dataesr/scientific-paragraphs-categorization huggingface dataset:
Different operators (e. g. "pooling") are generally added to reduce the number of parameters and summarize the information all along the network. The convolutive part of the model, as explained here, is then followed by a classification part which can predict a value (when predicting a scalar) or score (when predicting a label). So, in summary, a CNN takes as input an image, process it with a series of convolutive operations, and finally returns a number which, in the present case, allows to predict a label (e. g. a species name). A CNN is therefore a model dedicated to image analysis, with a huge quantity of parameters that must be estimated using a large amount of data (here, images) available in a "training" dataset with labeled images. Since the parameters estimation procedure is iterative, we used as a starting point a pre-trained model (see details after), which already contained generic features that can be relevant to deal with our teeth images.
For a subset of images, we manually cropped bounding boxes around the first molars with the VGG Image Annotator (VIA) (http://www. robots. ox. ac. uk/~vgg/software/via/), obtaining 450 bounding boxes of modern UM1 and 88 boxes around fossil UM1 that were used as training data to enhance automatic molar cropping. Note that a large amount of manual bounding box delineation of images of fossil/archaeological rodent teeth was not required to train the model dedicated to fossil images, since we used the model previously trained to crop modern molars as a starting point, again using a transfer learning approach. Finally, the trained models were used to crop all the studied images, retaining the box with highest confidence score. For this step, we use the Keras implementation of RetinaNet available at https://github.
Finally, we estimated a fifth "reference" 2-species model using all the available data in the training set. Therefore, an evaluation of its accuracy was not possible, but presumably, its performance was improved by increasing the number of pictures used to feed the model. This model was used to classify molars from an archaeological deposit that was not included in the training model. This procedure was used as a test of "real case" application, when molars from a new deposit would have to be classified based on a preexisting reference dataset. We trained each model with 10 epochs with batches of size 16. This pipeline was implemented with Keras 2. 3. 0.
The fossil specimens, projected on this space as supplementary specimens, felt within or close to the range of the corresponding species in the modern referential (Figure  3B ). The LDA on the aligned coordinates of the referential dataset provided 100% correct reclassification, even with the leave-one-out procedure (Table  1 ). Almost all fossils were also correctly reclassified, to the exception of one house mouse from Tuda. Note that the three species differ notably in tooth size, Mus musculus displaying the smallest and Acomys cahirinus the largest molars (Fig. 4 ). Insular populations tended to display larger molars than their continental relatives. This insular variation increased the overlap between the different species. In some cases, the fossil teeth tended to be larger than their modern counterparts, for instance for Deep learning for species identification of modern and fossil rodent molars -13 the wood mouse and house mouse from Tuda, and the house mouse from Mesnil Aubry.
2013; Ledevin et al. 2010; Renaud et al. 1996). The data acquisition for the ~1500 mice presented here therefore required, in total, several weeks of full-time fastidious work of an experienced operator. The integration of datasets collected by different operators require a procedure of cross-measurement to check for inter-operability. The deep-learning procedure requires the acquisition of a collection of well-identified pictures, like for the GMM analyses. However, once the pipeline is elaborated, our dataset only required few minutes to enter the set of thousand pictures and train the model, and only a few seconds to obtain classification results on few hundreds of pictures, using a graphics processing unit (GPU). Furthermore, running an existing pipeline of deep learning demands no in-depth knowledge of the biological group under study.
In both case, the validity of the results is however fully dependent of the initial identification of the specimens composing the dataset used in the training step. The interest of both methods is that this reference dataset can be elaborated from geneticallyidentified specimens, before a transfer to modern specimens without access to genetic analyses, or to fossil specimens. Importantly, both methods worked here without taking size into account. In the case of important size variations, for instance on islands  (Millien 2006)  or between fossil and modern representatives of the same species  (Cassaing et al. 2011; Thomas Cucchi et al. 2014) , this may be a crucial aspect for reliable classification results. Both methods will however face the limitation of transfer functions from the recent to the past.
Requirements for an efficient training dataset The deep learning procedure learns relevant informations from image patterns. It is therefore important that the pictures of the reference dataset are not too different in their setting from the ones to be classified  (Beery et al. 2018). A way to mitigate this issue and make the reference dataset transferable to a wide range of data is to make it as diverse as possible. This was achieved in our final "reference" CNN model by including an extensive sampling of modern and fossil molars. As a result, the model was able to almost completely classify pictures from Mesnil Aubry, which were taken by a different operator and with different settings than the pictures included in the reference dataset. Achieving this diversity in the reference dataset should be done first, by including as much intraspecific variation as possible.
For instance, in our case study, all the three species display important geographic variation, especially on islands  (Ledevin et al. 2016; Renaud et al. 2020; Renaud and Michaux 2007). If the specimens to be reclassified come from insular environment, such as those from the Tuda sequence, it is advisable that the range of morphological variation encountered on different islands is included in the reference dataset. A too stringent cleaning of the reference dataset from damaged or aged specimens, with used teeth, may be in that respect counterproductive. Second, variation in the pictures themselves (lighting, color range, focus…) should ideally be included as well, to make the algorithm more resilient when facing a diversity of picture settings, and hence, more easily transferable to another set of pictures, possibly taken by other operators with other devices and conditions.
Additionally, the enamel of fossil teeth often takes a different coloration from the original white tint. A mere transfer of the modern reference dataset to the classification of fossil teeth could thus appear problematic. A first way to deal with these issues was to develop an automated protocol to crop the UM1 from its background. A further way to mitigate this issue was to include some well-identified fossil specimens to the reference dataset, to make it more resilient to the background of the tooth.
For such purposes, the reference dataset could rely on pictures of the whole skull, which probably contain more phylogenetic information that the sole first upper molar  (Rychlik et al. 2006). Another important potential range of application for such algorithms regards the identification of fossil specimens, based on well-identified modern specimens. Despite limits due to inherent differences in pictures of modern and fossil specimens, our pilot study is highly promising in that respect. In the archaeological record, disentangling wild from domestic forms emerged as crucial to understand the process of domestication. GMM analyses allowed impressive progress in that respect  (Balasse et al. 2016; Thomas Cucchi et al. 2016; Thomas Cucchi et al. 2009; Evin et al. 2013; Owen et al. 2014). Having recognized the importance of disentangling these close taxa in archaeological contexts, the application of fast deep learning strategies may widen the scope of such discriminations, reserving the use of sophisticated GMM analyses to an in-depth understanding of the signature of domestication on the different species and bones  (Harbers et al.
2020; Renaud et al. 2011). The performance of deep learning systems in the field of zoology and archaeology will depend on the elaboration of extensive datasets of well-identified specimens, in order to train the models with as many pictures as possible. Using deep learning algorithms may democratize automated identification tasks, since writing only a few dozens of code lines can be sufficient to build a complete pipeline. However, despite being promising, these methods need to be rigorously evaluated to understand their potential limits and biases before extensive applications  (Wearn et al. 2019). With adequate sampling, these methods could even deliver relevant results in the cases of sibling species, that remain sometimes difficult to disentangle using GMM methods  (Dobigny et al. 2002).
Acknowledments We are grateful to all people who provided access to the biological material included in this study, especially  Jean-Christophe Auffray ,  Jean-Pierre Quéré ,  Johan R. Michaux ,  Guila Ganem ,  Maria da Luz Mathias ,  Jean-Louis Chapuis ,  Benoit Pisanu ,  Frank Chan ,  George Mitsainas ,  Eleftherios Hadjisterkotis ,  Petros Lymberakis , and  Ferhat Matur. We are further indebted to  Pierre Mein  and  Jean-Denis Vigne  for access to their collection of fossil material. We acknowledge  Laure Deschamps  for pictures of fossil material taken during a research internship. This manuscript is also a tribute to the late  Anne Tresset , who provided the pictures of the material from  Mesnil Aubry. This work was performed using the computing facilities of the  CC LBBE/PRABI. It was supported by funding from the  French National Center for Scientific Research (CNRS)  and the  Statistical Ecology Research Group (EcoStat)  of the CNRS.
, 2001). The mean grain size was calculated using  Folk and Ward (1957). The chronological data are based on the correlation of core facies with archaeological excavation data from Siraf. Juvenile marine shells contained in the anthropogenic facies were used for radiocarbon dating. The 14 C data were calibrated using Calib version 7. 1. 0 online version (http://calib. org) and a standard Persian Gulf marine reservoir age  (Southon et al. , 2002)  (Table  2 ). Historic wind directions and durations were obtained from the database of the Iranian Meteorological Office (http://irimo. ir). Bathymetric maps and 3D models of the seafloor were created based on hydrological maps of the Iranian National Cartographic Center and international chart series published by the United Kingdom hydrographic office in scales 1:100,000 for the Persian Gulf and 1:20,000 for the Shatt-al-Arab pro-delta area.
The RSL curve was obtained by a Glacial Isostatic Adjustment (GIA) simulation in which we adopted model ICE-6G (VM5a) of  Peltier et al. (2012)  and  Peltier et al. (2015). The simulation used the ICE-6G (VM5a) model through the program SELEN4  (Spada and Melini, 2019). SELEN4 solves the Sea-Level Equation by taking into account the horizontal migration of shorelines, for the transition from grounded to floating ice and for the effects of rotational feedbacks on sea level. The Sea-Level Equation was solved using a pseudo-spectral approach with a spatial resolution corresponding to the maximum harmonic degree lmax = 256, on a grid with a spacing of ~40 km.
Unit B (anthropogenic facies) 7. 3. 2. 1. Anthropogenic facies. Two important anthropogenic facies (A1 and A2) are recorded at 203-240 and 149-178 cm. According to radiocarbon analysis, A1 is dated to 44 cal. BC -425 cal. AD; it comprises a very compacted facies, with iron and ceramic fragments. A1 is located above US1 and the facies comprises 30-89% gravel and the mean grain size is between 1 to -1. 1 phi. The MS data manifests three peaks in this unit, with a maximum of 1. 03 × 10 -3 (SI) and an average of 7 × 10 -4 (SI). Meanwhile, we did not observe upper shore facies above A1 and beneath A2, suggesting the resumption of anthropogenic activities shortly after the RSL fall (Fig. 8 ). A2 contained coarser sediments (-0. 6 to -1. 11 phi) with 65-90% of the sediments comprising gravels. A sample from this second anthropogenic facies yielded a calibrated radiocarbon age of 113-477 cal.
Impact of RSL and Shamal winds on the geography of important harbour locations RSL in the central basin is controlled by global sea-level rise and salt tectonics  (Pourkerman et al. , 2020). The modeled relative sea level (RSL) curve for Siraf suggests that RSL was higher than present during the late Parthian and early Sassanid (Fig. 8 ) periods. Meanwhile, a rapid RSL fall started at ~50 cal. BC and with a lowstand between 50 and 150 CE. The chronological data suggest that the first anthropogenic activity in the coastal area of Siraf is recorded during the late Parthian to the first Sassanid empires (44 BC-452 CE). After a short time, marine facies overly A1 following a RSL rise. Seafaring in the Persian Gulf was vital to Persia's commercial and maritime activities during the Sassanian period.
Acknowledgements This work was supported by the  Center of International Scientific Studies and Collaborations (CISSC) ,  Iran National Science Foundation  (INSF grant number  94-44915 ), the  French Embassy in Iran, Campus France  ( PHC GUNDISHAPUR 2016-2017 , project number  35630QH ) and  Labex OT-Med  ( ANR-11-LABX-0061 ). Radiocarbon datings were funded through the project " LIA HAOMA " supported by the  Centre National de Recherche Scientifique (CNRS, France). The study was also supported by  Iran's National Elite Foundation.
Dijon for their feedback. We also thank MTurk Data for assisting with data collection. This research has been funded by the Laboratory of Excellence CORTEX (construction, cognitive function, rehabilitation and repair of the cortex) (ANR-11-LABX-0042) of Universit é de Lyon within the program "Investissements d'Avenir" (ANR-11-IDEX-007) operated by the French National Research Agency and by the DECISION project (A study on Dishonesty: from Emotions and Cognition to Institutions and Organizations) of the French National Research Agency (ANR-19-CE26-0019). The research has benefited from the support of Initiative of Excellence Lyon (IDEXLYON) from Universit é de Lyon (project Institutional Design and Economic Preferences: Theory and Experiments -INDEPTH) within the Program Investissements d'Avenir (ANR-16-IDEX-0005).
in Dijon for their feedback. We also thank MTurk Data for assisting with data collection. This research has been funded by the Laboratory of Excellence CORTEX (construction, cognitive function, rehabilitation and repair of the cortex) (ANR-11-LABX-0042) of Universit é de Lyon within the program "Investissements d'Avenir" (ANR-11-IDEX-007) operated by the French National Research Agency and by the DECISION project (A study on Dishonesty: from Emotions and Cognition to Institutions and Organizations) of the French National Research Agency (ANR-19-CE26-0019). The research has benefited from the support of Initiative of Excellence Lyon (IDEXLYON) from Universit é de Lyon (project Institutional Design and Economic Preferences: Theory and Experiments -INDEPTH) within the Program Investissements d'Avenir (ANR-16-IDEX-0005).
Procedure Participants first performed the IAT  (Greenwald et al. , 1998). The task was programmed and presented by using E-Prime 2 Professional (Psychology Software Tools, Pittsburgh, PA, USA). Participants then filled in the self-reported measures in paper form, including demographic information, explicit self-esteem (the Rosenberg Self-Esteem Scale [RSE];  Rosenberg, 1965) , and mobile phone use (Problematic Mobile Phone Use Questionnaire [PMPUQ];  Billieux et al. , 2008). We used the validated French version of these scales  (RSE: Valli� ere & Vallerand, 1990; PMPUQ: Billieux et al. , 2008). To avoid a carryover effect (i. e. , the possible effect of one measure on the following ones), we counterbalanced the order of completion of the self-reported measurement tools across participants.
notes on contributors xxiii Judit Törzsök completed an MA in Indic Studies at ELTE University, Budapest, and then continued her studies at the University of Oxford in 1993, where she was funded by the George Soros Foundation to do research on Abhinavagupta under Professor Sanderson's supervision. In 1994, having received the Domus Senior Scholarship at Merton College, she started working on the Siddhayogeśvarīmata for her DPhil, supervised by Sanderson. The years spent in Oxford under his guidance determined the course of her research, which has focused on the early history of yoginī cults ever since. After postdoctoral research fellowships at Emmanuel College, Cambridge, and at the University of Groningen (supervised by Professor Hans Bakker), she was elected Associate Professor (maître de conférences) in 2001 at the University Charles-de-Gaulle Lille III in France, and professor (directeur d'études) at the École pratique des hautes études (EPHE) in 2018.
Diwakar Acharya This essay concerns a pūjāstuti1 that guides its reciter through the mental or actual worship of the goddess Nityā. The text is composed in the first person but the author does not name himself in the text. The text is named Vāmakeśvarīstuti and attributed to Mahārājādhirāja Vidyādharacakravartin Vatsarāja in the colophon of the sole palm-leaf manuscript of the text available to me. However, the last verse of the text calls it Kāmeśvarīstuti and describes it using two adjectives, kāmasiddhi and atimaṅgalakāmadhenu. It is not unnatural, I think, to name this stuti using its first adjective. 2 The manuscript containing this stuti text is preserved in the National Archives, Kathmandu. It bears accession number 1-1077 and can be found microfilmed under NGMPP reel number A 39/15.
Acknowledgements I would like to thank all the participants of the  Symposium Śaivism  and the Tantric Traditions held in honour of  Professor A. G. J. S. Sanderson  for their questions, comments and criticism concerning the first version of this paper, in particular  Professor Sanderson  and  Professor Bakker. I am also indebted to  Csaba Kiss  and  Shaman Hatley  for comments on and corrections of the final draft. The written version of the paper reproduces some aspects of the oral presentation stylistically.
Acknowledgements Although my doctoral research was undertaken and completed at the  École Pratique des Hautes Études (Paris)  under  Lyne Bansat-Boudon 's supervision, I studied in Oxford under Alexis Sanderson's guidance in 2005 and 2006 while working on my PhD dissertation, and I am immensely indebted to him. He was kind enough not only to let me attend his classes there but also to organize many extra reading sessions so we could cover more of Abhinavagupta's Īśvarapratyabhijñāvimarśinī, and we ended up reading about two thirds of this lengthy and difficult text in less than two years. It was an extraordinary experience for me, not only owing to Alexis's remarkable erudition and exceptional philological acumen, but also because his generosity, humour and curiosity have always created among his students a unique atmosphere of intellectual freedom and enthusiastic research.
Kapstein ,  Matthew T. 2001. Reason 's traces. Identity and  Interpretation in Indian and Tibetan Buddhist Thought. Boston: Wisdom Publications. Acknowledgements My thanks go to  Elisa Freschi  and  Marcus Schmücker  for their careful reading of the first draft and  Harunaga Isaacson  for his generous correction of errors and felicitous reformulations of some textual passages. Acknowledgements This research has been made possible by financial support from the  European Research Council  synergy project " Beyond Boundaries: Religion, Region, Language and the State ," grant agreement no. 609823. Acknowledgements This research was funded by  ERC Synergy  Project No. 609823. I am grateful to  Dr. Michael Willis  and  Dr. Sam van Schaik  for their support and  Professor Harunaga Isaacson  for useful suggestions.
Acknowledgements I am very grateful to  Shaman Hatley  for his careful reading and extremely helpful comments, suggestions and edits of the text. Acknowledgements I would like to thank  Srilata Raman  and  Shaman Hatley  for inviting me to the conference in honor of  Alexis Sanderson , titled "Śaivism and the Tantric Traditions," at which I presented a shortened rendering of this essay. I also thank  Eric Steinschneider  for all he did to facilitate the running of this well-organized event. Prior to this, I delivered an earlier version of this essay in the  Hindu Studies Colloquium at Harvard Divinity School on February 20 ,  2014. I thank Anne Monius for the invitation to speak there and for her thoughtful and instructive comments on my lecture. I also thank  Ben Williams  and  James Reich  for their comments on my lecture and for their hospitality during my time in Cambridge, MA.
Acknowledgements I thank  Dominik Wujastyk  for his comments on a draft of this article, the research for which was carried out as part of the  Hatha Yoga  Project (hyp. soas. ac. uk). This project has received funding from the  European Research Coun-Acknowledgements  I would like to thank  Judit Törzsök ,  Dominic Goodall ,  Harunaga Isaacson  and  Gergely Hidas  for their constant help and support. I am especially grateful to  Shaman Hatley  and  Gergely Hidas  for reading and commenting on a draft of this article. Acknowledgements I would like to thank  Jacqueline Hargreaves  for her comments on numerous drafts, and also  Gudrun Bühnemann ,  Elizabeth de Michelis ,  Dominic Goodall ,  Shaman Hatley  and  Mark Singleton , who commented on earlier drafts. I'm particularly grateful to  James Mallinson  for his detailed comments and for sharing unpublished research.
Acknowledgements As my doctoral supervisor,  Professor Sanderson  gave of his time, enthusiasm, deep knowledge and friendship with extraordinary generosity. I owe him a very large debt of gratitude. For help on the present article thanks are due to  Verena Widorn , director of the  Western Himalaya Archive Vienna (WHAV) at the University of Vienna , for providing invaluable photographs; to  Cécile Ducher  for her overview of the Tibetan translations; and to  Jaroslav Poncar ,  Christian Luczanits  and  Christiane Papa-Kalantari  for photograph permissions.
The distance to the object to be analysed is achieved by gradually moving the instrument with the hands or with a microscopic stage until the video image is focused (Fig. 1 2 3 4 5 6 7 8 9 10 ). Background (acquired on a golden mirror) and spectra were collected between 3600 and 650 cm -1 with a resolution of 4 cm -1 by accumulating 128 scans (a few minutes). All samples were analyzed in several points to assess their homogeneity and the analytical reproducibility. The residual atmospheric contribution in recorded IR spectra (often weakly present despite the background correction performed) was corrected using the H 2 O/CO 2 compensation function of the OPUS software (Bruker) that also drives the spectrometer operation. In the SR-FTIR analyses, the reflectance spectra showed a large amount of bands with a pole shape profile.