dataesr/scientific-paragraphs-categorization huggingface dataset:
We further evaluated the importance of the different features used as predictors when classifying the calls, in order to detect whether some of them play a significantly larger role than others. We analyzed the features of the feature sets 'Bioacoustic' and 'DCT', but did not consider MFCC as there are too many features leading to very limitedand mostly meaninglessimpacts for the individual features. Different feature sets and classifiers may model differently the information present in the dataset. This suggests that they can be combined to achieve a better performance by cumulating their individual strengths while mitigating their individual weaknesses. So-called ensembling methods have been successfully developed for a large number of machine learning challenges. Bagging, boosting and stacking are three popular methods to combine different models  (Bauer & Kohavi, 1999) , depending on whether one builds models with different subsamples of the training dataset, one chains models to gradually reduce prediction errors (like in xgboost), or one integrates several parallel models applied to the same observations, which is the approach we implemented in this study.
596 with the bioacoustic set. This performance is comparable to that obtained by  Keenan, et al. (2020)  with the same method (57% of accuracy in a 5-category task with a slightly different call type labeling). In order to compare the results of the discriminant analyses to the chance level, modified datasets were created by recombination (see Section VI Methodology) and a DFA applied to them. This 1,000-fold iterated procedure provided a robust estimate of the distribution of random accuracies. The empirical p-values obtained after this recombination procedure were equal to p=0. 001. Leaving aside stacked learners, it can also be seen that i) the results obtained with the MFCC set are worse than those obtained with the bioacoustic or DCT sets, ii) the best performances are obtained with the xgboost approach, while the worst are obtained with the NN approach.
However, the impact of the unbalanced dataset is striking. With the ensemble configuration leading to the best performancethe stacking of the nine different configurationswe obtain, on the one hand, quite good performances (up to 79. 8% correct identification) for the four individuals contributing the most to the dataset (Jill, Zuani, Zamba, Vifijo). On the other hand, the performances are modest, though above chance, for the individuals that contribute less (e. g. , Bolombo = 17. 3% of correct identification; Busira = 28. 1%). Class imbalance thus has a significant impact on our results, despite the adoption of strategies to mitigate it. These results suggest that when a poor individual classification score is obtained, it is likely to be due to a faulty classifier and not to the absence of idiosyncratic features in an individual's calls.
Procedure In the automatic classification approach reported in the previous section, each observation unit consists of a single call. A problem may arise when these calls are extracted from the same vocal sequence, which is frequently the case in animal datasets. This situation violates the independence of the observations (pseudoreplication), and can potentially undermine the validity of the classification performance. Specifically, how can we be sure that certain features characterizing the call sequence as a whole are not used by the classifier to identify the emitter? If this is the case, as suggested for instance by  Clay and Zuberbühler (2011) , single call classification performance could be overestimated. Finally, the third strategy (Skewed strategy) consists in maximizing the proportion of sequences shared by both sets (but still with disjoint call sets).
Results are similar across our different metrics (see Supporting Information). One explanation to the previous observations may lie in how calls are specifically organized in sequences in our dataset. Out of the 571 sequences in our dataset of 1,560 calls, 259 sequences consist of only one call, and 111 consist of 2 calls. This may explain why the differences between the different strategies are limited: by definition, a one-call long sequence cannot be shared by the training and test sets… To further test this possibility, we built a subset consisting only of calls appearing in sequences of at least 3 elementsall 10 individuals were still present for a total of 1,079 calls in 201 sequences. We then followed the same approach as with our primary dataset, considering our three different strategies with 100 runs for each, after having estimated the best hyperparameters for our 9 different configurations of sets of predictors and classifiers.
The Skewed strategy should definitely be avoided since it dramatically overestimates the classification performances. On the contrary, one should try to implement a Fair strategy in order to get the least-biased performance estimation. If done manually, it can be tenuous, but optimization tools such as BALI provide an efficient way to alleviate the combinatorial burden. The Default strategy merely consists in the absence of any strategy and it is arguably a common practice when data leakage iswrongly or rightlynot identified as a problem. One should mention that when data paucity is not an issue, practitioners often adopt a conservative approach that avoids most data leakage, for instance by systematically assigning recordings done on different dates to the training and test sets. Our caveat is thus mostly relevant in the context of SUNG datasets.
We computed i) the overall average silhouette score over the whole dataset (overall average silhouette width) ii) the average silhouette score per class (average silhouette width). The outputs for the three configurations are found in the lower part of Figure  13  and Figure  14 , respectively for call types and individual signatures. Visual clustering of either call types or individual signatures is improved when one shifts from raw features to pDFA to the representation derived from the best ensemble classifier. This is confirmed by the increasing values of overall average silhouette values from one configuration to the other. Additionally, in the case of the best ensemble, silhouette shapes and values for the different groups are consistent with the confusion matrices in Figure  6  and Figure  9.
V. Main achievements and limitations of the approach While improving our understanding of how information is encoded in the bonobo vocal repertoire, this paper has primarily a methodological objective. Our goal is to assess the suitability of different approaches to vocalization processing (acoustic analysis, automatic classification and representation) to describe information encoded in vocalizations from a SUNG dataset representative of what is commonly obtained in bioacoustic studies (Figure  1 ). Our approach consists of an acoustic characterization in several feature spaces followed by the implementation of several classification algorithms and their combination to explore the structure and variation of bonobo calls (Figure  5 ) and the robustness of the individual signature they encode (Figure  8 ).
, 2020; Stowell, et al. , 2019; Stowell, 2022). Adopting multiple feature sets may also be good practice for extracting non-redundant information, particularly in small datasets. The complementarity between predefined acoustic features (Bioacoustic or DCT set) and agnostic spectro-temporal representation (MFCC set) echoes the conclusions of  Sainburg et al. (2020)  in the context of unsupervised visualization of a vocal repertoire. Automatic classification. We implemented three classifiers (svm, xgboost, and NN) and compared their performance to that of discriminant analysis (pDFA), a method classically used to analyze the information carried by animal vocalizations, in particular vocal signatures. Our results show that all three models significantly outperform pDFA for the identification of both call types and individual signatures.
Because it may not be sensitive enough, pDFA may miss the identification of acoustic categories: it is therefore not the most adequate means to assess the categorical nature of a complex vocal repertoire or the accuracy of vocal signatures. Furthermore, the performance achieved when evaluating individual signatures (illustrated by the confusion matrix in Figure  8 ) shows a striking contrast between individuals for which the dataset is very limited (around ~75 calls for each of the three least represented individuals) and the most represented individuals (~200 calls or more for three individuals). This difference reminds us that we have processed a very small dataset (Table  1 ) by the standards usually expected for machine learning. Moderately increasing the dataset size by only a few hundred observations per individual would certainly bring a significant improvement in the performance of the classifiers.
Highaccuracy classifiers could nevertheless be very useful when high accuracy is expected, for example when examining a new vocal repertoire, or for passive acoustic monitoring of wild animals  (Gibb, et al. , 2019; Kvsn, et al. , 2020 , Mcloughlin, Stewart, McElligott, 2019, for recent overviews). Performance assessment must be done with great care. Dealing with a SUNG dataset means taking into account data sparsity, class imbalance, and potential data leakage. Their detrimental effects can be mitigated to some extent, by adopting the right metrics and models, and by carefully controlling for leakage induced by confound factors between the training and test sets. The high level of robustness observed in speech or speaker recognition with very large corpora (based on thousands of speakers and various recording conditions, to mention only two aspects) is unreachable with relatively small datasets.
Even if one carefully assigns calls recorded at different times to the training and test sets, there is inherently some residual data leakage when assessing the individual signature, as the soundscape, recording conditions and equipment often vary between recordings. These caveats make it difficult to compare approaches and papers. In this regard, multicentric challenges offering a unified evaluation framework based on a shared dataset should be encouraged  (Stowell, et al. , 2019). Visualization. Whether using illustrative raw spectrogramsas in  Bermejo & Omedes (1999)  and de Waal (1988)or aggregated templates (as in this paper in Figures  3  and 4 ), graphical representations of vocalizations are essential to capture the structure and characteristics of any vocal communication system. The projection of a dataset into a feature space provides additional information about its variability and clustering.
Recent nonlinear reductions and projections such as UMAPs are particularly convenient and powerful, as convincingly stated by  Sainburg et al. (2020)  for large datasets. However, these authors acknowledged that in the context of small datasets, UMAP representations may fail to clarify the structure of the dataset. Here, we overcome this limitation by showing that classifiers provide an elegant means of transforming the dataset from its acoustic description into an informative, parsimonious and discriminative latent feature space. This intermediate representation can in turn be used as a preprocessing step to better account for the clustering and discriminant potential of the data (see also  Thomas, et al. , 2022).
Recommendations Our results lead to the identification of several practical approaches that are generalizable to any other animal communication system and can improve the standard bioacoustics workflow in terms of information gain and reproducibility, especially with SUNG datasets. These approaches may probably seem standard to some machine learning experts  (Stowell, 2022) , but the current literature shows that they are not yet adopted in a systematic way. However, this is not a magic recipe, and some thought should be given to whether these recommendations are relevant to the context of interest. We recommend: 1. comparing several acoustic parameterizations as they may be complementary; 2. adopting SVM (Support Vector Machines) rather than discriminant functional analysis as the baseline classification approach; 3.
This procedure is promising because conceptually it should work, but the results need to be confirmed on a larger task with stringent control of data leakage. Other neural network architectures have also been successfully implemented on large datasets (e. g. Ivanenko, et al. (2020)  on mouse ultrasonic vocalizations). Progress has also been achieved in segmenting long audio bouts into calls  (Oikarinen, et al. , 2019; Steinfath, et al. , 2021; Tachibana, et al. , 2020) , but the proposed solutions currently struggle with the adverse conditions typical of SUNG datasets (overlapping calls, non stationary noise, etc. ). All these approaches are nevertheless expected to improve classification accuracy on large-enough datasets but the main performance limitation comes from the dataset itself. As aforementioned, its low audio quality may partially be compensated by an adequate acoustic preprocessing but complementary approaches lie in its size artificial extension by data augmentation techniques  (Kahl, et al.
Data selection and preprocessing We selected a subset of individuals and call types from our initial dataset, specifically the five types P (peep), PY -peep-yelp), SB (soft bark), B (bark), and SCB (scream bark), and individuals with more than 70 calls in these five types, namely Bolombo, Busira, Djanoa, Hortense, Jill, Kumbuka, Lina, Vifijo, Zamba, and Zuani. This selection resulted in a data set of 1,560 observations (Table  1 ). One missing value was detected for f0. mid. A random forest algorithm trained on the observed values of the variables related to f0  (q1f, q2f, q3f, f. max, q1t, q2t, q3t, t. max, f0. sta, f0. mid, f0. end, f0. av, f0. max, tf0. max, f0. slope. asc, f0. slope. desc, f0. slope. 1st. half, f0. slope. 2nd. half, f0. onset, f0. offset) offered a plausible reconstruction.
Imbalance in the data Our dataset is characterized by an imbalance in the way our observations -the calls -are distributed across individuals and call types (not to mention other dimensions such as the context of emission). This number was equal to two thirds of the smallest number of vocalizations analyzed per call type (i. e. , 206×2/3) and per individual (i. e. , 71×2/3). These discriminant analysis models computed from training samples were used to classify all remaining occurrences. Thus, for each individual and for each type of vocalization, at least one third of the occurrences were included in the test sample. We performed 100 iterations of these discriminant analyses with randomly selected training and test samples. The performance metrics and the percentage of correct classification were obtained by averaging the results obtained for each of the 100 test samples.
In order to statistically compare the performance achieved by the discriminant analysis to the chance level, additional discriminant analyses were also performed on modified datasets created by recombination, following the permuted DFA paradigm. Recombination involves assigning a class label (call type or individual identity, depending on the task under consideration) to an observation by a random permutation on the dataset. The training and test sets were then drawn according to the same rules as for the original dataset, and a DFA was applied, leading to an accuracy characteristic of a random classification. This procedure was iterated 1,000 times, resulting in a robust estimate of the distribution of random accuracies. The empirical p-value of the performance achieved with the original discriminant analysis was then obtained by dividing the number of randomized datasets that revealed a percentage of calls or individuals correctly classified at least as large as that obtained with the original dataset by the total number of datasets tested  (Mundry & Sommer, 2007; Keenan, et al.
Training/testing procedure When working with a large dataset and considering a supervised ML technique, it is common to split it once between a training set and a test set. However, as the size of the dataset decreases, however, this binary sampling procedure gives rise to an increasing risk of sampling error. In other words, the results may depend significantly on the specific observations falling randomly into the two sets, as some observations are inherently more difficult to classify than others. It is therefore not prudent to assess performance with a single split 3. To account for this risk, we averaged performances over 100 repetitions, i. e. , over 100 pairs of training and test sets. We rely on 25-thread parallelization to reduce the time needed to obtain the results. The performance distributions clearly illustrate the effect and range of sampling error, and the overall symmetry of these distributions validates the use of mean values as an estimator of central tendency.
UMAP and silhouette scores We used the umap() function of the uwot package with the default settings, i. e. , 15 neighbors, a minimal distance of 0. 01, two dimensions and the Euclidean distance metric. For the silhouette scores (silhouette widths), we used the get_silhouette() function of the clues package with the Euclidean distance as a measure of dissimilarity between the UMAP coordinates of each data point. We generated UMAP and silhouette scores for three different configurations: i) the description of the calls with the combined features of the three sets Bioacoustic, DCT and MFCC, ii) the pDFA predictions with the Bioacoustic set, and iii) the predictions of the best ensemble. In the first case, 1,560 inputs were considered -the number of calls in our dataset. In the second and third cases, we considered all the predictions for the calls appearing in the 100 test sets of our approach to compute average performance.
Such imbalance is a common feature of SUNG datasets, and should be taken into account when considering classification approaches. Although pDFA is inherently designed to deal with this type of imbalance, we considered several options for the other approaches. Results are reported with class weights inversely proportional to the number of observations in each class, which yielded better results than undersampling, oversampling or SMOTE (synthetic minority oversampling technique). It should be noted that we only paid attention to the imbalance for the target domain of classification, i. e. , when classifying individuals, we only considered the different numbers of calls per individual and not for the call types, and vice versa. pDFAIn the first step of the permuted form of discriminant analysis, a training sample was used to compute a set of linear discriminant functions.
distributions of the occurrences of individuals in both sets. A second strategy (Fair strategy) consists in minimizing overlap (i. e. calls belonging to the same vocal sequence) by assigning as many sequences as possible to either the training set or the test set, so that the soundscapes of the sequences seen during training do not provide any information when classifying calls in the test phase. Full independence can be achieved in theory if enough data sequences are available to match the distributions of types and individuals between the training and test sets, but in practice the limited size of the dataset leads to residual leakage (see results below).
Acknowledgements Funding: We thank the  Ecole Doctorale SIS of the University of Saint-Etienne  (Ph. D. grant to SK), the  University of Saint-Etienne  (research sabbaticals to FL and NM, visiting professorship to VA and research grant), the  LABEX ASLAN  ( ANR-10-LABX-0081 ) (FL, VA, and  FP)  of the  University of Lyon  within the  Investments for the Future program  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency , the  Institut Universitaire de France (NM) , and The  Canada Graduate Scholarship (XSG) -Michael Smith Foreign Study Supplements (CGS-MSFSS ) of the  Social Sciences and Humanities Research Council of Canada (SSHRC)  and by the  Laboratoire Dynamique Du Langage  ( UMR 5596 ,  University of Lyon 2 ). We warmly thank the zoological parks of  Apenheul, Planckendael and La Vallée des Singes  for their welcome, and especially to the bonobo keepers for their support.
distributions of the occurrences of individuals in both sets. A second strategy (Fair strategy) consists in minimizing overlap (i. e. calls belonging to the same vocal sequence) by assigning as many sequences as possible to either the training set or the test set, so that the soundscapes of the sequences seen during training do not provide any information when classifying calls in the test phase. Full independence can be achieved in theory if enough data sequences are available to match the distributions of types and individuals between the training and test sets, but in practice the limited size of the dataset leads to residual leakage (see results below). To address this issue, we compared three different subsampling strategies to build training and test subsets. The first strategy (called Default) corresponds to the results reported in Section II.
Lifespan differences between sexes is a puzzling question in human and evolutionary biology. Sex chromosomes have been proposed to play a central role, and the Y male chromosome has been suspected of having a toxic genomic impact in this trait. As Y chromosomes are typically enriched in transposable elements (TE), this hypothesis suggests that in aged individuals, in whom TE are less repressed, an increase in somatic mutations and acceleration of the aging process will be observed. Using an unprecedented expression dataset from humans with atypical karyotypes, we show an increased TE expression related to the Y chromosome. These findings complement previous reports showing that the 47,XYY human population displays a shorter lifespan, and support the existence of a toxic Y effect on men lifespan.
Results and Discussion We tested prediction 1 using published blood RNA-seq data from the Genotype-Tissue Expression (GTEx) project. We compared TE expression in 46,XX female and 46,XY male individuals belonging to different age groups from 20 to 70 years old. We found that only a few TE families have expression patterns consistent with our prediction and that TE expression seems to show a peak of expression at 30-40 years old in males (see Supplementary Materials -Analysis of TE expression in the GTEx dataset and Fig. S1  to S8, Tables S1 to S6, Data S1). However, it was difficult to draw strong conclusions because of the intrinsic nature of the data from the GTEx database: the many donors in different conditions and the many different sequencing runs made the data quite noisy (potential confounding factors, no longitudinal follow-up of same individual, survival bias, etc…).
Moreover, the rather low sequencing depth and read length are not ideal for studying TE expression (see Materials and Methods). To test prediction 2, we generated RNA-seq data from blood samples of individuals with several gonosome compositions on the karyotypes. Blood samples were obtained from 25 individuals of different karyotype compositions (46,XX, 46,XY, 47,XXY and 47,XYY) (Table  S7 ) from the Lyon university hospital. For each sample, we performed deep RNA-seq for a median of 174 million reads (min-max: 132 million -206 million) per individual, producing an unprecedented dataset (Table  S7 ). We first analyzed gene expression (Fig. S3 , Data S1) to validate our wet and dry lab protocols (see Materials and Methods) and to assess whether certain gene aging pathways (Gene Ontology (GO) -term analysis) were impacted by the karyotype.