dataesr/scientific-paragraphs-categorization huggingface dataset:
02 Mya, and larger than 15 My for all other occurrences. We then discarded all occurrences without a defined genus classification. Following common practice in paleontology, we performed all subsequent analyses at the genus level, which are less contingent on preservation and sampling biases, correlate well with species-level patterns of diversity, and better reflect underlying diversification patterns (John  Sepkoski, 1998; Jablonski and Finarelli, 2009). To incorporate dating uncertainties, we produced 10 randomized datasets by sampling uniformly within the temporal range of each fossil occurrence  (Silvestro et al. , 2016). We then used the tropical and extra-tropical paleoenvironmental reconstructions to assign each fossil occurrence as tropical or extra-tropical according to their timing and paleocoordinates, and allowing a buffer diameter of 100 km, following the grain from the spatial reconstructions.
This resulted in 74, 722 fossil occurrences allocated across the two regions, including occurrences older than the Cenozoic (Table  S4 ). While we only considered the Cenozoic, including these occurrences is important for better estimation of sampling rates across genera that survived the K-Pg mass extinction. We then used the Dispersal-Extinction-Sampling ('DES') model  (Silvestro et al. , 2016) , which uses MCMC to estimate per-area dispersal, extinction, and sampling rates, while accounting for several sources of uncertainty such as sampling and preservation, and outperforms other methodologies that infer past diversification dynamics. We ran two separate models on the same dataset: first we constrained dispersal and extinction rates to be constant across the Cenozoic for each area, and then we allowed for time-heterogeneity by allowing rates to change according to stratigraphic ranges.
Acknowledgments We thank  Christopher R. Scotese  for sharing the palaeogeographic information. We thank  Daniele Silvestro ,  Juan L. Cantalapiedra  and  Juan D. Carrillo  for help with fossil analyses. We thank  Jérémy Andréoletti  and the  Morlon lab  in general for feedback on an earlier draft. We are grateful to the  Map of Life  team, in particular  Yanina Sica  and  Ajay Ranipeta , for support in preparing the spatial biodiversity data. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  ERC   CoG PANDA  to HM and  Marie Skłodowska-Curie  grant agreement No  897225  for IQ. MJL was supported by  NSF-DEB  grant  2040347. WJ acknowledges support from  NSF VertLife  grant  DEB 1441737.
Supervised deep learning approaches to underdetermined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corresponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised modelbased deep learning approach to musical source separation. Each source is modelled with a differentiable parametric sourcefilter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models' parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline.
Briefly, the contributions of this work are: • a novel unsupervised deep learning approach for audio source separation, • the integration of parametric source models in deep learning based audio source separation, • a new differentiable procedure to estimate stable timevarying all-pole filters with a DNN using line spectral frequency parameterization, • an extensive experimental evaluation of the proposed method on a musical source separation task and comparison to learning-free and supervised baselines, • the open source code 1 for the proposed method and experiments. The rest of the paper is structured as follows: In Section II we review related work on audio source separation and modelbased deep learning. The proposed method is explained in Section III and its experimental evaluation is outlined in Section IV.
Petermann et al. [23]  modified the conditioned U-Net  [25]  so that the target source can be selected and separated using its F0 information. Results show that this leads to improved objective separation quality compared to using non-informed source-specific models. However, ground truth source signals are needed for training and they are rare for choir recordings. This motivated Gover and Depalle  [24]  1 https://github. com/schufo/umss to synthesize choir singing from MIDI files and to use this synthetic data for training of a score-informed DNN. When tested on real choir recordings, the model is outperformed by the learning-free, score-informed NMF proposed in  [9]. This shows that the performance of supervised DNNs depends strongly on the quality and quantity of the training data. Therefore, learning-free methods are a powerful alternative in limited data settings.
There is a recent line of research that explores the combination of data-driven and knowledge-based methods to take advantage of both paradigms  [31] ,  [16] ,  [17]. The integration of differentiable source models in the DNN-based source separation process is inspired by this model-based deep learning research. Specifically related to our work are recent speech synthesis methods which use differentiable parametric voice models and estimate their parameters using DNNs  [32] ,  [33]. We use similar voice models but in a different context. Engel et al. [17]  implemented a code library for differentiable digital signal processing and show the advantages of model-based deep learning for tasks such as synthesis, timbre transfer and dereverberation. The DNN architectures and the differentiable signal processing implementations we use in our experiments are inspired by their work.
A. Source model The proposed method is not specific to any particular source model and any parametric model may be used as long as it can be formulated in a differentiable way. This is often facilitated by automatic differentiation software such as TensorFlow  [34]  or PyTorch  [35]. In this work, we use the source-filter model of speech production  [36]. It describes a signal as an excitation signal from a sound source (e. g. the glottis) which is modified by a time-varying filter (e. g. the vocal tract)  [36]. It is used to model a wide range of signals such as human voice  [36] ,  [33] ,  [37]  and musical instruments  [11] ,  [38]. An exemplary visualization of our source-filter model is presented in Fig. 2. In the following, we assume that the true source signal s j (t) is segmented into N frames of length T samples.
C. Unsupervised training The proposed training procedure requires only mixture signals, no isolated source signals are needed. During training, the task of the DNN is to reconstruct the observed mixture by 2 The formulation of Algorithm 1 which we present in this paper has been presented in  [61]. Some equations in the main body of  [61]  contain errors but the Matlab code in the Appendix is correct. A less general formulation is found in  [58, Ch. 8 ]. The conversion was formally introduced in  [60]. Algorithm 1 Compute filter coefficients a k from ω k  [60] ,  [61]  Input: (ω k ) k=1:K Define: x k = cos(ω k ) Initialize: p -1 = q -1 = 0; p 0 = q 0 = 1 Initialize: p 1 = -2x 1 ; q 1 = -2x 2 for k = 2 to K/2 do p k = -2p k-1 x 2k-1 + 2p k-2 q k = -2q k-1 x 2k + 2q k-2 for i = (k -1) to 1 do p i = p i -2p i-1 x 2k-1 + p i-2 q i = q i -2q i-1 x 2k + q i-2 end for end for for k = 1 to K/2 do p k = p k + p k-1 q k = q k -q k-1 end for for k = 1 to K/2 do a k = (p k + q k )/2 a (K/2+k) = (p (K/2-k+1) -q (K/2-k+1) )/2 end for Output: (a k ) k=1:K estimating the corresponding parameters of the source models.
D. Implementation details We implemented the proposed method using the PyTorch framework  [35]. For the differentiable source models, we make use of the DDSP library  [17]. We re-implemented it in PyTorch and added extensions such as Algorithm 1 and an all-pole filter. The code is available online  3. Using an all-pole filter in the proposed framework entails two challenges. Firstly, the autoregressive filtering process is slow because it does not allow for precise parallel processing of frames. Secondly, the filter is time-varying, i. e. its coefficients are different at every frame. Therefore, extra care must be taken to ensure a smooth transition between frames to avoid artefacts. The DNN operates at a frame rate which is determined by the FFT size T and hop size B used to compute the STFT of the mixture.
A. Data As training and validation data, we use the Bach Chorals (BC) dataset  4  and the Barbershop Quartet (BQ) dataset  5. The BC set contains 26 chorals sung by a vocal quartet with the voices Soprano, Alto, Tenor, Bass (SATB). The BQ set contains 22 songs performed by a vocal quartet comprising the voices tenor, lead, baritone and bass. All voices are available in isolation for both sets. This allows us to compare the proposed unsupervised approach to supervised baselines. We combine the BC and BQ sets to generate what we call the full training and validation sets. The full validation set comprises songs 8 and 9 of the BC set and songs 8 and 9 of the BQ set and has a total length of 9 minutes and 10 seconds. The remaining songs build the full training set with a total length of 91 minutes and 20 seconds.
We also build a small training set consisting of BC song 1 with a length of 2 minutes and 40 seconds and a small validation set consisting of BC song 2 with a length of 2 minutes and 20 seconds. When mixtures with less than four singers are created from the individual voice recordings, all possible combinations of the four voices with the desired number of singers are used with the constraint of using only one singer per voice. As test data, we use the Choral Singing Dataset  [7]. It comprises three songs performed by an SATB choir with four singers per voice. All 16 singer signals are available in isolation which allows to evaluate the separation with objective metrics. We add the signals of individual singers (max. one per voice) to produce the test mixtures. For mixtures of J = 4 singers, the test set has a length of 6 minutes and 48 seconds.
VI. CONCLUSION In this work, we presented a method for (musical) audio source separation which overcomes two limitations of stateof-the-art supervised deep learning methods: They do not separate homogeneous sources and require large datasets of mixtures with the corresponding sources in isolation for training. We proposed a novel unsupervised model-based deep learning approach. It integrates domain knowledge in the form of differentiable parametric source models in a datadriven method and exploits F0 information. Experiments show that it outperforms learning-free and supervised baselines. Furthermore, the method performs well even when trained on less than three minutes of audio data. It allows to apply powerful deep learning based separation in domains where training data is expensive or nonexistent.
Optical analysis of explanted thin-film electrodes The impedances of the implanted TIMEs were recorded until the last day of the clinical trial, prior to explantation. Following explantation, the implants were optically analyzed in detail after thorough cleaning. The available data from the optical analysis (111 stimulation contact sites out of 168 implanted) were compared to the recorded impedance data close to explantation. The impedances (Fig. 4a ) of the stimulation contact sites were linked with the optically analyzed status to five categories, from category 1 (like pristine) up to category 5 (heavy delamination of > 6 µm, disintegration of the metallization layers or compression or destruction of the PI substrate) (Fig. 4b ). Details can be found in the Methods section. Could a certain impedance range be assigned to a classification of mechanical damage? An optically and mechanically pristine contact site corresponding to category 1 should exhibit a low impedance (<150 kΩ), whereas an entirely destroyed contact site with for example missing metallization and corresponding to category 5 should either exhibit a very high impedance or even not be measurable.
The median impedances of all categories were between 46 kΩ and 79 kΩ (Fig. 4c ), thus within the definition of an electrically functional stimulation contact site (<150 kΩ). The same applied for the median values, which were even in a narrower band of 50 kΩ to 67 kΩ. The majority of the impedance with 82% came under category 1 to 3. Only 7%, respectively 11% were related to category 4 and 5. In Fig. 4c  the data was summarized. A non-parametric Kruskal-Wallis method was applied to assess differences between the impedances of the categories (Fig. 4a ). The categorized impedances displayed no statistically significant difference at the α-level of 0. 05. Detailed images of the stimulation contact sites (111 out 168 available) were acquired using SEM. Especially on contact sites classified in category 5, traces of external mechanical impacts could be detected (Fig.
Box plots corresponding to the categories containing the mean (small box), median (bar) and the quartiles were generated. The numbers marked with a star represent not available impedances, n corresponds to the available impedances used for analysis. There was no statistically significant difference between the impedances of the categories at the α-level of 0. 05 (Kruskal-Wallis). b Acquisition samples of the optical thin-film analysis, using light microscopy, white light interferometry (WLI) and scanning electron microscopy (SEM) in combination with focused ion beam (FIB). Five categories were defined for the state of metallization integrity. Category 1 represents no impact and category 5 the highest. c Acquired impedance data from last day of the clinical trial, referred to the categorization of the optical analysis.
Simulation of intrinsic mechanical stress Thermo-mechanical intrinsic stress was simulated using COMSOL Multiphysics® (version 5. 3, COMSOL Inc. , Burlington, MA, USA). If not otherwise stated, standard parameters given by the software were applied. Two concepts of the large area ground contacts were designed. First, a rectangular design used for the TIME-3H  14, 33  , i. e. with an exposed metallization area of 1 mm × 0. 25 mm (A = 0. 25 mm 2 ) and second, a split ground with 109 circular exposed metallization contacts with a diameter of d = 0. 08 mm (A = 0. 55 mm 2 ). For simplification, a polyimide-platinum sandwich was simulated without adhesion layers and without SIROF. Platinum is the main conductive material, representing the tracks, MFI structures and the base of the stimulation and contact sites.
Chronic human implant The TIME-3H_V2 and TIME-4H implants (called TIME implants if specification not relevant) were assembled out of four submodules (Fig. 8c ; for details see  14  ). First, the thin-film part To this in turn 16 helically wound MP35N-wires covered by a medical-grade silicone rubber hose were soldered and terminated on the opposite side in a commercial connector. Colored silicone rubber and an identification tag made of laser-structured platinum were used for distinct identification. containing the contact sites for stimulation and the ground contacts to close the electrical circuit. Second, a screen-printed interconnecting ceramic to mechanically and electrically connect the thin-film electrode to the third part, a 40 cm long cable. The fourth module was a commercially available connector (NCP-16-DD, Omnetics Connector Corporation, Minneapolis, USA).
The voltage sweep was performed between -0. 6 V and 0. 8 V, with 200 mV steps for 250 cycles. Three neural stimulators: STIMEP (INRIA, Montpellier, France & Axonic, Vallauris, France), EARNEST (University of Cagliari, Italy) and the Grapevine Neural Interface System (Neural Interface Processor, 512 Channels of Potential, Ripple LLC, Salt Lake City, UT, USA) were used to inject current into the nerves of the subjects by means of TIME implants (details are provided in the supplementary materials). For the characterization of the electrode-nerve impedance in vivo and the subjects' response to intraneural stimulation, we delivered trains of cathodic-first, biphasic and symmetric square-shaped, current-controlled stimulation pulses of variable intensity, duration, and frequency, through a dedicated software controlling one of the before mentioned, external, electrical neural stimulators.
3 mCcm -2 ) to avoid overstimulation  14. Gathering the voltage response using a picoscope (PicoScope 2000 Series, Pico Technology, St. Neots, UK) and evaluation of its data using MATLAB (MATLAB, The MathWorks, Inc. , Natick, US). To gather compound overviews of the ground contact sites with a high degree of details, images with a tabletop SEM (Phenom Pro Desktop SEM, Thermo Fisher Scientific Phenom-World B. V. , Eindhoven, Netherland) were acquired and highlighted with CorelDRAW X7 (Corel Graphics Suite X7, Corel GmbH, München, Germany).
Statistical analysis Statistical methods were applied for data analysis of the electrochemical experiments. The data acquired in vivo was longitudinal and unbalanced for each time point (weeks). Moreover, the data was not normal distributed and the variance was not homogeneous as well. Therefore, we applied a linear mixed effect model using the software R (version 3. 5. 2, The R Foundation for Statistical Computing, Vienna, Austria) and RStudio (Version 1. 1. 463, RStudio Inc. , Boston, MA, USA), which is robust to non-normality and unbalanced data sets. Further detailed statistical analysis of the impedance data was performed elsewhere  7. A non-parametric Kruskal-Wallis method was applied, to assess differences between the impedances classification, comparing optical and electrochemical data.
ACKNOWLEDGEMENTS The authors would like to thank the whole working groupespecially those who are not on the list of authorsof the EPIONE and NEBIAS projects for their contributions and collaboration in the projects. Graphical artwork has been drawn by  Danesh Ashouri Vajari  (D. A. V. ). European Commission  (EC) grant  EPIONE FP7-HEALTH-2013-INNOVATIO-1 602547  (P. Č. , G. V. , T. G. , A. H. , F. P. , S. R. , I. S. , G. G. , E. F. , P. R. , K. Y. , W. J. , J. L. D. , D. G. , S. M. , T. S. ). European Commission  (EC) grant  NEBIAS FP7/2007-2013-611687  (G. V. , M. M. , F. P. , S. R. , I. S. , G. G. , E. F. , P. R. , M. B. , S. M. , T. S. ). Bertarelli Foundation  (F. P. , S. R. , I. S. , S. M. ). KNMF at  Karlsruhe Institute of Technology  granted working hours at the  FIB ID 2016-016013709  (P.
TESS discovery light curves HD 190622 (TIC 366989877) was observed by TESS in Sectors 13 (June 2019) and 27 (July 2020) with a two-minute cadence. The data were processed by the TESS Science Processing Operations Center (SPOC,  Jenkins et al. 2016) , which included aperture photometry, flagging of low-quality data, removal of trends related to systematic sources (pre-search data conditioning), and finally a search for transiting planets  (Jenkins 2002; Jenkins et al. 2020; Twicken et al. 2018; Li et al. 2019 ). The Sector 13 processing resulted in the identification of a planet candidate that satisfied all of the validation criteria and was subsequently ranked as TESS object of interest (TOI) 1054. 01  (Guerrero et al. 2021). The vetting procedure included the acquisition of speckle data on the Southern Astrophysical Research telescope (SOAR; Tokovinin 2018) and photometric observations from Las Cumbres Observatory Global Telescope (LCOGT;  Brown et al.
2013) , which confirmed that the candidate was on target with the expected ephemeris. The TESS presearch data conditioning simple aperture photometry (PDCSAP  Stumpe et al. 2014 Stumpe et al. , 2012;; Smith et al. 2012 ) light curve of this target does not show any strong signs of residuals of systematic features or stellar activity. The TESS pixel scale is ∼21 ′′ pixel -1 , and photometric apertures typically extend out to roughly 1 arcminute, which generally results in multiple stars blending in the TESS aperture. We acquired ground-based time-series follow-up photometry of TOI-1054 as part of the TESS follow-up observing program sub-group 1 (TFOP SG1; Collins 2019) 2  to attempt to rule out or identify nearby eclipsing binaries (NEBs) as potential sources of the detection in the TESS data and detect the transit-like event on target.
We observed full predicted transit windows of TOI-1054. 01 in Pan-STARRS z-short band using the LCOGT 1. 0 m network node at Siding Spring Observatory on UTC 2019 August 31 and UTC 2020 July 06. We used the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013) , to schedule our transit observations. The 1 m telescopes are equipped with 4096 × 4096 SINISTRO cameras with an image scale of 0. ′′ 389 per pixel, resulting in a 26 ′ × 26 ′ field of view. The images were calibrated by the standard LCOGT BANZAI pipeline  (McCully et al. 2018). Differential photometric data were extracted using AstroImageJ  (Collins et al. 2017). The first observation intentionally saturated TOI-1054 to check for deep NEB events in the fainter Gaia DR3 stars within 2. ′ 5 that are sufficiently bright in the TESS band to cause the TOI-1054.
HARPS radial velocity follow-up We acquired 117 useful high-resolution (R ≈ 115 000) spectra of HD 190622 using the High Accuracy Radial Velocity Planet Searcher (HARPS;  Mayor et al. 2003 ) spectrograph mounted at the ESO-3. 6 m telescope at La Silla Observatory. The observations span more than 3 yr between 18 September 2019 and 24 November 2022 (UT); they were performed as part of the high-precision Doppler follow-up program of TESS small planets carried out by the KESPRINT consortium using the HARPS spectrograph (IDs: 1102. C-0923 and 106. 21TJ. 001; PI: Gandolfi). We set the exposure time to 1200-2100 s depending on the sky conditions and schedule constraints, leading to a median (S/N) of about 120 per pixel at 550 nm. The spectra were reduced and extracted using the dedicated HARPS data reduction software (DRS;  Pepe et al.
Transit photometry and RV joint analysis We performed a global analysis of the observational data using different tools simultaneously in order to check the robustness of the results. We used the PyCHEOPS code  (Maxted et al. 2022) , the TLCM code (Csizmadia 2020), and pyaneti  (Barragán et al. 2019 (Barragán et al. , 2022) )  for a combined solution of the photometry and the a (au) 0. 123 6 ± 0. 002 0 0. 921 ± 0. 015 ρ planet (g cm -3 ) 1. 43 ± 0. 21 g planet (m s -2 ) 7. 9 ± 1. 3 -RV amplitude. The results of the three joint analyses are consistent with each other and we present as baseline those of pyaneti in Table  3. We performed the joint analysis of the TESS and CHEOPS transit photometry, and the HARPS TERRA Doppler measurements using the software suite pyaneti, which combines a Bayesian approach with MCMC sampling in order to determine the planetary system parameters.
The TESS and CHEOPS phase-folded transit light curves are displayed in Fig. 4  along with the best-fitting transit models for the two band passes. The HARPS TERRA RV time series, the phase-folded RV curves of HD 190622b and c, and the best-fitting models are shown in Fig. 5. As a final sanity check, we fitted two independent planet-tostar radius ratios (R p /R ⋆ ) for the TESS and CHEOPS transit light curves, while modeling all the remaining parameters jointly. We find that TESS and CHEOPS data provide consistent values A183, page 6 of 14 of the planetary radius of HD 190622b that are well within the nominal error bars.