dataesr/scientific-paragraphs-categorization huggingface dataset:
Bacterial artificial chromosome (BAC) sequencing and sequence analysis Rice RFLP marker RGR1963 was used to screen a pearl millet BAC library  (Allouis et al. 2001). Eleven positive clones were identified, of which BAC 293B22 was selected for sequencing. BAC DNA was isolated from clone 293B22 and shotgun libraries prepared as described  (Dubcovsky et al. 2001). A total of 1152 subclones were sequenced from both ends using Sanger technology. PHRED, PHRAP, and CONSED were used with default parameters for base calling and quality control, sequence assembly, and contig ordering, respectively. The sequence of BAC 293B22 has been deposited in GenBank under accession number KC463796. Gene prediction was performed using FGENESH with the monocot training set (www. softberry. com). Repetitive DNA was identified by BLASTN searches against the Gramineae repeat database (http://plantrepeats.
2000; Devos 2005 ). Genes selected from the rice and sorghum genomic sequences were used in BLASTN searches to find corresponding expressed sequence tags from other grass species. The genomic and expressed sequence tag sequences were aligned using the program Multalin  (Corpet 1988) , and primer sets were designed against conserved exon regions flanking an intron. For polymorphism screening and mapping, amplification products were separated on mutation detection enhancement (MDE) acrylamide gels to display single-strand conformation polymorphisms  (Martins-Lopes et al. 2001). The primer sequences, annealing temperature and location in the sorghum, rice, and Setaria italica genomes for all markers are given in Supporting Information, Table  S1. To facilitate interpretation of the data, all markers, irrespective of whether they were developed from rice or sorghum, were named after their sorghum ortholog.
Comparative analyses The protein corresponding to the primary transcript and its location in the genome was retrieved for all annotated genes in the region between 54. 31 and 64. 31 Mb on sorghum chromosome 7 (JGI v. 1. 0) (Paterson et al. 2009). Similarly, we retrieved the protein corresponding to the primary transcript for all annotated genes in S. italica (35,158 loci in nine chromosomes, JGI v2. 1)  (Bennetzen et al. 2012) , Oryza sativa (34,781 representative gene loci in 12 chromosomes, IRGSP build 5)  (Matsumoto et al. 2005) , and Brachypodium distachyon (23,558 loci in five chromosomes, JGI v. 1. 0) (Vogel et al. 2010 ). In the first instance, a BLASTP search was carried out with the sorghum proteins as queries against the S. italica proteins. The top hit was recorded if the E-value was less than 1e 25 and the maximum number of hits at the threshold value was four.
Homologous pairs were used to detect syntenic blocks by MCscan (multiple collinearity scan)  (Tang et al. 2008 ) and colinear segments were identified using the empirical scoring scheme min {2log 10 E, 50} for one gene pair and 21 gap penalty for each 102kb distance between any two consecutive gene pairs. Syntenic blocks with scores. 300 and an E-value ,1e 210 were retrieved. S. italica proteins located within these syntenic blocks were then used to identify the syntenic regions in rice and B. distachyon. The positions of orthologous gene pairs were plotted using a script in R (R Development Core Team 2005), and the dot plots were used to identify rearrangements. The precise breakpoints of the rearrangements were determined manually.
Five plants did not survive the seedling stage, and one plant was removed because of the presence of nonparental alleles, so the fine-mapping and phenotyping was performed on 23 recombinants. Determining the genotype at the d2 locus The height of the F 2:3 plants varied both within and between F 3 families and ranged from 37 to 270 cm (Table  S2 ). Five F 3 families comprised only dwarf plants, and the corresponding F 2 plants were genotyped as d2d2 (Table  1 ). Five F 3 families were segregating for plant height in a 3:1 (tall:dwarf) ratio, and the corresponding F 2 plants were genotyped as D2d2. Nine F 3 families contained either no plants ,110 cm (7 families) or a single plant ,110 cm (2 families) and the corresponding F 2 plants were genotyped as D2D2. Integrating these data with the genotypic data for markers B224C4P2 and PSMP305 confirmed the location of d2 in the B224C4P2-PSMP305 interval.
The sequence of BAC 293B22 assembled into a single scaffold consisting of three contigs. De novo gene identification as well as homology-based annotation identified three genes in the order Ca_Sb07g023860 2 1. 7 kb 2 Ca_Sb07g023850 (which corresponds to RGR1963) 2 60. 1 kb 2 Ca_Sb07g023840. Marker B224C4P2 was located 3. 1 kb from Ca_Sb07g023840, and both markers were separated by a minimum of one and a maximum of three recombination events in the Tift 23DB • ICMP 451 map (Ca_Sb07g023840 was scored as a dominant marker; hence, not all recombination events could be identified). Combining the genetic mapping data with the gene order information from BAC 293B22 indicated that part of the d2 region was inverted in pearl millet compared to sorghum (Figure  3 ). To better understand the evolution of the d2 region in grasses, we conducted a comparative analysis at the genome level of the distal 10 Mb of sorghum chromosome 7 (54.
Preliminary validation of ABCB1 as a candidate for d2 Several forward and reverse primers were designed against the sequence of gene Sb07g023730. One primer combination, Ca_Sb07g023730F1/R5 (Table  S1 ), yielded a strong amplification product in the tall inbreds ICMP 451, P1449-2 and Tift red but did not amplify in the dwarf inbreds Tift 23DB, PT 732B and 81B. Sanger sequencing of the fragment amplified from the tall inbred ICMP 451 (File S1) and BLASTX analysis of the resulting sequence to the 'nr' section of GenBank confirmed that the amplified fragment was derived from gene ABCB1 (96% identity with both the sorghum and maize ABCB1 protein). Mapping of this product in the informative progeny of the Tift 23DB • ICMP 451 and PT 732B • P1449-2 F 2 populations showed Ca_Sb07g023730 to fall within the cluster of markers that cosegregated with d2 (Figure  2, A  and B ).
Orthologous markers in pearl millet and sorghum are connected with solid lines or, for markers that are inverted in pearl millet relative to sorghum, with dotted lines. Pearl millet markers for which no ortholog could be identified in the depicted sorghum region are indicated with X. Ca_Sb07g023860, RGR1963, Ca_Sb07g023840, and B224C4P2 were located on pearl millet BAC clone 293B22 and distances between those markers are drawn to scale. Distances between other markers in pearl millet are taken from sorghum. Markers shown in the d2 region in pearl millet in the same color could not be separated by recombination events based on data from both the Tift 23DB • ICMP 451 and PT 732B • P1449-2 mapping populations. Marker Ca_Sb07g023730 (indicated in bold italic) represents the gene underlying the dw3 phenotype in sorghum.
ACKNOWLEDGMENTS We thank  W. Hanna  for providing seeds of Tift 23DB and Tift red, and generating the Tift 23DB • ICMP 451 F 2 mapping population. Early work on the PT 732B • P1449-2 cross was carried out by F. K. P. , S. F. , and  K. M. D. at the  Comparative Genomics Unit, John Innes Centre, Norwich, UK. The research was funded by fellowships from the  Pioneer Hibred-Generation Challenge Program  (to V. J. ), the  Kirkhouse Trust  (to N. N. ), and by a  National Institute of Food and Agriculture (NIFA)   1890_CSREES Capacity Building Grants  (grant  2008-38814-04740 ; PI:  B. Singh ,  Fort Valley State University, GA ).
, 2007). To give some examples of other topics:  Gawda (2007)  tests the associations among neuroticism, extraversion, and paralinguistic expression. Rosenberg and Hirschberg (2005, 2009)  deal with acoustic/prosodic and lexical correlates of charismatic speech. Gregory and Gallagher (2002)  demonstrate that US president election outcomes can be predicted on the basis of spectral information beneath. 5 kHz, and  Oberlander and Nowson (2006)  employ textual features for personality classification of weblogs. Obviously, correlations among speaker states and traits exist; for example, in  (Byrd, 1994)  it was shown that both the speaker's sex and the dialect region affect pronunciation. Sex and dialect related variation was studied using the TIMIT database, displaying effects on phonetic characteristics such as of central vowels, speech rate, flapping.
Both form and function of paralinguistic phenomena can be straightforward, easy to obtain, and frequent, or complex, difficult to extract/annotate, and sparse. The computation of pitch register is relatively straightforward; it is normally always obtainable for speech, and thus it is a frequent parameter, and one of its functions, namely telling apart males from females, is often encoded in databases as well. Moreover, telling gender apart automatically is attractive for many applications. So is age, such as for selecting acoustic and language models of speech recognisers according to children or senior speakers  (Wöllmer et al. , 2011). The ground truth can be provided fully objectively, and it is known that age influences, for example, voice quality such as the jitter and shimmer parameters.
In contrast, for instance, laryngealisation as marker of social status cannot always be observed, and it is often speaker-specific; it is not easy to tell apart its different functions, or to annotate it. So far, we do not know of any large database annotated with this phenomenon and such functions. Therefore, age and gender were natural trait candidates for a first paralinguistic challenge; in addition, we employed a database with the emotion-related state 'interest', in order to cover both long term traits and short term states.
Speech and Language Resources Speech databases used for training and adaptation comprise the stored audio of exemplary speech for model learning (training) and testing; a transcription of the spoken content may be given, together with labels for phenomena such as emotion, age, or personality. It is common wisdom in automatic speech processing that training data should be as close as possible to the data used for testing. For some applications, read, i. e. , non-spontaneous, non-realistic speech data will do because the data used for testing will be read as well; examples are the screening of pathological or non-native speech or speaker verification for access control. A majority of applications, however, calls for non-prompted, non-acted speech data. There is an obvious clash between this demand and the majority of studies that still deal with prompted, acted data.
emotion categories vs. dimensions)  (Mori et al. , 2011) ; reliable annotations either by the speaker herself or by a higher number of annotators to avoid skewness (at least three but more is better); additional perception tests by independent labellers to provide a comparison of human performance for the task; balanced distribution of instances across classes or within the dimensional continuum; knowledge of the targeted distribution; high diversity of speaker ages, gender, ethnicity, language, etc. ; and normally, high spoken content variation. Finally, one wishes for well-defined test, develop, and training partitions without prototypical selection of 'friendly' cases for classification; free availability of the database; and well-documented meta-data. At the same time, the privacy of the speakers has to be preserved, which can be in contradiction to the other requirements  (Wyatt et al.
Looking at data collection, we can regroup several types of speaker states and traits into two big classes of databases. First, the class of databases with 'common' traits (e. g. , age, gender), which always are present despite the fact that pertaining information is not always annotated. Obviously, there is more of this type of corpora than of the second class of corpora, which are especially tailored for the study of 'more specific' traits. Second, the class of databases with 'less usual' states and traits that are sparser in data collected in real-life scenarios, and more difficult to record in large quantity; often, their labelling is more ambiguous  (Mower et al. , 2009). Due to this sparseness in real-life, specific databases are built for specific analyses such as alcoholised speech, sleepy speech  (Schuller et al.
, 2011a) , or emotions. In the last ten years, large efforts in the community have been undertaken towards collecting such corpora. Regrettably, most of them used by different teams have high levels of privacy and cannot be shared  (Devillers et al. , 2005). For a few databases, several types of paralinguistic factors can be analysed: In the aGender database, age, gender, and likability  (Burkhardt et al. , 2011) ; in the TUM AVIC database, interest and diverse types of non-linguistic vocalisations. These corpora are introduced in sections 6. 3 and 6. 4. Some corpora have initially not been recorded aiming at modelling speaker states and traits; however, their rich meta-data makes this possible: The TIMIT corpus  (Fisher et al. , 1986) , originally recorded for automatic speech recognition, can be used for speaker trait analysis  (Mporas and Ganchev, 2009; Schuller et al.
, 2011e) ; the "Vera am Mittag" (VAM) corpus  (Grimm et al. , 2008) , recorded for three-dimensional affect recognition, can be used for age and gender recognition  (Schuller et al. , 2011d). Note that speaker trait databases require a basically different approach compared to speaker state databases. For a reasonable modelling, a very high number of different speakers is needed (typically around 1 000). For speaker states, one can collect data from only a few speakers (typically around ten to 100, cf. , e. g. ,  (Stuhlsatz et al. , 2011) ), but with varying state. Speaker independence is a must when dealing with traits -unless longitudinal studies can be conducted, and speaker adaptation may not be an option, while one can choose to deal differently with these topics when looking at states.
Computational Analysis In Figure  1 , a unified overview of typical paralinguistic speech and language analysis systems is given. Each component in the chain of processing is described in the following. Speech databases (training/adaptation phase): They comprise the stored audio of exemplary speech for model learning and testing. In addition, a transcription of the spoken content may be given together with labels for emotion, age, personality, or other phenomena (section 4). Preprocessing: Subsequent to capturing the speech sound-a complex sequence of changes in air pressure-by a single microphone or an array of microphones, and its sampling and quantisation, preprocessing follows. This step usually aims at the enhancement of the speech signal or at the separation of multiple speakers' signals.
90. 0 % of the test sentences (with a length of five seconds each) were correctly classified. By including speech rate (morae per time unit) and local perturbation of power (power peaks per time unit), the accuracy was increased to 95. 3 %. Shafran et al. (2003)  used HMM based classifiers on MFCCs and F0 features to recognise age in a five class task: < 25, 25, 26 -50, 50 and >50, all in years. A database of spontaneous English phone calls to a customer care system (65 % women, 35 % men) was used for training. Results for age recognition showed: 68. 4 % correct classifications using only MFCCs, and 70. 2 % correct using a combination of cepstral and F0 features. Minematsu et al. (2003)  conducted a study with male speakers on Japanese read speech (age groups 6 -12, 20 -60, and 60 -90 years).
, 2003). The best accuracy for the seven class task was again obtained using MLPs, with an overall accuracy of 63. 5 %. A first non-public comparative study of speaker age recognition is reported in  (Metze et al. , 2007). The evaluation data were taken from the German SpeechDat II corpus, which is annotated with age and gender labels as given by callers at the time of recording. The database consists of 4 000 native German speakers who called a recording system over the telephone and read a set of numbers, words and sentences. For each class, 80 speakers were selected for training and 20 speakers for testing (weighted age and gender structure). Training data consisted of the whole utterance set of each person, up to 44 utterances. For further analysis, a sub-set of short utterances and another set of longer sentences was created.
In order to evaluate the performance on data that originate from a different domain, the systems were also tested on VoiceClass data. These data were collected at the Deutsche Telekom and consists of 660 native speakers of German who called a voice recorder and freely talked for about five to 30 seconds on the topic of their favourite dish. Here, age structure was not controlled, and the data consist of many children and youth but almost no seniors. Four systems A, B, C, and D were compared which are described in more detail in  (Metze et al. , 2007). The accuracy for the entire evaluation set ranged between 27 % and 54 % for precision while recall ranged between 46 % and 55 %. System A (based on class-specific phone recognisers) yielded the best performance. However, performance dropped for the short utterances which was attributed to the temporal structure realised in the phone bi-grams.
, 2002)  and audivisual interest detection in meetings  (Gatica-Perez et al. , 20005) ; or (children's) tutoring systems  (Mota and Picard, 2003). Moreover, there has been some research on interest detection on the Challenge corpus (TUM AVIC, cf. section 6. 4) prior to the Challenge. This work was mainly based on acoustic cues  (Vlasenko et al. , 2008; Schuller and Rigoll, 2009) , additional linguistic cues including non-verbal vocal outbursts  (Schuller et al. , 2006a) , and even audiovisual cues  (Schuller et al. , 2007a (Schuller et al. , , 2009a)). Note that in these studies, non-verbal vocal outbursts were recognised automatically, cf. also for the same dataset  (Schuller et al. , 2008a; Schuller and Weninger, 2010; Eyben et al. , 2011a). Looking at the 'opposite side' of interest, i.
e. , boredom, there exist several studies recognising it in line with other prototypical emotion categories; however, they usually employ acted data. Probably the most frequently used (acted) data set is the Berlin Emotional Speech database  (Burkhardt et al. , 2005a) -for results on this set cf. , e. g. ,  (Schuller et al. , 2010c; Gaurav, 2008). Besides this set, others exist, such as the one used in  (Pao et al. , 2010).
To account for the different age intervals of the groups, Children and youth should be uniformly distributed into two year clusters, and Adults and Seniors into five year clusters. This means, for example, that 25 children from seven to eight years and 20 young-aged females The content of the database was designed in the style of the SpeechDat corpora. Each of the six recording sessions contained 18 utterances taken from a set of utterances listed in detail in  (Burkhardt et al. , 2010). The topics of these included command words, embedded commands, month, week day, relative time description, public holiday, birth date, time, date, telephone number, postal code, first name, last name, yes/no with free or preset inventory and appropriate 'eliciting' questions such as "Please tell us any date, for example the birthday of a family member.
45 hours), and the other 770 speakers as a Train set (53 076 utterances, 38. 16 hours). The latter was further subdivided into Train (32 527 utterances in 23. 43 hours of speech of 471 speakers) and Develop (20 549 utterances in 14. 73 hours of speech of 299 speakers) sets. Overall, this random speaker-based partitioning resulted roughly in a 40 %/30 %/30 % Train/Develop/Test data distribution. Table  2  lists the number of speakers and the number of utterances per class in the Train and Develop sets and Figure  2  depicts the number of speakers as a histogram over their age. Decisive for the Age Sub-Challenge was the age group {C, Y, A, S} by classification as indicated in Table  2 , and not the age in years. The age group could be handled either as combined age/gender task by classes {1,.
Then, a 50-dimensional speaker factor vector is estimated from an aGender utterance and classified with a multi-class SVM. The fourth system is another system based on a JFA based speaker recognition system. Data of 235 speakers of the aGender Train set are used to build speaker dependent models. New data are scored against these models, resulting in a 235-dimensional feature vector, which is then classified with a multi-class SVM. The sixth system is a GMM-SVM system based on 26 perceptual linear predictive (PLP) features. Kockmann et al. won the Age Sub-Challenge and reached second place in the Gender Sub-Challenge, significantly behind the system of Meinedo et al. , who presented two similar, but separate systems for the Age and the Gender Sub-Challenge. The gender classification system of  Meinedo and Trancoso (2010)  is also a system based on late fusion of six sub-systems using multi-class linear logistic regression.
Visual and voice data were recorded by a camera and two microphones, one headset and one far-field microphone. For the Challenge, the lapel microphone recordings at 44. 1 kHz, 16 bit were used. 21 subjects took part in the recordings, three of them Asian, the remaining European. The language throughout experiments is English, and all subjects are non-native, but experienced English speakers. More details on the subjects are summarised in Table  5. In the Challenge, the speech data of the subjects were used exclusively for analysis. To acquire reliable labels of a subject's 'Level of Interest' (LoI), the entire video material was segmented into speaker-and sub-speaker-turns and subsequently labelled by four male annotators, independently from each other. The annotators were undergraduate students of psychology.
, 2000) ). This continuous annotation allows for mapping to larger chunks such as syllables, words, turns, etc. (Wöllmer et al. , 2010) , e. g. , by averaging. More, synthesised and agglomerated data, and cross-corpus modelling: Paralinguistic databases are still sparse-in particular publicly available ones. But, this seems to be changing: There are more databases increasingly available, and hopefully, this trend will continue in the future. Then, multi-corpus and cross-corpus evaluations such as the ones described above for age and gender (cf. section 6. 1) and as recently done for emotion  (Stuhlsatz et al. , 2011; Schuller et al. , 2010c; Eyben et al. , 2010a ) could be part of future research on combined speaker trait analysis. In addition, this allows for data agglomeration  (Schuller et al.
Unified overview of typical paralinguistic speech analysis systems. Dotted boxes indicate optional components. Dashed lines indicate steps carried out only during system training or adaptation phases, where s(n), x, y are the speech signal, feature vector, and target vector, respectively high comma indicates altered versions, and subscripts diverse vectors. Speech Database Hier. Functional Extraction Feature Selection / Gen. x Parameter Selection Model Learning s(n),y Chunking Acoustic Model Language Model s(n) Preprocessing s'(n) Low Level Descr. Extraction Feature Reduction x Classification/ Regression y i y' Encoding y Fusion y j Figure 1:.