dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, achieving crosscorpora generalization between such diverse corpora-pair is challenging. The language-specific cross-corpora study can provide further insights for solving this challenging situation. Still, compared to the baseline, we get the best-case EER improvements of 4. 55% for the LDC-test and 6. 28% for KGP-test data. On the other hand, for both same-corpora and cross-corpora scenarios, IIITH utterances show consistent performance improvements. IIITH contains both BN and CTS data, and it has a moderately large size. We believe the meta characteristics of this corpus have some commonalities with both corpora. So, achieving cross-corpora generalization over the IIITH corpus is relatively easier. Overall, the LID performance improvements we achieve are promising, considering the fact that the corpora we use are low-resourced and developed independently with distinct differences in the meta-data characteristics.
Assessing domain diversification We have proposed the maximally diversity-aware cascaded augmentations for effective diversification of the training data that can improve the cross-corpora generalization. We further analyze here whether the domain diversity is improved due to such augmentation methods. The domain diversity measures adopted in this work are data-driven, where we use embeddings from the LID model trained on augmented data. We now conduct this diversity analysis with a LIDindependent model trained on different audio data. We select a pre-trained model trained for acoustic scene classification tasks. In particular, we choose OpenL3 embedding to explore the background and environmental differences across the three corpora  [33]. Fig. 5  visualizes the non-augmented training set embeddings for the three databases excluding and including IIITH augmented training set.
We observe that three corpora consisting of non-augmented original training data (Fig. 5 (a) ) are somehow separated by the OpenL3 embeddings, and this indicates the presence of strong non-lingual differences. We observe that the OpenL3 embeddings of the IIITH-augmented utterances (Fig. 5 (b )) are well distributed across all three corpora demonstrating effective domain diversification with the proposed method. We have also analyzed the overall LTAS of the three databases along with the augmented IIITH LTAS in Fig. 6. The augmented spectrum shows closer proximity with the KGP-LTAS spectrum, indicating improved cross-corpora performance. We also present a language-specific LTAS comparison using two languages: Bengali and Punjabi. These two plots indicate that the cross-corpora mismatch is non-uniform across the languages.
The results in Table  12  show that applying constraints on the content repetition helps to improve the LID performance for the two test conditions: IIITH and KGP. The meta information says that IIITH and KGP both are mainly broadcast news data. So, the manner of articulation, accent, and dialect remains standard for these databases. Whereas LDC being conversational data, should contain diverse variations in accents and dialects. For such a database in evaluation, the constraint on content repetition during data augmentation does not help. We consider further exploration of content vs. nonlingual diversity as an interesting future work in the context of domain diversification.
Domain-invariant vs. domain-aware approaches The LID performance of the explored domain generalization methods reveals a trend that the adversarial models are suitable for cross-corpora generalization while the multitask learning models help to improve same-corpora LID performance. Therefore, we have two apparently opposite approaches to address the domain information during language training: domain-invariant and domain-aware  [1]. The same-corpora data is more likely to have relatively higher domain similarity with the created pseudo-domains compared to the cross-corpora utterances. So, the additional task of pseudo-domain classification in the multitask learning architecture potentially makes the LID model aware of the possible diversities in the same-corpora test utterances. On the contrary, the domains of the cross-corpora evaluation utterances could be less similar to the pseudo-domains.
Conclusions This study has explored the suitability of domain diversification and domain generalization for improving cross-corpora generalization in low-resourced LID tasks. We have explored existing and new data audio data augmentation methods for domain diversification. For domain generalization, we investigate adversarial learning, multitask learning as well as MMD loss, and its new variants with pseudo-domains. In addition to extensive experiments on three widely used Indian LID corpora, we also perform a blind evaluation on an unseen corpus to further validate the effectiveness of the proposed approaches. We summarize the main findings of this work as follows: • Different evaluation corpora can contain diverse non-lingual characteristics. Hence, along with the conventional audio augmentations, exploring new sources of perturbing the audio data further diversify the training data, which is effective for unseen cross-corpora generalization.
For example, the proposed speech enhancement or codec-based augmentations are most promising for improving robustness against LDC and KGP test data for the IIITH-trained LID models. • The fundamental motivation for applying domain diversification is to reduce the crosscorpora mismatch by simulating the non-lingual characteristics related to the potential unseen domain. Hence, augmenting audio data in a more realistic manner is important. For this reason, we propose the maximally diversity-aware cascaded augmentation with an optimized fold-factor, which is a more effective solution in blind evaluation than the conventional augmentation methods. • We aim to achieve better LID generalization across diverse unseen domains with limited training data. Hence, instead of attaining robustness for a specific target domain using the conventional domain adaptation methods, we opt for domain generalization.
Acknowledgements The first author would also like to thank the  Linguistic Data Consortium (LDC), University of Pennsylvania, USA , for the LDC Data Scholarship. We also thank  Prof. KS Rao  ( IIT Kharagpur ) and  Prof. AK Vuppala  ( IIIT Hyderabad ) for their help with the KGP and IIITH datasets. Finally, the authors would like to thank the associate editor and the reviewers for their careful reading, detailed comments, and constructive suggestions, which substantially enhanced the content of the manuscript.
Theses regressions are coupled with linear interpolation in the Bellman values or the cash generated at the different points of the grid. The method can be used for non linear problems only in low dimension for two reasons. The most obvious one is the computing time that explodes with the dimension and computer clusters are necessary to face to this computing cost even with a number of reservoir limited to 3 or 4. The second reason which is in fact the first limiting one is the need to store in memory the Bellman values needed by the software. This has led the development of algorithms splitting the Bellman values on the memory of the different nodes of the computer cluster (see  [MVW07]  and the StOpt library  [Gev+18]  for a recent implementation). But even with this kind of approach it is impossible to optimize problems with more than 4 or 5 reservoirs.
The data collected by this programme are used to monitor the spatial extension of the infection as well as its progression within these already infected wild populations, by estimating the prevalence level of the infection in badgers in the different clusters. Since the prevalence is simply the proportion of the population that is infected, it is easily understood by non-specialist local communities, which is important to keep stakeholders informed and involved in the programme. Of course, cattle bTB prevalence and incidence are key stone parameters to follow, especially to maintain the national official free status, but because of the multi-host system in place, the sole monitoring of cattle prevalence would not capture the complex epidemiological situation of bTB in an area. Therefore, estimating such a parameter in wildlife populations, easily comparable from one year to another, would also be essential to monitor the epidemiological situation and evaluate the impact of the control measures.
Then, we use this model as a basis to develop simpler indicators of the prevalence that also account for all the aforementioned difficulties. These simpler indicators can be easily understood by all the stakeholders and used to monitor both the mean prevalence level and the mean prevalence trend over a given period. Our work in this paper is summarized on Fig 1. during different years, tested for M. bovis. We first fit a complex Bayesian model to this dataset accounting for many characteristics of the infection (left part). We then focus on highly infected communes and use the average predictive comparison to estimate the mean proportion of the cluster population becoming infected in one year, and we use the model to estimate the mean prevalence in these infected communes during the year in the middle of the period.
Sylvatub programme and database The national surveillance system is described in details in  Réveillaud et al. (2018). Briefly here, in the communes from level 2 and 3 departments (i. e. , communes from infected areas), trapping and culling badgers is implemented as a control measure aiming at reducing badgers abundance. To do so, licensed field stakeholders (hunters, trappers, pest control officers) trap badgers, mostly between March and August. Trapped badgers are culled, and sent to the local veterinary laboratory for necropsy and M. bovis testing in the framework of Sylvatub. Not all dead animals are tested: national prescription is to analyze at most 2 animals in each commune and each year, which we suppose in the following to be a random sample of the sent animals. The result of the test for each analyzed animal is stored in a local database and then compiled in the national Sylvatub database.
For each trapped animal, the following data were stored: date of trapping, name of the commune where the animal has been trapped, results of the test (M. bovis positive, M. bovis negative), type of first-line test carried out (bacterial culture; PCR), date of the analyses, surveillance level of the commune of trapping, and sex and age class (young; adult) of the animals (though this latter information is not systematically reported by the field partners).
To save some memory space, we thinned the chains by selecting one sample every 1000 iterations. We checked the mixing properties of the chains both visually and using the diagnostic of  Gelman and Rubin (1992). We checked the goodness of fit of our model, using the approach recommended by  Gelman and Meng (1996) : each MCMC iteration r generated a sampled value θ r of the vector of parameters of interest θ = (τ, α, β, ρ, u) t. For each simulated value θ r , we simulated a replication of the Sylvatub dataset (i. e. , we simulated a random infection status for each trapped animal of the dataset with the fitted model parameterized by the vector simulated by the r-th MCMC iteration). We then compared summary statistics calculated on the observed Sylvatub dataset with the distribution of the same statistics derived from the simulated datasets.
To summarize the overall effect of the year on the prevalence in a given dataset,  Gelman and Pardoe (2007)  proposed to calculate the mean value ∆ t of the predicted comparisons over the probability distribution of the inputs (years and communes) estimated with the data, and over the posterior distribution of the parameters. This averaging is equivalent to consider all pairs of animals (i, j) in the data, corresponding to pairs of transition of (t i , v i ) to (t j , v j ) in which the commune v i = v j is held constant. Technical details on the calculation of the Average Predictive Comparison (APC) are given in appendix B. When positive, the APC estimated the proportion of the population animals becoming infected in one year in each cluster. When negative, the APC estimated the proportion of the population becoming sane in one year due to the death of infected animals, the birth of uninfected animals, and a decrease of the infection rate, which collectively lead to a decreasing prevalence in the population.
We carried out two sets of simulations to assess the ability of the simpler regression model to estimate the two target quantities. For these two sets of simulations, we simulated different infection situations, using the Dordogne/Charentes cluster as an example. We simulated datasets covering 7 years in this cluster. We generated a number of trapped animals for each year and each commune of the cluster from a binomial negative distribution with mean µ and dispersion parameter θ = 0. 48 (value estimated from our dataset by maximum likelihood). In each set of simulations, we simulated four possible values of µ corresponding to four levels of trapping pressure: µ = 0. 5 animals trapped per commune and per year in average, µ = 1, µ = 3 and µ = 10 (as a point of comparison, remember that in our dataset, µ ≈ 1.
5 in all clusters). For each simulated animal, we simulated a probability of infection with the help of equation (  3 ). Different values of the slope β and intercept α were specified for the different simulated situations (see below). We simulated an iCAR process to generate random commune effects u i using equation (  4 ), setting τ = 0. 73 (corresponding to the mean value estimated by the model with the Sylvatub dataset in the Dordogne/Charentes Cluster, see results). For each animal, we could calculate the probability of infection p it from the vector of (α, β, {u i }) with equation (3). We then simulated a random infection status for each animal using equation (  2 ), fixing the correlation coefficient ρ = 0. 04 (also corresponding to the value estimated in the Dordogne/Charentes cluster using the Sylvatub dataset, see results).
More precisely, we set the intercept α = -1. 38 and the slope β randomly drawn from a uniform distribution bounded between -0. 4 and 0. 4. For each combination of trapping pressure µ and simulated situation (either low but increasing prevalence or high prevalence), we simulated 1000 datasets. For each dataset, we estimated the true proportion ∆ u of animals of the area becoming infected in one year in the highly infected communes (i. e. those with simulated random effect greater than 0) with the APC procedure. We applied the linear regression (5) to the data simulated in these communes. We then compared the estimated slope b with the APC ∆ u of the simulated model, which should in theory be equal if the two models are equivalent. In the second set of simulations, we wanted to assess the ability of our regression model to estimate the mean prevalence level during the middle year.
We simulated data with our Bayesian model, using different values of the intercept α = -4, -3, -2, -1, 0, describing different mean prevalence levels. We then randomly sampled a slope β from a uniform distribution bounded between -0. 4 and 0. 4. We simulated 1000 datasets for each combination of value of intercept α and of trapping pressure µ. For each simulated dataset, we considered only the highly infected communes (i. e. , with u i > 0) and we calculated the true mean prevalence over the area during the middle year of the study period. We then applied the linear regression (5) to the data simulated in these communes. We then compared the estimated slope a with this true mean prevalence, which should be equal if the two models are equivalent.
Computational aspects All our analyses and simulations were carried out with the R software  (R Core Team, 2023). We used the package nimble for model fit  (Valpine et al. , 2017) , coda for the analysis of the fit  (Plummer et al. , 2006) , and tidyverse  (Wickham and Grolemund, 2017)  and ggplot2  (Wickham, 2016)  for data manipulation and graphical display respectively. We have programmed an R package named badgertub, available at https://github. com/ ClementCalenge/badgertub, containing all the code and data used to fit the model. It can be installed in R with the package devtools , using the function devtools::install_github("ClementCalenge/badgertub", ref="main"). This package includes a vignette describing how the user can reproduce easily the model fit and simulations (vignette available with the command vignette("badgertub") once the package has been installed).
Acknowledgements We warmly thank all the stakeholders of Sylvatub, including the  French Reference Laboratory for Tuberculosis (Anses, Maisons-Alfort, France ), local state veterinary services, departmental hunters federations, the local services of the  French Agency for Biodiversity , departmental laboratories for analysis, local animal health defense associations, pest control officers, trappers and their associations. We are also indebted to  Julie Rivière , the national coordinator of Sylvatub from 2011 to 2013, and the members of the National steering commitee and coordination subcommitee.
Data, script, code, and supplementary information availability All the data, script and codes, as well as the supplementary information have been packaged in an R package named badgertub, available on Github, at https://github. com/ClementCalenge/badgertub. We have also stored this package on Zenodo, where it has been given the following Digital Object Identifier: https: //dx. doi. org/10. 5281/zenodo. 8006604. The raw dataset used in this paper has also been stored as a text file on Zenodo, and is available at the following URL: https://dx. doi. org/10. 5281/zenodo. 8010664.
The framework introduces a characteristic saturation scale Q s which describes the color-charge density fluctuations, where Q sA ∝ A 1=3 for the target nucleus. In p ↑ þ A collisions, measuring TSSAs can be used as a probe for the saturation scale in the nucleus  [31]. In addition, TSSAs in the forward region from p ↑ þ A collisions help to disentangle differing mechanisms and clarify the origin of the TSSA. Previous calculations of TSSAs incorporating gluon saturation at small-x suggest that TSSAs in p ↑ þ A may or may not be A-dependent, depending on the mechanisms involved  [31] [32] [33] [34] [35] [36]. On the experimental side, the A N of positively charged hadrons at 0. 1 < x F < 0. 2 in PHENIX  [37]  and π 0 results at 0. 2 < x F < 0. 7 in STAR in p ↑ þ p and p ↑ þ A collisions have been reported recently  [38].
A. Dataset The integrated luminosity of the data in this analysis is 37 pb -1 from p ↑ þ p, 593 nb -1 from p ↑ þ Al, and 112 nb -1 from p ↑ þ Au collisions. The data was recorded using the hadron trigger in combination with the BBC trigger. The hadron trigger selected high-momentum tracks when the last MuID gap for a track pass through is MuID Gap2 or Gap3, with the bending at the middle plane of MuTr being less than three cathode strips  [43].
ACKNOWLEDGMENTS We thank the staff of the  Collider-Accelerator and Physics Departments at Brookhaven National Laboratory  and the staff of the other PHENIX participating institutions for their vital contributions. We also thank  D. Pitonyak  for the theory calculation. We acknowledge support from the  Office of Nuclear Physics in the Office of Science of the Department of Energy , the  National Science Foundation ,  Abilene Christian University Research Council, Research Foundation of SUNY , and Dean of  the College of Arts and Sciences, Vanderbilt University  (U. S. A),  Ministry of Education, Culture, Sports, Science, and Technology  and the  Japan Society for the Promotion of Science (Japan) ,  072016-9   Natural Science Foundation of China (People's Republic of China) ,  Croatian Science Foundation and Ministry of Science and Education (Croatia) ,  Ministry of Education, Youth and Sports (Czech Republic) ,  Centre National de la Recherche Scientifique, Commissariat à l'Énergie Atomique , and  Institut National de Physique Nucléaire et de Physique des Particules (France) , J.
Furthermore, there are emerging parallels between stages for approval for medical AI interventions and the regulatory approval of small molecules or medical devices through clinical trials  [9] [10] [11]. Pioneering research in the medical field and elsewhere  12, 13  has demonstrated that using large and diverse datasets during model training results in more accurate models. Such models are also expected to be more generalizable to other clinical settings. Other studies have shown that models trained with data from limited and specific clinical settings demonstrate bias toward specific patient populations  [14] [15] [16]  , and such data biases can lead to models that appear promising during development but have lower performance in wider deployment  17, 18. A given static model may be susceptible to distribution shifts for the model's input or the model's target, or both  19.
Such an approach should allow collaborative model training and evaluation on large, multi-institutional and representative datasets while complying with privacy and regulatory requirements. However, the degree to which these requirements can be met during collaborative training is still an open research question  43. Here we present MedPerf, an approach focused on broader data access during model evaluation, which we believe will best support model generalization as well as improve clinician and patient confidence. MedPerf was built upon the group's experience leading and disseminating efforts such as (i) the development of standardized benchmarking platforms (e. g. , MLPerf for benchmarking machine learning training  20  and inference  21  across industries in a pre-competitive space -https://mlcommons.
org/#MLPerf); (ii) the implementation of federated learning software frameworks (e. g. , NVIDIA CLARA, Intel OpenFL  22  , and Flower by Adap/University of Cambridge); (iii) the ideation and coordination of federated medical challenges across dozens of clinical sites and research institutes (e. g. , BraTS 23 and FeTS 24 ); as well as (iv) other prominent medical AI and machine learning efforts spanning multiple countries and healthcare specialties (e. g. , oncology  25  and COVID  26  ). MedPerf should also illuminate cases where better models are needed, increase adoption of existing generalizable models, and incentivise further model development, data annotation, curation, and data access, while preserving patient privacy. The development of this approach requires (a) consistent and rigorous methodologies to evaluate performance of AI models for real-world use in a standardized manner, (b) a technical approach that enables measuring model generalizability across institutions, while maintaining data privacy and respecting model intellectual property, and (c) a community of expert groups to employ the evaluation methodology and the technical approach to define and operate mature performance benchmarks.
This step is expensive, highly human-dependent, and error-prone, while carrying additional costs related to annotation correction, versioning, and dataset maintenance  30. 5. Review: Data need to be reviewed for regulatory, legal, and policy compliance, and patients or patient groups need to be consulted for the design and perception of the use case. 6. Licensing: Data must be licensed in a manner that fulfills business and/or scientific interests while complying with existing regulations. 7. Sharing: Data must be physically shared with licensees, through complex legal agreements, which may require secure transmission of large data volumes or the creation of specially designed data enclaves. Navigating these steps can be costly. The technical part of the process is also complex, requiring a mix of medical, artificial intelligence, and software engineering skills.