dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , hit contains more amino acids than bait); on the contrary, many orange PSMs have a positive ∆m (data not shown). Hence, one reason for which our method is not able to unambiguously interpret bait could be the absence of too many bait amino acids in hit. This problem could be intrinsic to the protein database, which in some cases may not be representative of certain peptides. The score system we defined for SpecGlob to determine the best alignment can also influence the classification of PSMs. In the tests we carried out, a small variation in the scores had little effect on the results (data not shown). We favoured the insertion of mass offsets to align the maximum number of hit amino acids, which could lead to the insertion of a large number of mass offsets. Moreover, we have shown that each insertion of a mass offset presents the risk of introducing a mass alignment error (namely, a b-ion mass aligned to a y-ion mass).
Delivering more than one PSM per bait and then selecting the 'best' hit returned by SpecGlob (considering e. g. the score of the obtained alignment and/or the colour of the corresponding PSM) would probably increase the number of green PSMs in the final results. The FDR filtering of PSMs before any alignment of paired spectra, as it is usually done in association with OMS methods, is also questionable. For example, based on the FDR, PSMs that can be fully explained by SpecGlob, such as the actual PSM (GVPTEVK, GDPITVK), are discarded because of a low SPC (8 here). As shown in Figure  8 , a large number of green PSMs come from the decoy database. This means that a bait from the target database can have a hit coming from the decoy database, and still end up as a perfect interpretation by SpecGlob, despite several modifications and a relatively low SPC.
Spectra dataset and protein database We ran SpecGlob on the human proteome, downloaded from Ensembl99, release GrCh38  [29]. The protein database includes the target database (110,210 proteins with the annotation "protein coding" plus the 116 contaminants from the cRAP database) and the decoy database, generated by reversing all the protein sequences. After in silico digestion by trypsin, peptides whose lengths are strictly out of the range 7 to 30 amino acids were removed. Theoretical spectra generated from the human proteome are then compared to themselves (self-identification excluded). All peptides from the database thus simultaneously play the role of experimental spectra (in which case they are called baits), and the role of peptides (in which case they are called hits).
PSMs generation with SpecOMS SpecOMS  [9]  is a very fast OMS identification software used to identify experimental spectra, by comparison with theoretical spectra generated from a peptide database. This comparison is based on the number of shared peaks (Shared Peaks Count, or SPC) between pairs of spectra. Note that, in our work, only the masses of the peaks are considered, while intensities are not taken into account. Hence, we consider the terms peak and mass as totally equivalent. First, given a bait, SpecOMS selects all hits that share at least t peaks with the bait (where t is set by the user). Next, in order to produce the PSM (bait, hit), the bait that shares the maximum number of peaks is chosen according to either the raw SPC (when parameter shift=false), or to the shifted SPC (when parameter shift=true) -see  [21]  for details.
Execution time measurement SpecGlob is implemented in Java, and has been executed on a laptop equipped with an Intel i7 (2. 6 GHz) with 16 Gb of memory allocated to the JVM, running under Windows 10. On the dataset we tested, it took approximately 3 minutes to run SpecGlob in the standard mode, while its execution time was approximately 5 minutes when the two-steps algorithm was used.
In our example, hitModified is interpreted as follows: D is aligned to the bait without any mass offset, W is not found in the bait, Y is aligned but the alignment requires a mass offset of -186. 08 Da (which is the mass of W), I is aligned with a mass offset of 87. 03 Da (which is the mass of S), and R is aligned without any mass offset. Thus, starting from hitModified, it is possible to retrieve the bait sequence DYSIR using a post-processing algorithm which will infer both the deletion of W and the insertion of S after Y. In this dataset, the number of modifications predicted by SpecGlob varies between 1 to 16 in each PSM. Figure Legends.
Availability of data and materials The human proteome was downloaded from Ensembl 99, release GrCh38 on the Ensembl FTP server ftp://ftp. ensembl. org/pub/release-99/fasta/homosapiens/pep/. Proteins predicted with the annotation "protein coding" were added to the contaminant proteins downloaded from the cRAP contaminant database ftp://ftp. thegpm. org/fasta/cRAP. The SpecOMS software is available at https://github. com/dominique-tessier/SpecOMS.
As an example in the context of infrastructure maintenance and preserving structural integrity, Hitachi is already commercially deploying partially autonomous UAVs that collect data from IoT sensors embedded in large structures, such as the San Juanico and Agas-Agas Bridges in the Philippines  [4]. Research into UAV-aided data collection from IoT devices or wireless sensors include the H. Bayerlein and D. Gesbert were partially supported by the French government, through the 3IA Côte d'Azur project number ANR-19-P3IA-0002, as well as by the TSN CARNOT Institute under project Robots4IoT. M. Caccamo was supported by an Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research. This article was presented in part at IEEE GLOBECOM 2020  [1]. The code for this work is available under https://github.
The best way to describe our learning approach is by decentralized deployment or execution with centralized training. As DDQN learning requires an extensive experience database to train the neural networks on, it is reasonable to assume that the experiences made by independently acting agents can be centrally pooled throughout the training phase. After training has concluded, the control systems are individually deployed to the distributed drone agents. The rationale behind this concept is that we investigate a team of homogeneous UAVs with identical capabilities and tasks, therefore all experiences are useful for the training of all agents. In a real-world deployment of a team of quadcopter UAVs, all UAVs would be required to regularly return to a charging station, as flying time remains strongly limited by available on-board battery capacity.
pecially fascinating opportunities for both linguistic and psycholinguistic inves tigations of compound words  (Joyce, 2002;  2004; Masuda and Joyce, 2018). As contributions to the ongoing construction of a larger database project of Japan ese lexical properties  (Joyce, Hodošček, and Masuda, 2017; Joyce, Masuda, and Ogawa, 2014) , which aims to facilitate such investigations in terms of experi mental designs and stimuli preparation, this paper reports on two new database components for threekanji (3KCWs) and fourkanji compound words (4KCWs) respectively. More specifically, the paper focuses on the results of analyzing their morphological structures. In contrast to 3KCWs, where the dominant mor phological structure is attaching suffixes to existing twokanji compound words (2KCWs), such as 可能性 /kanōsei/ potentiality; possibility [[can + ability = possi ble; potential] + nature; ity ending], for 4KCWs, the dominant structure is com pounding with two 2KCWs combined, such as 自分自身 /jibunjishin/ oneself.
The next block of five papers deals with applications of the computer in grapholinguistics: Jee, Tamariz & Shillcock study soundgraphic system aticity in various fonts; Sheng, Colin & Perono Cacciafoco attempt to de cipher Linear A by a brute force attack; Salgarella & Castellan present a palaeographical database for Linear A; Donnelly describes a system for digi tizing Swahili in Arabic script; Xu presents a semantic index for the Dongba script of the Naxi people of Southwest China. Speech and writing are not the only modalities of languages. There is also gestuality, used in sign languages. Two papers deal with the written transcription of sign languages: Danet et al. present the TYPANNOT system; Bianchini discusses metalinguistic awareness. Among the authors of  Danet et al. is also Dominique Boutet who succumbed to the COVID19 disease a few weeks before the conference.
Against such background considerations, this paper reports on the construction of two new databases of 3KCWs and 4KCWs, which have been compiled as components of a larger database project concerned 8. However, in order to more appropriately contextualize this comment, it should also be noted that  Kobayashi et al. 's (2016)  chapter outline only includes sections up to fourcharacter SJ words. As they explain, although it is theoretically possible to construct SJ words of unlimited lengths, such words are inevitably combinations of compound word elements. In illustration,  Kobayashi, Yamashita, and Kageyama (2016, pp. 114-115)  analyze 新社屋建設案発表会 /shinshaoku kensetsuan happyō kai/ presentation of plan for construction of new company building according to its compo nent structure, working from its head of 発表会  [[disclose + diagram = presentation]  + gathering] for the announcement of the 建設案 [[build + establish = construction] + plan] for the  新社屋 [new + [company + building] ].
with Japanese lexical properties  (Joyce, Hodošček, and Masuda, 2017; Joyce, Masuda, and Ogawa, 2014). The overarching objective of the larger database project is to compile a database of scale, which can support linguistic and psycholinguistic research on the Japanese lexi con, such as facilitating the selection of stimuli for psycholinguistic sur veys and priming experiments  (Masuda and Joyce, 2018). Consistent with common practice  (Kobayashi, Yamashita, and Kageyama, 2016) , the component databases for the larger database project focus on differ ent aspects of the Japanese lexicon, as such compound words according to their overall lengths and targeted lexical properties. For instance,  Masuda and Joyce (2005)  supplemented a list of 2KCW headwords ex tracted from the fifth edition of  Kōjien (Shinmura, 1995)  with various data relating to morphological family sizes, morphological structures and semantic categories, while  Masuda, Joyce, et al.
(2014)  focused on semantic transparency ratings for 2KCWs. The present paper focuses primarily on the analyses of the new database components in terms of the morphological structures of the 3KCWs and 4KCWs, respectively. As the target compound words were extracted according to their graphe matic representations, without explicitly controlling for lexical strata, while the majorities of the 3KCWs and 4KCWs will be SJ, inevitably, some proportion of both databases will be either NJ or hybrid compound words. After briefly outlining the extracting and cleaning of the two database lists in Section 2, Sections 3 and 4 present the results of analyz ing the morphological structures of the 3KCW and 4KCWs, respectively. The paper ends with a short section of concluding remarks.
In order to derive lists of more practical lengths for analyses, the re spective second stages commenced by first applying the criterion that the BCCWJ lemma frequencies (token counts) should be either equal to or greater than 10. Moreover, reflecting the automatic nature of the methods used in extracting the CWL source corpus, additional clean ing work was required to remove some nonwords, some proper nouns and to merge for cases of lemma replications. Accordingly, Stage 2 pro cessing resulted in database lists of  23, 046 3KCWlemmas and 23, 159 4KCWlemmas. Although the application of the frequency criterion yielded highly comparable lists in terms of the overall numbers of com pound word lemmas that each database component contains, naturally, the impact of eliminating compound words with frequencies of less than 10 was far greater in the case of the 4KCWs.
That is, although Stage 2 processing for the 3KCWs yielded a list that was 13. 5% of the Stage 1 extracted list, Stage 2 processing for the 4KCWs yielded a list that con tained only 7. 75% of the Stage 1 extracted list. It should also be noted that while the distributions of lemma frequencies are generally consistent for both database lists, with both being typical of corpus frequencies, the 3KCWs are generally of higher token frequency counts compared to the 4KCWs, as the plots of logtransformed frequencies in Figure  1  indi cate. More specifically, for the 3KCWs, the frequency range is from 10 to 18,395 with a mean of 88. 5 and median of 25, while for the 4KCWs, the frequency range is from 10 to 4,127 with a mean of 43. 1 and a median of 20.
Fıgure 1. Logtransformed lemma frequencies of the 3KCWs and 4KCWs Even though the BCCWJ's original lexical strata codes (i. e. , NJ, SJ or hybrids) are retained within the respective databases compiled from the CWLs, the primary criterion for inclusion has been the appropri ate lemma length of 3KCWs and 4KCWs, respectively. Thus, while ac knowledging that both database lists contain some proportions of NJ and hybrid compound words and that awareness concerning the lex ical stratum of the components often greatly informs the appropriate classifications of the compound words, the analyses of morphological structures reported in the subsequent sections do not explicitly con sider the lexical stratum of the component elements. The conducted analyses of both database lists adopted similar conventions for denoting the constituent component kanji, which were designated as A, B, and C (3KCWs), as well as D (4KCWs), respectively, with squarebrackets used to indicate internal structures, such as  [AB] +C to indicate a 2KWC with a C addition and [AB]+  [CD]  to indicate a combination of two 2KCWs.
The Morphological Structures of the 3KCW Database Although  Joyce and Masuda (2019)  tendered an initial report about com piling this database list of 23,046 3KCWs, this paper describes the re sults of analyzing their morphological structures in a little more detail. In addition to presenting a summary table of the morphological struc tures, Section 3. 1 includes a table of the top 20 most frequent 3KCWs, as well as some general analyses of the A and C additions to 2KCWs. Three further subsections focus on the morphological structures, with Section 3. 2 on the primary structure of [AB]+C, Section 3. 3 on the sec ondary structure of A+  [BC] , and Section 3. 4 on the remaining 3KCW structures.
Morphological Structures of the 3KCW Database: Summary and A + C Additions Table  3  presents the breakdown of the 3KCW database list according to their morphological structures, with both type counts and their cor responding percentages. As the morphological structures of 3KCWs are generally transparent, it has been possible to confidently classify the database list according to eight morphological structures. 11 As Ta ble 3 clearly indicates, the primary morphological structure of [AB]+C is highly dominant in accounting for 77. 1% of the database list. In con trast, the secondary structure of A+  [BC]  only accounts for 21. 3% overall, which is about onethird of the primary structure's percentage. How ever, taking the primary and secondary structures together, they ac count for the vast majority of 3KCWs, at 98.
4% for the database list, with six other structures underlying the remaining 1. 6%. Firmly under scoring the profound significance of 2KCWs within the Japanese lexicon 10. Although we regard the [AB]+C classification as being the more plausible in terpretation, we are also planning to conduct psycholinguistic surveys to investigate the extent to which alternative structures might be activated in the processing of such compound words. 11. Table  3  also includes an adjustment category of multiple types for a few 3KCWs that are open to alternative analyses. (Joyce, 2011; Joyce, Hodošček, and Masuda, 2017; Kobayashi, Yamashita, and Kageyama, 2016; Nomura, 1975; 1988) , the majority of 3KCWs are 2KCWs combined with an additional morpheme; either predominately attached to the end or, in considerable cases, inserted at the beginning.
Primary Morphological Structure of [AB]+C 3KCWs As Table  3  vividly attests, 3KCWs overwhelmingly conform to the mor phological structure of [AB]+C, where a single morpheme is appended to an existing 2KCW. Accordingly, Table  6  first presents the ten most fre quent Cadditions in terms of their type counts, which indicates their productivity in combining with multiple 2KCWs, then Table  7  presents the most frequent 3KCWs by token counts, for each of the 10 most fre quent Cadditions. Being wholly consistent with  Kobayashi et al. 's (2016, p. 127 ) com ment that 的 /teki/ AN ending 13 is "a representative, highly productive SinoJapanese affix that combines with a variety of bases," it is not in the least surprising to find that it is the most productive of the Cadditions observed within the 3KCW database list.
Indicative of its wide applic ability, 的 is a Ccomponent of 3KCWs across the database's entire fre quency range. In addition to being a Caddition to four of the 20 most 12. Kobayashi, Yamashita, and Kageyama (2016, pp. 95-96)  classify onecharacter SJ morphemes as free (会 /kai/ meeting) or bound-either connectives (運転中 /un tenchū while driving), or the bases of verbs (信じる /shin. jiru/ believe), of ANs (急な /kyū. na/ abrupt), of adverbs (実に /jitsu. ni/ actually), of adnominal/adverbial modi fiers (単なる /tan. naru/ mere). Kobayashi et al. also  regard some bound morphemes as affixes due to their positional constraints, such as 最 /sai/ most as a prefix of 最先端 /saisentan/ cutting edge [most + front + edge] (p. 108). 13. As  Kageyama and Saito (2016, p. 18 ) note, the lexical category of adjectival noun does not exist in English or other European languages.
Secondary Morphological Structure of A+[BC] 3KCWs Although not as common as the primary structure of [AB]+C 3KCWs, the secondary structure of A+  [BC]  accounts for approximately onefifth  (21. 3%)  of the 3KCW database list. Table  8  presents the ten most fre quent Aadditions in terms of their type counts, while Table  9  presents, for each of the ten most frequent Aadditions, the most frequent A+  [BC]  3KCWs with the respective Aadditions. While  Kobayashi, Yamashita, and Kageyama (2016, p. 123 ) acknowl edge that there are considerable cases of SJ words "where the distinction between affix and compound constituent is not clear," they also stress that Aadditions often represent substantive semantic concepts. Indeed, they provide a number of examples, which they organize according to five semantic functions, including (a) limiting or modifying the base meaning, (b) verbal meaning that corresponds to the base noun's argu ment, (c) limiting the base noun's reference, (d) adverbially modifying a predicatelike base, and (e) indicating negation.
Examples for each of these five semantic functions are given in  (7). (7)   Instructively,  Kobayashi, Yamashita, and Kageyama (ibid. ,  differentiate between the four Aadditions that signify negative senses in terms of their nuances and the categories of bases to which they attach. Consistent with its fourth place ranking amongst the most productive Aadditions,  Kobayashi, Yamashita, and Kageyama (ibid. ) comment that 不 /fu/ and /bu/ negative is the most productive and at taches to nouns, adjectival nouns and verbal nouns, as in  (8) , respec tively. The next most productive Aaddition with negative connotations is 無 /mu/ lacking, nonexistent, which attaches to nouns and verbal nouns, but not adjectival nouns, as in  (9). As an Aaddition of 45 3KCWs within the database list, the third most productive of the Aadditions with negative connotations is 未 /mi/ not yet, which attaches to nouns and verbal nouns, but not adjectival nouns, as in  (10).
Other 3KCW Morphological Structures Although our analysis of the morphological structures of the 3KCW database reveals that the two structures of [AB]+C and A+  [BC]  account for the vast majority (98. 4%) of 3KCWs, as Table  3  also indicates, six other morphological structures underlie a small percentage of 3KCWs. Accordingly, this section turns to present examples of 3KCWs that con form to those other morphological structures. Albeit on a distinctly smaller scale (0. 7%), the third most frequent morphological structure is [A(C*)]+  [BC] , where the Ccomponent of an  [AC]  2KCW is omitted and the resultant A is attached to a related [BC] 2KCW. The practice of omitting the Ccomponent of an  [AC]  2KCWs is undoubtedly a form of clipping that is common with SJ words  (Kobayashi, Yamashita, and Kageyama, 2016, p.
Morphological Structure Results for the 4KCW Database Having presented the results of analyzing the morphological structures of the 3KCW database component in some detail, this paper now turns to present the results for the 4KCW database. Adopting a similar organiza tion to the previous section, Section 4. 1 starts with a summary table of the morphological structures and a table of the top 20 most frequent 4KCWs. Four further subsections focus on the various morphological structures, with Section 4. 2 on the primary structure of [AB]+[CD], Section 4. 3 on the second structure of [ABC]+D, Section 4. 4 on the tertiary structure of A+  [BCD] , and Section 4. 5 on the remaining 4KCW structures.
Morphological Structures of the 4KCW Database: Summary Table  10  presents the breakdown of the database list of 23,159 4KCWs according to their morphological structures, with both type counts and their corresponding percentages. As the morphological structures of 4KCWs are also generally highly transparent, it has been possible to confidently classify the database  list according to 11 morphological structures. 16 Similar to the results for the 3KCWs, the analyses of the morphological structures within the 4KCWs reveals that one structure dominates in accounting for 85. 3% of the 4KCW types. However, in the case of 4KCWs, the primary mor phological structure is [AB]+  [CD] , which is consistent with  Kobayashi et al. 's (2016, p. 113 ) comment that "fourcharacter SJ words are words composed of four SJ morphemes, which are typically divided into two words, each consisting of two morphemes".
g. , 二千一年 2001). As such, their oc currences within the top 20 4KCWs should be attributed to the relative frequency levels of these compound word lemmas within the BCCWJ based CWLs that are the basis for the 3KWC and 4KCW database lists. That is, while these particular 4KCWs are of high frequencies among the 4KCW database list, as noted earlier, the 4KCWs are generally of lower frequencies compared to the 3KCWs. It is also germane in this context to note that, although 12. 0% of the 3KCWs are combinations of number kanji with various numerical units and classifiers, only 1,134 (4. 9%) of the 4KCW list are of such combinations, with another 332 (1. 4%) 4KCW that are only numbers. Of the 1,134 4KCWs that are combinations of a number and a unit or classifier, understandably, 991 (87. 4%) of those are [ABC]+D structures.
It also bears noting that compound words that consist of three number kanji and a numerical unit/classifier (e. g. , 三十一 + 日) are likely to far less frequent in occurrence compared to both a single number kanji and classifier (i. e. , 2KCWs such as 一回 /ikkai/ onetime [one + time]) and two number kanji and classifier (i. e. , 3KCWs such as 三十分 30 minutes). frequent 4KCWs, in terms of their token counts, for each of the most fre quent ABcomponents. Highly consistent with the largescale BCCWJ corpus from which the 4KCW database list has been derived, the most frequent ABcomponents are related to general areas of human activity, such as 経済 /keizai/ eco nomics, 社会 /shakai/ society, 経営 /keiei/ business and 政治 /seiji/ politics. The most productive ABcomponent is the adjective 当該, which appears as the ABcomponent of 112 4KCWs within the database list, such as in 当該各号 /tōgaikakugō/ relevant items  [[relevant + abovestated]    1,007  Kobayashi, Yamashita, and Kageyama (2016, pp.