dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the opening displacement and the test time, the opening loading rate can be calculated. A second Matlab script was used to determine the current position of the crack tip and the arm displacement profiles during the test by post-processing the images taken with Camera 2. First, the displacement of the longitudinal outer edge of each specimen arm is detected and a polynomial expression is adjusted to each outer arm edge by a curve fitting process, as shown in Fig. 10a. Then, to obtain the crack length required as input data in Methods 1 and 2a, the edge points detected and the fitted curves are translated to the middle of the specimen where the crack is actually located, as shown in Fig. 10b. Finally, the crack tip is detected by the intersection of the two polynomial curves. It is important to mention that the outer edges of the specimen are used instead of the inner ones since an elastic behaviour with no shear effects is assumed and these surfaces are not affected by the waviness or small defects caused during the crack propagation, i.
Mode I fracture toughness: validation The results of the mode I interlaminar fracture toughness of the composite material for the different loading rates tested are shown in Fig. 12. For the adhesively bonded joint, the results of the mode I fracture toughness for the different loading rates tested are shown in Fig. 13. First, these figures are used to compare the results obtained with the quasi-static GDCB (GDCB QS) and the DCB (DCB QS) test methods using the Method 1 data reduction procedure in both cases (Eq. (  6 ) for the GDCB and its equivalent equation without the axial-load effect for the DCB). Analysing the DCB QS results from Fig. 12 , these are slightly higher than those for the DCB QS-CBT. However, this is acceptable since the load-independent method can present some differences with respect to the CBT method because of how the data is collected and the simplifications introduced in the beam theory  [29].
Conclusions The rate-dependencies of the mode-I fracture toughness in a thermoset-matrix carbon-fibre composite and an adhesively bonded joint were assessed. Three different data reduction methods have been developed and compared. The study comparing the three different methods has been performed at different loading rates (from quasi-static to high-rate). In general, the results of the methods presented show a similar overall behaviour, but with clear differences in scattering. The results no rate-dependency on the mode I fracture toughness higher than the uncertainty for the materials and the loading rates considered. A larger specimen sample per configuration could help to study the variability of the results and obtain a more reliable conclusion about the loading rate effect on the mode I fracture toughness.
Acknowledgments This project has received funding from the  Clean Sky 2 Joint Undertaking (JU)  under grant agreement No. 886519. The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme  and the  Clean Sky  2 JU members other than the  Union. The first author acknowledges the grant for doctoral studies  IFUdG2017/43  and the financial support from the  Spanish Ministerio de Ciencia, Innovación y Universidades  through the project  RTI2018-099373-B-100.
Phase stability We described in Sect. 2. 4 the data reduction pipeline, aiming at estimating each baseline's complex coherence to compute the CP, useful to retrieve spatial information of the observed object. In the case of a point source, the objects complex visibility modulus is 1, and its phase is 0, and Eq. (1) becomes: µ nn ′ = A n A n ′ e i∆Φ nn ′ with ∆Φ nn ′ = 2πσδ nn ′ [2π], ( 7 ) where σ is the wavenumber in nm -1 and δ nn ′ is the OPD in nanometers. We focus here on the characterization of δ nn ′. To do so, we acquire data on the SCExAO internal calibration source. We obtain 500 frames of 50 ms exposure each. Using the data reduction pipeline, we extract the phase from the baseline complex coherences. In order to extract the OPD value, we first need to unwrap the phase, fit a linear polynomial, and extract the slope of the latter following: ∂∆Φ nn ′ ∂σ = 2πδ nn ′.
First On-sky demonstration on the Subaru Telescope We present in this section the first on-sky tests performed with FIRST on the Subaru Telescope. Two observation programs were conducted: the first one on a point source target, to test and validate our instrument and data reduction process, the second one on a binary star, to demonstrate FIRST's capability to measure relative positioning of the two binary components. A log of all the data acquired for these studies is presented in Table  3. As desccribed in Sect. 2. 4, the best end product of the FIRST interferometric data reduction are the CP measurements. For an unresolved point source, the CP signal should be zero, for all triangles. However, this is usually not the case in practice, as instrumental biases remain and need to be calibrated.
This is why the observation of calibrator stars before and/or after the target of interest is required. The calibrator data allow one to compute CP measurements that are subtracted to the target of interest CP measurements thus removing the instrument bias and producing a CP signal originating solely from the target of interest. We acquired data on Keho'oea (α Lyrae or Vega, m V = 1. 25, m R = 0. 1), during an Engineering night on the 13th of July 2020 UTC at the Subaru Telescope (Proposal ID: S20A-EN13, PI: Olivier Guyon, Support Astronomer: Sébastien Vievard, Telescope Operator: Andrew Neugarten). The data were acquired between 6:50 a. m. and 7:39 a. m. UTC. The conditions reported by the Canada France Hawaii Telescope Weather Tower 2  showed a seeing measurement of 0. 6 arcsec (measured at 500 nm).
The SCExAO PyWFS pickoff was set to 850 nm Short Pass Filter, meaning that FIRST received light from about 650 nm to about 800 nm (since the PyWFS pickoff is at an angle, the transmitted wavelength cut-off is lower than 850 nm). Because of limited time to perform our tests, we were not able to acquire data on another unresolved target during that night. In order to calibrate our Keho'oea data, we decided to take, during the slewing of the telescope, data on the SCExAO internal calibration source. Although it does not include bias that could be induced by the telescope and AO188, we can at least calibrate the bias originating from SCExAO. 3. 1. 2. Data analysis: from the fringes to the closure phase  100 ms, and the camera EM gain was set to 50. We acquired a total of 50 cubes of 100 images each.
The raw images typically present a curvature caused by astigmatism induced by the dispersing prism  (Huby et al. 2012a ). This curvature is estimated and corrected thanks to our wavelength calibration data (using the AvaLight-CAL-Neon-Mini Spectral Calibration Light Source). The source is injected in one of the FIRST fibers, and the spectral lines are imaged on the detector. Once the curvature is corrected on each frame, we can compute the Power Spectral Density (PSD). Figure  9 -right shows the PSD computed from stacking 30 cubes of the acquired data. From this image, we fit the V-groove step by fitting the peaks for several spectral line. The white dots show the result of the peak fitting. 28 peaks are present, for each spectral channel fitted, since 8 subapertures were actually used during the observations, due to a broken segment on the MEMS.
For example, on graph number 1, both CP values are a few degrees above 0. However, for the graph number 49, the bias is very small. We can qualitatively confirm that the bias amplitude for both targets is the same in a first approximation. We note on the Keho'oea data that for some triangles, there is a small "bump" centered around 765 nm. This is probably due to a decrease of the fringe contrast at this wavelength, induced by the telluric oxygen A band absorption line around 760 to 765 nm. We do not see this bump in the SCExAO calibration source data. Finally, the calibration source data show a disrupted signal on several triangles  (2, 7, 12, 13, 14, 15)  appearing as high frequency (in the wavelength dimension) oscillations. It is a periodic signal, as the function of the wavelength, that we have noticed several times on the SCExAO internal source, but which origin is still uncertain (and under investigations).
Calibration of the field rotation On September 16th 2020, we acquired 19 cubes of 500 frames each on Hokulei. The exposure time was 30 ms and the EM gain was 300. For calibration purposes, we acquired 20 cubes of 3 https://github. com/scexao-org/Binary_analysis 500 frames each on ρ Persei. The exposure time was 40 ms and the EM gain was 300. Following the same procedure explained in Sect. 3. 1 we extracted the 28 baseline complex coherences from the 8 subapertures, and computed the 56 CPs. We show on Fig. 15  the signal of the 56 CPs (in blue on the graphs) extracted from one of the Hokulei data cubes, and calibrated with the ρ Persei calibrator measurements. We can differentiate two categories in these CP plots. (1) The triangles which do not detect the companion. These triangles are formed by baselines whose on-sky projected spatial frequency do not overlap with the spatial frequency of the binary.
The best random error on the separation and PA were respectively 0. 1 mas and 0. 1 •. Systematic errors on the separation, due to an uncertainty on the subaperture projected diameter, were estimated to be about 1 mas. Acquiring more data on well known binary systems will be essential to calibrate systematic errors. With these new capabilities, and the demonstration of the instrument to perform measurements below the diffraction limit of the telescope, we will be able to fully commission FIRST at the Subaru telescope and offer it to the scientific community for open use. The success of demonstrating/commissioning such instrument on SCExAO is an important stepping stone for future interferometric instrumentation on extremely large telescopes. Their large collecting area and small diffraction limit (about 4 mas A84, page 13 of 16 in the optical wavelengths), coupled with spectral dispersion capabilities, would offer unique high contrast and high resolution capabilities with high sensitivity for Earth-like exoplanet research and characterization.
The most common simulators are Neuron's HOC  [30] , NEST  [31]  and Brian simulator  [32]. In  [33] , authors show that using different simulators (Neuron's HOC and NEST) give different results due to a different mathematical representation of the neurons processing. Hence, they propose PyNN, a python package, that gives a common model specification independently from the simulator. In this work, we choose Brian simulator because it gives a variety of neural model representations that we used to model our SNN.
A. Data Retrieval After having a surgery, patients follow a quick training on Smart Angel system features at the hospital before going back home. Patients are invited to answer a questionnaire as well as to take physiological measurements when they are at home. The measurements are planned to be done periodically, at least every seven hours apart, but they can be done more often when it is needed (special cases of additional measurements will be described in Section III-D1). The data retrieved is taken by various sensors such as an oximeter (Heart Rate and Oxygen Saturation) or a blood pressure monitor. Smart Angel's patent  [34]  explain the system for the personalized monitoring of the health status. A clinical study  [35]  has been performed from May 2018 to September 2018 to validate the recovery of the data, in particular the transfer of data across the networks and the validity of the sensors.
B. Data Description The types and norm range values of the data retrieved and used by the system are described in Table  I. Patients are followed up 20 days before their operation to allow medical staff to enter their average constants as well as their personal data. Only physiological and factual data is stored on the Smart Angel health data host to ensure patient anonymization on the server. The personal data of the patients are only accessible by the medical staff in the hospital database. The connection between the two databases to know the identity of the patients is not allowed by Smart Angel system. For this article, we could not use patient data from the clinical study, as this data is internal to the clinical study and is sensitive and un-sharable medical data without patient consent.
Data Set We reanalyzed a published data set consisting of n = 1328 adult mouse motor cortex (MOp) neurons  17  , which had been characterized transcriptomically and electrophysiologically using Patch-seq. We downloaded the read count data from https:// github. com/berenslab/mini-atlas and the electrophysiological traces from https://dandiarchive. org/ dandiset/000008. The authors used Smart-seq2 to obtain single-cell transcriptomes for these neurons. Out of n = 1328 cells, n = 1213 cells passed transcriptomic quality control and were assigned a transcriptomic cell type using the 1000 genes that were most variable across this subset of cells. For electrophysiological characterization, the authors injected negative to positive constant currents for 600 ms time windows starting at -200 pA with steps of 20 pA to positive currents beyond 400 pA or until the cell died.
In order to account for the 25 °C temperature at which Patch-seq experiments were performed, we used a temperature coefficient Q 10 = 2. 3 to scale the kinetics with which gates in ion channels open and close. Parameter r SS , further scales the rates with which sodium and potassium currents with maximal conductances g Na and g Kd reach steady states. In total, 13 parameters in the model can be tuned in order to fit observed electrophysiology of n = 955 MOp cells (Table  1  for details). We implemented the model with the Brian2 toolbox  50  in Python, which can efficiently transpile and simulate models in C++. In order to understand the significance of adding the r SS in reducing model misspefication on a "model implementation" level rather than training the density estimator, we compared to the same HH model but with scaling parameter r SS = 1 (Fig.
First, we tried to include only simulations which position themselves close to experimental observations in summary statistic space (Table  S1 , Best Euclidean). Closeness was measured by calculating the Euclidean distance between simulation and observation after standardizing all summary statistics. Second, we introduced different levels 11/18 of isotropic Gaussian noise to the summaries of those simulations (Table  S1 , Add x amplitude noise to ephys). Third, besides adding noise to summary statistics, we introduced isotropic Gaussian noise to the model parameters with which the close simulations were generated (Table  S1 , Add 0. 05 amplitude noise to ephys and model parameters). Finally, we experimented with a mixture of non-manipulated close simulations and close simulations with noise added to their summary statistics (Table  S1 , Data augmentation).
Acknowledgements We thank  Ziwei Huang  for discussion. We thank the  Deutsche Forschungsgemeinschaft  ( Heisenberg  Professorship  BE 5601/8-1  and  Excellence Cluster 2064 "Machine Learning -New Perspectives for Science ", ref. 390727645 ) and the  Federal Ministry for Education and Research (BMBF ,  FKZ 01IS18039A ). The work was also funded by the  European Union (ERC , " NextMechMod ", ref. 101039115 ). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Additional support comes from the  National Institute of Mental Health and National Institute of Neurological Disorders And Stroke  under Award Number  U19MH114830.
Data and code https://github. com/berenslab/ mini-atlas Read count data https://github. com/berenslab/hh_sbi Electrophysiological data https://dandiarchive. org/dandiset/ 000008/ Electrophysiological features https://github. com/berenslab/ EphysExtraction HH simulator: Brian2 https://brian2. readthedocs. io/en/ stable/ Inference: SBI Toolbox 56 https://www. mackelab. org/sbi/ tSNE: openTSNE https://github. com/pavlin-policar/ openTSNE Parallel processing: Pathos 57 https://mmckerns. github. io/project/ pathos/wiki. html Table 3 Overview of all ressources used 14/18.
In LID literature, most work considers the performance by training system on the train set and evaluating it on the dedicated evaluation set from the same corpus. However, for the low-resourced corpora, the data diversity within the dataset can be limited. As a consequence, even a decent performance on its own evaluation set may question its generalization on evaluations with different data. Hence, we explore cross-corpora evaluations to assess the generalization ability of the LID system. For each corpus, we independently train LID models and evaluate them with the test subsets for all the available corpora. Our preliminary investigation  [17]  with different low-resourced LID corpora revealed that the crosscorpora LID performances are strikingly inferior compared to the same-corpora performances.
Related works on cross-corpora evaluations The majority of the research in cross-corpora evaluation is so far conducted for computer vision applications, including object detection  [39, 15] , facial expression recognition  [104] , deepfake detector  [61] , person re-identification  [94]. In speech processing applications, cross-corpora evaluations are often adopted to assess the robustness, especially for tasks, such as emotion recognition and speaker recognition anti-spoofing, where dataset variability is limited due to the constraints in data collection. In the speech emotion recognition (SER) field, the study reported in  [77]  performed crosscorpora SER with speaker-wise feature normalization using six different corpora. Cross-corpora SER performance is severely degraded except when the test data has background similarities with the train data.
They evaluated the robustness of different statistical and neural network models for cross-corpora LID. However, they did not conduct additional modifications to improve the cross-corpora generalization. Another recent work  [23]  proposed a discriminative hierarchical model for language recognition and used a combination of NIST LRE, RATS, NIST SRE, VoxLingua107, Panarabic, CALLHOME and CALLFRIEND databases for training and a combination of LRE15, LRE17, LASRS, KALAKA and Crowdsource (CROWD) databases for evaluation. The aim of this work was to develop a discriminative probabilistic linear discriminant analysis (PLDA) framework as a better alternative to the commonly used generative modeling. The study in  [45]  incorporated hierarchical phonotactical information using multitask learning in LID.
They evaluated their proposed algorithms on the NIST LRE17 and AP17-OLR databases independently. The LRE17 evaluation utterances are sampled from narrow-band MLS14 and wide-band VAST databases, which were not used in training and development in LRE17. In  [46] , the authors separately used the NIST LRE17 and AP17-OLR corpora and removed the redundancies in the pre-trained multilingual wav2vec2. 0 XLSR model based speech representations by using squeeze-excitation (SE) and linear bottleneck modules. In  [2] , the authors used a custom training set for the unconstrained LID task of the OLR21 challenge. They combined training utterances of OLR21 with the Mozilla Common Voice  [59]  data. The authors then used the pre-trained wav2vec 2. 0 model and fine-tuned it using the custom training set. Most of the above works violated the cross-corpora evaluation standards by mixing data from multiple corpora, and their key objectives were not focused on explicitly reducing the crosscorpora performance mismatch.
Description of corpora We consider three corpora that are most widely used in the Indian LID literature. They are: IIITH-ILSC (IIITH)  [92] , LDC 2017S14 (LDC)  [38] , and IITKGP-MLILSC (KGP)  [51]. For our experiments, we have selected the five languages, Bengali, Hindi, Punjabi, Tamil, and Urdu, which are common to all three corpora. In Table  1 , we have summarized the meta-data of the three corpora. The meta-data comparison in Table  1  lists the differences and similarities among the three corpora. The duration of speech indicates that these are low-resource corpora compared to the LID corpora in major languages. Out of these three, the KGP is the smallest one, with just four The accents and dialects are also varied across the corpora. For example, the Bengali utterances present in the IIITH and KGP corpora contain dialects spoken mostly in India, whereas the LDC corpus contains dialects spoken in different parts of Bangladesh.
This is also very recently adopted for language recognition tasks  [37, 85]. The ECAPA-TDNN model modifies the basic x-vector architecture in different ways. Instead of the TDNN layers, three squeeze-excitation (SE) based residual blocks (SE-Res2) are used. Multi-layer feature aggregation (MFA) is performed with the outputs of these three blocks. The aggregated output is then fed to a channel and context attentive pooling layer (Attn Pooling) followed by a fully connected layer. We use batch normalization (BN) for 1d-convolutional (Conv1D) layers and used a drop-out of 0. 25 for the linear layers. Table  3  presents a detailed description of this architecture. The TDNN architectures are implemented using the PyTorch library  [66]  of Python. We use a batch size of 32, AdamW optimizer  [50] , reduce on plateau based learning rate scheduler with an initial value of 0.
Cross-corpora LID: Domain diversification Low-resourced LID corpora contain a substantially lesser number of utterances. Mostly the data collection source and configurations are very limited. So, the amount of non-lingual diversities in terms of varying backgrounds, recording instruments, room environments, and speaker populations are limited. These create corpora-specific non-lingual biases across the databases leading to inferior cross-corpora performances  [17]. Therefore, this section explores extensive audio augmentation methods to diversify the low-resourced corpora. In the speech processing literature, audio augmentation techniques, such as noise and non-speech addition, volume, pitch, and speed perturbations, are commonly used to artificially increase the amount and variations in the training data  [60, 75, 96].
LID performance of signal-feature level cascaded augmentations We apply cascaded data augmentation by performing feature-level augmentations during the feature extraction steps of the signal-level augmented utterances. We choose all seven signallevel augmentation categories and sample them with γ = 2. For feature-level augmentation, we evaluate SpecAug and mixup separately. The corresponding LID performances (for IIITH as training data) are presented in Table  8. Analyzing Table  8 , we observe that cascading the different signal-level and feature-level augmentation categories does not help in improving the LID generalization. It may be possible that the signal-feature augmented utterances are over-diversified, and the language classification task becomes challenging. As a consequence, in Section 6.
The comparison reveals a relative C avg improvement of 2. 03% and 12. 60%, respectively, for the LDC and KGP test corpora. If we study the LID performances for the different experiments we conduct, we can observe a clear trend that whenever LDC/KGP trained LID models are evaluated on KGP/LDC test utter-ances, the performance remains inferior compared to other cross-corpora train-test pairs. The meta information comparison, presented in Table  1 , reveals a prominently higher corpora mismatch between these two corpora. For example, KGP is the smallest database, and its utterances are collected from broadcast news recordings. Whereas LDC is the largest database containing conversational telephone speech (CTS) data. The LDC utterances have a strong influence on regional dialects, and they are recorded in diverse backgrounds.