dataesr/scientific-paragraphs-categorization huggingface dataset:
V. CORRECTING FOR DETECTOR EFFECTS Our goal is to measure differential cross sections in a form that is optimal for testing FSR calculations. Therefore we are obliged to remove the effects of detector resolution and efficiency (including reconstruction, isolation, and trigger efficiency). The corrections for the muons follow the techniques developed for the DY cross section measurements  [17]. The corrections for photons are applied using an unfolding technique, as discussed in this section. We apply small corrections to the muon momentum scale as a function of muon p T , η μ , and ϕ μ  [21] ; they have almost no impact on our measurements. The muon reconstruction  efficiency (taken from simulation and corrected to match the data as a function of p T and η μ ) is taken into account by applying weights on a per-event basis.
We do not correct for the approximately 0. 5% increase in the isolation efficiency coming from the way we handle FSR photons falling within the muon isolation cone. The energy scale and efficiencies for photons are more central to our task. Most PF photon energies correspond to the true energies within a few percent. However, in about 13% of the cases the photon energy is significantly underestimated. The simulation reproduces this effect very well. We construct a "response" matrix that relates the PF energy to the true energy as a function of η γ and ΔR μγ. The angular quantities η γ and ΔR μγ are themselves well measured. We use the iterative D'Agostini method of unfolding  [22]  as implemented in the ROOUNFOLD package  [23]. By default, we unfold in the three quantities E T , η γ , and ΔR μγ simultaneously after subtracting backgrounds; as a crosscheck we also unfold the E T and ΔR μγ distributions one at a time, and we also use a single-value decomposition method  [24].
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES.
However flawed this methodology, alternative options were limited. Anybody seeking to use our accumulated experience of the past to predict likely patterns of history-making in the future has been limited by how much knowledge they could personally command, given the difficulties of accessing information, the limitations of brains (especially memory and processing power), and the shortness of scholars' lifespans. To overcome these very human frailties, what has long been needed is a computerized database of global history in which patterns of correlations across space and time between variables of interest could be reliably tracked using statistical tools. Seshat: Global History Databank, a vast collection of information gleaned from the work of scholars who study the human past, will provide a new way of addressing this challenge (Seshat: Global History Databank 2015a).
Seshat builds upon and radically expands a number of more established initiatives, including the Human Relation Area Files (HRAF; Human Relation Area Files 2015) and the Standard Cross-Cultural Sample (SCCS;  Murdock and White 1969). The crucial added value of Seshat is its longitudinal depth. Pre-existing databases are too historically shallow to understand long-term persistence and trend reversals, but Seshat will provide dynamic data revealing how different aspects of societies change with time. Our work also builds upon earlier efforts to construct and analyze more modest datasets using ethnographic material derived from both HRAF and the SCCS (Atkinson and Whitehouse 2011) and independently assembled archaeological materials  (Whitehouse et al. 2013). We started working together on this massive undertaking about five years ago.
By the end of 2015, with the help of many colleagues who have joined forces with us along the way and the generous support of several funding bodies, we will have coded information pertaining to over 500 political systems (or 'polities') in 30 'natural geographic areas' (NGAs; Seshat: Global History Databank 2015b; 2015c). We call them 'NGAs' because they each cover a geo-ecological zone that has retained its distinctive character over the millennia, even while the scale and structure of human social systems have changed, often quite dramatically. Our NGAs were selected to cover as broad a range as possible of social, cultural, political, and economic variation in world history selected from Africa, Europe, the Pacific, the Americas, and all the major regions of Asia. For each world region, we selected NGAs capable of furnishing examples of polities at all levels of sociocultural complexity  (categorized as 'low,' 'medium,' or 'high').
Some NGAs yielded polities with richly documented histories, and, taken together, these polities span very long time periods. Other NGAs had much shallower histories and offered thus a smaller number of polities. Overall, however, this sampling approach provided us with a rich slice of variability in human history (see Table  1 ). For each of the many hundreds of polities included in Seshat, we have been coding for a great range of variables pertaining to social complexity, warfare, ritual, governance, law, stratification, social mobility, religion, quality of life, economy, technology, agriculture, and demography. At the time of writing, we have about 78,000 data points already coded, and, by the end of the year, we expect this to have risen to around 100,000. Coding our initial sample of 30 NGAs is only the starting point, however; by 2025, we expect to hit our target of 100 NGAs, allowing us to test a great variety of hypotheses about the history of humankind and laying the groundwork for others to follow suit when the database 'goes public.
' As well as enabling us to avoid the charge of cherry-picking examples to fit our arguments, Seshat will allow us to test predictions formulated before we compiled the database, paving the way for a scientific approach to learning from history. Here, we set out five predictions concerning the relationship between ritual and various wider features of society. For each of these predictions, we provide a rationale or motivation based on the theory of 'Divergent Modes of Religiosity' (hereafter DMR theory). When the first phase of data assembly is completed early.
This early database, allowing synchronic comparison, generated a number of predictions that will be testable using a longitudinal dataset such as Seshat. For each polity coded in Seshat, we record details for five kinds of rituals: the largest scale, the most widespread, the most frequent, the most euphoric, and the most dysphoric. For each of these five rituals, we assemble information on the frequency, duration, scale, and inclusiveness of performances along with evidence of the quality and intensity of the emotions evoked through participation. This information is gathered through direct input from domain experts and through extensive literature reviews conducted by research assistants. As such, the data reflect the most recent scholarly understanding of ritualistic behavior for each of the polities.
There is some evidence that these processes began in Western Asia with the Neolithic transition from foraging to farming. Over a period of nearly two thousand years, we have attempted to unpick the intricately entwined relationships between agricultural intensification, ritual frequency, and the standardization of communal identities  (Whitehouse and Hodder 2010). We then constructed a database of Anatolian and Levantine sites from the end of the Paleolithic through the beginning of the Bronze Age, showing a gradual shift from imagistic to doctrinal modes of group formation as the cultivation of crops and the domestication of animals emerged and slowly intensified  (Whitehouse et al. 2013 ; see above). Eventually, Seshat will be able to link to databases of this kind so that we can compare the complex relationships between agriculture, ritual, and social morphology across multiple regions and time periods, potentially on a global scale.
And a Drum Roll Making predictions is a scary business. The initial analysis of our first database of 645 rituals from 74 cultures (Atkinson and Whitehouse 2011) was accompanied by much anxious anticipation. Prior to it, there had been no shortage of scholars claiming to have shot down DMR theory on the grounds that they had found a lowfrequency low-arousal ritual, or a high-frequency high-arousal one. Explaining that DMR theory was claiming to pick out statistical tendencies across space and time, not to explain every individual case, was often met with great affront as if one were claiming that the theory could not after all be empirically refuted. To be fair, it was more like claiming that the theory could not be tested on the evidence currently available. With the new rituals database, this changed for the first time.
We were finally able to see whether rituals statistically clustered as predicted around imagistic and doctrinal 'attractor positions. ' To our immense relief, they did. But this was only the beginning. Not every prediction of DMR theory was capable of being tested by the Atkinson and Whitehouse database. In some cases, this was simply because we were unable to find compelling proxies for variables of interest. For example, levels of social cohesion were surprisingly difficult to measure using ethnographic sources. In other cases, the problem was simply that the rituals database was synchronic-we couldn't test hypotheses about causation based on the assumption that causes usually precede effects. With Seshat, this will change. Seshat will allow us to test hypotheses longitudinally for the first time-not only in one culture area or geographical region but across the entire globe, reaching as far back into the mists of times as historians and archaeologists have been able to go.
The five predictions plucked out here for consideration will not be everyone's cup of tea, but they will nevertheless allow us to demonstrate proof of concept. In the years to come, we will be able to test many more hypotheses with ever more precision, providing a whole new way of thinking about the human past. We will at last bring history under the purview of experimental science. Surely this is an act that deserves a drum roll? "The Role of Ritual in the Evolution of Social Complexity" is one of several recent publications that introduce the Seshat databank and its potential contributions to social science (e. g. Turchin, Whitehouse, et al. 2012; Turchin, Brennan, et al. 2015). Here Whitehouse and colleagues provide a number of interesting hypotheses concerning the relationship between ritual and social complexity that will be testable once Seshat is more complete.
I have two critiques and one comment to make about this interesting paper. First, Whitehouse and colleagues argue that one of the most important elements of Seshat is its "longitudinal depth," implying that the Human Relations Area Files (HRAF) lacks such depth. This is not wholly true. A number of the cultures (e. g. Iroquois) in the HRAF World Cultures archive do have longitudinal depth; that is, there are ethnographic materials from several focal periods. This longitudinal depth is not great-several centuries at most-but it does allow for some diachronic analysis. In addition, the HRAF Archaeology archive is designed around longitudinal sequences that explicitly provide data for diachronic analyses. HRAF Archaeology, however, does not extend into the historic period. Whitehouse and colleagues would be more accurate in saying that HRAF does not provide data that extends from the historic period into the past.
At the end of the day, there is no clear conclusion, and the success of a theory comes right back down to its prima facie plausibility. The form of empirical inquiry has been followed, but its spirit has been exorcised. Seshat promises to change this by allowing the rigorous testing of historical hypotheses against a database that is large both synchronically and diachronically. What's not to love? I am excited about Seshat, and optimistic that it will teach us a great deal. I do wonder, however, about the following. Correlation vs. causation: Seshat will be able to generate correlational data, but these are of limited value in assessing causal claims. To be sure, if there is no correlation where a causal claim has been made or if the putative effect precedes the putative cause, then Seshat will be able effectively to disconfirm a causal hypothesis.
But in many cases it seems that cultures evolve as complexes: for instance, we might imagine that agriculture and the state tend to coevolve because a state can organize large-scale irrigation projects, and large-scale irrigation projects increase tax revenue and strengthen the state. There is a causal influence here, but it is non-linear. If the variables change together, it is doubtful that Seshat can discriminate causal from coincidental relationships, or tell us anything about the particular causal linkages. Which variables? The database is obviously designed to include all the variables relevant to Whitehouse' Modes theory, and so it might reasonably be expected to provide an excellent test of those hypotheses. But if the database merely substitutes cherry-picked variables and gerrymandered categories for cherrypicked examples, I don't think we will come out much ahead.
Does the database contain the variables relevant to the Ritual Frequency Hypothesis, a rival to Whitehouse' Modes theory? Or is the Modes theory to be tested only against the null hypothesis, that there is no pattern at all? Will the database include the variables required to actually compare specific predictions, or merely to confirm the existence of some kind of pattern? Psychology has recently faced something of a crisis of conscience over the discovery that some of its most famous findings are either not replicable or were tested by statistical measures insufficiently powerful to establish their claims. Here I must confess that I am the type that prefers classical mathematics to statistics, and that I have a hard time following statistical arguments. Given how easy it is to "lie" (intentionally or unintentionally) using statistical methods, I cannot help but feel doubt that statistical arguments are going to resolve anything.
In short, I wonder whether Seshat will not really be a giant, digital Golden Bough. Frazer, in writing the original, gathered all the examples he could, and tied them together into a coherent narrative about social evolution. He clearly intended to carry his argument precisely by the weight of examples he cited-a kind of intuitive statistics, without numbers. Ultimately, his work was rejected because he took his examples out of their contexts. But isn't this exactly what a database is designed to do? To permit the cross-cultural comparison of variables stripped of all their particular contexts? If this was such a problem for Frazer's undertaking, has it now ceased to be a problem? I understand that Seshat includes hundreds of variables from different societies, and that these can-potentially-be invoked to provide some degree of context.
I am certain that all of my questions occurred long ago to Sestet's designers. This project is indubitably worthwhile-indeed, it is the most exciting thing I have seen in a long time. I hope it lives up to its promise. Harvey Whitehouse, Pieter François, and Peter Turchin. Can there be a science of history? Response to commentaries on "The Role of Ritual in the Evolution of Social Complexity: Five Predictions and a Drum Roll" Our recent article invited readers of the SEF to imagine a drum roll in the background as we announced the imminent arrival of a vast global databank that will bring history under the purview of experimental science for the first time. We are extremely grateful to Peter Peregrine and Brian Malley for their willingness to comment on the article-and for raising such big and interesting questions, to boot.
But first, let us emphasize that our purpose here is to explain our approachthis is a work in progress, not a set of results. Our main rationale is that we want our key predictions to have appeared in print before putting them to the test through the first phase of analysis in 2016. Both commentators express reservations about the capacity of Seshat: Global History Databank to demonstrate causal links between the variables of interest. Malley concedes that the database could be used to disconfirm certain causal claims (e. g. , where a hypothesized effect precedes its putative cause), but because socio-cultural systems are so complex, he suspects that the causal role of one or a few variables is likely to be "non-linear. " To some extent, this concern may illustrate Peregrine's point about the dangers of misunderstanding probability in social science.
Yes, we cannot cover all possible hypotheses-that's a practical and logical impossibility-and, yes, we have started with variables of relevance to theories of particular interest to us as Seshat's creators. This is one of the great privileges of pioneers in any domain: you get to make decisions about what to prioritize. But we also build a solid foundation and neutral platform for others to add to the database by collecting data on additional variables to test additional theories. As the stock of variables increases, the job of each next researcher wishing to test an additional theory becomes easier. A second difference between the approach taken by Frazer and ours is that we do not "cherry-pick" facts. Instead, we employ a systematic procedure by collecting all data available for all societies that occupied a set of 30 points on the globe between the Agrarian Revolution and the Industrial Revolution.
We are limited only by what historians and archaeologist know about these societies. (And one of the "products" of the Seshat Databank will be to highlight important gaps in our knowledge that can be filled with additional research. ) By turning Seshat into a public resource available to everyone to test theories they consider to be the most compelling or timely, we believe to have put in a place the infrastructure necessary to analyze history using the scientific method. Science, like other domains of culture, evolves-and we believe a science of history may evolve faster and more spectacularly than most.
Turchin, Peter, Rob Brennan, Thomas E. Currie, Kevin C. Feeney, Pieter François, Daniel Hoyer, J. G. Manning, Arkadiusz Marciniak, Daniel Mullins, Alessio Palmisano, Peter Peregrine, Edward A. L. Turner, and Harvey Whitehouse (2015). Seshat: The Global History Databank. Cliodynamics: The Journal of Quantitative History and Cultural Evolution, 6(1): 77-107. Turchin, Peter, Harvey Whitehouse, Pieter Francois, Edward Slingerland and Mark Collard (2012). A Historical Database of Sociocultural Evolution. Cliodynamics: The Journal of Quantitative History and Cultural Evolution, 3 (2): 271-293.
Acknowledgements This work was supported by an  ESRC   Large Grant  to the  University of Oxford , entitled "Ritual,  Community , and  Conflict"  ( REF RES-060-25-0085 ), a  John Templeton Foundation  grant to the  Evolution Institute , entitled "Axial-Age Religions and the  Z-Curve of Human Egalitarianism ," a  Tricoastal Foundation  grant to the Evolution Institute, entitled " The Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability ," and a grant from the  European Union   Horizon 2020 research and innovation programme  (grant agreement No  644055  [ALIGNED]; Aligned 2015). We gratefully acknowledge the contributions of our team of research assistants, post-doctoral researchers, consultants, and experts. Additionally, we have received invaluable assistance from our collaborators in the construction of Seshat.
The designed method is strongly based on bivalve's respiratory physiology and ethology. The developed platform for valvometry was built using lightweight electrodes (approximately 100 mg each) linked by thin flexible wires to high-performance electronic units. These electrodes are capable to measure the position of the opening of a mollusk shell with an accuracy of a few µm. This system allows the bivalves to be studied in their natural environment with minimal experimental constraints. Statistical approaches have already been used for the analysis of this data  [Sow et al. (2011); Tran et al. (2003) ;  Schmitt et al. (2011) ]. The goal of the present work is to estimate the velocity of the valve opening/closing activities of the bivalve (i. e. an oyster in our case) from the measured position of shell.
We have used the experimental data obtained from a population of 16 oysters living in the bay of Arcachon (France). We have tried to examine whether there exist any relationship between the velocity and the contamination of water as the quality of the water influence the behavior of the oyster population. Three different methods of differentiation were used for estimating the velocity, namely: an algebraic differentiator  [Mboup et al. (2009) ], a higher order sliding mode (HOSM) based differentiator  [Efimov and Fridman (2011) ] and a homogeneous finite-time differentiator  [Perruquetti et al. (2008) ]. Their performances were then compared. The methods were chosen because of their individual merits. For example, HOSM differentiator has the global differentiation ability independently on the amplitude of the differentiated signal and measurement noise, but it has some chattering in the response, while the homogeneous finite-time differentiator has no chattering, but it is more sensitive to the amplitudes of the signals to be differentiated.
MEASUREMENT SYSTEM DESCRIPTION The monitoring site was located in the bay of Arcachon, France, at the Eyrac pier (Latitude: 44 • 40 N, Longitude: 1 • 10 W). Sixteen Pacific oysters, Crassostrea gigas, measuring from 8 cm to 10 cm in length were permanently installed on this site. These oysters were all from the same age group (1. 5 years old) and came from the same local supplier. They also all grew in the bay of Arcachon. They were immersed on the sea bottom (at 3 m to 7 m deep in the water, depending on the tide activity). We analyzed the data for the year 2007. The electronic equipment has been first described in  [Tran et al. (2003) ] and slightly modified (adapted to severe open ocean conditions) in  [Chambon et al. (2007) ]. A considerable advantage of this monitoring system is that it is completely autonomous and made to work without in situ human intervention for one full year.
Each animal is equipped with two light coils (sensors) of approximately 100 mg each, fixed on the edge of each valve. One of the coils emits a high-frequency sinusoidal signal, that is received by the other coil. One measurement is performed every 0. 1 sec (i. e. at 10 Hz) for one among the sixteen animals. So the behavior of a particular oyster is measured every 1. 6 sec. EPOC's website MolluScan-Eye 2 can be consulted to see the recorded data sets of information. So, every day, 54000 triplets (1 distance, 1 stamped time value, 1 animal number) are collected for each oyster. The strength of the electric field produced between the two coils is proportional to the inverse of distance between the point of measurement and the center of the transmitting coil leading to the measurement of the distance between coils.