dataesr/scientific-paragraphs-categorization huggingface dataset:
THE DATA Flickr has emerged as the data-source of choice for most recently developed data-driven applications in computer vision and graphics, including visual geo-location  [6, 2, 11]. However, the di culty with Flickr and other consumer photosharing websites for geographical tasks is that there is a strong data bias towards famous landmarks. To correct for this bias and provide a more uniform sampling of the geographical space, we turn to Google Street View -a huge database of street-level imagery, captured as panoramas using specially-designed vehicles. This enables extraction of roughly fronto-parallel views of building facades and, to some extent, avoids dealing with large variations of camera viewpoint. Given a geographical area on a map, we automatically scrape a dense sampling of panoramas of that area from Google Street View.
DISCOVERING GEO-INFORMATIVE EL-EMENTS Our goal is to discover visual elements which are characteristic of a given geographical locale (e. g. the city of Paris). That is, we seek patterns that are both frequently occurring within the given locale, and geographically discriminative, i. e. they appear in that locale and do not appear elsewhere. Note that neither of these two requirements by itself is enough: sidewalks and cars occur frequently in Paris but are hardly discriminative, whereas the Ei↵el Tower is very discriminative, but too rare to be useful (< 0. 0001% in our data). In this work, we will represent visual elements by square image patches at various resolutions, and mine them from our large image database. The database will be divided into two parts: (i) the positive set containing images from the location whose visual elements we wish to discover (e.
g. Paris); and (ii) the negative set containing images from the rest of the world (in our case, the other 11 cities in the dataset). We assume that many frequently occurring but uninteresting visual patterns (trees, cars, sky, etc. ) will occur in both the positive and negative sets, and should be filtered out. Our biggest challenge is that the overwhelming majority of our data is uninteresting, so matching the occurrences of the rare interesting elements is like finding a few needles in a haystack. One possible way to attack this problem would be to first discover repeated elements and then simply pick the ones which are the most geographically discriminative. A standard technique for finding repeated patterns in data is clustering. For example, in computer vision, "visual word" approaches  [21]  use k-means clustering on image patches represented by SIFT descriptors.
Our Approach From the tens of millions of patches in our full positive set, we randomly sample a subset of 25, 000 high-contrast patches to serve as candidates for seeding the clusters. Throughout the algorithm, we represent such patches using a HOG+color descriptor. First, the initial geo-informativeness of each patch is estimated by finding the top 20 nearest neighbor (NN) patches in the full dataset (both positive and negative), measured by normalized correlation, and counting how many of them come from Paris. Figure  3  shows nearest neighbors for a few randomly-selected patches and for the patches whose neighbors all come from Paris. Note that the latter patches are not only more Parisian, but also considerably more coherent. This is because generating a coherent cluster is a prerequisite to retrieving matches exclusively from Paris: any patch whose matches are incoherent will likely draw those matches randomly from inside and outside Paris.
We keep the candidate patches that have the highest proportion of their nearest neighbors in the positive set, while also rejecting near-duplicate patches (measured by spatial overlap of more than 30% between any 5 of their top 50 nearest neighbors). This reduces the number of candidates to about 1000. Some good elements, however, get matched incorrectly during the nearest-neighbors phase. Figure  4  shows a patch that contains both a street sign and a vertical bar on the right (the end of the facade). The naïve distance metric doesn't know what's important, and so it tries to match both. Yet too few such patches exist in the dataset; for the remainder, the algorithm matches the vertical bar simply because it's more frequent. To fix this problem, we aim to learn a distance metric that gives higher weight to the features that make the patch geo-discriminative.
So, we iterate the SVM learning, using the top k detections from previous round as positives (we set k = 5 for all experiments). The idea is that with each round, the top detections will become better and better, resulting in a continuously improving detector. However, doing this directly would not produce much improvement because the SVM tends to overfit to the initial positive examples  [20] , and will prefer them in each next round over new (and better) ones. Therefore, we apply cross-validation by dividing both the positive and the negative parts of the dataset into l equally-sized subsets (we set l = 3 for all experiments). At each iteration of the training, we apply the detectors trained on the previous round to a new, unseen subset of data to select the top k detections for retraining.
In our experiments, we used three iterations, as most good clusters didn't need more to converge (i. e. stop changing). After the final iteration, we rank the resulting detectors based on their accuracy: per-  centage of top 50 firings that are in the positive dataset (i. e. in Paris). We return the top few hundred detectors as our geo-informative visual elements. Figure  5  illustrates the progression of these iterations. For example, in the left column, the initial nearest neighbors contain only a few windows with railings. However, windows with railings di↵er more from the negative set than the windows without railings; thus the detector quickly becomes more sensitive to them as the algorithm progresses. The right-most example does not appear to improve, either in visual similarity or in geo-discriminativeness.
This might be explained by the relative lack of stylistic coherence and uniqueness in American cities (with its melting pot of styles and influences), as well as the supreme reign of the automobile on American streets. In addition to the qualitative results, we would also like to provide a more quantitative evaluation of our algorithm. While validating data-mining approaches is di cult in general, it is possible to measure 1) to what extent our elements are specific to particular locations, and 2) do users find them subjectively geo-informative in a visual discrimination task? To evaluate how geo-informative our visual elements are, we ran the top 100 Paris element detectors over an unseen dataset which was 50% from Paris and 50% from elsewhere. For each element, we found its geo-informativeness by computing the percentage of the time it fired in Paris out of the top 100 firings.
The average accuracy of our top detectors was 83% (where chance is 50%). We repeated this for our top 100 Prague detectors, and found the average accuracy on an unseen dataset of Prague to be 92%. Next, we repeated the above experiment with people rather than computers. To avoid subject fatigue, we reduced the dataset to 100 visual elements, 50 from Paris and 50 from Prague. 50% of the elements were the top-ranked ones returned by our algorithm for Paris and Prague. The other 50% were randomly sampled patches of Paris and Prague (but biased to be high-contrast, as before, to avoid empty sky patches, etc). In a web-based study, subjects (who have all been to Paris but not necessarily Prague) were asked to label each of the 100 patches as belonging to either Paris or Prague (forced choice). The results of our study (22 naive subjects) are as follows: average classification performance for the algorithm-selected patches was 78.
CONCLUSION So, what makes Paris look like Paris? We argued that the "look and feel" of a city rests not so much on the few famous landmarks (e. g. the Ei↵el Tower), but largely on a set of stylistic elements, the visual minutiae of daily urban life. We proposed a method that can automatically find a subset of such visual elements from a large dataset o↵ered by Google Street View, and demonstrated some promising applications. This work is but a first step towards our ultimate goal of providing stylistic narratives to explore the diverse visual geographies of our world. Currently, the method is limited to discovering only local elements (image patches), so a logical next step would be trying to capture larger structures, both urban (e. g. facades), as well as natural (e. g. fields, rivers). Finally, the proposed algorithm is not limited to geographic data.
pubescens. a Measurement information measured inside the enclosure chamber are daily averaged; PAR is in µmol m -2 s -1 , temperature T in • C, relative humidity RH in %, photosynthetic net assimilation Pn in µmol CO 2 m -2 s -1 and stomatal conductance Gw in mmol H 2 O m -2 s -1. b Percentage of speciated BVOC relative to total BVOC and to non-isoprene BVOC (brackets). c Emission factors EF (µgC g -1 DM h -1 ) formed the best-fit slope of ER vs. CL × CT as in  Guenther et al. (1993). d Total monoterpenes emissions measured from the PTR-MS were derived from absolute concentrations at m/z 137. Since methanol release, as other alcohols, is strongly stomatal dependent, its maximum relative contribution to the emitted carbon was observed at dawn: up to 6. 9 % of the carbon emitted as BVOC (data not shown) compared to 3.
Acknowledgements. We are very grateful to  J. -P. Orts ,  I. Reiter ,  P. E. Blanc ,  J. C. Brunel  and other OHP technical staff for support before and during the campaign. We thank  D. Coutancier , post-graduate student of  IUT d'Orsay  for his efficient help in the analysis of LSCE sample tubes and results computing. We thank members of the DFME team from IMBE:  S. Greff ,  C. Lecareux ,  S. Dupouyet  and  A. Bousquet-Melou  for their help during measurements and analysis. This work was supported by the  French National Agency for Research  ( ANR 2010 JCJC 603 01 ),  INSU (ChARMEx) ,  CNRS   National program EC2CO-BIOEFECT  ( ICRAM  project),  CEA , and  Université Paris Diderot. We are grateful to  FR3098 ECCOREV  for the O 3 HP facilities (https://o3hp. obs-hp. fr/index. php/fr/), Europe (FEDER)  and  ADEME/PACA  for PhD funding.
The 238 U to 235 U fission cross section ratio has been determined at n TOF up to ∼1 GeV, with two different detection systems, in different geometrical configurations. A total of four datasets have been collected and compared. They are all consistent to each other within the relative systematic uncertainty of 3-4%. The data collected at n TOF have been suitably combined to yield a unique fission cross section ratio as a function of the neutron energy. The result confirms current evaluations up to 200 MeV. A good agreement is also observed with theoretical calculations based on the INCL++/Gemini++ combination up to the highest measured energy. The n TOF results may help solving a long-standing discrepancy between the two most important experimental dataset available so far above 20 MeV, while extending the neutron energy range for the first time up to ∼1 GeV.
An extension of the energy range up to higher energy would also be desirable, as data above 200 Me could prove useful for refining theoretical models, in particular those used in modern Monte Carlo codes for high-energy neutron transport. To address the need of new, accurate data to improve current cross section standard, and to extend the current energy limit, a series of measurements of the 238 U(n,f )/ 235 U(n,f ) cross section ratio were performed at the n TOF facility at CERN, from 0. 5 MeV to 1 GeV, in different experimental campaigns and with different setups. After a description of the various setups and analysis procedures, in Section II, the n TOF results are presented and compared with each other in Section III. Suitably combined in a unique dataset, the n TOF results are then compared with previous experimental data, with current evaluations and with the predictions of a theoretical model extending up to the highest measured energy.
It is important to note that the ratio does not depend neither on the shape nor on the absolute value of the neutron flux, since the two samples, having the same diameter, are measured simultaneously and in the same experimental setup, i. e. are exposed to the same neutron flux. As a consequence, the final uncertainties in the cross section ratio will mostly be determined by the uncertainty on the areal density of the two samples, and on the ratio of the efficiency and dead-time corrections. The data reported here were collected with two detection systems: a Fission Ionization Chamber (FIC), in which a single Fission Fragment (FF) is detected, and an array of Parallel Plate Avalanche Counters (PPAC), in which the two FF are detected in coincidence. This last detection system was used in two geometrical configurations, i.
e. perpendicular to the neutron beam direction, and in a tilted configuration, with the detectors mounted at an angle of 45 • relative to the neutron beam direction. A brief description of the detectors and other experimental details is given below for each setup, together with the systematic uncertainties affecting the corresponding results. All data here reported were collected with a large-aperture collimator (8 cm diameter). With such an arrangement, the beam profile is essentially flat over the surface of samples with diameter ≤8 cm. This feature is of great advantage in this as in other high-accuracy fission cross section measurements at n TOF, since it minimizes the effect of possible inhomogeneities of the fissile deposit. Another important aspect of the n TOF neutron beam regards the neutron energy resolution in the range reported here.
In both measurements, the chambers were positioned at approximately 190 m downstream of the spallation target. In the measurement with FIC1, two 235 U and four 238 U samples were mounted, in the form of U 3 O 8 compound. The samples were prepared at the Institute of Physics and Power Engineering, Obninsk, Russia by means of the painting technique. Their purity, checked by means of α-spectroscopy, was in all cases greater than 99%, with only trace contamination of other U isotopes. The uniformity of the fissile deposit was always between 5 and 10%. The deposit of fissile material was 8 cm in diameter, while the areal density of each sample was between 6×10 -7 and 8×10 -7 atoms/barn, with a declared uncertainty on the mass of each sample of ∼ 1. 3% (see Table  I ). Due to a large γ-flash signal, it was not possible to collect useful data with this setup above 10 MeV.
These results, on the other hand, are affected by a small systematic uncertainty on the mass of each sample, and possible inhomogeneities of the samples are averaged out when combining the different samples. Therefore, these data were used as basis for the normalization of the second dataset, affected by larger systematic uncertainties. The second setup used in the measurement consisted of the FIC2 chamber, inside which two 235 U and one 238 U samples were mounted. All three samples had a diameter of 5 cm. The areal density of the two 235 U samples was around 6×10 -7 atoms/barn, while a much thicker 238 U sample, of 2. 6×10 -6 atoms/b was used in this second setup, to collect a sufficient statistics below the fission threshold as well. The uncertainty on the mass of this last sample was >3%, and no information was available on the homogeneity of the deposit.
The efficiency, estimated by means of detailed FLUKA simulations  [14]  of the energy deposition in the gas as a function of the threshold used in data analysis, was found to be 94. 9% and 96. 2% for the thin 235 U and 238 U samples mounted in FIC1, respectively. Contrary to the efficiency, dead-time corrections had to be applied to both FIC1 and FIC2 data, since they depend both on the sample and neutron energy. Such corrections, based on the non-paralyzable model, were below 5% in the whole neutron energy range for both thin samples, while it reached ∼10% for the thick 238 U sample used in FIC2.
7 µm Al backing in the third measurement. The characteristics of the samples used in the first two cases are listed in Table  II. Their thickness and homogeneity was checked by means of the Rutherford Back Scattering spectroscopy (RBS), which also provides the chemical composition of samples and backings. Apart from a contamination of ∼6% of 238 U in the 235 U sample, taken into account in the analysis, only trace concentrations of other isotopes were found in the samples (see Refs. [15, 18] ). The samples of the last measurement ("PPAC tilted 2") have not been characterized with the necessary accuracy. As a consequence, this last dataset has been normalized to the ENDF/B-VII. 1 evaluated ratio between 3 and 5 MeV. In both perpendicular and tilted configurations, a correction had to be applied for the loss of efficiency related to the stopping of one of the fission fragments in the sample backing.
Because of the angular dependence of the efficiency, the fission fragment angular distribution (FFAD) had to be considered. To this end, corrections were calculated, by means of Monte Carlo simulations, on the basis of existing experimental results on the FFAD as a function of the neutron energy, reported in the EXFOR database for both isotopes  [19]. More details on the procedure can be found in Refs. [15, 18]. The FFAD is expressed as the sum of even Legendre polynomials in terms of the cosine of the emission angle (because of the forward-backward symmetry of the emitted FF). In this work, polynomials up to second order only were considered, as there is no reliable information in literature about higher order coefficients. The energy-dependent coefficients were determined from the so-called anisotropy parameter, A(E), defined as the ratio between the emission probability of the FF at 0 and 90 • , W(E,0 • )/W(E,90 • ).
The angular anisotropy of 235 U and 238 U was obtained by fitting data present in literature, available in the EXFOR database  [19]. Since data only go up to ∼20 MeV neutron energy, the anisotropy was constrained to asymptotically approach unity above this energy. The data and the results of the fit are shown in Figure  1. The detection efficiency of the PPAC setup as a function of the FF emission angle is shown in Figure  2  for the perpendicular and tilted configuration. The figure clearly shows the difference between the two configurations: in the perpendicular case, the efficiency is flat up to a given angle (around ∼60 • ), and drops to zero above this limit. On the contrary, in the tilted configuration all FF emission angles are covered. The gain in efficiency at large angles is obviously compensated by a reduction of the efficiency at smaller angles, due to the cut in the azimuthal angle related to the stopping of the fragments in the dead layers (sample backing and PPAC windows).
III. RESULTS AND DISCUSSION Figure  4  shows the results of the different measurements of the 238 U/ 235 U fission cross section ratio, for neutron energies in the range 500 keV-20 MeV. For comparison, the ratio extracted from the ENDF/B-VII. 1 evaluated cross sections, is also shown in the figure. We remark here that for neutron energies below 20 MeV a wealth of data are available in literature, with the standard cross section of both isotopes characterized by an uncertainty of less than 1%. Therefore the very good agreement between n TOF results and evaluations, evident in Figure  4 , provides a strong confidence on the accuracy of the new data, in particular on the systematic uncertainty related to the sample mass and on the efficiency corrections. Some minor differences can nevertheless be noticed in the Figure, in particular around 12 MeV neutron energy, where the dip corresponding to a valley in the 238 U(n,f ) cross section is less pronounced in the n TOF dataset, relative to ENDF/B-VII.
Setup Samples Efficiency Others Total FIC 2 1 3 3. 5 PPAC perpend. 1. 1 3 <1 3 PPAC tilted 1 1. 1 2 <1 3 PPAC tilted 2 (∼1) 1 <1 2. 5 238 U fission threshold, slightly above 1 MeV. In this region, the n TOF data are systematically shifted on the right, with respect to evaluations, possibly indicating a slight underestimate of the value of the fission threshold in the evaluations. Figure  5  shows the results of the four different measurements at n TOF, from 500 keV to 1 GeV. The error bars on the symbols indicate the statistical errors only. Above 20 MeV some differences can be observed between the various results. In particular, the FIC results are systematically lower than all the PPAC ones. Among these last ones, the results obtained in the first measurement with the tilted configuration are systematically higher than the other two datasets, with the difference becoming more pronounced above ∼500 MeV.
It should be noticed, however, that all observed differences are well within the combined systematic uncertainties affecting the results here reported, as discussed below. Each of the four datasets collected at n TOF represents a new result by itself, and should be considered independently from each other, for example in re-evaluating the fission cross section ratio. Nevertheless, for the purpose of this paper it may be convenient to combine all four datasets in a single one. To this end, a weighted average was performed, taking into account only the statistical errors (since the systematic errors are similar for the different datasets, in first approximation they can be neglected in the weighted average). The ratio obtained with this procedure can be more easily compared with data from previous measurements, as well as with evaluations and results of theoretical calculations.
Furthermore, it can be used to analyze the dispersion of the n TOF datasets and draw further conclusions on their accuracy. This last point is illustrated in Figure  6 , which shows the divergence of each n TOF result from their weighted average, as a function of the neutron energy. Except for some details, the difference in all cases is within the ∼3% systematic uncertainty associated with each dataset (in the crude approximation adopted here, not taking into account the existing correlations between some datasets, the systematic uncertainty on the weighted average is around 1% up to 200 MeV, and between 1 and 2% above this energy). This observation provides further strength on the accuracy of the results here reported. The unique n TOF fission cross section ratio discussed above is compared with the two major datasets extending to high energy in Figure  7  (the data from Nolte et al.
IV. CONCLUSIONS The 238 U/ 235 U fission cross section ratio has been measured at n TOF, for the first time up to 1 GeV, with two different detection systems, one of which used in two different geometrical configurations. The results of the different measurements agree with each other within the estimated systematic uncertainty on each dataset of approximately 3%. Furthermore, the results are in all cases in agreement with evaluated cross sections and standard values between 0. 5 and 20 MeV, thus providing confidence on their accuracy. The different results have been combined in order to obtain a unique value of the ratio, which has then been compared with previous results and evaluations, up to 200 MeV, and with theoretical calculations based on intranuclear cascade model coupled with an evaporation-fission code, all the way up to 1 GeV.
The extracted ratio rules out the results of Shcherbakov et al. , and is in very good agreement both with current evaluation and standards, up to 200 MeV, and with theoretical calculations up to 1 GeV. These results could be used to improve the accuracy of current libraries, and in particular of the standard used in a variety of applications. 2 U/ 235 U (n,f ) cross section ratio obtained from the weighted average of the four n TOF datasets compared with previous results from Ref. [4]  and  [6] , with the ratio extracted from the ENDF/B-VII. 1 library and with the results of model calculations from Ref. [22].