Wellcome  grants ( 208391/Z/17/Z ,  223745/Z/21/Z ), and the  EU   H2020  project  EPIC-XS [823839 ]. Con icts of interest L. H. is an employee of Laminlabs. H. W. is an employee of Storm Therapeutics. L. B. is an employee of BioNTech SE. L. N. is an employee of OpenMS Consulting. O. K. , T. S. and S. W. are o cers in OpenMS Inc. , a non-pro t foundation which manages the international coordination of OpenMS development.
Power supply (low and high voltages) will be provided via other pairs of wires within the same custom wiring harness in order to minimize the number of connectors on the FEB on which very limited space was available. Outside of the chamber, all the LVDS data from the different FEBs of a given detector module are distributed to a GBTxEMU  [62, 63]  concentration board used for data aggregation, slow control and synchronization of the ASICs. The GBTxEMU board receives the digitized data via a mezzanine connection board (FMC) and outputs the aggregated data via optical fiber to the data acquisition server. On this server, a PCI-express GERI board  [64]  is used to catch the data frames sent by the different GBTxEMU needed for the full array and perform their synchronization. Since this readout system is triggerless, the event correlation with other detectors used in the experiment is performed by recording the timestamp of the accepted trigger of the RIBF DAQ.
Integration of the STRASSE readout into RIBF DAQ The STRASSE electronics is triggerless, i. e. an event is generated whenever a signal on a given channel crosses the threshold. The STRASSE DAQ will run in a stand-alone mode but will record the timestamp of any accepted trigger from the RIBF DAQ dealing with the other detectors. This will allow to merge offline the different data sets by applying a coincidence window using this timestamp. This approach also allows for different coincidence time windows, to make sure no events are erroneously discarded as noise. The concept of the method developed to build, synchronize and transfer this timestamp generated by the RIBF DAQ to the STRASSE data stream via a charge injection (CI) board is shown in Fig. 22. First of all, a client LUPO module  [71]  with a dedicated FPGA add-on is used to send the accepted trigger signal of the RIBF DAQ and to transfer the master clock (CLK), generated by a separate master LUPO module (not represented in Fig.
For the SAMURAI data acquisition system, the trigger rate is limited by the standard SAMURAI detectors, in particular the multi-wire drift chambers. A maximum limit of 4-5 kHz can be achieved with the newly purchased electronics and DAQ modules at SAMURAI. Assuming a total beam intensity of 10 5 pps, an inclusive ( p, 2 p) cross section of 50 mb  [44, 72] , a 50% twoproton detection efficiency, a 50% reaction loss when using a 150 mm thick LH 2 target, one gets a trigger rate of 0. 65 kHz. A more reliable estimation is obtained from a full Geant4 simulation of ( p, 2 p) and INCL-based fragmentation events in the STRASSE target with a realistic CATANA geometry and efficiency, as well as the foreseen trigger with detection thresholds of 30 MeV for the individual CATANA crystals. The total counting rates in CATANA is estimated to be 1.
Acknowledgements H. N. L. , M. E. , A. F. , V. G. -A. , A. O. , T. P. and Y. L. S. acknowledge the support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project no. 279384907-SFB 1245. H. N. L. is supported by the  Fundamental Research Funds for the Central Universities of China. F. F. acknowledges the support from the  Région Normandie  ( RIN Recherche 2019 , Chaire d'excellence SIREN). H. W acknowledges the support by  MEXT KAKENHI  Grant Nos. JP21H04465  and  JP18H05404.
We investigated the performance of query expansion using word embeddings. We also analyzed the use of a pre-trained Amharic BERT model for masked language modeling, next sentence prediction, and text classification tasks. Amharic ad hoc information retrieval test collections that contain word-based, stem-based, and root-based text representations were used for evaluation. We conducted a detailed empirical analysis on the usability of word embeddings and BERT models on word-based, stem-based, and root-based corpora. Experimental results show that word-based query expansion and language modeling perform better than stem-based and root-based text representations, and fastText outperforms other word embeddings on word-based corpus.
In recent years, word embeddings and variants of BERT models have brought new solutions to learn better representations for NLP and IR tasks. Pre-trained word embeddings  [8] , paragraph embeddings  [9] , and sentence embeddings  [10]  have become essential elements in modern NLP systems. Word embedding is a distributed dense representation of a text and refers to a set of language modeling and features of learning techniques in NLP. Recently, word embedding has gained more attention and has been successfully employed in various NLP and IR tasks  [11, 12]. It is considered to be one of the most significant breakthroughs of deep learning for solving challenges in NLP. It represents words in the form of real-valued vectors that make similar words to have similar vector representations  [8]. Prominent word embedding methods include word2vec, global vector (GloVe), and fastText.
The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative. The performance of word2vec and GloVe with LSTM classification is almost similar. However, fastText improves the performance by 4%, with a score of 88. 35%, using the impact learning method. The combination of fine-tuned Bangla-BERT and LSTM leads to an accuracy of 94. 15%. Pre-trained language models have been generated for many languages. Although Amharic is the most widely spoken language in Ethiopia and a large volume of text is available on the Web, it is lagging behind in computational analysis including BERT and word embeddings.
For example, the surface form ሰበረ /səbərə 'he broke'/ consists of the template form (CVCVCV), root consonants (ስ-ብ-ር /s-b-r 'break'/), and vowel pattern (ኧ /ə/). While the root ስ-ብ-ር /s-b-r/determines the basic meaning of the word, the template CVCVCV, along with the vowel pattern, provides additional morpho-syntactic information such as aspect and tense. Many affixes, such as person, /s/, approaches. Protasha et al. [36]  examined the effect of word embeddings and fine-tuned BERT for sentiment classification. The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative.
The performance of word2vec and GloVe with LSTM classification is almost similar. However, fastText improves the performance by 4%, with a score of 88. 35%, using the impact learning method. The combination of fine-tuned Bangla-BERT and LSTM leads to an accuracy of 94. 15%. Pre-trained language models have been generated for many languages. Although Amharic is the most widely spoken language in Ethiopia and a large volume of text is available on the Web, it is lagging behind in computational analysis including BERT and word embeddings. Some research has been conducted to create pre-trained Amharic models. To mention them: fastText  [37, 38] , word2vec  [26, [38] [39] [40] , and XLMR  [41]. Some of these models were trained for cross-lingual purposes and are not usable for the needs of most NLP tasks.
For example, the surface form ሰበረ /səbərə approaches. Protasha et al. [36]  examined the effect of word embeddings and fine-tuned BERT for sentiment classification. The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative. The performance of word2vec and GloVe with LSTM classification is almost similar. However, fastText improves the performance by 4%, with a score of 88. 35%, using the impact learning method. The combination of fine-tuned Bangla-BERT and LSTM leads to an accuracy of 94. 15%. Pre-trained language models have been generated for many languages.
Although Amharic is the most widely spoken language in Ethiopia and a large volume of text is available on the Web, it is lagging behind in computational analysis including BERT and word embeddings. Some research has been conducted to create pre-trained Amharic models. To mention them: fastText  [37, 38] , word2vec  [26, [38] [39] [40] , and XLMR  [41]. Some of these models were trained for cross-lingual purposes and are not usable for the needs of most NLP tasks. Moreover, most of them are not publicly accessible. Because of this, Amharic NLP tasks have been performed using classical text representations such as stems and roots  [42, 43] , and the impact of learned text representations on roots, stems, and words to the development of various applications is not yet investigated. Thus, the construction of pre-trained Amharic models is a long sought resource for the research community.
For example, the surface form ሰበረ /səbərə 'he broke'/ consists of the template form (CVCVCV), root consonants (ስ-ብ-ር /s-b-r 'break'/), and vowel pattern (ኧ /ə/). While the root ስ-ብ-ር /s-b-r/determines the basic meaning of the word, the template CVCVCV, along with the vowel pattern, provides additional morpho-syntactic information such as aspect and tense. Many affixes, such as person, and approaches. Protasha et al. [36]  examined the effect of word embeddings and fine-tuned BERT for sentiment classification. The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative.
Protasha et al. [36]  examined the effect of word embeddings and fine-tuned BERT for sentiment classification. The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative. The performance of word2vec and GloVe with LSTM classification is almost similar. However, fastText improves the performance by 4%, with a score of 88. 35%, using the impact learning method. The combination of fine-tuned Bangla-BERT and LSTM leads to an accuracy of 94. 15%. Pre-trained language models have been generated for many languages. Although Amharic is the most widely spoken language in Ethiopia and a large volume of text is available on the Web, it is lagging behind in computational analysis including BERT and word embeddings.
For example, the surface form ሰበረ /səbərə 'he broke'/ consists of the template form (CVCVCV), root consonants (ስ-ብ-ር /s-b-r 'break'/), and vowel pattern (ኧ /ə/). While the root ስ-ብ-ር /s-b-r/determines the basic meaning of the word, the template CVCVCV, along with the vowel pattern, provides additional morpho-syntactic information such as aspect and tense. Many affixes, such as person, approaches. Protasha et al. [36]  examined the effect of word embeddings and fine-tuned BERT for sentiment classification. The authors integrated CNN-BiLSTM with BERT to address the problem of sentiment analysis for the Bangla language. They also compared word2vec, GloVe, fastText, and BERT. The experiment was conducted on 8952 samples from various sources. From these samples, 4325 samples were positive and the rest of the samples were negative.
For example, the surface form ሰበረ /səbərə 'he broke'/ consists of the template form (CVCVCV), root consonants (ስ-ብ-ር /s-b-r 'break'/), and vowel pattern (ኧ /ə/). While the root ስ-ብ-ር /s-b-r/determines the basic meaning of the word, the template CVCVCV, along with the vowel pattern, provides additional morpho-syntactic information such as aspect and tense. Many affixes, such as person, /s-b-r 'break'/), and vowel pattern ( 5 of 23 ined the effect of word embeddings and fine-tuned e authors integrated CNN-BiLSTM with BERT to adlysis for the Bangla language. They also compared T. The experiment was conducted on 8952 samples ples, 4325 samples were positive and the rest of the ance of word2vec and GloVe with LSTM classifica-Text improves the performance by 4%, with a score method. The combination of fine-tuned Bangla-BERT.
Amharic Pre-Trained Word Embeddings We built three types of word embedding models, namely word2vec, GloVe, and fastText. The number of hidden layers between the input layer and the output layer is 1, as the word embeddings learn only one vector representation for each word. These embedding models learn each word representation considering the window size. For example, given the sentence "ጽሁፍ የተጻፈው የህክምና እርዳታ ለመስጠት አይደለም /tsʼɨhufɨ jətətsʼafəwɨ jəhɨkɨmɨna ʔɨrɨdata ləməsɨt ətɨ ʔəjɨdələmɨ/" along with its corresponding stem and root representations and the window size of 3, the word2vec model learns the representation of each word using the pair of input and output training datasets as presented in Table  1. The size of the window in the case of word2vec, GloVe, and fastText is presented in Table  2.
Amharic Pre-Trained Word Embeddings We built three types of word embedding models, namely word2vec, GloVe, and fastText. The number of hidden layers between the input layer and the output layer is 1, as the word embeddings learn only one vector representation for each word. These embedding models learn each word representation considering the window size. For example, given the sentence "ጽሁፍ የተጻፈው የህክምና እርዳታ ለመስጠት አይደለም /tsʼɨhufɨ jətətsʼafəwɨ jəhɨkɨmɨna ʔɨrɨdata ləməsɨt'ətɨ ʔəjɨdələmɨ/" along with its corresponding stem and root representations and the window size of 3, the word2vec model learns the representation of each word using the pair of input and output training datasets as presented in Table  1. The size of the window in the case of word2vec, GloVe, and fastText is presented in Table  2.
Word Training Sample Type ጽሁፍ (ጽሁፍ, የተጻፈው), (ጽሁፍ, የህክምና), (ጽሁፍ, እርዳታ) Word (ጽሁፍ, ጻፍ), (ጽሁፍ, ህክም), (ጽሁፍ, ርድ) Stem (ጽ-ህ-ፍ, ጽ-ህ-ፍ), (ጽ-ህ-ፍ, ህ-ክ-ም), (ጽ-ህ-ፍ, ር-ድ) Root የተጻፈው (የተጻፈው, ጽሁፍ), (የተጻፈው, የህክምና), (የተጻፈው, እርዳታ), (የተጻፈው, ለመስጠት) Word (ጻፍ, ጽሁፍ), (ጻፍ, ህክም), (ጻፍ, ርድ), (ጻፍ, ስጥ) Stem (ጽ-ህ-ፍ, ጽ-ህ-ፍ), (ጽ-ህ-ፍ, ህ-ክ-ም), (ጽ-ህ-ፍ, ር-ድ), (ጽ-ህ-ፍ, ስ-ጥ) Root የህክምና (የህክምና, ጽሁፍ), (የህክምና, የተጻፈው), (የህክምና, እርዳታ), (የህክምና, ለመስጠት), (የህክምና, አይደለም) Word (ህክም, ጽሁፍ), (ህክም, ጻፍ), (ህክም, ርድ), (ህክም, ስጥ) Stem (ህ-ክ-ም, ጽ-ህ-ፍ), (ህ-ክ-ም, ጽ-ህ-ፍ), (ህ-ክ-ም, ር-ድ), (ህ-ክ-ም, ስ-ጥ) Root እርዳታ (እርዳታ, ጽሁፍ), (እርዳታ, የተጻፈው), (እርዳታ,የህክምና), (እርዳታ,ለመስጠት), (እርዳታ, አይደለም) Word (ርድ, ጽሁፍ), (ርድ, ጻፍ), (ርድ, ህክም), (ርድ, ስጥ) Stem (ር-ድ, ጽ-ህ-ፍ), (ር-ድ, ጽ-ህ-ፍ), (ር-ድ, ህ-ክ-ም), (ር-ድ, ስ-ጥ) Root ለመስጠት (ለመስጠት, የተጻፈው), (ለመስጠት, የህክምና), (ለመስጠት, እርዳታ), (ለመስጠት, አይደለም) Word (ስጥ, ጻፍ), (ስጥ, ህክም), (ስጥ, ርድ) Stem (ስ-ጥ, ጽ-ህ-ፍ), (ስ-ጥ, ህ-ክ-ም), (ስ-ጥ, ር-ድ) Root along with its corresponding stem and root representations and the window size of 3, the word2vec model learns the representation of each word using the pair of input and output training datasets as presented in Table  1.