dataesr/scientific-paragraphs-categorization huggingface dataset:
In Front of Namib Dune Over the sol range when Curiosity was in front of Namib Dune, multiple REMS EBs were acquired. Mean wind speeds in the 11:00-12:00/14:00-16:00 EB periods vary from ~4 to 7 m s À1 , have modes between 0 and 5 m s À1 , and are strongest from the west (Table  4a  and Figures 9  and 11 ). These records provide important information on wind flow around the dune independent of the interest in change detection  [Newman et al. , 2017]. Close to the rover, the distributed Namib near-field (Nnf) M100 sequence and clast survey (Ncs) sequence provide images with ground resolution near 0. 2 mm/pixel (Figure  12 ). Not including thumbnails and compressed images, and restricting comparisons to midday images  (11) (12) (11) (12) , data were obtained on sols 1198 (Nnf1, Ncs1), 1200 (Nnf2), 1201 (Nnf3), 1202 (Nnf4), 1206 (Nnf8, Ncs4), 1210 (Nnf11, Ncs7), and 1214 (Nnf17, Ncs13).
In the post-sieve pile, several ~500 μm bright particles appear or disappear between the frames. Similar particles seem absent from the pre-sieve pile. However, over the 2 h time period, the left margin of the main pre-sieve pile, where it contacts the bright bedrock vein, moved ~1-2 pixels (0. 2 mm) to the left, which is ~ESE based on the instrument pointing azimuth. The hourly summaries of the REMS EBs spanning 12:00 to 15:00 on sol 1253 show that mean wind speeds are higher than for all but two Hebron data points, and the mode of the wind speed of 5. 8-6. 0 m s À1 from 12:00 to 13:00 (second row of red text in Table  4b ) is greater than the other blocks analyzed during the campaign (Figure  16 ). The results from MarsWRF, orbital HiRISE tracking, and the Curiosity rover consistently show that the Bagnold Dune Campaign occurred in the period of lowest aeolian activity during the Martian year.
The MarsWRF maximum predicted wind speeds at 1. 5 m during the L s range of the Bagnold campaign are predicted to be ~4-8 m s À1 (Figure  4 , top left). The measured mean wind speeds for hours 11:00-12:00 and 14:00-16:00 sampled by REMS during the campaign are 5-9 or 3-7 m s À1 , depending on whether values above 20 m s À1 are included or not, respectively, in the average. These two data sets therefore show fairly good agreement and are consistent with the very limited dune ripple displacement seen with HiRISE. These values can be converted to friction speeds to compare to predictions for the sand motion threshold on Mars. Assuming a range of roughness heights (z 0 ) of 10-100 μm, a 1. 5 m wind speed of 3-9 m s À1 converts to friction speeds of ~0. 1-0. 3 m s À1 (equation (  1 )). This is about an order of magnitude below the predicted fluid threshold for basalt particles on Mars but above the impact threshold for fine sand (Figure  1 ).
, 2016]. With a lower density, at ~2730 km m À3 , than typical basalt at 3000 kg m À3 , it will have only a slightly lower threshold (Figure  1b ). Therefore, taken at face value, the REMS and MarsWRF results predict that sand should never be mobilized during the period of the Bagnold Dune Campaign. However, as we have shown, there is at least some wind-related surface activity seen by Curiosity's cameras and HiRISE, and -as evinced by the intrasol changes detected -at least some of it occurs during the warmer periods when wind speeds could be measured by REMS. Three explanations are likely. First, the REMS 1 Hz wind data have an asymmetric distribution that peaks at lower wind speed, with a decreasing tail extending to higher values. This type of wind speed frequency distribution is typical of many windy environments and is commonly described utilizing a Weibull distribution function  [Seguro and Lambert, 2000] , which has been applied to Viking Lander 2 wind speed measurements  [Lorenz, 1996] : f u ð Þ ¼ k=c ð Þ u=c ð Þ kÀ1 exp À u=c ½ k (3) where u is the wind speed, c is the scale speed, and k is a dimensionless curve shape parameter >1 that is proportional to the gustiness, and therefore the shallowness of the high-speed tail.
5 m, this correlates to a mean friction speed of 0. 3-0. 4 m s À1. Similarly, what processes did not occur are as significant as those that did. Whereas grain scrambling can be attributed to surface particle rolling and sliding, or perhaps limited saltation in gusts, and other processes such as grain flow to non-aeolian effects, the movement of ripples would provide strong evidence of sustained saltation, yet this was not observed. Therefore, considering the REMS results combined with this negative outcome, average winds above ~8-10 m s À1 are probably needed for such activity to occur (Figures  7  and 8  and Tables 4a  and 4b ). Lorenz, 1996] , but do not match these REMS data well. This 1 h period had the second highest median wind speed (both using all sampled data and those only below 20 m s.
Acknowledgments Reviews by  Paul Geissler  and an anonymous reviewer improved this paper. We thank  Jasper Kok  for providing the impact threshold calculations in Figure 1. We acknowledge  Matthew Chojnacki  and the  University of Arizona/HiRISE Operations Center  who produced the stereo-derived  T9/S1-2 (Table 1 ) digital elevation model. We are grateful to  Tim Parker  for helping generate the rover traverse map seen in Figure 2. Great appreciation is extended to the  Mars Science Laboratory Project  for the time and effort they extended in conducting the  Bagnold Dune Campaign. Funding for this work in the U. S. was provided by the  Mars Exploration Program  to the  MSL  Project and, for HiRISE studies by the first author, the  MRO  Project and the Mars Data Analysis Program. S. Silvestro was supported by  ASI  through the  ASI-CISAS agreement I/018/ 12/0 : " DREAMS EDM Payload-ExoMars 2016 ".
It is also strongly based on the gradients of the images. Finally, due to the Rician noise nature of cardiac MR images, the last technique that we tested was the non-local means approach with Rician bias correction  [10]  implemented in Dipy  [11]. For ORNLM, each 3D volume was processed using non-local means filtering with automatic noise standard deviation estimation of the background, search volume of 1331 neighbors and neighborhood size of 26 neighbors. The three denoising filters were implemented in an inhouse built software using third party libraries. This software was written in Python and was run on a Windows 7 platform. The ORLNM implementation was taken from Dipy (http://nipy. org/dipy/). For the anisotropic diffusion filter, the 3D implementation of the algorithm was taken from Sim-pleITK (http://www.
Image analysis The SNR and the contrast-to-noise ratio (CNR) were calculated on a mid-ventricular short-axis slice at end-diastole for each data set. Regions of interest (ROIs) were drawn in the blood pool, in the myocardium and in the background of the image for noise estimation. The same regions of interest were used with three different filtering strategies and the background area was located outside the animal in the bottom right of the image. As the SNR is higher than 4, the Rician noise distribution can be approximated by a Gaussian distribution  [12] , and then the SNR of the blood and the myocardium were calculated as the ratio between the mean signal intensity in the corresponding ROI and the standard deviation of the signal in the background of the image. The CNR was calculated as the difference between the SNR of the blood and the SNR of the myocardium.
A limitation of this study concerns the comparison between in vivo and ex vivo data for the measurement of LVM. Although weighing the left ventricle ex vivo is the best way to have absolute comparison, the different physiological conditions (between in vivo and ex vivo) and the excision of the heart can induce errors in measurements which could decrease the correlation and the agreement between ex vivo  and in vivo results. Moreover, the choice of 16 phases of the cardiac cycle was made as a trade-off between image quality and acquisition time, and this sample rate may not be high enough to capture cleanly the systolic motion. However, it is unclear whether more phases would have improved the quality of the manual segmentation. A limitation of the results is that reproducibility was evaluated on only one set of in vivo image acquisition, thus evaluating the influence of filtering on the variation due to segmentation only.
The KDF executed in the server side and the key delivered to the UE, eliminates the need for the latter to make computations. The MICs are calculated in a standard, recommended way  [10] ,  [11]  and therefore, the computational cost is normal. The MTCs can choose the KDF from the recommended ones in MTCsInfo. This can be very useful for the MTCd as the MTCs can, for example, select a suitable KDF to generate DMKey. Therefore, we evaluate the computational costs and computing power as minimum to garantee robust security features. As for memory, some bytes are needed to store keys. The nonces require some more bytes to store previously used values but in very constrained MTCd, they can be replaced by counters. The MTCs needs to maintain a database linking MTCd IDs with the PSKey, DMKey and nonces but memory shouldn't be a problem at a server level.
Washing/enrichment with Lovo device Prior to washing, percentage of packed cell volume (PCV) calculation was performed as follows per the Lovo operator's manual: ([total volume of cellular material: red blood cells (RBCs), white blood cells (WBCs) and platelets]/[total source product volume]) x100. The HPC bags selected covered the low, medium and high ranges of the spectrum of cell content (PCV range, 3. 4-22. 5%), representing the variation intrinsic to these products and met in our daily routine (Supplementary Table  S1C ). The HPC bags that were used at the prototype stage originated from the same apheresis product cryopreserved in two identical bags. Paired bags were run on each of the wash cycles using Lovo 0. 0. Whether Lovo 1. 0 or Lovo 2. 0 software was used, the procedure, as detailed in Figure  1 , involves a pre-wash step (setting up the instrument and kit) and source prime (with sequential priming of multiple source bags by adding an equal volume of buffer being unique to Lovo 2.
Lovo 1. 0: two-cycle wash of one HPC bag Following this first evaluation, the upgraded Lovo 1. 0 software was used, yet with a faster source priming rate (50 mL/min instead of 20 mL/min), targeting shorter processing times. Single HPC bags (n = 12) with low PCV (4% [2. 9-6. 9; median, IQR]) were washed, resulting in comparable viable CD34 + cell recoveries than the Lovo 0. 0 (81% versus 90%, respectively), as well as comparable CD34 + cell viabilities (91% versus 90%, respectively) and DMSO elimination efficacies (96% versus 96%, respectively; Table  I ). The fast source priming rate was re-evaluated for its efficacy in terms of CD34 + cell recovery versus timesaving advantage and accordingly the initial slow source priming rate was re-adapted for the following upgraded version.
A difference noted in the CD34 + cell recovery between Lovo 0. 0 and 1. 0 is possibly related to the thawed HPC bag source prime volume addition rate, which was fixed at 50 mL/min. Additionally, the Lovo 1. 0 would allow users to target a final volume without a final rinse of the tubing line leading into the retentate bag. Therefore, it is suggested that these two differences in the software led to differences in CD34 + cell recovery between Lovo 0. 0 and 1. 0. The volume of the graft destined for pediatric recipients is 10-20 mL/kg. Accordingly, the two-cycle and three-cycle procedures were designed to target a lower final volume to correspond to this patient age group. We and others have adopted a protocol for transport of washed HPC products at +18-24°C thanks to a proven stability up to 4 hours post-washing and in compliance with FACT-JACIE standards  [18].
Numerical results In the previous section we described how to set up the NLO-renormalized HT method for the (φ 4 ) 2 theory in finite volume. In this section we will present the numerical results which come out of this implementation. Recall that we are working in the units in which m = 1. Our code is written in python and was run on a cluster with 100 Gb RAM nodes. As an example of required computational resources, one NLO-HT data point in figure  1  for L = 10 and E T = 20 requires 40 CPU hours and about 80 Gb RAM. Running time and memory requirements quickly decrease with E T. The whole scan for E T = 10 -20 in steps of 0. 5 for a given g takes about 140 CPU hours. For the raw and leading LO renormalized HT, the maximal attainable E T was limited by available RAM, while the running time was faster than for the NLO-HT.
At the same time, the local correction term ∆H loc 2 (E * ) varies continuously with the cutoff, and so is unable to compensate the effects of discreteness. 15 n the other hand, the correction term ∆ H in the NLO-HT method adjusts itself discontinuously with the cutoff, because the sum over states just above the cutoff if performed exactly and not in the local approximation. For this reason the NLO-HT provides a much smoother dependence on E T , as Figs. 1  and 2  demonstrate. This makes the NLO-HT data well amenable to a fit. We tried various fitting procedures, and the one which seemed to worked best is to fit the NLO-HT points by a polynomial in 1/E T of the form F (E T ) = α + β/E 3 T + γ/E 4 T. (4. 1) From these fits we extract predictions for the eigenvalues at E T = ∞, with error estimates, which will be used in the subsequent sections.
Acknowledgements We thank  Richard Brower ,  Ami Katz ,  Robert Konik ,  Iman Mahyaeh ,  Marco Serone ,  Gabor Takács ,  Giovanni Villadoro  and  Matthew Walters  for the useful discussions. SR is supported by the  National Centre of Competence in Research SwissMAP  funded by the  Swiss National Science Foundation , and by the  Simons Foundation  grant  488655  (Simons collaboration on the Non-perturbative bootstrap). The work of LV was supported by the  Simons Foundation  grant on the Nonperturbative Bootstrap and by the  Swiss National Science Foundation  under grant  200020-150060. The computations were performed on the  BU SCC  and  SISSA Ulysses clusters.
A new modular humidity calibration setup For improving the efficiency of humidity calibrations, VTT MIKES has developed a modular humidity calibration setup that can easily be installed any commercial temperature test chamber with inner volume of 200 dm 3 or larger. In this setup, the humidity in a measurement chamber is controlled by two thermal mass flow controllers located outside the temperature test chamber. These controllers are connected to the measurement chamber system with flexible tubes equipped with quick couplings. This enables easy assembling and disassembling of the system. A LabView computer program was developed for enabling automatic calibrations in static and non-static conditions. The schematic diagram of the system is in Fig. 4. An MBW373 LHX chilled mirror hygrometer is used as the dew-point temperature reference.
Acknowledgments This study was supported by the  National Natural Science Foundation of China  ( 41125004 ),  National Basic Research Program of China  ( 2013CB956303 ), and  National Youth Top-notch Talent Support Program in China. Philippe Ciais ,  Ivan A Janssens  and  Josep Peñuelas  acknowledge support from the  European Research Council  through Synergy grant  ERC-2013-SyG-610028  " P-IMBALANCE ".
We describe the new version (v2. 73y) of the code hfodd which solves the nuclear Skyrme Hartree-Fock or Skyrme Hartree-Fock-Bogolyubov problem by using the Cartesian deformed harmonic-oscillator basis. In the new version, we have implemented the following new features: (i) full proton-neutron mixing in the particle-hole channel for Skyrme functionals, (ii) the Gogny force in both particle-hole and particle-particle channels, (iii) linear multi-constraint method at finite temperature, (iv) fission toolkit including the constraint on the number of particles in the neck between two fragments, calculation of the interaction energy between fragments, and calculation of the nuclear and Coulomb energy of each fragment, (v) the new version 200d of the code hfbtho, together with an enhanced interface between hfbtho and hfodd, (vi) parallel capabilities, significantly extended by adding several restart options for large-scale jobs, (vii) the Lipkin translational energy correction method with pairing, (viii) higher-order Lipkin particlenumber corrections, (ix) interface to a program plotting single-particle energies or Routhians, (x) strong-force isospin-symmetry-breaking terms, and (xi) the Augmented Lagrangian Method for calculations with 3D constraints on angular momentum and isospin.
Introduction The method of solving the Hartree-Fock (HF) equations in the Cartesian harmonic oscillator (HO) basis was described in the publication, Ref. [1]. Six versions of the code hfodd were previously published in six independent publications: (v1. 60r)  [2] ,(v1. 75r) [3] , (v2. 08i)  [4] , (v2. 08k)  [5] , (v2. 40h)  [6] , and (v2. 49t)  [7]. Version (v2. 08i)  [4]  introduced solutions of the Hartree-Fock-Bogolyubov (HFB) equations. Below we refer to these publications by using roman capitals II-VII. The User's Guide for version (v2. 40v) is available in Ref. [8]  and the code home page is at http://www. fuw. edu. pl/~dobaczew/hfodd/hfodd. html. The present paper is a long write-up of the new version (v2. 73y) of the code hfodd. This extended version features the full proton-neutron mixing in the particle-hole channel for Skyrme functionals; full Gogny force in both the particle-hole an particle-particle channels; linear multi-constraint method at finite temperature; fission toolkit including the constraint on the number of particles in the neck between two fragments, calculation of the interaction energy between fragments, and calculation of the nuclear and Coulomb energy of each fragment; enhanced interface to the new version 200d of the code hfbtho; enhanced hybrid MPI/OpenMP parallel programming model with several restart options for large-scale calculations on massively parallel computers; the Lipkin translational energy correction method with pairing; higher-order Lipkin particle-number corrections; interface to a program plotting single-particle energies or Routhians; strong-force isospin-symmetry-breaking (ISB) terms; and the Augmented Lagrangian Method (ALM) for calculations with 3D constraints on angular momentum and isospin.
In serial mode, it remains fully compatible with all previous versions. Information provided in previous publications  [2] -  [7]  thus remains valid, unless explicitly mentioned in the present long write-up. In Section 2 we review the modifications introduced in version (v2. 73y) of the code hfodd. Section 3 lists all additional new input keywords and data values, introduced in version (v2. 73y). In serial mode, the structure of the input data file remains the same as in the previous versions, see Section I-3. In parallel mode, two input files, with strictly enforced names, must be used: hfodd. d has the same keyword structure as all previous hfodd input files, with the restriction that not all keywords can be activated (see updated list in Section 3. 2. 1); hfodd_mpiio. d contains processor-dependent data, see Section 3.
(49) We see that both isoscalar (T = 0) and isovector (T = 1) coupling constants define the potential energies in the particle-hole channel, and only the isovector ones define those in the particleparticle channel. Therefore, Eqs. (  42 )-(  44 ) can now be written as E dir = 1 2 i µτ τ ′ V (iD) Sτ τ ′ m ′ mn ′ n ρ µτ n ′ ,m ′ G i,dir m ′ mn ′ n ρ µτ ′ n,m , (50) E exc = 1 2 i µτ τ ′ V (iE) Sτ τ ′ m ′ mn ′ n ρ µτ n ′ ,m ′ G i,exc m ′ mn ′ n ρ µτ ′ n,m , (51) E pair = i µτ V (iP ) S1 m ′ mn ′ n κ µτ * n ′ ,m ′ G i,pair m ′ mn ′ n κ µτ n,m , (52) where the particle-hole coupling constants, V Sτ τ ′ and V (iE) Sτ τ ′ , are defined as V (iD) Snn = V (iD) Spp = V (iD) S0 + V (iD) S1 , V (iD) Snp = V (iD) Spn = V (iD) S0 -V (iD) S1 , (53) V (iE) Snn = V (iE) Spp = V (iE) S0 + V (iE) S1 , V (iE) Snp = V (iE) Spn = V (iE) S0 -V (iE) S1 , (54) and explicitly read V (iD) 0nn = V (iD) 0pp = W i + 1 2 B i -H i -1 2 M i , (55) V (iD) 0np = V (iD) 0pn = W i + 1 2 B i , (56) V (iD) 1nn = V (iD) 1pp = 1 2 B i -1 2 M i , (57) V (iD) 1np = V (iD) 1pn = 1 2 B i , (58) and In Table  2 , we show a benchmark comparison of hfodd against a spherical-harmonicoscillator Gogny code developed in Madrid and used in studies of neutron-rich nuclei  [15, 16].
Calculations were performed in 120 Sn with the D1S interaction in a spherical basis with N shell = 10 full shells and an oscillator length b = 2. 0390475 fm. The direct and exchange part of the Coulomb force were switched off for the test. For the kinetic energy term, we use the default value hard-coded in hfodd for the D1S parametrization, 2 /2m = 20. 736676229 MeV. fm 2 ; see the test input and output files provided with the source code. We emphasize that a possible source of numerical differences is in the density-dependent term of the interaction. Implementations of this term depend on the Gauss quadrature integration schemes, which are different: Gauss-Laguerre in the spherical code and Gauss-Hermite in hfodd. Nevertheless, the largest relative difference observed in Table  2  is for the pairing energy, where the 20 eV difference between the two codes represent about 0.
p. ν they are N ′ 1,ν = cos 2 θ µν N 1,ν + sin 2 θ µν N 1,µ -sin θ µν cos θ µν [ω νµ (-∞) -ω νµ (z N )], N ′ 2,ν = cos 2 θ µν N 2,ν + sin 2 θ µν N 2,µ -sin θ µν cos θ µν ω νµ (z N ). (112) In the current implementation of the localization method in the code hfodd, the code first searches for all possible pairs (µ, ν) such that |∆E| = |E µ -E ν | ≤ ∆, the localization of both q. p. is ℓ µ , ℓ ν ≤ ℓ max and their occupation is N µ , N ν ≥ N min. The quantities ∆, ℓ max , and N min are user inputs. The localization of q. p. is only performed on those q. p. states that match all these conditions. The code also allows to perform several successive q. p. rotations. Note that, after the first rotation, the HFB matrix is not diagonal anymore: the definition of ∆E is, therefore, not based on q. p. energies but on the diagonal elements E µµ and E νν of the rotated HFB matrix.
Interface with hfbtho Since version (v2. 49t), the code hfodd includes the hfbtho DFT solver as a module. In version (v2. 49t), the hfbtho kernel was the one published in  [22]. In the current version (v2. 73y), we have upgraded the hfbtho kernel to the version 2. 00d of  [23]. This offers additional capabilities such as the constraints on (axial) multipole moments, the breaking of parity to describe asymmetric nuclear shapes, the finite-temperature HFB theory, etc. In addition to being able to initialize a hfodd calculation with the hfbtho solution, version (v2. 73y) also offers the reverse option: the code can read a previous hfodd solution, use it to initialize and run hfbtho before to convert back again to hfodd. Such an option can be useful in axial multi-constrained calculations in heavy nuclei (where hfodd calculations can be time-and resource-consuming) which serve as initial conditions for triaxial calculations.
As a result, the user can choose to spread one HFB calculation across several MPI processes (typically, between 4 and 8), each MPI process being able to invoke several threads. Note that this option is only available when running in MPI mode and requires specifying at compile time the number of MPI processes handling a given hfodd calculation; see section 4. 2 for details. In such a case, the program handles three different levels of parallelism for: The total number of cores required in such a calculation is thus N cores = N ×N p ×N t. Internally, the program splits MPI_COMM_WORLD into three sets of communicators: one corresponding to each group of processes handling a HFB calculation; one including all the master processors across all groups; the last one including all the slaves across all groups.