dataesr/scientific-paragraphs-categorization huggingface dataset:
To deal with the heterogeneity of the system, EMMI simulates many replicas, r, of the system. The overlap between model GMM and data GMM is estimated over the ensemble of replicas as an average overlap per GMM component 𝑜𝑣 MD,$. This forward model overlap can then be compared to the data GMM self-overlap 𝑜𝑣 DD,$ = ∫ 𝜙 D (𝒙)𝜙 D,$ (𝒙) d𝒙 Metainference. Metainference is a Bayesian approach for modelling statistical ensembles by combining prior information on a system with experimental data subject to noise or systematic errors  17. This framework is particularly well suited to structural ensemble determination through molecular dynamics simulations, in which the prior (i. e. the force field) is updated with information from experimental methods, such as NMR spectroscopy, SAXS, or cryo-EM data.
Note 𝜎 SEM is calculated per data point (𝜎 $ SEM ), while 𝜎 B is computed per datapoint i and replica r as σ $,. B. In the present case, the likelihood 𝑝@ 𝑑 $ | | 𝑋, 𝜎 $ SEM , 𝜎. ,$ B C takes the form of a Gaussian function 𝑝@𝑑 $ <𝑿, 𝜎 $ SEM , 𝜎. ,$ B C = 1 √2𝜋 K @𝜎. ,$ B C / + @𝜎 $ SEM C / exp P- 1 2 Q𝑜𝑣 DD,$ -𝑜𝑣 MD,$ R / @𝜎. ,$ B C / + @𝜎 $ SEM C / S (4) where 𝑜𝑣 MD,$ is the ensemble average of the overlap. The metainference energy function for multiple replicas then becomes 𝐸 MI (𝑿, σ) = 𝐸 MD (𝑿) + 𝑘 B 𝑇 2 U Q𝑜𝑣 DD,$ -𝑜𝑣 MD,$ R / @𝜎. ,$ B C / + @𝜎 $ SEM C / % R ,% D. ,$ + 𝐸 0 ( 5 ) where 𝐸 0 represents the energy associated with the error 𝜎 = (𝜎 B , 𝜎 SEM ) 𝐸 0 = 𝑘 B 𝑇 U -log@𝜎. ,$ B C + 1 2 log W@𝜎. ,$ B C / + @𝜎 $ SEM C / X % R ,% D. ,$ (6). 𝐸 MD represents the molecular dynamics force field.
The unbiasing procedure used here is analogous to the standard umbrella-sampling technique by Torrie and Valleau 47. Now the ensemble average 𝑜𝑣 F!,$ (𝑿) is given by 𝑜𝑣 F!,$ (𝑿) = ∑ 𝑤(𝑿. , 𝑡) 𝑜𝑣 F!,$ (𝑿. ) % *. &' (12) The MEMMI energy 𝐸 MEMMI is thus equal to Eq. 5. Initial fibril structure. We build the initial structure of the full fibril by starting from a deposited fibril structure (PDB:6Y1A), which only contains the core of the fibril, and extending each of the 16 peptide chains by adding the missing 12-residue N-terminal sequence with guidance from the cryo-EM density map EMD-10669. We use the macromolecular modelbuilding program coot  72.
MEMMI simulations. We first express the experimental voxel map data as a data GMM containing 10,000 Gaussians in total, resulting in a 0. 975 correlation to the original voxel map EMD-10669 61 , using the gmmconvert utility  75. We continue by extracting 32 configurations from the previous NVT equilibration and initiate a MEMMI simulation, consisting of 32 replicas, resulting in an aggregate runtime of 5. 49 μs, using PLUMED. 2. 6. 0-dev  76  and gromacs-2020. 6  77. The simulation is performed in the NVT ensemble at 310 K using the same MD parameters as in the equilibration step. Configurations are saved every 10 ps for postprocessing. The cryo-EM restraint is updated every 2 MD steps, using neighbour lists to compute the overlaps between model and data GMMs, with a neighbour list cut-off of 0.
Conformational heterogeneity of the fuzzy coat. A structure of the IAPP fibril core (residues 13-37) has been previously published (PDB:6Y1A) using the cryo-EM data used also in the present work (EMD-10669). Here, we determine a structural ensemble of the whole IAPP fibril (residues 1-37). We model the system as a stack of eight peptides per side (Figure  2A ). While the core of the fibril maintains a parallel b-sheet structure, the flanking region (residues 1-12) exhibits a large conformational heterogeneity (Figure  2B ). While we find the cores residues 12-37 to be largely in a b-sheet conformation, we also note significant heterogeneity for residues 23-24, 32-34 and 37. As shown in Figure  2B , residues 23-24 interact with TYR37 and maintain mostly a coil structure, while residues 32-34 interact with the fuzzy coat and maintain mostly a coiled structure.
We also detected a small population of a-helical conformations in the region of residues 5-9. Correlation between experimental and calculated cryo-EM maps. We estimate the correlation of the MEMMI structural ensemble with the experimental cryo-EM map (Figure  3 ). We find that using a structural ensemble IAPP (residues 1-37) correlates better with the experimental cryo-EM map than a single structure (PDB:6Y1A) (Figure  3A, B ). The coefficient of correlation of the structural ensemble to the experimental electron density map is on average 0. 92. Furthermore, an important feature of MEMMI is its ability to estimate the error in the experimental electron density map (Figures  3C  and S5 ). We find that the relative error per Gaussian data point is on average 0. 09, where the relative error is the error of each Gaussian data point with respect to the total overlap between all data GMM and the i th component of the data GMM  35.
Here, we analysed lake water, tributaries, and hydrothermal vent fluids from Yellowstone Lake for their dissolved Si concentrations, isotope composition (𝛿 30 Si) and Ge/Si ratios to evaluate the sources of variability in the lake's Si cycle. Bulk elemental composition and biogenic SiO 2 (bSiO 2 ) content, together with 𝛿 30 Si and Ge/Si ratios from a single diatom species, Stephanodiscus yellowstonensis, were analysed in two sediment cores spanning the last 9880 cal. yr BP. We investigate these datasets to identify long term Holocene changes in hydrothermal activity and effects of large and short-term events i. e. , hydrothermal and a volcanic eruption. Combinations of bSiO 2 , 𝛿 30 Si and Ge/Si with XRF and lithology data revealed that Yellowstone Lake has a resilient biogeochemical system: hydrothermal explosions are visible in the lithology but have no identifiable impact on bSiO 2 accumulation or on the 𝛿 30 Si signature.
, 2010). Stable Si isotope data are reported as deviations of 30 Si 28 Si and 29 Si 28 Si from the NBS-28 standard in per mil ( ), denoted 𝛿 30 Si and 𝛿 29 Si as follows: 𝛿 x Si = ⎛ ⎜ ⎜ ⎜ ⎝ ( x Si 28 Si ) sample ( x Si 28 Si ) NBS28 -1 ⎞ ⎟ ⎟ ⎟ ⎠ ⋅ 1000, ( 2 ) where x is either 30 or 29. Each sample was measured three to four times in a single measurement session with a standard-sample-standard bracketing technique. Full chemical replicates were analysed for the majority of samples (n = 292, total measurements = 1875). Accuracy and reproducibility over three years on the secondary reference materials Diatomite (𝛿 30 Si = 1. 24 ± 0. 20 , 2SD, n = 285), Big-Batch (𝛿 30 Si = -10. 63 ± 0. 34 , 2SD, n = 109) and IRMM (𝛿 30 Si = -1. 77 ± 0. 22 , 2SD, n = 195) were in good agreement with values from a previous interlaboratory comparison  (Reynolds et al.
51 ± 0. 06 (2𝜎 -internal analytical) at 680 cm to 1. 96 ± 0. 07 (2𝜎) at 170 cm, with a mean of 1. 09 ± 0. 31 (1SD, n = 126). The 𝛿 30 Si is positively correlated with changes in bSiO 2 concentration (𝑝-value = 5. 8 ⋅ 10 -9 , R = 0. 54), which may indicate an effect of diatom production. We test the relationship between the biogenic silica and silicon isotopes, where in limited system, we expect to see positive relationship and strong correlation. Our regression shows that only part of our data follows this trend and that in a relatively unlimited system, like Yellowstone Lake, the isotopic values cannot be used to predict the bSiO 2 productivity completely. The 𝛿 30 Si and the bSiO 2 concentration show no long-term changes triggered by hydrothermalism. We also examine Si isotopes signal relationship with the hydrothermal proxy As.
Acknowledgements This work was supported by The  Royal Physiographic Society in Lund  to PZ, RC, the  Swedish Research Council  to DJC, and  NSF  Grant No. 1515377  to SCF. DJC has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  (Grant agreement No. 833454 ) and from the  Knut and Alice Wallenberg Foundation. PZ was funded by The  Charles University Grant Agency (GA UK) , project No. 40217 , supported by  UNCE Center for Geosphere Dynamics ,  UNCE/SCI /006  and  Swiss National Science Foundation  grant  200020_204220. The research was conducted under  Yellowstone National Park  research permits  YELL-2016-SCI-7018 ,  YELL-2016-SCI-5054 ,  YELL-2018-SCI-5054 and  YELL-2018-SCI-7084. Yellowstone Lake coring was aided by  M.
1A ). If not otherwise stated, the trap displacements were 1. 5 µm within 3 s, the laser power per trap was fixed at 200 mW, and the acquisition was performed over 3 min. Image and Data Analysis. To analyze the length of junctions, we performed image segmentation with Tissue Analyzer  (39). The length changes presented in the figures are averaged over 5 s. To measure the strain rate, the curves are first smoothed according to a spline interpolation defined on a 5-s time window (applied to the data acquired with 1-s frame rate). To analyze the intensity of Myo-II, we corrected for photobleaching (Fiji) and then measured the signal intensity at the junctions from the segmented image. Myo-II intensity is defined by the ratio of the integrated signal at the junction to the junction length. Kymographs are made from the images with KymoResliceWide, after photobleaching correction and registration to remove tissue drift have been performed (all procedures were performed in Fiji).
Despite the accumulation of data and studies, deciphering animal vocal communication remains highly challenging. While progress has been made with some species for which we now understand the information exchanged through vocal signals, researchers are still left struggling with sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets offer a valuable but distorted vision of communication systems. Adopting the best practices in their analysis is therefore essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species.
Introduction Cracking animal vocal communication codes has always constituted a motivating challenge for bioacousticians, evolutionary biologists and ethologists, and impressive breakthroughs have been achieved in the last decade thanks to advances in both data collection and machine learning, in conjunction with well-designed experimental approaches (e. g. Coye, et al. , 2016; Turesson, et al. , 2016; Mielke & Zuberbuhler, 2013). Building upon these success stories, can we conjecture that the puzzle of animal vocal communications will be soon solved? Probably not really. The high level of understanding achieved for a few dozens of species should not obscure the fact that for the vast majority of animal species, not much is known yet. For most animal species, neat, clean, and massive datasets are out of reach and bioacousticians have to cope with Small, Unbalanced, Noisy, but Genuine (SUNG) datasets characterized by data paucity, unbalanced number of recordings across individuals, contexts, or call categories for instance, and by noisy and sometimes reverberant recording environments.
Despite their imperfection, the worth of such datasets, which are often expensive to collect and annotate, is invaluable as they inform us about the complex communication systems of animal species that may be dramatically endangered or of major scientific interest. As a result, it is becoming increasingly important to adopt the best possible practices in the analysis of such datasets, both to provide reproducible studies and to reach robust and reliable conclusions about the species communication systems, and beyond that, about more general questions concerning the evolution, diversity, and complexity of communication systems. In mammals, individuals often produce vocalizations potentially informative to their conspecifics in their "here and now" context of emission. Additionally, these signals can also provide idiosyncratic clues to the emitter identity, which is often an essential information for territorial defense, social interaction and cohesion (e.
A commonbut sometimes overlookeddetrimental issue occurs when the classifier's decision is correct, but based on faulty premises because the samples in the training and test sets are not drawn from independent datasets and share confounding properties other than the category label itself (e. g. , a background acoustic noise that leaks information about the identity of the recorded individual), a phenomenon known as data leakage in data mining and machine learning  (Kaufman, Rosset, & Perlich, 2012 ; see also the "Husky vs Wolf'' experiment in Ribeiro, Singh & Guestrin, 2016, for a striking illustration). The pDFA was specifically designed to mitigate the risk of overestimating discriminability due to such a non-independence artifact, although it is not immune to such shortcomings. It has become the standard approach for bioacoustic analysis and it is still routinely used nowadays despite its limitations.
Specifically, it is neither the best nor the most accurate classification algorithm for assessing discriminability with SUNG datasets. As a paradoxical consequence, pDFA can be expected, on the one hand, to underestimate the information present in a dataset (because of suboptimal classification), and, on the other hand, to overestimate the class discriminability (because of residual non-independence). Meanwhile, in other scientific fields, impressive improvements have been made to address similar issues, by implementing more powerful statistical and machine-learning algorithms in more controlled configurations of evaluation. As aforementioned, such algorithms, including deep-learning neural networks, have recently been successfully applied to animal communication, mainly in situations where data paucity was not an issue (e.
g. ,  Aodha, et al. , 2018, but   see Turesson, et al. , 2016 and Clink & Klinck, 2019  for applications to smaller datasets; Stowell, 2022 for a recent review  ; Valletta, et al. , 2017  for a broader perspective). In addition to automatic classification, graphical exploration of a corpus of animal calls projected into an informative feature space is often a helpfuland essentialstep in understanding the structure of their repertoire. Since multiple acoustic features are usually involved, a reduction in dimensionality is required to get a human-friendly low-dimensional representation, usually in two (plane) or three (volume) dimensions. While this reduction was traditionally achieved through linear or near-linear transformations, such as Principal Component Analysis or Multidimensional Scaling, innovative nonlinear approaches such as t-distributed stochastic neighborhood embedding (t-SNE,  Van der Maaten & Hinton, 2008)  and Uniform Manifold Approximation and Projection (UMAP,  McInnes, Healy, & Melville, 2020)  have recently emerged.
These methods generally result in intuitive representations of the local structure present in complex datasets, at the expense of the significance of the global structure. Both t-SNE and UMAP have already been successfully applied to animal communication, either as exploratory methods to assess repertoire discreteness vs. grading, or to compare the relevance of several feature sets (see  Goffinet, et al. , 2021; Smith-Vidaurre, Araya-Salas, & Wright, 2020; Valente, et al. , 2019; Valente, et al. , 2022  for examples;  Sainburg, Thielk, & Gentner, 2020 and Thomas, et al. , 2022  for thorough discussions of the potential benefits of these methods, as well as their limits with small datasets). In this paper, we apply an automatic classification workflow to a SUNG dataset whose structure should seem fairly conventional to most bioacousticians: several recordings of calls produced in sequences of varying lengths, belonging to half a dozen types, and produced by a dozen individuals.
This dataset consists of audio recordings of calls emitted by captive bonobos (Pan paniscus). It provides a case study where the identification of individuals on the basis of their vocalizations and the identification of call types is not trivial. The bonobo vocal repertoire was described several decades ago in two seminal studies that highlighted its graded nature  (Bermejo & Omedes, 1999; de Waal, 1988). It is structured on almost a dozen prototypical types that conjugate modulated voiced vocalizations with scream components, also exhibiting nonlinear phenomena. Although quantitative studies with bonobos are still rare, it has recently been shown that an individual signature is detectable in bonobo vocalizations and that the reliability of this signature differs from one call type to another  (Keenan, et al.
, 2020). Here we implement a systematic comparison of several classification approaches to assess the strength and stability of individual bonobo signatures in the vocal repertoire. Our results establish whether the level of performance is the result of a lack of intrinsically encoded information in the vocalizations or a suboptimal classification approach. The research question we have addressed is therefore: can state-of-the-art automatic classification approaches lead to a more accurate estimation of the information encoded in a SUNG dataset and to a more comprehensive understanding of the functioning of an animal communication system? We present and evaluate the relevance of several methods that can be used to overcome the difficulties inherent in raw naturalistic dataset. In the end, the objective of this paper is to propose an operational workflow that can be used by bioacousticians in concrete situations.
In section III, we provide an in-depth evaluation of the impact of data leakage due to non-independence on classification results. We demonstrate why data leakage should not be overlooked by reporting the performances reached when maximizing the independence between the training and test datasets, compared to results from random partitions or from a maximally dependent (purposefully ill-designed) partition. We show how the use of a genetic algorithm allows the construction of training and test datasets leading to an almost unbiased accuracy estimation. Section IV illustrates the visual improvement in terms of UMAP representation when an appropriate classifier is applied as a preprocessor to get informative latent features over the representation directly extracted from the acoustic parameters.
However, playback experiments have shown that bonobos are able to discriminate a familiar from an unfamiliar congener based on low-arousal calls (peep-yelp;  Keenan, et al. , 2016). Note that this kind of individual signature depending on the type of call had already been reported in other mammal species  (Bouchet, et al. , 2012; Leliveld, Scheumann, & Zimmermann, 2011). A SUNG dataset is characterized by data paucity, the imbalance in the number of recordings between individuals, between contexts or between call types, and by noisy and sometimes reverberant recording environments. A first set of these constraints is inherent in the field work conditions, and is not specific to bonobos. Whether in zoos or in the wild, recordings of vocalizations often involve few individuals (typically less than two dozen), in an unbalanced proportion of call types and individuals.
Which feature is the most important from the animal's point of view? In this case, the answer is probably both, but this example aims to highlight that the choice of the right evaluation metrics (average recognition rate, accuracy, etc. ) among the dozens available may influence the resulting ethological interpretation (see Methodology subsection "Performance Metrics"). In this study, we use a dataset from the corpus analyzed in  Keenan et al. (2020). It comes from 20 adult bonobos housed in three zoos (Apenheul in The Netherlands, La Vallée des Singes in France and Planckendael in Belgium), totaling 380 hours of recording. Recordings were made during daytime using a Sennheiser MKH70-1 ultradirectional microphone and a Zoom H4 Digital Multitrack Recorder (44. 1 kHz sample rate, 16 bits per sample,.
wav files), in various contexts (foraging, grooming, aggression, etc. See  Keenan, et al. , 2020 for details). Vocalizations were manually segmented, identified and then double-checked by two other experimenters with Praat  (Boersma, 2006) , based on the visual inspection of signals on spectrograms. The audio quality of the recordings was variable, with many calls being recorded in a reverberant and challenging environment for automatic F0 detection, often leading to uncertainties in Praat. The temporal modulation of F0 was thus derived semi-automatically from the narrow-band spectrograms, thanks to a homemade Praat script based on mouse input of at least two points on the F0 trace on the spectrogram by the experimenter, allowing an interpolated trajectory to be estimated. Figure  2  illustrates why this dataset can be qualified as SUNG: the recorded environment may be a distant free-ranging enclosure or an indoor room; there is a quite noticeable imbalance in the number of calls per individual, and the audio bouts are noisy, reverberant, and complex to analyze.
We worked with a dataset consisting of 1,971 calls from 20 subjects to perform the preliminary quantitative study to characterize the tonal contour of each type (Section II and Figure  2 ) (after removing grunts and screams that do not have any tonal component). The following 10 call types are thus described: Peep (P), Yelp (Y), Hiccup (H), Peep Yelp (PY), Soft Bark (SB), Bark (B), Scream Bark (SCB), Whining Whistle (WW), Whistle (W), and Scream Whistle (SCW). For the automatic classification tasks reported in Sections II, III, and IV, we selected the ten individuals for whom at least 70 calls were available from the five most frequent call types Bark (B), Soft Bark (SB), Peep (P), Peep Yelp (PY) and Scream Bark (SCB) (1,560 calls, split by call types and individuals in Table  1 ).
Although they achieve by far the best performance today in computer vision and natural language processing, with models now involving up to hundreds of billions of parameters (e. g. ,  Chowdhery, et al. , 2022) , these large networks require (very) large training datasets, which do not fit very well in the context of SUNG datasets, despite recent attempts  (Leroux, et al. , 2021). Instead, we will consider 'shallow' dense neural networks (two to four fully connected layers, including the output layer) well suited for small datasets and the size of our different sets of predictors, as they have proven effective in similar applications (e. g. ,  Mielke & Zuberbühler, 2013; Robakis, Watsa & Erkenswick, 2018). Each of our 'state-of-the-art' approaches involves the tuning of a number of hyper-parameters, which values can impact performance on a given dataset  (Ramasubramanian & Moolayil, 2019).
While the parameters are adjusted during the training phasee. g. , the values of the connections between neurons in a NN -, the hyper-parameters are not and have to be specified otherwise -e. g. , the number of neurons in each layer of a NN. We have implemented a usual approach in machine learning, which is to consider not only a training set and a test set, but also a validation subset taken off the training set and used alongside the remaining part of the training set to find the optimal values of the hyperparameters for the data at hand, in a process known as hyper-parameter tuning. In order to evaluate the performance of our different classification techniques, we had to specifically consider the imbalance in the dataset. Indeed, not all metrics are appropriate to face this situation. In particular, the accuracy, which is easily interpreted, returns results biased towards the more represented classes.