dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Tanju Gleisberg  for his collaboration in the initial stages of the development described above; he contributed in an important way to the tools described here. We are also grateful to  Kemal Ozeren  for his collaboration and input, and we thank  Joey Huston  for his continual encouragement. We also thank  David Saltzberg , and  Eric Takasugi  for discussions and feedback. This research was supported by the  US Department of Energy  under contracts  DE-AC02-76SF00515  and  DE-FG02-13ER42022. DAKs research is supported by the  European Research Council  under  Advanced Investigator Grant   ERC-AdG-228301. DM's work was supported by the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement number  PITN-GA-2010-264564  (LHCPhenoNet). SH's work was partly supported by a grant from the  US LHC Theory Initiative  through  NSF  contract  PHY-0705682.
2. The beam is extracted from the SPS and injected into the decay ring. The ring is oriented so that the neutrino beam is directed towards Fréjus in France. A beta beam facility, using the isotope pair 6 He= 18 Ne and a baseline from CERN to Fréjus in France has been studied within the European Framework Program 6, the FP6 EURISOL Design Study  (2005) (2006) (2007) (2008) (2009)   [12]. The studied scenario is based on CERN infrastructure and machines and on existing technologies. After the EURISOL Design Study there was an estimated significant deficit in the production of 18 Ne. The European FP7 EUROnu beta beam proposal was based on a new idea to produce neutrinos from the decay of 8 Li and 8 B with an internal target in a production ring  [13]  to be able to get the required rates of neutrinos.
, of the order of 1%), the large negative chromaticity may induce resonance crossing and losses. A first attempt to include sextupoles in the lattice to compensate the chromaticity led to dynamic aperture problems. A trade-off between the increase in tune spread and the reduction in dynamic aperture has to be found. Moreover, a large second order dispersion in the straight sections leads to a nonzero dispersion in the rf cavity for particles with a 1% momentum offset and to a 10% difference in the cooling section, which may need to be taken into account. This lattice, which still needs to be tuned for optimizing the cooling efficiency, is used to set up tracking simulations and for identifying the parameters to reduce the blowup  [34]. 6. The code SIXTRACK for the production ring SIXTRACK  [19]  is a fully 6D, single-particle tracking code, based on high order truncation of Taylor expansion, which is widely used at CERN for dynamic aperture studies and for collimation.
The code had to be adapted for the production ring simulations. The production target has been implemented in the code as a special element and the interaction with matter modeled by simple analytical formulas. Since SIXTRACK can only deal with protons, an equivalent proton beam is tracked, with the same rigidity (Bρ) and the same momentum Δp rf =p recovered at the rf cavity. Before the interaction with the target, the proton energy is converted to the 7 Li equivalent and then back again after the target  [27]. The equivalent proton energy is 19 MeV and the energy recovered at the rf cavity is ΔE rf ¼ 0. 22 MeV for the reference particle. The rf voltage and synchrotron phase, for a harmonic number h ¼ 1, have been set to V ¼ 860. 6 kV and ϕ s ¼ 15°, from considerations of bucket height, but this can be further tuned.
The software used for the data acquisition is a customized version of CAEN WAVEDUMP, able to handle and synchronize two or more digitizers. Three different kinds of information are expected to be obtained processing the scintillator signals: the energy release of the impinging particles, its time of flight and the pulse shape discrimination between neutrons and gammas. Data are processed using algorithms able to perform RC/CR filters and constant fraction discriminator emulator. A proper baseline subtraction is computed from the raw data. Energy calibration of the BC501 detectors was done using 137 Cs, 60 Co and 88 Y gamma sources. Silicon detectors calibration was performed using a triple Am-Pu-Cm alpha source. The neutron gamma discrimination was achieved both by the time-of-flight and the zero-crossing method that rely on the longer tail of the neutron signals with respect to the gamma ones in liquid scintillators.
Summary The primary goal of this experiment was the measurement of the angular distribution of the 8 Li produced in the reaction dð 7 Li; pÞ 8 Li at 25 MeV. The experimental method takes advantage of the two-body nature of the process. 8 Li angular distribution is obtained by measuring the angular distribution of the protons. A by-product of this experiment is the total 8 Li production cross section. Considering that only the ground state is included, the cross section at 25 MeV is in good agreement with data from literature.
The black dots indicate the points at which the tune and losses were measured. There are many measurements close to each other; on the plot we see measurement where one tune has been held constant and the other has been varied. The color plot is then obtained by a MATLAB© interpolation function. The losses are quoted in terms of the derivative of the BCT signal dN=N. The plot identifies the lines from which the working point should be kept far from. It indicates that the most destructive line is the 2q x þ q y ¼ 1 and that the fourth order line 4q y ¼ 1 is not excited, but the third order 3q y ¼ 1 is.
The whole rf program was calculated by using MATHEMATICA  [109]. We will respectively note V 1 , ϕ 1 , V 2 , and ϕ 2 the voltage and phase of the first and second cavity. We will use r ¼ V 2 =V 1 the ratio between the voltages of the two cavities and ϕ 21 ¼ ϕ 2 -2ϕ 1 the phase difference.
Simulation of the merging To illustrate the different steps of the merging, we have drawn the injected beam at different moments. The simulation was performed after applying the rf program illustrated in Fig. 42   [111]. First of all, the fresh beam is injected with an offset of 5% in energy. This energy offset was determined to enable the insertion of a septum blade between the stored and injected beams at the injection  [87]. In order to optimize the capture, the beam does not enter exactly in phase with the stored beam and was tilted. The longitudinal emittance of the entering helium 6 beam was taken equal to 1 eV s according to the FP6 database  [112]. The beam is shown in Fig. 43  for several steps during the merging process. After one quarter turn, the beam is on momentum but is late compared to the stored beam.
ACKNOWLEDGMENTS We acknowledge the financial support of the  European Community  under the  European Commission Framework Program 7 Design Study: EUROnu , Project No. 212372. We would like to thank  L. Maran ,  S. Marigo ,  A. Minarello , and  L. Pranovi  for the excellent technical support during the experiment at LNL for the measurements of 8 Li and cross section and we are grateful to  Professor S. A. Goncharov  for the theoretical ZR-KO-DWBA calculations and the fruitful discussions. We also acknowledge the help for costing of the different pieces of equipment, given by the expert groups at  CERN  ( L. Bottura ,  G. de Rijk ,  E. Todesco ,  D. Tommasini ,  L. Tavian ,  A. Perin ,  J. -P. Burnet ,  J. Osborne ,  C. Waaijer ,  A. Kosmicki ,  L. Scibile ,  T. Pettersson ,  R. Catherall ,  H.
In our method we use Self-Organizing Topological Maps (SOM) which are clustering methods based on neural networks  [15]. They provide a discretization of a learning dataset into a reduced number of subsets, called classes, which share some common statistical characteristics. Each class corresponds to an index. For each class, the hidden data attributed to it is represented by a referent vector, which approximates the mean value of the elements belonging to it. These referent vectors are used to label any other data of the same dimension with the index of the "nearest" referent vector. The indexes represent the states of the HMM and their referent vectors are used to generate sequences of indexes of states that serve to learn the emissions and transition probabilities. The SOM training algorithm forces a topological ordering upon the map, and therefore any neighboring classes have referent vectors that are close in the Euclidean sense in the data space.
It is important to note that this is just a projection of the data on the first plane and that we did not reduce the dimension of our vectors by applying this PCA. The SOM maps were trained with the algorithms provided by the matlab som toolbox  [15] , specifically the functions som_make, som_batchtrain, som_bmus in order to train our maps and classify our data. The SOM maps were used to classify the datasets and generate two sequences of state indexes, SI obs and SI hid. These sequences were subsequently used to train the Hidden Markov Model according to the procedure presented in Section 2, and to estimate the HMM parameters. We made the assumption that, due to exterior factors such as heavy cloud coverage or satellite instrument malfunction, only during some (or none) of these time-steps there were observations available.
Its value is determined by an iterative optimization process on an independent test data set. In the case of the application (Section 3. 2) its value is taken to be equal to 9. The impact of neighboring states on the probabilities has been schematized in Figure  A1. It is important to retain that in order to apply PROFHMM_UNC, we require a training data set of concurrent hidden and observable states sequences. This, unlike more complicated cases of HMMs (such as voice recognition software), permits for an estimation of the initial emissions and transition probabilities through the use of a counting algorithm such as hmmestimate of the matlabstat_toolbox.
Acknowledgements The research presented in this paper was financed by  Centre National de l'Etude Spatial (CNES, French  national center of spatial studies), and the  Delegation Gouvernemantale pour l'Armement (DGA, French Military Research Delegation) , which we wish to thank for their support. We wish to also thank  Cyril Moulin  and  Laurent Bopp  from  LSCE  for their help with NEMO-PISCES and  Michel Crepon  for his input on the method.
This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent the space of candidate translations generated by the grammar when applied to an input sentence. General-purpose PDA algorithms for replacement, composition, shortest path, and expansion are presented. We describe HiPDT, a hierarchical phrase-based decoder using the PDA representation and these algorithms. We contrast the complexity of this decoder with a decoder based on a finite state automata representation, showing that PDAs provide a more suitable framework to achieve exact decoding for larger synchronous context-free grammars and smaller language models. We assess this experimentally on a large-scale Chinese-to-English alignment and translation task.
This is the general approach adopted in the HiFST decoder  (Iglesias et al. 2009a; de Gispert et al. 2010) , which first represents T as a Recursive Transition Network (RTN) and then performs expansion to generate a WFSA. Figure  1b  shows the space of translations for this example represented as an RTN. Like the hypergraph, it also has a 1:1 correspondence between each production in the CFG and paths in the RTN components. The recursive RTN itself can be expanded into a single WFSA, as shown in Figure  1c. Intersection and shortest path algorithms are available for both of these WFSAs. Pushdown Automata. Like WFSAs, PDAs are easily generated from RTNs, as will be described later, and Figure  1d  gives the PDA representation for this example. The PDA represents the same language as the FSA, but with fewer states.
HiPDT: Hierarchical Phrase-Based Translation with PDAs We introduce HiPDT, a hierarchical phrase-based decoder that uses a PDA representation for the target language. The architecture of the system is shown in Figure  2 , where we contrast it with  HiFST (de Gispert et al. 2010). Both decoders parse the sentence with a grammar G using a modified version of the CYK algorithm to generate the translation search space as an RTN. Each decoder then follows a different path: HiFST expands the RTN into an FSA, intersects it with the language model, and then prunes the result; HiPDT performs the following steps: 1. Convert the RTN into PDA using the replacement algorithm. The PDA representation for the example grammar in Section 1 is shown in Figure  1. The algorithm will be described in Section 3.
2. 2. Apply the language model scores to the PDA by composition. This operation is described in Section 3. 3. 3. Perform either one of the following operations: (a) Shortest path through the PDA to get the exact best translation under the model. Shortest distance/path algorithm is described in Section 3. 4. (b) Pruned expansion to an FSA. This expansion uses admissible pruning and outputs a lattice. We do this for posterior rescoring steps. The algorithm will be presented in detail in Sections 3. 5 and 3. 5. 2. The principal difference between the two decoders is the point at which finite-state expansion is performed. In HiFST, the RTN representation is immediately expanded to an FSA. In HiPDT, the PDA pruned expansion or shortest path computation is done after the language model is applied, so that all computation is done with respect to both the translation and language model scores.
The use of RTNs as an initial translation representation is somewhat influenced by the development history of our FST and SMT systems. RTN algorithms were available in OpenFST at the time HiFST was developed. HiPDT was developed as an extension to HiFST using PDA algorithms, and these have subsequently been included in OpenFST. A possible alternative approach could be to produce a PDA directly by traversing the CYK grid. WFSAs could then be generated by PDA expansion, with a computational complexity in speed and memory usage similar to the RTN-based approach. We present RTNs as the initial translation representation because the generation of RTNs during parsing is straightforward and has been previously presented  (de Gispert et al. 2010). We note, however, that RTN composition is algorithmically more complex than PDA (and FSA) composition, so that RTNs themselves are not ideal representations of T if a language model is to be applied.
Composition of PDAs with FSAs will be discussed in Section 3. 3. Figure  3  continues the simple translation example from earlier, showing how HiPDT and HiFST both benefit from the compactness offered by WFSA epsilon removal, determinization, and minimization operations. When applied to PDAs, these operations treat parentheses as regular symbols. Compact representations of RTNs are shared by both approaches. Figure  4  illustrates the PDA representation of the translation space under a slightly more complex grammar that includes rules with alternative orderings of nonterminals. The rule S→ X 1 s 2 X 2 , t 1 X 1 X 2 produces the sequence 't 1 t 3 t 4 t 5 t 6 ', and S→ X 1 s 2 X 2 , t 2 X 2 X 1 produces 't 2 t 5 t 6 t 3 t 4 '. The PDA efficiently represents the alternative orderings of the phrases 't 3 t 4 ' and 't 5 t 6 ' allowed under this grammar.
In these experiments we take HiFST as a contrastive system for HiPDT, but we do not present experimental results with hypergraph representations. Hypergraphs are widely used by the SMT community, and discussions and contrastive experiments between HiFST and cube pruning decoders are available in the literature  (Iglesias et al. 2009a; de Gispert et al. 2010 ). We will propose a two-pass translation decoding strategy for HiPDT based on entropy-pruned first-pass language models. Our complexity analysis prompts us to investigate decoding strategies based on large translation grammars and small language models. We describe, implement, and evaluate a two-pass decoding strategy for a large-scale translation task using HiPDT (Section 5). We show that entropy-pruned language models can be used in first-pass translation, followed by admissible beam pruning of the output lattice and subsequent rescoring with a full language model.
We analyze the search errors that might be introduced by a two-pass translation approach and show that these can be negligible if pruning thresholds are set appropriately (Section 5. 2). Finally, we detail the experimental conditions and speed/performance tradeoffs that allow HiPDT to achieve state-of-the-art performance for largescale SMT under a large grammar (Section 5. 3), including lattice rescoring steps under a vast 5-gram language model and lattice minimum Bayes risk decoding (Section 5. 4). With this translation strategy HiPDT can yield very good translation performance. For comparison, the performance of this Chinese-to-English SMT described in Section 5. 4 is equivalent to that of the University of Cambridge submission to the NIST OpenMT 2012 Evaluation. 1.
Composition Once we have created the PDA with translation scores, Step 2 in Section 1. 1 applies the language model scores to the translation space. This is done by composition with an FSA containing the relevant language model weights. The class of weighted pushdown transducers is closed under composition with weighted finite-state transducers  (Bar-Hillel, Perles, and Shamir 1964; Nederhof and Satta 2003). OpenFST supports composition between automata T 1 and T 2 , where T 1 is a weighted pushdown transducer and T 2 is a weighted finite-state transducer. If both T 1 and T 2 are acceptors, rather than transducers, the composition of a PDA and an FSA produces a PDA containing their intersection, and so no separate intersection algorithm is required for these automata. Given this, we describe only the simpler, special case of intersection between a PDA and an FSA, as this is sufficient for most of the translation applications described in this article.
Prior to expansion of a PDA T to an FSA T ′ , the shortest distance d in T is calculated. Transitions e = (q, a, w, q ′ ), a ∈ Π, are expanded as transitions e = ((q, z), q, w, (q ′ , za)) in T ′ only if c(e) ≤ d + β, as calculated by Equation (  7 ). The pruned expansion algorithm implemented in OpenFST is necessarily more complicated than the simple description given here. Pseudo-code describing the Open-FST implementation is given in Appendix B. The pruned expansion operation can be applied in any semiring having the path property.
2006; Xiao et al. 2009)  or scope pruning  (Hopkins and Langmead 2010). Of course, practical translation systems introduce various pruning procedures to achieve much better decoding efficiency than the worst cases given here. We will next describe the translation grammar and language model for our experiments, which will be used throughout the remainder of this article (except when stated otherwise). In the following sections we assess the complexity discussion with a contrast between HiFST (FSA representation) and HiPDT (PDA representation) under large grammars.
Translation Grammars and Language Models Translation grammars are extracted from a subset of the GALE 2008 evaluation parallel text; 6 this is 2. 1M sentences and approximately 45M words per language. We report translation results on a development set tune-nw (1,755 sentences) and a test set test-nw (1,671 sentences). These contain translations produced by the GALE program and portions of the newswire sections of the NIST evaluation sets MT02 through MT06. 7   In tuning the systems, MERT  (Och 2003)  iterative parameter estimation under IBM BLEU 8  is performed on the development set. The parallel corpus is aligned using MTTK  (Deng and Byrne 2008)  in both sourceto-target and target-to-source directions. We then follow published procedures  (Chiang 2007; Iglesias et al. 2009b ) to extract hierarchical phrases from the union of the directional word alignments.
Exact Decoding with Large Grammars and Small Language Models We now compare HiFST and HiPDT in translation with our large grammar G. In this case we know that exact search is often not feasible for HiFST. We run both decoders over tune-nw with a restriction on memory use of 10 GB. If this limit is reached in decoding, the process is killed. 9 Table  3  shows the number of times each decoder succeeds in finding a hypothesis under the memory limit when decoding with various entropy-pruned LMs M θ 1. With θ = 7. 5 × 10 -9 (row 2), HiFST can only decode 218 sentences, and HiPDT succeeds in 703 cases. The difference in success rates between the decoders is more pronounced as the language model is more aggressively pruned: for θ = 7. 5 × 10 -7 HiPDT succeeds for all but three sentences. As Table  3  shows, HiFST fails most frequently in its initial expansion from RTN to FSA; this operation depends only on the translation grammar and does not benefit from any reduction in the language model size.
Subsequent intersection of the FSA with the language model can still pose a challenge, although as the language model is reduced, this intersection fails less often. By contrast, HiPDT intersects the translation grammar with the language model prior to expansion and this operation nearly always finishes successfully. The subsequent shortest path (or pruned expansion) operation is prone to failure, but the risk of this can be greatly reduced by using smaller language models. In the next section we contrast both HiPDT and HiFST for alignment.
The computational analysis from the beginning of this section applies to alignment. The language model M is replaced by an acceptor for the target sentence, and if we assume that the target sentence length is proportional to the source sentence length, it follows that |M| ∝ |s| and the worst-case complexity for HiPDT in alignment mode is O(|s| 6 |G|). This is comparable to ITG alignment  (Wu 1997 ) and the intersection algorithm of  Dyer (2010b). Our experimental results support the complexity analysis summarized in Table  1. HiPDT is more efficient in ITG alignment and this is consistent with its linear dependence on the grammar size, whereas HiFST suffers from its exponential dependence. This use of PDAs in alignment does not rely on properties specific either to Hiero or to ITGs. We expect that the approach should be applicable with other types of SCFGs, although we note that alignment under SCFGs with an arbitrary number of nonterminals can be NP-hard  (Satta and Peserico 2005).
1. Such a two-pass strategy is widely used in automatic speech recognition  (Ljolje, Pereira, and Riley 1999). The steps in two-pass translation using entropy-pruned language models are given here, and depicted in Figure  14. Step 1. We translate with M θ 1 and G using the same parameters obtained by MERT for the baseline system, with the exception that the word penalty parameter is adjusted to produce hypotheses of roughly the correct length. This produces translation lattices that contain hypotheses with exact scores under G and M θ 1 : Π 2 ({s} • G) • M θ 1. Step 2. These translation lattices are pruned at beamwidth β: [Π 2 ({s} • G) • M θ 1 ] β. Step 3. We remove the M θ 1 scores from the pruned translation lattices, reapply the full language model M 1 , and restore the word penalty parameter to the baseline value obtained by MERT.