dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, and more strangely, in order to be accepted by the community, an alternative to SRIM must provide results identical or at least very close to the ones of SRIM. Indeed, as mentioned below, SRIM has been used so intensively and for so many years that any code which gives different results, whatever their experimental accuracy and physical correctness will not be adopted by the community. Finally, in the nuclear context, an alternative to SRIM should include the QC of damage, which is very commonly used in this community. Keeping the objective of providing an alternative to SRIM, we chose to implement QC in the Iradina code  [11]  originally developed by Christian Borschel and Carsten Ronning from the university of Jena. Compared to the few other modern BCA codes (see below), Iradina has some advantages.
First, it is truly open-source. Second being written in C, it is easy to read. Third, it proves very fast. This is because it relies on the formalism implemented originally in the Corteo code  [12]  which has been developed by François Schiettekatte for ion beam analysis. In this code, the calculation of the scatterings use logarithmicallyscaled stopping tables which greatly speeds up the calculation. This formalism is much faster than the one based on the so-called Magic formula implemented in SRIM. Other modern BCA codes we are aware of include Mytrim  [13] , SDTrimSP  [14]  and IM3D  [15]. To our knowledge, SDTrimSP is not open-source. From what we understand, MyTrim uses the Magic formula (instead of fast Corteo's formalism) which should make it quite slow. Finally, contrary to what authors claim, IM3D is not open-source.
Indeed the transport routines at the heart of this code are included in an executable archive, which one has to download separately. Strangely enough, the input variables of IM3D have almost identical names to the ones of Iradina. Marlowe code has recently be renewed in connection with mixed BCA-MD simulations  [16]. But it remains not very user-friendly and much more complex than BCA codes considering random material. It can therefore not be regarded as an alternative to SRIM. We present in the following, how we realized the implementation of QC in Iradina. We then compare our results with the ones of SRIM. Finally, we discuss QC in non-elemental solids and give our opinion on how to choose between FC and QC calculations. 2 Implementation of QC in Iradina.
Second, the damage energy must be converted into damage production by a damage formula. Various formulations appeared in the late 1960s and early 1970s  [18]. Nowadays, the so-called modified Kinchin-Pease or Norgett-Robinson-Torrens (NRT)  [17]  formula is used: n d E ð Þ ¼ 0 if E < E d 1 if E d < E < 2:5E d 0:8 E 2E d if E > 2:5E d : 8 > < > : ð5Þ We implemented formulas (1) to (  5 ) in Iradina and set up a new simulation type for which the code calculates the trajectory of the sole ion and the damage created by the recoil of each collision by the above formulas. When actual damage is created (n d (E) > 0), the code assigns it entirely to the point where collision took place. The damage is eventually stored in the vacancy file. In QC, one cannot distinguish replacements and interstitial creations.
While this interface is quite elegant and useful, it was not conceived to run SRIM like calculations. Moreover, it is not open source and thus cannot be adapted to such calculations. We therefore preferred to design a new GUI. This GUI is briefly described in Appendix A. Finally, Iradina has recently been made available on source forge  [22]. The SRIM-like GUI is also available on source-forge with packages to be used in linux or windows environments to perform SRIM like calculations with the Iradina code embedded in it  [23]. 3 Choosing between FC and QC Users of SRIM or Iradina have to choose between two ways of calculating the damage profile in ion-irradiated materials. We want in this section to elaborate on this choice and present our opinion about it. We shall first present some comparisons between these two schemes for elemental or non-elemental solids based on the same test cases we used before.
They consider series of nuclear reactions or elastic collisions of a neutron with atoms. The amount of damage created by the result of a nuclear reaction or an elastic collisions are secondary outputs of the neutron code irrelevant for the main calculation. Because such codes consider interactions with one atom at a time, the damage formulas depend on the sole nature of the atom interacting with the neutron. Physically, this is as wrong as in ion irradiation codes, but the consequences for neutronics are zero. Indeed, damage productions being simple pieces of information, they do not affect the fate of the neutrons which is the actual object of neutronics codes. One may suppose that NRT formulas were implemented in neutron codes, and then simultaneously introduced in SRIM for consistency with these codes even for multi-component systems where they are physically irrelevant.
Bathymetric map The bathymetric map was produced by processing and interpolating interferometric sidescan sonar data for the deeper parts of the roadstead (from 2 m to 18 m related to L. A. T) and from aerial LIDAR (Light Detection and Ranging) for the shallower part (from the emerged land to 2 m depth in relation to L. A. T). High-resolution shallow-water bathymetric data were obtained from a geophysical survey on board the R/V Haliotis (IFREMER), during spring (from 3 to 17 May 2009), equipped with a Geoacoustics Geoswath system. Its phase measuring sonar system, working at frequency of 250 kHz, allowed the acquisition of backscatter (cannot be used in this study due to acquisition complications) and bathymetric data in very shallow water depths (but deeper than 2 m) with a vertical resolution of around 20 cm for the considered water depths.
Vessel navigation was carried out by DGPS-RTK (Differential Global Positioning System -Real Time Kinematic) using a reference station located at a short distance on the coast, providing a positioning accuracy better than 10 cm. The RTK system allowed us to correct for tide variations. The bathymetric survey consisted of 330 bathymetric profiles. To correct for refraction errors, acoustic velocity profiles were repeated every day before the start of the daily survey. Sonar data were treated manually using the CAR-AIBES software (©IFREMER) to clean aberrant soundings (40%) and correct data for tide variations. These data allowed us to create a 1-m grid bathymetric Digital Elevation Model (DEM), edited with ©QGIS software. LiDAR data were from the Litto3D Digital Elevation Model (DEM) distributed by ROL (Reseau d'Observatoire du Littoral Normandie -Haut de France) and SHOM (Service Hydrographique et Océanographique de la Marine)  (Shom-ROL, 2018).
33; Sample RI: 1. 55; Imaginary RI: 0. 1). The GRADISTAT statistical Excel package  (Blott & Pye, 2001)  was used to obtain statistical parameters using the  Folk and Ward (1957)  method. The final sedimentary maps (Main map) were produced by Principal Component Analysis (PCA) using R language (R Core Team, 2016). The interpretation of the properties of sub-populations is based on the calculated eigenvectors and relative weighting of the scalar quantities of these eigenvalues.
Acknowledgements We would like to thank the crews of the  R/V. 'Côtes de la Manche' of INSU/CNRS and 'Haliotis' of GENAVIR/IFRE-MER. We are also grateful to the  SHOM  and  ROL  for providing the LIDAR data (https://doi. org/10. 17183/LIDAR_NHDF_V20180501 ). Dr M. S. N. Carpenter  post-edited the English style and grammar. We greatly appreciate the revisions of the five reviewers which improved an earlier version of the manuscript.
Genotyping and data analyses The following Y-chromosomal biallelic markers were genotyped: M9, M130, M217, M77, F6379, B469, B80, B90, L1373, M407, M217, M48, M401, М207, Page07, Z93, Z95, Z2125, M343, M269, M412, Z2105, М458, М558, M478, M124, M175, M122, M119, M268, M95, P203, P201, M7, M231, P43, TAT, M128, F2930, F4205, CTS6967, B478, B525, B187, M2118, YAP, M174, M35, M201, P15, M170, M423, 12f2, M410, Page08, M242, M25 and M346. Genotyping was performed using PCR and subsequent direct sequencing or restriction fragment length polymorphism analysis. For genotyping new markers (F6379, B80 and B469) discovered from the refined phylogenetic tree inside C3c1 sub-clade, we designed primers with Primer3 software  [22, 23]. Primer specificity was first assessed with Primer-BLAST  [24]  and GenomeTester v.
Whole Y chromosome sequencing To reconstruct the phylogeny of haplogroup C3 we combined the sequences of 10 novel Y chromosomes with 28 published by us earlier  [31]  (Supplementary Table  S6 ). The ten whole Y chromosome sequences reported in this study are deposited in the European Nucleotide Archive (http://www. ebi. ac. uk/ena) under the accession number PRJEB27561. Variants that did not have previously reported rs numbers were submitted to NCBI dbSNP database (https://www. ncbi. nlm. nih. gov/projects/SNP/) (dbsnp official release version 153). All novel samples were sequenced using the Illumina HiSeq 2500 platform following Y chromosome capture with a proprietary capture protocol available at Gene by Gene (Family Tree DNA) using the commercially available "BigY" service (https://www.
Mapping and calling the Y chromosome variants The fastq files of the newly sequenced genomes were mapped with BWA-MEM (v0. 7. 12)  [32]. Read duplicates were removed using Picard (v2. 0. 1) (http://broadinstitute. github. io/picard/). GATK (v3. 5)  [33]  was used to perform local realignment around known indels and for base quality score recalibration. Variant calling was performed with GATK tools HaplotypeCaller and GenotypeGVCF, which were asked to report variant and non-variant calls over the whole Y chromosome.
Filtering Individual genotypes were filtered with bcftools (v1. 4)  [34]  in the raw VCF files that included all Y-chromosomal sites. The filtered Illumina and Complete Genomics data sets were merged using CombineVariants from GATK (v3. 8)  [33]. All the positions with >5% of missing genotypes in the combined dataset were masked out resulting in the final effective overlap between the sets. In addition, regions with poor mapability as described in ref. [31]  were also masked out. This resulted in a final total of 9. 2 Mb of usable sequence.
Phylogeny reconstruction We reconstructed the phylogeny for Y chromosome haplogroup C3 with hg C7a as an outgroup and estimated coalescent times using the software package BEAST v. 1. 7. 5  [35]  (Fig. 2 ). We chose Bayesian skyline coalescent model as the tree model  [36]  the general time reversible substitution model  [37]  with γ-distributed rates  [38]  and a relaxed lognormal clock model  [39]. We used previously published  [31]  age of hg C (50,865 years, 95% confidence interval = 49,191-52,699) as the calibration point to get coalescent times for the inner structure. The run was performed with a piecewise-constant coalescent model with seven groups. The number of groups was obtained by dividing the sample size by 5. Marcov Chain Monte Carlo method had 30 million iterations with a sampling made every 2000 steps.
We visualized the BEAST run in Tracer v1. 5 (http://beast. bio. ed. ac. uk) and confirmed that effective sample size was above 200. The tree was visualized using FigTree 1. 4. 2. (http://tree. bio. ed. ac. uk/software/figtree/). One newly sequenced sample was left out of BEAST analysis due to quality issues but can be seen on annotated tree indicated with dashed line (GRC171156839) (Supplementary Fig. S5 ). Throughout the study, nomenclature of Karmin et al. [31]  and its updates were followed.
This time estimate, however, is significantly younger than the estimation based on sequence data at 1076-2011 years (Supplementary Table  S7  and Fig. 2 , node 16). The differences in TMRCA estimates based on network analysis of Y-STRS vs. BEAST analysis of sequences may be caused by using different methods and different datasets. Non-random selection of samples for sequences to cover as much variation as possible provides the maximum TMRCA estimate for the clade and rho-statistic calculations based on STR network give us the time estimate for the pronounced founder event in the populations. It should be noted that the mutation process of microsatellites is less well understood and repeat length changes can occur in both directions, leading to underestimation of times.
Acknowledgements This research was supported by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 16-0125 ),Project No. 2014-2020. 4. 01. 16-0771  and Project No. 2014-2020. 4. 01. 15-0012. This work was supported by the  Estonian Research Council  grant ( PUT1339  and  PRG243. This work was supported by institutional research funding  IUT  ( IUT24-1 ) of the  Estonian Ministry of Education and Research. This work was supported by the  EU European Regional Development Fund through the Centre of Excellence in Genomics  to the  Estonian Biocentre  and by the  Estonian Institutional Research  grant  IUT24-1.
This question becomes especially relevant when we consider the significant conceptual and performance progress in machine learning. Deep learning, or the use of neural networks with many hidden layers, is the tool which allows us to analyze low-level LHC data without constructing high-level observables. This directly leads us to standard classification tools in contemporary machine learning, for example in image or language recognition. The goal of this study is to see how well different neutral network setups can classify jets based on calorimeter information. A straightforward way to apply standard machine learning tools to jets is so-called calorimeter images, which we use for our comparison of the different available approaches on an equal footing. Considering calorimeter cells inside a fat jet as pixels defines a sparsely filled image which can be analyzed through standard convolutional networks  [12] [13] [14].
8 of the jet axis as well. No matching is performed for the QCD jets. We also require the jet to have |η j | < 2. The constituents are extracted through the Delphes energy-flow algorithm, and the 4-momenta of the leading 200 constituents are stored. For jets with less than 200 constituents we simply add zero-vectors. Particle information or additional tracking information is not included in this format. For instance, we do not record charge information or the expected displaced vertex from the b-decay. Therefore, the quoted performance should not be considered the last word for the LHC. On the other hand, limiting ourselves to essentially calorimeter information allows us to compare many different techniques and tools on an equal footing. Our public data set consists of 1 million signal and 1 million background jets and can be obtained from the authors upon request  [29].
CNN One standard top tagging method applies a convolutional neural network (CNN) trained on jet images, generated from the list of per-jet constituents of the reference sample  [16]. We perform a specific preprocessing before pixelating the image. First, we center and rotate the jet according to its p T -weighted centroid and principal axis. Then we flip horizontally and vertically so that the maximum intensity is in the upper right quadrant. Finally, we pixelate the image with p T as the pixel intensity, and normalize it to unit total intensity. Although our original method includes color images, where the track p T 's and neutral p T 's are considered separately, for this dataset we restrict ourselves to gray-scale images. For the design of our CNN, we take the DeepTop architecture  [15]  as a starting point, but augment it with more feature maps, hidden units, etc.
ResNeXt The ResNeXt model is another deep CNN using jet images as inputs. The images used in this model are 64×64 pixels in size centered on the jet axis, corresponding to a granularity of 0. 025 radians in the ηφ space. The intensity of each pixel is the sum of p T of all the constituents within the pixel. The CNN architecture is based on the 50-layer ResNeXt architecture  [31]. To adapt to the smaller size of the jet images, the number of channels in all the convolutional layers except for the first one is reduced by a factor of 4, and a dropout layer with a keep probability of 0. 5 is added after the global pooling. The network in implemented in Apache MXNet  [32] , and trained from scratch on the top tagging dataset.
This is a proper rotation, not a simple rotation in the η-φ plane, and thus preserves the jet mass (but can distort quantities like N -subjettiness  [7, 34] ). The TopoDNN tagger presented here has a similar architecture as a tagger with the same name used by the ATLAS collaboration  [35]. The architecture of this TopoDNN tagger was optimized for a dataset of high p T (450 to 2400 GeV) R = 1. 0 trimmed jets. Its hyperparameters are the number of constituents considered as inputs, the number of hidden layers, the number of nodes per layer, and the activation functions for each layer. For the top dataset of this study we find that 30 constituents saturate the network performance. The ATLAS tagger uses only 10 jet constituents, but the inputs are topoclusters  [36]  and not individual particles.
The P-CNN used in this paper follows the same architecture as the CMS particle-based DNN tagger. However, the top tagging dataset in this paper contains only kinematic information of the particles, while the CMS particle-based DNN tagger uses particle tracks and secondary vertices in addition. Therefore, the network components related to them are removed for this study. The P-CNN is similar to the ResNet model  [46]  for image recognition, but only uses a 1-dimensional convolution instead of 2-dimensional convolutions. One of the features that distinguishes the ResNet architecture from other CNNs is that it includes skip connections between layers. The number of convolutional layers is 14, all with a kernel size of 3. The number of channels for the 1-dimensional convolutional layers ranges from 32 to 128.
Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a widely used unsupervised learning technique used in generative modelling for collections of text documents  [53]. It can also uncover the latent thematic structures in jets or events by searching for co-occurrence patterns in high-level substructure features  [54]. Once the training is performed the result is a set of learned themes, i. e. probability distributions over the substructure observable space. For the top tagger, we use a two-theme LDA model, aimed at separate themes describing the signal and background. We then use these distributions to infer theme proportions and tag jets. Both the training and inference are performed with the Gensim software  [55]. Before training, we pre-process the jets and map them to a representation suitable for the Gensim software.
The EFP-multigraph correspondence yields simple visual rules for translating multigraphs to observables: vertices contribute an energy factor and edges contribute an angular factor. Beyond this, the multigraphs provide a natural organization of the EFP basis when truncating in the number of edges (or angular factors) d, with exactly 1000 EFPs with d ≤ 7. For the top tagging EFP model, all d ≤ 7 EFPs computable in O(M 3 ) or faster are used (995 observables total) with angular exponent β = 1/2. Linear classification was performed with Fisher's Linear Discriminant  [59]  using Scikit-learn  [42]. Implementations of the EFPs are available in the EnergyFlow package  [60].
Only constituent 4-momentum information is used here. Exploring the importance of particle-type information for top tagging is an interesting direction for future studies. The PFN architecture parameterizes the functions Φ and F with neural networks. For the top tagging EFN and PFN models, Φ and F are parameterized with simple three-layer neural networks of (100, 100, 256) and (100, 100, 100) nodes in each layer, respectively, corresponding to a latent space dimension of = 256. A ReLU activation is used for each dense layer along with He-uniform parameter initialization  [62] , and a two-node classifier output is used with SoftMax activation, trained with Keras  [63]  and TensorFlow  [41]. Implementations of the EFNs and PFNs are available in the EnergyFlow package  [60].
It is implemented as a fully connected network with 5 layers containing 100-100-100-20-2 nodes. All activation functions are ReLu, apart from the final layer's SoftMax. Training is performed with the Adam  [30]  optimizer with an initial learning rate of 0. 001 and binary cross-entropy loss. We train for up to 50 epochs, but terminate if there is no improvement in the validation loss for two consecutive epochs, so a typical training ends after 5 epochs. The training data is provided by individual tagger output on the previous test sample and split intro three subsets: GoaT-training (160k events), GoaT-testing (160k events) and GoaT-validation (80k events). We repeat training/testing nine times, re-shuffling the events randomly between the three subsets for each repetition. The standard deviation of these nine repetitions is reported as uncertainty for GoaT taggers in Tab.
Conclusion Because it is experimentally and theoretically well defined, top tagging is a prime benchmark candidate to determine what we can expect from modern machine learning methods in classification tasks relevant for the LHC. We have shown how different neural network architectures and different representations of the data distinguish hadronically decaying top quarks from a background of light quark or gluon jets. We have compared three different classes of deep learning taggers: image-based networks, 4-vector-based networks, and taggers relying on additional considerations from relativistic kinematics or theory expectations. We find that each of these approaches provide competitive taggers with comparable performance, making it clear that there is no golden deep network architecture. Simple tagging performance will not allow us to identify the kind of network architectures we want to use for jet classification tasks at the LHC.
Acknowledgments First and foremost we would like to thank  Jesse Thaler  for his insights and his consistent and energizing support, and  Michael Russell  for setting up the event samples for this comparison. We also would like to thank  Sebastian Macaluso  and  Simon Leiss  for preparing the plots and tables in this paper. In addition, we would like to thank all organizers of the ML4Jets workshops in Berkeley and at  FNAL  for triggering and for supporting this study. Finally, we are also grateful to  Michel Luchmann  for providing us with Fig. 1. PTK and EMM were supported by the  Office of Nuclear Physics of the U. S. Department of Energy (DOE)  under grant  DE-SC-0011090  and by the  DOE Office of High Energy Physics  under grant  DE-SC-0012567 , with cloud computing resources provided through a  Microsoft Azure for Research Award.
2  in linear plot (Fig. 2a ) and semi-log plot (Fig. 2b ) displaying the typical exponential dependence. A fit of the data with Simmon's model  46  allows the estimation of the barrier thickness (3 nm) and the mean barrier height (2. 01 ± 0. 03 eV) using an effective mass of the electron of 0. 23m e  47. As an additional check of the tunnel regime, we show in the Supplementary Note 4 that the current is proportional to the area of the device for four different devices and we also show that the J(V) curve does not depend on temperature. Antenna surface plasmon emission. We now turn to the optical characterization of the emitted surface plasmons. The optical observation of the SPPs emitted by the Al/AlOx/Au junction is based on leakage radiation microscopy (LRM) using an inverted optical microscope.