dataesr/scientific-paragraphs-categorization huggingface dataset:
e. the gross overestimate in BBN models of the primordial abundance of Lithium. Although neutron-induced reactions of relevance for Nuclear Astrophysics are being studied since many decades at neutron facilities worldwide, some open issues in stellar and primordial nucleosynthesis still remain to be addressed in order to reach a more comprehensive understanding of the elemental abundance distribution and of the galactic chemical evolution. To this end, an intense experimental program is undergoing at the neutron facility n_TOF (CERN) since almost two decades, in order to reduce the uncertainty on neutron capture and (n, charged particle) cross sections for some key isotopes, and ultimately improve the reliability of astrophysical models. Thanks to the innovative characteristics of the neutron beam, to the development of high-performance detection and data acquisition systems, and to the use of refined analysis and simulation techniques, a number of valuable results of relevance for the field of Nuclear Astrophysics have been produced so far.
Measuring the neutron capture cross section of these three isotopes is a challenging task because of the high specific activity of these radionuclides, typically resulting in a poor signal-to-background ratio, and because of the difficulty in the procurement of a sufficient amount of material with adequate purity, as well as in sample preparation and characterization. Because of these problems, very scarce or no data have been collected so far on these isotopes, and at present one can only rely on highly uncertain theoretical calculations, as reported in Table  I , or on a single activation measurement for 147 Pm  [10]. The availability of intense neutron beams at n_TOF finally provided the unique opportunity to collect, for the first time, high quality data at a time-of-flight installation for these reactions.
A 6 Li-glass detector was used in the transmission measurement, performed in the experimental station placed at 50 m distance from the neutron-producing target. High-resolution neutron capture yields for a high-purity, metallic 25 Mg sample measured in EAR1 at n_TOF, were combined with the transmission data collected at GELINA in a simultaneous R-matrix analysis performed on both data sets. This allowed to univocally assign the spin and parity of some important excited states of 26 Mg with natural parity just above the neutron separation energy, not easily accessible in the direct a+Ne reactions. Based on the n_TOF/GELINA data, a new, more reliable estimate of the 22 Ne(a,n) cross section below 800 keV a-particle energy was obtained. The result, shown Figure  2 , puts firmer constraints on the reaction rate of this important stellar neutron source.
The precise knowledge of (anti-)neutrino fluxes is one of the largest limitation in accelerator-based neutrino experiments. The main limitations arise from the poorly known production properties of neutrino parents in hadron-nucleus interactions. Strategies used by neutrino experiment to constrain their fluxes using external hadroproduction data will be described and illustrated with an example of a tight collaboration between T2K and NA61/SHINE experiments. This enabled a reduction of the T2K neutrino flux uncertainty from ∼25% (without external constraints) down to ∼10%. On-going developments to further constrain the T2K (anti-)neutrino flux are discussed and recent results from NA61/SHINE are reviewed. As the next-generation long baseline experiments aim for a neutrino flux uncertainty at a level of a few percent, the future data-taking plans of NA61/SHINE are discussed.
Hadroproduction experiments 2. 1. Introduction Data taken in hadroproduction experiments can be sub-divided into two categories : Thin target where the primary p-A interaction can be studied. In particular the total p-A inelastic (or production) cross section can be measured as well as the differential multiplicity of various secondary particles (π ± , K ± , neutral strange particles, protons). As for T2K, at the peak energy, it has been shown that 60% of the ν μ and ν e neutrinos originate from the decay of hadrons produced in the primary interaction  [5]  (in positive focusing mode). Replica target of the corresponding neutrino experiment is used. The differential yield per incoming beam proton of charged pions escaping the target is measured. At T2K, at the peak energy, 90% of the ν μ and ν e neutrinos originate from hadrons produced in the target  [5]  (in positive focusing mode).
NA61/SHINE The NA61/SHINE experiment (successor of the NA49 experiment) is located at the H2 beamline of the CERN SPS. It is a large acceptance spectrometer with 5 TPCs -two of them are inside a superconducting magnet -and a time of flight detector  [11]. NA61/SHINE has taken data for the T2K experiment (31 GeV/c proton on a graphite target) between 2007 and 2010 with two graphite target configurations : thin (up to 5. 4 M triggers) and a T2K replica (up to 10 M triggers), and is now conducting measurements for the NuMI-based experiments  [12]. The charged particle identification is performed through a combined ToF-dE/dx (dE/dx only) analysis for particle with momentum above (below) 1 GeV/c. Neutral strange particles are selected using a standard invariant mass fit criteria. Using the thin target data, the differential multiplicity of π ± , K ± , p, K 0 S and Λ production has been measured  [13, 14, 15, 16]  in bins of secondary particle momentum and polar angle.
Conclusions and prospects The tight collaborations between hadroproduction and long baseline neutrino experiments lead to precise knowledge of the neutrino flux. Without hadroproduction data, the neutrino flux error would amount to about 25%. Using thin-target tuning, the flux uncertainty down to about 10% and the replica-target tuning can push the uncertainty down to about 5%. Future long baseline experiments (DUNE, Hyper-Kamiokande) are computing their sensitivities with the assumption that the flux error will be known at a level below 5%. Therefore, new sets of data taking are scheduled, in particular at NA61/SHINE  [12] , with various targets and beam energies to better understand and constrain the primary and secondaries interactions in and out of the target.
| MATERIALS AND METHODS This study received approval by institutional review boards, and was conducted in compliance with established good clinical practice as stated in the current Declaration of Helsinki. 25 Written informed consent was obtained from all subjects at the time of their enrollment. The study is registered at clinicaltrials. gov (NCT01708564). Following review of the 30 ± 6 hour safety and PD coagulation data by an independent, external Data Monitoring Committee (DMC), 5 of these subjects were randomly selected and 5 new subjects received the second treatment dose. Following a similar safety review by the DMC, the 5 remaining subjects treated at dose level 1, and the 5 newly treated subjects from dose level 2 were treated with the highest dose.
Study design A retrospective single-center study was conducted in Grenoble University Hospital on the outcomes of CR after acute ischemic stroke. We collected data on all patients who had undergone CR from July 2010 to June 2014 from the vascular surgery database. Patients with asymptomatic carotid stenosis and patients requiring combined CR and cardiac bypass were excluded. Data from patients with symptomatic carotid stenosis were combined with the stroke unit database. All patients had brain images and patients who presented with TIA were excluded. Thus, all consecutive patients who underwent CR in our department within three months after the stroke were included. Data collected included demographics, medication, neurological presentation. Neurologic presentation included: symptom onset, neurological de icit and NIHSS (National Institute of Health Stroke Score: neurological gravity score after stroke) on admission.
Only one patient was lost on follow-up in the 30-day postoperative period. Despite the small number of patients included resulting in a low power of statistical analysis, the exclusion of patients with TIA reduced the interpretation biases concerning the outcomes of early CR, which could have been increased by the usually excellent outcomes in these patients. We acknowledge several limitations to our study. First, it was a retrospective study. However, data collection was thoroughly performed. NIHSS was missing for 17 patients. Detailed neurologic examination on admission allowed us to evaluate retrospectively NIHSS whether patients were secondarily transferred or primarily admitted in our facility. The retrospective evaluation limits the interpretative part of NIHSS. Second, Group A was rather small due to a delay in the management of patients presenting with strokes when patients were referred secondarily to the university hospital.
To perform quantitative comparison to theoretical predictions  17  , we measured specific heat, magnetization and ac susceptibility for this field direction, on the same single crystal. The raw data are presented in Methods Fig. 7a  and in Supplementary Figs  2  and 3. Analysis. To get an idea about the effect of a field dependence of the pairing strength on H c2 , a simple back-of-the-envelop calculation can be useful. For a superconductor in a weak coupling scheme, T sc is given by: T sc $ O exp À 1 l À m Ã ð1Þ O is the characteristic frequency of the pairing interaction (proportional to the Debye temperature for electron-phonon coupling), l is the pairing strength, which can now vary under field (l ¼ l(H)), and m Ã is the Coulomb repulsion parameter (usually of order 0. 1-0. 15). Like all heavy fermion superconductors, UCoGe is in the clean limit.
The idea for UCoGe is that this second term could be dominant: a large negative dl dH would lead to a much reduced initial slope, so that the anisotropy of H c2 could just reflect that of the field suppression of the ferromagnetic fluctuations. Another consequence of this dominant term is that the orbital effect would play little role in the temperature dependence of H c2 , opening new routes to explain the data of Fig. 1. To study quantitatively this hypothesis of a field-dependent pairing strength, we choose an new angle of attack: instead of searching for a model that can reproduce H c2 (T), we extract from the experimental data of Fig. 1a  the field and direction dependence of l required to reproduce them. For this, we calculate H c2 for a series of fixed values of the pairing strength l, with the help of a simple strong-coupling model for the upper critical field 33 described in ref.
Discussion Apart from the high-field behaviour along the transverse directions, the theoretical framework in ref. 32 successfully explains the positive curvature of H c2 in UCoGe for H//c, as well as the anomalous anisotropy between c-axis and the (a,b) plane. This theory includes no element specific to UCoGe, and should apply equally to URhGe. The validity of our claims for UCoGe is challenged by the usual negative curvatures and the small anisotropy of H c2 in URhGe  35. A major difference between the two systems is that in URhGe, M z /M 0 has a much weaker field dependence for H//c than in UCoGe  44  (B3% instead of 60% in UCoGe, at 0. 5 T). This is the key element to understand, according to equation (  6 ), the smaller suppression of l for H//c. Quantitatively, we can take advantage of the specific heat data available for H//b in this system  44  , to determine the value of lð0Þ $ 0:75 from comparison to H c2 (ref.
These anomalies could be suppressed by using a better fitting procedure for the thermometers' calibration (and for the Addenda raw data), so we worked on the PPMS log file, and re-performed the data analysis with the improved calibration laws. Although the normal phase of UCoGe follows roughly the classical behaviours of a Fermi liquid (that is, for resistivity, r ¼ r 0 þ AT 2 , and for specific heat, C p /T ¼ g), there are difficulties in the analysis of the normal phase properties of this system. They arise from the closeness of its Curie temperature T Curie $ 2:5 K ð Þ to the superconducting transition T sc $ 0:5 K ð Þ. For example, it is hard to determine precisely the A coefficient of resistivity, because the temperature range for the T-square fit is not large enough. As regards the specific heat measurements, the C p /T values are significantly enhanced around the ferromagnetic transition.
v F h i band was considered to be field independent. Determination of k(0) in URhGe. As explained in the main paper, the effects of the suppression of l under field in URhGe for H//c are much weaker, so the same method as used for UCoGe is not applicable. However, contrary to UCoGe, the specific heat has been estimated from magnetization measurements using Maxwell relations  44  for the three directions. At the field H R parallel to b,where the superconducting temperature is maximum, the specific heat coefficient is increased by more than 30%. If we believe that this increase of C p /T is because of the reinforcement of the pairing mechanism, this puts severe constraints on the value of l(0), as lðHÞ¼ gðHÞ gð0Þ ð1 þ lð0ÞÞ À 1. So we used the data along the b-axis in URhGe, to adjust the value of l(0).
Acknowledgements We thank  V. Mineev  for his help and reactivity on the analysis of our results. We also thank  M. Zhitomirskii ,  G. Knebel ,  A. Pourret ,  J. Flouquet ,  Y. Tokunaga  for useful discussions, and  A. Gourgout  for experimental support. The work was supported by the  ERC NewHeavyFermions , the programs  KAKENHI  ( 15H05882 ,  15H05884 ,  15K21732 ,  16H04006 ,  15H05745 ), and the  ANR  grant  SINUS.
The quality requirement is an important issue for modern companies. Many tools and philosophies have been proposed to monitor quality, including the seven basic tools or the experimental design. However, high quality requirement may lead companies to work near their technological limit capabilities. In this case, classical approaches to monitor quality may be insufficient. That is why on line quality monitoring based on the neural network prediction model has been proposed. Within this philosophy, the dataset is used in order to determine the optimal setting considering the operating point and the product routing. An inverse model approach is proposed here in order to determine directly the optimal setting in order to avoid defects production. A comparison between the use of a classical multi-inputs multi-outputs NN model and a sequence of different multi-inputs single-output NN models is performed.
Multilayers perceptron The classical multilayer percepton (MLP) is a feedforward neural network including only one hidden layer using a sigmoidal activation function and on output layer using an activation function which can be linear for regression problem or sigmoidal for classification problem. Its structure is given by (for the output k): 0 1 2 1 0 1 2 1 1 1 2. 1. n n k ki ih h i k i h z g w g w x b b with k n = =     = + +         = ∑ ∑ (1) where, k z are the 2 n outputs and 0 b is the bias of the output neuron k, g2(. ) is the activation function of the output neuron. Because of the problem is to obtain the optimal setting of controllable parameters, we are faced to a regression problem, so g2(. ) being chosen linear. No normalisation is performed on the dataset. This fact implies to use an initialisation algorithm able to take into account the different value ranges between the inputs  (Nguyen and Widrow 1990).
The dataset is a real industrial dataset polluted with outliers. So the learning algorithm used must be robust to these outliers  (Thomas et al. 1999). In order to evaluate the generalization capabilities of the model, the dataset must be divided into learning and validation datasets. The learning dataset is used in order to adapt the parameters of the MLP when the validation one is used to estimate the performance of the model. The accuracy of the neural model depends on the structure (number of hidden neurons, inputs and parameters). Too few parameters or hidden neurons, and the learning can't find accurate parameters. Too much parameters, and the learning can lead to the well-known overfitting problem. To avoid this problem, the learning is performed on a largely oversized structure with too much hidden neurons and a pruning procedure is used to reduce this structure  (Thomas and Suhner 2015).
Tuning of controllable parameters The main idea is to determine the optimal setting of the controllable parameters by using MLP model. This model is designed by using the dataset constituted by the controllable and uncontrollable factors collected upstream of the process, and the defects types collected downstream of the process. This model is designed under the inverse concept where the outputs of the model are constituted by some inputs of the process, when the inputs of the models includes some inputs of the process and its outputs (figure  3 ). To do that, the classical and simplest approach is to design a multi-inputs multi-outputs (MIMO) neural network. However, in this case, the pruning phase don't allow to determine if a defect type (input) is related to a particular setup parameter (output).
These parameters can be classified into: Three environmental factors (temperature, humidity, pressure). Five product routing parameters (number of passes, time per table, litre per table, number of layers, drying time) Three setup parameters (load factor, basis weight, number of products). These different factors and parameters are able to have an impact on thirty different defects types. Considering the inverse model structure, the dataset is constituted by three outputs (setup parameters) and thirty eight inputs (environmental factors, product routing parameters, and types of defects). The dataset includes 2167 data and is split into two datasets for identification (1088 data) and validation (1079). In order to limit the risk of local optimum trapping, the learning of all the NN models is performed with twenty initial parameters sets and the best one is retained.
The selection criterion used is the classical Root Mean Square Error (RMSE) calculated on the learning and validation datasets: ( ) 2 1 1 ( ) ( ) N n RMSE z n y n N = = - ∑ (2) where z(n) is the output given by the network for the data n and y(n) is the corresponding target. The first NN model designed is the MIMO one. The initial number of hidden neuron is set to twenty. Table  1  presents the RMSE values obtained with the best MIMO model for the three different outputs. These values highlight the difference between the variation ranges and amplitudes of the outputs. This fact may have an impact on the learning accuracy. During the learning, the criterion to minimize is the errors sum squared performed on the three outputs. In this case, the risk is that the learning algorithm favours one output over the others.
The results obtained with the MIMO model must be compared to those obtained with the sequence of MISO models. To design the sequence of MISO models, it is necessary to determine in which order the setup parameters must be considered. The selected order is the one which maximize the sequence accuracy. Different MISO NN models must be designed with different structures. For all these models the initial number of hidden nodes is setup to twenty. The inputs number depends of the order of the sequence. The first MISO model must determine its output by using the same thirty eight inputs of the MIMO model. The second one shall have one additional input (the output of the first MISO model). The third one shall have two additional inputs (the outputs of the two preceding MISO models). The table 2 presents the RMSE values obtained for the learning and the validation datasets for the different MISO NN models designed.
The first line indicates if the considered model is the first, the second or the third of the sequence. The second line indicates which the supplementary inputs if available are. First, it can be noticed that the use of one or two supplementary inputs improve the accuracy for the three outputs. As example, the RMSE value for the validation dataset for load factor is reduced with a reduction from 0. 4336 to 0. 2856 (34% improvement) when product number is used as supplementary input. The same observation may be performed for the two other outputs. So it is necessary to determine the optimal sequence of MISO models. To do that, the decrease in terms of percent of RMSE (compared with the results obtained with the MIMO model) is studied and the sequence which minimizes the sum of the three "RMSE decreased in terms of percent" values for the three outputs (on the validation dataset) is selected.
No specific analyses for postpartum care were performed with this database. Shifting early postnatal care from in-patient to community-based settings requires intensified network building between inpatient and outpatient care providers, which is seen as an intermediate position between full service segregation and full integration  [25]. Yet, fragmentation and lack of continuity among maternity care services often hamper adequate follow-up care for mothers and their newborns, even in high-resource countries  [26] [27] [28] [29] [30]. New forms of inter-professional and inter-organisational collaboration have been developed  [31, 32]  and implemented for patients with chronic diseases  [33] , however this has rarely been the case in perinatal care  [34]. A comparative study in four Canadian health regions showed that the regions with the highest level of inter-organisational collaboration had the most accessible perinatal health services and the most continuous care delivery  [28].
Methods We used the database of the largest health insurer in Switzerland, the Helsana Group, to recruit the study population. The study included mothers insured by the Helsana Group and who delivered between January 2012 and May 2013 in the canton of Basel Stadt (BS), the intervention canton where Familystart began in November 2012, and their newborn children. We also included mothers and their newborns from five control cantons without a coordinated postpartum care model (Aargau, St. Gall, Solothurn, Thurgau and Zurich). Mothers with a basic compulsory insurance coverage were identified through a claim with a delivery-related DRG starting in this period. Newborns were identified either through the unique family number at Helsana or by combining the mother's address and the date of birth of the child from the hospital where the mother had delivered.
In a pre-/post design, post-discharge costs covered by the Helsana Group were analysed, first for a 10-month period before the introduction of Familystart and then for a seven-month period after the introduction of Familystart. The shorter period of measurement in the post-intervention phase was due to time restrictions imposed by the research programme funding the study. Based on a hospital code, data could also be analysed separately for the two hospitals participating in Familystart. A subsequent cross-sectional comparison was made between mothers and their newborns delivered in BS and those from the five control cantons for the period 1 January 2012 to 31 May 2013. For the study period, costs could be retrieved for 97% of all bills relating to mothers. Costs were calculated as the average (and median) monthly post-discharge costs for mothers and for those children also insured with Helsana Group.