dataesr/scientific-paragraphs-categorization huggingface dataset:
Usages The subscriber's element resulted in a strong pro-occidental bias in versions 1 and 2, owing to the absence of non-occidental social networks, and a particular effort has been made in version 3 to complement the 11 initial sources with analogous applications from the rest of the world. The criteria, chosen for complementing the sources, involved retaining social networks with more than 100 million subscribers. The data measured is the repartition of subscribers per country; when no source is identified, the data is established from the traffic per country, data obtained from the Similarweb service, and extended to all countries through extrapolation (see 2. 5. 2 Extrapolation). The repartition of subscribers per country, after extrapolation of each element, is weighted in function of the total number of subscribers per application and is finally transformed in percentage per language by weighting with the demo-linguistic matrix.
In version 3, the complementation considerably reduced the bias against non-occidental countries and indirectly against non-European languages. The complete list of social networks processed is reported in Annex 1 of Supplementary material. For the e-Commerce element, as mentioned previously, the source is unique but remarkably trustable. For video streaming, the model utilizes only two sources at this stage: the percentage of Netflix subscribers per country and YouTube penetration per country. Clearly, this sub-indicator should be extended in the next release of the model with alternative streaming applications beyond YouTube and Netflix, with a special effort for non-occidental countries. Nevertheless, the element receives a low weighting. For open contents, this sub-indicator clearly needs to be extended in the next release of the model with more data related to openness, especially in the field of MOOCs.
Some of the languages favored in previous versions by their high presence in Wikipedia remain in high positions in version 3, but not at the first positions; this keeps validate the statement that languages of countries (or regions) with highest performance in Information. Model. Pre-processing The main part of the data reported by Ethnologue is in the form of an Excel matrix of 11 500 lines in the following format: "ISO639, Language name, Country name, number of L1 speakers, number of L2 speakers", along with a large number of related parameters not used for this method, which have been removed. To obtain the format required by the model (a matrix with all the countries considered in columns and all the languages considered in rows), a series of steps was implemented with the support of different programs written in the form of VBA macros.
One of the most complex steps involved merging all the data from the languages belonging to the same macro-language. This process involved 60 macro-languages comprising 434 different languages: for example, the Arabic macro-language contains 29 languages, such as Egyptian, Arabic, or Moroccan Arabic (see details in Annex 6 of Supplementary material). After concluding this step, the next process involved reducing the full list of languages to retain only those languages that are handled by the model (number of L1 speakers higher than one million), carefully summing all the remaining numbers by country in a single line for the rest of languages. It is important to understand that the adoption of Ethnologue data entails the acceptance of its rules of presentation, which are based on purely linguistic considerations: -Grouping of macro-languages.
-List of countries and corresponding English denominations. The list of countries treated by Ethnologue is larger than the list treated by the ITU for the provision of Internet connection rates according to country: the ITU, as a United Nations entity, does not separate, for example, Martinique from France. In this case, the ITU rule prevails, and the requirement involves carefully gathering Ethnologue data for the 29 countries not considered by the ITU (for the complete list, see Annex 7 of Supplementary material) into a single column, "Other countries".
The -character ISO code assigned to each of the languages identified. Virtual Basic Applications, a language used to create executable macros in Excel. A significant example is the case of the Serbo-Croatian macrolanguage whose definition includes, in alphabetical order, Bosnian, Croatian, Montenegrin, and Serbian. This grouping does not at all meet geopolitical criteria and could even be considered controversial from this point of view. Moreover, as some sources clearly separate the languages and the countries concerned, this entails a risk of error in the results, even if the entry of the sources has been transformed to take this situation into account (the risk arises when the figures must not be summed but rather averaged as in Wikipedia's depth indicator).
Source management for micro-indicators The whole process of managing sources for micro-indicators is the most difficult and demanding task of the project, with a high consumption of human resources. For this purpose, several steps are required: (a) For each indicator, check that the sources of previous versions are still available and up-to-date, otherwise search for other comparable sources on the Internet. In Annex 5 of Supplementary material, the complete list of sources is presented, for each indicator. To perform step (4), the data must be transformed into Excel format, with the country and language names matching those in the template and in the same sequential order. In step (3), all sources are collected from a specific URL (see Annex 5 of Supplementary material for the complete list of URLs), and most sources are obtained in HTML format.
Certain sources are in PDF format, and a limited subset (mainly that of the ITU and the World Bank) is in Excel format, which is the formattargeted to transform all sources. The process of converting from PDF to Excel can be relatively simple in most cases, when the tables are well structured. However, in some cases, an incompatibility exists, and certain tricks are needed, such as going through an intermediate. doc format. The process of transforming from HTML to Excel can often be a challenging task requiring a lot of imagination, including in cases, searching for the data inside the HTML source, and thereafter, trying to build a table using Excel's conversion function after cleaning up the HTML code surrounding the data. In an increasing number of cases, the source offers geographical access to the data (clickable maps), that, except when the number of countries or languages is limited and manual copying is not cumbersome, makes automated processing impossible or requires the outsourcing of a manual collection work, which is tedious and requires great concentration and discipline to avoid errors.
Credit is due to the institutions (generally, international organizations or NGOs) that report the data in a computer-readable format (Wikimedia provides, for example, in its English version, HTML tables that can be transformed directly into Excel format without loss of structure). Obtaining a copy of the source in Excel or compatible format (usually a table of country names or languages with associated values or percentages) does not signal the end of the process. With 215 countries and 329 languages to process and, instead of using unambiguous ISO code, the common usage of literal names that can be in different languages and in non-standard spellings, the integration of data into the model cannot be performed by hand. Two programs have been designed for this process, both of which required recursive tuning to accommodate the different spellings.
The program outputs are Excel files that can be used directly to integrate the data into the model. In addition to the appreciable time-saving quality of this computerized method, it guarantees that the obtained data is free of error. It may happen that reliable data is in a format that prohibits automated exploitation. The recursive process recognizes new spellings and ends when the error check no longer identifies unknown spellings. Notably, the management of macro-languages has rendered this process even more complex, because the grouping of languages in the corresponding macro-language must be performed in the source data before processing using the macro. Considering a few examples, the frequent occurrences of Egyptian or Moroccan Arabic in the sources have been cumulated into the Arabic macro-language, whereas those of Serbian, Bosnian, Croatian, and Montenegrin have been merged into Serbo-Croatian (the number of similar cases being quite high).
The results of the model can be consulted in CC-BY-SA-4. 0 in https://obdilci. org/lc2022 and can be read in  Pimienta (2022). Further releases of the model are accessible in https://obdilci. org/ Results. To cross-check the results, the model has been run separately with L1 data only and with L2 data only (see Annex 9 https://www. ethnologue. com/language/srp The language families include, for each region, the languages which are native of that region. English, French, and Spanish are European languages and, following the Ethnologue classification that we use, Russian is classified as European language while Turkish and Hebrew as Asian languages. Less than % of African language's speakers connected to the Internet and very low virtual presence and content productivity are obtained. of Supplementary material for the corresponding results, which represent a quite positive indirect control of the method).
InternetWorldStats biases The figures of IWS differ slightly from those of the Observatory, primarily because the sources of demo-linguistic data are not identical, and that, especially for L2 figures, the differences between sources could be considerable (see 2. 4. 2). However, another difference exists in terms of the management of L2 figures. The Observatory computes the world language percentages for L1+L2 over the number of L1+L2 speakers, a figure 43% greater than the world population, following the Ethnologue source, whereas IWS computes L1+L2 figures over the world population (named as the zero-sum approach). Unless there is a trick hidden somewhere in the computations, the zero-sum approach seems to provoke an error by overrating the 10 languages mentioned, error hidden in the remaining languages figure, which will eventually become negative if the number of languages is extended to the point where the sum of L1+L2 speakers crosses the L1 value.
W Techs biases The method used by W3Techs involves applying a languagerecognition algorithm to the home page of 10 million websites that are selected as the most visited sites by certain Web traffic-analysis services (Alexa. com or tranco-list. eu, until the end of 2022). The differences between W3Techs' and Observatory's figures are immense and generally in a ratio of 1 to 3; sometimes, such as for Chinese and Hindi, this ratio is higher than 1 to 10. Table  4  presents these differences using W3Techs data for 24/8/22 and Observatory data of V3. 1 for 8/2022. In the figures, those we are using, Ethnologue counts the world population (total number of L speakers) at and the total number of L +L speakers at. Cited from the IWS website: "Indeed, many people are bilingual or multilingual, but here we assign only one language per person in order to have all the language totals add up to the total world population (zero-sum approach)".
From the data computed, the range of English contents would slide from the 50%-60% window displayed by W3Techs into the 20-30% windows displayed by the Observatory or the Greek universities study limited to EU ccTLD. The Observatory have been encouraging the colleagues from Greece to apply their algorithm to the Tranco list of websites, with some promising answer. This would contribute in a definitive manner to that debate since their method does give due credit to the multilingualism of the web. This is an optimistic prospect for the coming months for whoever is interested in that subject.
Acknowledgments The work conducing to version 1 and 3 of the described model has been realized thanks to the funding of  Organisation de la Francophonie. The Brazilian government and the  International Portuguese Language Institute contribution  allowed for version 2 of the model, an indispensable step toward version 3. Daniel Prado  is to be credited for many founding ideas of the method, since 2012, particularly the idea, of collecting many indirect sources for indirect approximation and for using country data crossed with demolinguistic figures to compensate the scarcity of language figures. He is also to be credited for the first historical version of the Excel model.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The model presented stands in 3 type of data: (1) Demo-linguistic data have been bought to Ethnologue and are proprietary with non-disclosure agreement (2) Countries connectivity data are updated yearly by ITU and publicly accessible (3) A large set of data accessible freely on the Internet have been searched and used. Except Ethnologue's all URLs to access input data are provided in the Supplementary material as well as the data output of the model.
Article https://doi. org/10. 1038/s41559-023-02089-7 Data Fig. 1b, c ). We assessed if these chromosomes carried translocations, internal deletions or terminal deficiencies. Using a Y-linked fluorescent transgene, we counted the number of red fluorescent protein (RFP)-positive F 1 females, corresponding to potential translocations of the RFP marker from the Y chromosome to another chromosome. Depending on the sgRNA, translocation frequency was between zero and 0. 5% (Extended Data Fig. 1d ), indicating that the interchromosomal translocation frequency is at least 80 times lower than the incidence of modified Y chromosomes. Using several X-linked GFP transgenes, we established that the modified Y chromosomes did not carry additional genetic material derived from the X chromosome, corresponding to X-to-Y translocations (Extended Data Fig.
1e ). Next, to determine the frequency of internal versus terminal deletions, we selected 90 modified Y chromosomes and carried out a PCR survey of all the Y chromosome coding genes. We identified 46 terminal deletions, 44 complete Y deletions (X0 males) and no internal deletions (Extended Data Fig. 1f  and Extended Data Table  1 ). Altogether, these results indicate that our approach allows the generation of terminal Y chromosome deletions, at high frequency. We then probed if the sgRNAs produced fragments of the anticipated size on the basis of the location of their predicted cleavage sites along the chromosome. We used a Y chromosome with two genetic markers: one on the tip of the short arm and the other on the long arm. With the sgRNA targeting the Pp1-Y2 locus on the short arm, we obtained truncations of the targeted arm but also at lower frequency long arm deletions (or even double short arm and long arm truncations; Extended Data Fig.
2a-d ). Although the effects of the Y chromosome on the epigenome were observed in D. melanogaster almost 100 years ago  53  and have been studied extensively, the physiological relevance of this mechanism to sex-specific differences has remained unclear  16  , until the work of ref. 12. In this recent work, flies with different sex chromosome karyotypes (XXY females; X0 and XYY males) were generated and it was observed that the number of Y chromosomes correlated with the average lifespan. However, the experimental design could not completely discriminate between genetic background or Y chromosome effects. Our results using isogenic lines demonstrate that the Asterisks highlighting comparisons across sexes are displayed in grey boxes; those highlighting comparisons within male datasets are displayed in blue boxes.
P values from log-rank tests for lifespan experiments are (NS) P = 0. 43, ***P < 0. 0001. In all panels, n = number of flies, except for the linear regressions in b and c where n indicates the number of repeats (each repeat is composed of 150 flies). Asterisks highlighting comparisons across sexes are displayed in grey boxes; those highlighting comparisons within female and male datasets are displayed in red and blue boxes, respectively. See also Extended Data Fig. 3.
Reverse transcription and quantitative PCR RNA was extracted from five whole flies using TRIzol (Invitrogen). Complementary DNA was synthesized using the iScript cDNA synthesis kit (Bio-Rad) from 500 ng of total RNA. Quantitative PCR was performed by mixing cDNA samples (5 ng) with iTaq Universal SYBR Green Supermix (Bio-Rad, 172-5124) and the relevant primers in 384-well plates. Expression abundance was calculated using a standard curve for each gene and normalized to the expression of the rp49 control gene. For data display purposes, the median of the expression abundance was arbitrarily set at 100% for young XY males and percentage of that expression is displayed for all sexes and genotypes. The following quantitative PCR primer pairs were used: copia (5′-GCATGAGAGGTTTGGCCATATA AGC-3′, 5′-GGCCCACAGACATCTGAGTGTACTACA-3′), rp49 (5′-CTT CATCCGCCACCAGTC-3′, 5′-CGACGCACTCTGTTGTCG-3′).
Statistics and data presentation All statistical analyses were carried out in GraphPad Prism v. 7. 04. Comparisons between two genotypes and/or conditions were analysed with the Mann-Whitney-Wilcoxon rank sum test. Multiple comparisons between a single control condition and different genotypes were analysed using one-way non-parametric analysis of variance (ANOVA). These two non-parametric tests do not require the assumption of normal distributions, so no methods were used to determine whether the data met such assumptions. For lifespan experiments, log-rank tests were used. All graphs were generated using GraphPad Prism v. 7. 04. All confocal and bright field images belonging to the same experiment and displayed together in our figures were acquired using the exact same settings. For visualization purposes, level and channel adjustments were applied using ImageJ to the confocal images shown in the figure panels (the same correction was applied to all images belonging to the same experiment) but all quantitative analyses were carried out on unadjusted raw images or maximum projections.
P-values from one-sided t tests are (ns) p > 0. 07, ***p < 0. 0001. (f) Impact of transformer F (tra F ) on lifespan. The lifespan of females mutated for tra F (tra F KO ) and constitutively feminized males carrying a tra F knock-in allele (tra F KI ). n = number of repeats (each repeat is composed of 150 flies). P-values from one-sided t tests are ***p < 0. 0001. In all panels, boxplots display the minimum, the maximum, the sample median, and the first and third quartiles. Asterisks highlighting comparisons across sexes are displayed in grey boxes; those highlighting comparisons within female and male datasets are displayed in red and blue boxes respectively.
In all panels, n = number of flies, except in panel (g) where n indicates the number of repeats (each repeats composed of 50 fly heads). Asterisks highlighting significant comparisons within male datasets are displayed in blue boxes. Extended Data Fig. 3 | The Y chromosome heterochromatin is not toxic and does not contribute to the sex gap in longevity. (a) Impact of the Y chromosome on the age-related loss and dispersion of the heterochromatin marker H3K9me2 in adult intestinal enterocytes was quantified by measuring the average intensity of the H3K9me2 signal by nucleus, as well as the area of the signal compared to the area of the nucleus. Representative pictures from young (2 days) and old (50 days) individuals showing H3K9me2 signal in green and DAPI in blue. Scale bars, 10 μm. n = number of cells.
Acknowledgements We thank  W. Theurkauf  for the anti-Stellate antibody;  G. Reuter ,  T. Jenuwein ,  P. Meier  and  M. Amoyel  for fly lines; the  Bloomington Drosophila Stock Center  for providing Drosophila fly lines; the iBV platforms-A. Loubat from the flow cytometry platform,  B. Monterroso  and  S. B. Aicha  from the imaging facility and  A. Mencser  for fly food;  F. Besse  and  S. Merabet  for comments; and all the members of the B. H. laboratory for discussions and comments. This work was supported by the  Université Côte d'Azur ,  CNRS  ( ATIP-Avenir programme ), INSERM,  European Research Council  ( ERC starting grant CellSex , grant no. ERC-2019-STG 850934 ) and the  French Government (National Research Agency, ANR)  through the  'Investments for the Future' programmes LABEX SIGNALIFE   ANR-11-LABX-0028-01  and  IDEX  UCAJedi  ANR-15-IDEX-01.
P values from one-sided ANOVA are ***P = 0. 0005. h, Viability of XXY females carrying X chromosomes with deleted rRNA locus. Such females must carry a Y chromosome with an intact rRNA locus to survive. P values from one-sided ANOVA are (NS) P > 0. 34, *P = 0. 0143. i, Testes from males carrying different Y chromosomes stained with an anti-Stellate antibody (red, Stellate aggregates (asterisks)) and DAPI (blue, DNA). Scale bars, 25 μm. Immunohistochemical analyses were repeated three independent times. j, Diagram of the Y chromosome truncations. n = number of flies. Asterisks highlighting comparisons within female and male datasets are displayed in red and blue boxes.
Restricted Boltzmann Machines are simple and powerful generative models that can encode any complex dataset. Despite all their advantages, in practice the trainings are often unstable and it is difficult to assess their quality because the dynamics are affected by extremely slow time dependencies. This situation becomes critical when dealing with low-dimensional clustered datasets, where the time required to sample ergodically the trained models becomes computationally prohibitive. In this work, we show that this divergence of Monte Carlo mixing times is related to a phenomenon of phase coexistence, similar to that which occurs in physics near a first-order phase transition. We show that sampling the equilibrium distribution using the Markov chain Monte Carlo method can be dramatically accelerated when using biased sampling techniques, in particular the Tethered Monte Carlo (TMC) method.
This sampling technique efficiently solves the problem of evaluating the quality of a given trained model and generating new samples in a reasonable amount of time. Moreover, we show that this sampling technique can also be used to improve the computation of the log-likelihood gradient during training, leading to dramatic improvements in training RBMs with artificial clustered datasets. On real lowdimensional datasets, this new training method fits RBM models with significantly faster relaxation dynamics than those obtained with standard PCD recipes. We also show that TMC sampling can be used to recover the free-energy profile of the RBM. This proves to be extremely useful to compute the probability distribution of a given model and to improve the generation of new decorrelated samples in slow PCD-trained models.
Introduction In Physics, Restricted Boltzmann Machines (RBMs) are only a spin system or an Ising model in which the coupling matrix between spins has a bipartite topology. In Machine Learning, it is a famous generative model introduced many decades ago by Smolensky  [1]  as one of the first generative models capable of successfully learning complex/real datasets. It was later popularized by Hinton  [2, 3] , who (at the time) proposed a viable algorithm for training these machines. After a brief period of notoriety in which RBMs were used as a pretraining method for larger neural networks  [4, 5] , they fell into disuse after 2010 with the introduction of new generative models such as Generative Adversarial Network (GAN)  [6]  and Variational Autoencoder (VAE)  [7]. Indeed, these models have a clear advantage: despite eventual instabilities of the training process, they do not rely on complex and costly Monte Carlo simulations.