dataesr/scientific-paragraphs-categorization huggingface dataset:
This approach is fitted by solving n Lasso problems (one per row of Y) by means of the R package glmnet. 3. Third, we consider an adaptation of the fused-Lasso (hereafter FL2D). Indeed, as illustrated in the introduction, the basic 2-dimensional fused-Lasso for signal approximator is not tailored for recovering change points. We thus consider the following variant, which applied a fused-Lasso penalty on the following linear model: Y =          1 n 0 n • • • • • • 0 n I n 0 n 1 n. 1 n 0 n. 0 n • • • • • • 0 n 1 n I n          X (F L)            β (F L) 1. β (F L) n β (F L) n+1. β (F L) 2n            B (F L) + E where 1 n (resp. 0 n ) is a size-n column vector of ones (resp. zeros), I n a n × n-diagonal matrix of ones and Y, E are defined as in Equation (2.
3). The FL2D method detects a change-point in columns (resp. in row) if two successive values β (F L) i and β (F L) i+1 with 1 ≤ i ≤ n -1 (resp. n + 1 ≤ i ≤ 2n -1) are different. To solve this problem, we must fit a general fused-Lasso problem. We rely on the R package genlasso for this task. 4. Finally, our own procedure, that we call blockseg, is implemented in the R package blockseg which is available from the Comprehensive R Archive Network (CRAN,  [17] ). Most of the computation are performed in C++ using the library armadillo for linear algebra  [18]. In what follows, all experiments were conducted on a Linux workstation with Intel Xeon 2. 4 GHz processor and 8 GB of memory.
Statistical performances We evaluate the performance of the different competitors for recovering the true change-points in the 4 scenarii defined in Section 4. 1 for an increasing level of difficulty. We draw 1000 datasets for each scenario for a varying level of noise σ ∈ {1, 2, 5, 10} and for a problem size of n = 100. Note that we use this relatively small problem size to allow the comparison with methods HL and FL2D that would not work for greater values of n. Figure  7  shows the results in terms of receiver operating characteristic (ROC) curves for recovering the change-points in rows, averaged over the 1000 runs. Similar results hold for the change-points in columns. This Figure exhibits the very good performance of our method, which outperforms its competitors by retrieving the change-points with a very small error rate even in high noise level frameworks.
Application to HiC data In this section, we apply our methodology to publicly available HiC data (http: //chromosome. sdsc. edu/mouse/hi-c/download. html) already studied by  [4]. This technology is based on a deep sequencing approach and provides read pairs corresponding to pairs of genomic loci that physically interacts in the nucleus, see  [12]  for more details. The raw measurements provided by HiC data is therefore a list of pairs of locations along the chromosome, at the nucleotide resolution. These measurement are often summarized as a square matrix where each entry at row i and column j stands for the total number of read pairs matching in position i and position j, respectively. Positions refer here to a sequence of non-overlapping windows of equal sizes covering the genome. The number of windows may vary from one study to another:  [12]  considered a Mb resolution, whereas  [4]  went deeper and used windows of 40kb (called hereafter the resolution).
Conclusion In this paper, we proposed a novel approach for retrieving the boundaries of a block wise constant matrix corrupted with noise by rephrasing this problem as a variable selection issue. Our approach is implemented in the R package blockseg which is available from the Comprehensive R Archive Network (CRAN). In the course of this study, we have shown that our method has two main features which make it very attractive. Firstly, it is very efficient both from the theoretical and imsart-ss ver. 2014/10/16 file: BlockSeg_Brault_Chiquet_Levy. tex date: September 7, 2016 practical point of view. Secondly, its very low computational burden makes its use possible on very large data sets coming from molecular biology.
Our package imsart-ss ver. 2014/10/16 file: BlockSeg_Brault_Chiquet_Levy. tex date: September 7, 2016 σ = 1 σ = 2 n = 50 n = 100 n = 250 n = 50 n = 100 n = 250 blockseg 0. 896 0. 972 0. 993 0. 791 0. 923 0. 982 (0. 0425) (0. 0162) (0. 00463) (0. 0789) (0. 0398) (0. 00865) FL2D 0. 814 (0. 132) 0. 906 (0. 0997) X 0. 679 (0. 133) 0. 753 (0. 128) X HL1 0. 574 0. 619 0. 66 0. 467 0. 527 0. 611 (0. 0598) (0. 0426) (0. 0255) (0. 0899) (0. 084) (0. 0513) HL2 0. 56 0. 573 0. 59 0. 424 0. 451 0. 472 (0. 101) (0. 0642) (0. 0432) (0. 0972) (0. 0713) (0. 0467) CART 0. 445 0. 479 0. 498 0. 487 0. 487 0. 512 (0. 123) (0. 108) (0. 0589) (0. 125) (0. 114) (0. 0708) σ = 5 σ = 10 n = 50 n = 100 n = 250 n = 50 n = 100 n = 250 blockseg 0. 646 0. 739 0. 91 0. 577 0. 642 0. 766 (0. 127) (0. 11) (0. 0394) (0. 112) (0.
The DONUT  [3]  experiment employed a combination of magnetic and passive shielding to clear muons from their emulsion target, which was located 36 m downstream of the target. SHiP will accumulate three orders of magnitude more protons on target, and hence requires a much larger reduction in muon flux for both the emulsion and the hidden sector particles search. To achieve this SHiP will employ the novel magnetic shielding concept described in this paper. The basic magnetic configuration that addresses these issues is presented in the next section. Section 3 describes the optimization procedure with a simplified Monte Carlo tool to obtain a layout which reduces the muon flux sufficiently for a minimum size of the system. The optimization is checked by implementing the obtained layout in the GEANT4-based SHiP software package, and its performance is presented in section 4.
Active muon shield performance The SHiP experiment simulations are performed in the software package FairShip  [1]. FairShip is mostly a collection of libraries and scripts based on the FairRoot framework  [10] , which is fully based on the ROOT software framework  [13]. In the framework, users can construct their detectors, and perform simulation and analysis without any code dependence on a specific Monte Carlo simulation software. FairRoot uses the ROOT geometry package TGeo to build, browse, track and visualize a detector geometry. The muon shield can be made of two different magnet types, that are depicted in figure  3. When there is enough air-gap between the iron yokes with the opposite field direction to place a coil a magnet of type a) in figure  3  is used. If the optimization requires an air-gap which is too small to accommodate a coil, a magnet of type b) in figure  3 , which has place for coils at the top and bottom, is used.
After validating mathematical models with available experimental data, ADRS is simulated by using Simulink-Matlab software to achieve optimum times for various processes. The results of simulation show that the cooling capacity of the system with Zeolite 13x/CaCl 2 -water is more than the other types. The results have shown that the arrangement of adsorbents affects cooling capacity of combined ADRS significantly. In Type A, Zeolite 13x/CaCl 2 -water has been used as an adsorbent for both top and bottom cycles. This type not only has more cooling capacity than the other types, but also the effect of hot water temperature on cooling capacity of this type is less than the others. Furthermore, a sensitivity analysis has been done to determine the importance of each parameter on ADRS system because the cooling capacity and the COP are influenced by many constant parameters.
Peptide-protein docking protocol A reduced version of the pepATTRACT protocol (  12 ) is performed, using only the rigid-body stage. Three idealized peptide model structures are generated from sequence using the Python library PeptideBuilder  (22). Missing side chain atoms in the protein are completed using the program PDB2PQR  (23). The protein and peptide structures are converted to the ATTRACT coarse-grained atom type representation  (13). Global rigid body docking is performed with ATTRACT  (13, 14) , using 100 000 random starting positions per peptide model. The rigid body docking solutions are ranked by ATTRACT score. The top 10,000 docking solutions are clustered by pairwise ligand RMSD using a cutoff of 1 Å.
Web server input The web server requires: The structure of the protein receptor in PDB format. If the receptor consists of multiple chains, they are concatenated. In case of nuclear magnetic resonance structures, the first model is selected. Only PDB ATOM records are considered. Co-factors (HETATM records) are ignored. Missing side chains are modeled using the program PDB2PQR  (23). Modified amino acid side chains are replaced by dummy atoms. The maximum size of the protein receptor is 10 000 heavy atoms. The sequence of the peptide (one-letter code). Modified amino acids are not supported. During the docking, the peptide will be modeled as an ensemble of three idealized peptide conformations. In theory, pepATTRACT enforces no particular maximum length of the peptide sequence, but note that as the length of the peptide grows, the probability decreases that its structure is well approximated by one of the three idealized peptide conformations that are used in the docking.
Web server output The server returns 50 atomic models, with each model being the lowest-energy structure of a docking cluster. These models can be downloaded and they are also visualized directly in the browser. The interactive display relies on the PV--JavaScript Protein Viewer (https://biasmv. github. io/pv/). The interface propensity of every residue is also given (see below). Note that all protein residues have been renumbered from 1, their residue number may be different than in the original PDB. Finally, the ATTRACT force field energy of each model is shown in a table. Note that this energy should be used only to identify the correct model, it cannot be used to predict binding affinities.
Interface prediction performance The web server also returns an analysis of the protein residues that are most prevalent in protein-peptide contacts among the top 50 docking models (before clustering). The interface propensity (the average number of proteinpeptide contacts) of each residue is visualized in a table. In the docking model visualization, the interface propensity can be projected onto the receptor protein. When the top N residues with the highest interface propensity are selected (with N being the total number of interface residues), the specificity (precision) and sensitivity of the interface prediction are both 37. 2%. This is considerably better than two existing web servers that we tested. PEP-SiteFinder (8) achieved a sensitivity/specificity of 27. 3% on the same dataset. PepSite  (7) , which only accepts peptides up to 10 amino acids, achieved 13.
Examples of web server output Example 1. In demonstration mode, the web server performs a docking between cyclophilin A (unbound crystal structure; PDB code: 2ALF) and the HAGPIA peptide from the HIV-1 capsid protein. Figure  1  shows the results of the docking. On the left, the 50 best peptide poses are depicted. One sees that the peptide tends to always interact with the same region on the receptor surface. Among the top 50 models, the third model has an interface RMSD of 0. 83 Å toward the crystal structure of the cyclophilin A--peptide-protein complex (PDB code: 1AWR) (right--the experimental peptide conformation is in magenta). Example 2. The WD40 domain of WDR5 represents a class of histone methyl-lysine recognition domains that is important for recruiting H3K4 methyltransferases to K4dimethylated histone H3 tail as well as for global and genespecific K4 trimethylation.
5-1. 5 mm of the 12 mm deep gap as shown in figure  3  (right)  [24]. Deposition profiles for deuterium (figure  3  (right)) and metals have several characteristic features: (i) low D content at the very entrance to the gap, (ii) increase of the concentration with maximum reached at about 0. 5 mm and then sharp decrease; (iii) very small content of metals (at least three orders of magnitude lower compared to deuterium). In absolute number, the total deuterium retained within the castellated gaps remains small: 3% of the total fuel inventory. It should also be stressed that the amount of carbon is very low thus confirming the low amount of carbon impurities in JET-ILW. Deposition of deuterium inside the Be castellated structures has been modelled using 3D-GAPS code  [24, 37]. Steep profiles have been reproduced for narrow gaps (0.
g. ERO code  [39, 44] ). The passive spectr oscopy experiments for physical and chemical erosion data  [45]  were interpreted using the simulations by the ERO code  [39, 44]. For the determination of the Be yields and erosion data assessment, dedicated experiments were performed where the limiter plasmas were shifted towards the inner wall with single plasma-wall interaction poloidal contact point  [46]. It was shown that the fit of the effective physical sputtering yield (based on molecular dynamic and binary-collision approximation simulations) assuming Be with 50% deuterium in the interaction layer with the PFC is recommended for the erosion modelling of the Be plasma-wetted surfaces  [46]. A procedure for numerical (ERO) or analytic  [47]  generation of angle and energy distributions of sputtering ions on their impact with surface was suggested and proved to be of importance.
In addition, an important influence of the coupled RF power from ICRH antenna on erosion (factor 2-3 increase of erosion yield compared to cases without ICRH) was investigated by antenna sequential toggling experiments (switching on and off the antennas) simulated by the ERO code  [48]. The analytical procedure necessary for calculating the local effective yields was adapted to take into account an additional surface biasing effect due to RF sheath rectification  [49]. It allows using the same sputtering assumptions ('ERO-min' fit) as at the inner wall to reproduce the antenna toggling effect. Several important issues like for instance the fraction of chemically assisted physical sputtering versus physical sputtering, the amounts of Be-D molecules released (fraction of BeD 2 , BeD 3 ) need further investigation.
Typically, in the DSMC approach the simulation of large number of model particles are evolved in small time steps in which their free motion and collisions are uncoupled. Based on this approach, a novel divertor gas simulator code (DIVGAS) is capable of modelling complex neutral gas flows in a tokamak sub-divertor. For validation of the code, demonstration of its feasibility and general benchmark, experimental data from JET (L-mode plasma cases with the JET ITER-like wall configuration) are successfully replicated over a wide range of density. To be more specific, the pressure gauge readings taken in the subdivertor region, properly corrected for the influence of the low conductance connection pipe so as to get representative values for the subdivertor volume, are well described using EDGE2D-EIRENE calculations as boundary conditions on the plasma side  [83].
APODIS was trained with more than 8000 JET discharges corresponding to carbon wall operations and it is being used during the ILW campaigns without any retraining since 2010. The initial result just after the first three ILW campaign (991 discharges) has shown a success rate of 98. 36% (with a warming time on average 426 ms prior to the disruption time) and a false alarm rate of 0. 92%  [100]. The requirement of a large database of disruptive discharges to train the predictor is not compatible with safe ITER and DEMO operations. Therefore, adaptive predictors have been proposed with high learning rates using a limited disruption database  [101] [102] [103] [104]. The strategy is to retrain the predictors on a wider database only after missed disruption detection. The most recent predictor developed in JET avoids the use of past discharges for training purposes  [105, 106].
Knowledge of the L-H threshold power is therefore critical to the optimization of the time sequence of the experimental programme towards D-T operation and availability of the ITER systems. In the active phase of ITER operation, the anticipated threshold power on ITER is 53 MW in deuterium and 43 MW in deuterium-tritium mixture (at a density of 5 × 10 19 m -3 at 5. 3 T) and is extrapolated from a multi-machine scaling  [112] , based on density, magnetic field and machine size. Many hidden parameters are known to affect P L-H leading to large scatter (factor 4) in P L-H in the present database and induce considerable uncertainty in the extrapolation. Because the available external heating power on ITER (73 MW max) is only marginally above the expected threshold, there is an ongoing effort to understand the underlying physics behind these additional dependencies and to evaluate if any could be used to lower the power threshold on ITER and widen the operational space.
This new experimental observation suggests that ETG destabilisation plays a role in explaining the anomalous electron heat transport. Non-linear single-scale ITG/TEM turbulence simulations performed with the GENE code  [148]  in the local limit showed that the electron heat flux driven by ITG/TEM turbulence alone is not enough to match the experimental data, and that the observed level of electron stiffness is higher than the simulated one. Including ETG turbulence could provide a better match of the experimental electron heat flux, but it requires taking into account interactions between ion and electron scales, with the ion zonal flows being a saturating mechanism for ETGs. First results of these costly multi-scale simulations, the first of this kind for JET plasmas, indeed indicate a significant fraction of electron heat flux carried by ETGs in the experimental conditions studied.
Finally, the strong dependence with ν * in JET-ILW is related to an improvement in the pedestal stability at low collisionality (figure  20 ). The Peeling-Ballooning stability analysis shows that this improvement at low collisionality is due to three factors: (i) the increase of the bootstrap current, (ii) the reduction of the pedestal width, and, (iii) the reduction of the relative shift between the positions of the pedestal density relative to the pedestal temperature. The behaviour of the pedestal stability with collisionality might suggest an explanation for the difference in the scaling of the normalized confinement versus ν * observed in JET-C and JET-ILW. Indeed, the pedestal width and relative shift of the JET-C dataset are not affected by collisionality. In this context, the relative shift between the electron density and the temperature pedestal position, hereafter referred to as 'pedestal relative shift', seems to play an important role.
Comparison of JET-C and JET-ILW data low δ-baseline operational regime within the same range of β and ν * shows that JET-C H-mode regimes have systematically a smaller 'pedestal relative shift' compared to JET-ILW, and, that α values decrease with the relative shift (figure  21  (right))  [159]. Normalized pressure gradient has been calculated for extended dataset of JET-ILW baseline low δ pulses and compared with low δ JET-C pulses. In agreement with the exper imental results, stability analysis shows an improvement in the pedestal stability when the relative shift is reduced. Moreover, for similar values of the 'pedestal relative shift', the nor malized pedestal pressures, α, are comparable for the two types of wall materials. The origin of the 'pedestal relative shift' is still unclear and is currently under high invest igation.
In addition non-ideal and non-linear MHD JOREK simulations, which use a low viscosity and resistivity MHD model, have been systematically performed to assess the pedestal pressure at the onset of the MHD modes on a subset of the JET-ILW database  [53, 54]. The critical non-ideal MHD limit is determined by running JOREK, starting from a stable pedestal pressure, p ped value, and increasing it progressively (together with the corresponding bootstrap current deduced from the Sauter's model) until a MHD mode becomes unstable. For comparison, the critical ideal MHD limits have also been estimated on the same database by increasing p ped values (and self-consistent bootstrap current) until the finite-n Peeling Ballooning modes growth rates deduced from ELITE  [159, 164, 165]  indicate that the stability threshold has been crossed.
Particle transport Particle transport in tokamaks has received much less attention than electron and ion heat transport channels. It is still often not treated self-consistently in transport modelling and predictions for future tokamaks. As a consequence, particle transport and fuelling have remained one of the major open questions in understanding the ITER physics. The shape of the density profile has a significant influence on fusion performance and impurity transport. Extensive database studies in JET showed that density peaking scales with several plasma parameters, the most dominant ones being collisionality ν * , Greenwald fraction and NBI fuelling  [176] [177] [178]. Collisionality was found to be the dominating parameters in JET. On the other hand, while the database studies suggested the dominant role played the collisionality in peaking the density, other particle transport analyses in JET emphasise the importance of the particle sources  [179] [180] [181] , i.
The extrapolation to future tokamaks, like ITER, is the final goal here and based on these results, the density peaking may not be as high as predicted in the earlier database papers  [176] [177] [178] [179]  in the absence of core particle sources. In addition to core particle transport studies, several edge diagnostics were exploited to diagnose the neutral sources and understanding edge particle transport and fuelling  [183]. Experimental analysis suggests that particle source inside separatrix is fairly narrow and it does not contribute much inside the pedestal top. Inward convection of the order of 5 m s -1 at the plasma edge is needed to sustain the steep pedestal. This is also supported by time-dependent EDGE2D-EIRENE modelling that was performed for roughly over one modulation cycle  [184].
D-T projection of the expected fusion performance has been first calculated based on present plasma scenario but assuming that the experimental profiles and confinement enhancement factors could be translated at higher field, current and applied powers (i. e. temperature and density profile shapes are kept constant with a fixed ratio of the density normalised to Greenwald density when increasing the plasma current). With these assumptions the predicted fusion performance is 7. 5 MW for the baseline scenario (at H IPB98(y,2) = 0. 8) at high current (4. 5 MA) and 13 MW for the hybrid regimes with 40 MW of applied power at lower current (2. 5-3. 0 MA). To complement this simple prediction, more sophisticated time dependent integrated scenarios modelling have been performed  [175]  using Trap Gyro-Landau Fluid (TGLF) model  [205]  for the core transport validated on the JET database  [204].
This is due to a stronger turbulence stabilization of core turbulence in D-T than in D-D, which also leads to an increase of density peaking for D-T. Therefore, the equivalent fusion power also significantly increases in D-T when including the effective mass change on transport, from P fus ≅ 11 MW to 16 MW. A simple explanation has been proposed by taking into account the mass effect in the effectiveness of the E × B flow shear for quenching ITG transport (it scales as the square root of the ion mass) and has been confirmed by performing D-D and D-T simulations with and without ExB flow shear stabilization  [175]. The isotope effect has also been analyzed by performing gyro-kinetic simulations with the GENE code for the ITER hybrid scenario including kinetic electrons, collisions, electromagnetic effects and up to five species  [206].