dataesr/scientific-paragraphs-categorization huggingface dataset:
To give evidence for the nonzero value of s 1 (r) at r = 0. 1, we extrapolate the data to N = ∞ for each value FIG. 19. The opEE, S1, of the non-unitary time-evolution operator (see Eq. 115) for the Haar ensemble with forced measurements. The top panel is for r = 0. 1, deep in the entangled phase where there is a plateau in S1/N in the N → ∞ limit. The extrapolated N → ∞ value is shown as the black dashed line; the extrapolation in N is shown for a few exemplary time points in the inset. The bottom panels correspond to r = 0. 3 and r = 0. 75, the latter being the putative rc obtained from the Haar tree. Note that the value of S1/N is already quite small in the entangled phase at r = 0. 3. All data is averaged over 5000 realisations. of the time. 29 This N → ∞ extrapolation is shown as a dashed line in the figure.
On the other hand, at r = 0. 75 (lower right panel), S 1 /N decays exponentially to zero, with a very weak Ndependence and no indication of saturation at large t. In fact, the trend with increasing N is in the opposite direction to the cases r = 0. 1 or 0. 3. Thus, the opEE for these system sizes is consistent with an entanglement transition, occurring below the classical critical point, and with the expected plateau for S/N in the entangled phase. However, the rapid decay of the plateau value s(r), and the weak N -dependence, make it hard to pin down the position of the transition. We have checked (but do not show) that the data for S 2 is qualitatively similar to that for S 1. We find the same qualitative features for the Haar circuit with true measurements. Figure  20  shows the case r = 0.
ACKNOWLEDGMENTS We thank  Patrick Draper ,  John Chalker ,  Andrea De Luca ,  Tianci Zhou ,  Toby Swann ,  Michael Gullans ,  David Huse ,  Ehud Altman ,  Andreas Ludwig ,  Romain Vasseur ,  Yi-Zhuang You , and  David Kesler  for useful discussions. AN was supported by a  Royal Society University Research Fellowship. SR is supported by  EPSRC  Grant No. EP/S020527/1. JR acknowledges the funding of the  Israeli Science Foundation  under grant No. 994/19  and the support of the Alon fellowship by the  Israeli Council of Higher Education.
Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The "rate of change" is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning.
This paper provides a unique analysis of long-term texture obtained over 19 years for 14 sites located on the A1(M); the UK's longest north to south transecting highway. Data are obtained from operational surveys using traffic speed condition survey (TRACS) measurements, as part of annual pavement monitoring undertaken by Highways England. Sensor measured texture depth (SMTD) data are analyzed using novel spatial filtering techniques and compared with meteorological and traffic data sets to produce long term evolution trends, and first compelling evidence of seasonal conditioned events in macrotexture measurements.
Selecting the Sites and Legacy Data The study considers legacy raw texture depth data captured using TRACS, a purpose-built road survey vehicle  [30] , over a 19-year period, commencing from the 11th August 1998. The data were collected on behalf of Highways England as part of the annual maintenance survey of the UK Strategic Road Network (SRA)  [31]. The TRACS vehicle was calibrated in accordance with the UK Standard  [30] , to safeguard the quality of data during the stages of data collection, storage, and post processing. The TRACS texture data were obtained at a traffic speed of 50 km/h using laser triangulating profile sensors  [32]. The texture data were measured by TRACS in a 300 mm wide swath, positioned over the nearside wheel track of the lane being surveyed. The vehicle has inertia-corrected global positioning system (GPS) data in conjunction with distance measurement, to reference the location of a texture reading to a longitudinal accuracy of ±1m.
Texture data were obtained for 14 sites located on the A1(M). At 659. 8 km it is the longest north to south highway in the UK, connecting Edinburgh with London. Study sites were selected where long-term (typically >12 years) legacy data were available for the same surface material. Hot rolled asphalt (HRA) surfaces were selected as they have been used widely on the SRA and thereby provide the largest, longest, and most representative dataset. HRA is a type of hot mix asphalt often used in the United Kingdom. It is a dense gap graded asphalt of mineral aggregate, sand, filler, and typically a 40/60 penetration bitumen binder. There is a high proportion of sand in the mix resulting in a low percentage of air voids when it is compacted. High polished stone value bitumen coated 20 mm aggregate are rolled into the surface of the mixture after laying to provide a skid resistance surface  [33].
Sites with HRA were selected for comparison in the North and South of England, to allow the greatest potential differences in traffic flow and meteorological conditions for study purposes. Sites were also selected at locations with traffic counters. A total of 10 suitable sites were identified in the North of England, located at approximately 20 km intervals (refer to Figure  1a ) and four in the South of England (refer to Figure  1b ). Fewer sites were available in the South due to frequent resurfacing, disrupting the availability of consistent long-term records for a single surface material. The A1(M) varies along its route from one to four lanes. Data for the sites were collected from the inside lane of the southbound side of the A1(M). The southbound inside lane was considered more likely to have the heaviest traffic usage being the lane favoured by heavy goods vehicles (HGV).
Comparison of SMTD with Traffic Flow The rate of change in SMTD data was compared with both annual average daily traffic (AADT) flows and HGV AADT flows; and linear best fit lines added to the plotted data. To determine the variability in these data, the rate of change was characterised both for the full time period of SMTD measurement available at an individual site, and for the linear trend occurring between the pronounced "troughs" at point (I) and (II) on Figure  2  for the northern sites and point (I) and (II) on Figure  3  for the southern sites. A decadal AADT was calculated from WebTRIS Highways England Traffic  [37]  flow data, to differentiate the traffic flow usage at the different sites on the A1(M). The data were collected using a series of fixed induction loop road sensors. The AADT flows were calculated from the latest available data (from 1 January 2010 to 31 December 2019), there was no significant fluctuation in AADT flows over this period at individual sites.
Changes in Road Texture The filtered mean SMTD data for the A1(M) northern sites are shown on Figure  2. SMTD data for nine sites (Sites 1 to 8 inclusive and Site 10) were first measured on either the 11 or 12 August 1998. The initial mean SMTD measurements vary on Figure  2a  for these sites, from 1. 18 mm to 1. 88 mm. The construction records available of Sites 1 to 8 indicated that these HRA pavement surfaces were first constructed between 1976 and 1993, so the youngest pavements had been laid for a minimum of five years when they were first measured. No clear correlation was found between the dates of construction and the initial measurement of SMTD recorded on August 1998. This suggests that the evolution of pavement SMTD is influenced by site specific processes, such as meterological conditions and traffic flow.
The general linear evolution of filtered mean SMTD for the HRA surfaces studied was found to be repeatedly disrupted by extreme precipitation events. Other meteorological triggers were investigated including ground surface temperature, minimum and maximum ambient temperature, and number of frost days, but no clear correlation with the transient drop or "troughs" in the mean SMTD was established. Coatings 2021, 11, 735 9 of 17 A detailed study was made of the "peaks" and "troughs" occurring in the filtered mean SMTD data for each of the sites, within the northern and southern groups. It was found that the long-term trends in filtered mean SMTD against time in days could be modelled as a 7th order polynomial for all the sites. The 7th order polynomial was found to represent the long-term trend, from which the "peaks" and "troughs" can be detected.
The hypothesis that precipitation is occasionally a trigger for transient change in SMTD data with local road surface conditions governing, is reinforced by the data presented in Figure  8  (see point (I)), where a "trough" occurs on the 30 May 2014 early within the summer period for a southern site. The winter of 2013/2014 was recorded as being particularly wet across south-east England, being at the time the wettest since 1766  [40]. In the month of May 2014, 83. 5 mm of precipitation fell. In the week prior to the SMTD reading being taken on the 30 May 2021, 57% or 47. 6 mm of this precipitation was recorded. This intense period of precipitation occurred after a dry spell of seven days. The data indicate that "peaks" and "troughs" occur where there has been above average precipitation for several days within the week prior to a measurement being taken, suggesting that processes occurring on the HRA pavement surface are temporarily interrupted by surface processes such as precipitation.
Conclusions An investigation of the long-term evolution of SMTD for HRA surfaces was undertaken for 14 sites located on the north to south transect of the A1(M) in the UK and compared with traffic flow and metrological dataset for 19 years. Sensor measured texture depth (SMTD) data were analyzed using novel spatial filtering techniques. The long-term evolution of SMTD for HRA surfaces, when seasonal signals are discounted in the "rate of change", follow a linearly increasing trend at a gradient (∅) correlated to the order of magnitude of the AADT. The results also demonstrate for the first time a seasonal signal in SMTD measurements corresponding with above average precipitation occurring within the week prior to the SMTD measurement being taken. The pattern observed demonstrates positive deviation from the long-term evolution of filtered mean SMTD in the summer and negative deviations in the winter.
Acknowledgments We thank  L. Shao  for setting up the initial experiments,  X. Cui  for taking the SEM images of the nanoparticles, and  A. A. R. Neves  and  R. Verre  for fruitful discussions. F. Schmidt and  G. Volpe  acknowledge partial supported by the  ERC   Starting Grant  ComplexSwimmers (grant number  677511 ) and by  Vetenskapsrådet  (grant number  2016-03523 ). A. Würger acknowledges support from  ANR  through contract Hotspot  ANR-13-IS04-0003  and from  ERC  through contract Hiphore grant number  772725. H. Šípovà-Jungová ,  M. Käll  and  G. Volpe  acknowledge support from the  Knut and Alice Wallenberg Foundation.
Study site, species, and design The experiment was conducted in French Guiana at the Paracou experimental station (https:// parac ou. cirad. fr/ websi te; 5° 16′ 26″ N, 52° 55′ 26″ W), which represents a lowland tropical rainforest  (Gourlet-Fleury et al. 2004 ). The warm and wet tropical climate of French Guiana is highly seasonal due to the North-South movement of the Intertropical Convergence Zone. Mean (± SE) annual air temperature is 25. 7 °C ± 0. 1 °C and the mean annual precipitation is 3,102 mm ± 70 mm (data between 2004 and 2014;  Aguilos et al. 2019). There is a dry season lasting from mid-August to mid-November, during which rainfall is < 100 mm month -1. We sampled only dominant canopy trees, and only growing on terra firme. A total of 50 trees belonging to 18 species and 50 trees were sampled, with three trees per species for 14 species, and two trees per species for four species (Bocoa prouacensis, Iryanthera sagotiana, Moronobea coccinea, Qualea rosea).
These 50 trees represent a subset of the sampling in  Ziegler et al. (2019) , for which stem embolism resistance was initially measured. The same dataset was used by  Levionnois et al. (2020) , who added data on leaf embolism resistance. Measurements of stem or leaf embolism resistance were particularly challenging for species for which data on only two individuals were available. Although several samples have been collected for a given species, only two vulnerability curves (VC) per species could be obtained  (Ziegler et al. 2019; Levionnois et al. 2020 ). The studied species covered a broad phylogenetic diversity such that the main clades of the flowering plants were represented, i. e. magnoliids, rosids, and asterids. Branches were sampled by professional tree climbers. We calculated desiccation time at the shoot level, with a shoot defined in the context of this study as an unbranched and leafy stem, with the largest possible number of leaves and with all nodes bearing a leaf.
Stem embolism resistance Stem embolism resistance was measured as the stem water potential inducing 50% and 88% loss of conductivity (P 50,stem and P 88,stem , respectively; MPa; Table  1 ). Sample collection was held between January and July 2017, with one 3-m-long canopy branch sampled per tree. Only sun-exposed branches were sampled, except for the understory species Gustavia hexapetala. Vulnerability curves were measured with the flow-centrifugation technique using a Cavi1000 system (DGMeca, BIOGECO lab, Gradignan, France). A detailed description of the methods and the data for each species were provided and discussed by  Ziegler et al. (2019).
Acknowledgements We thank the climbing team for canopy sampling:  Jocelyn Cazal ,  Valentine Alt ,  Samuel Counil ,  Anthony Percevaux , and  Elodie Courtois. We thank the following colleagues for technical assistance during field work:  Oscar Affholder ,  Louise Authier ,  Anne Baranger ,  Maxime Bellifa ,  Benoit Burban ,  Maëlle Cario ,  Bruno Clair ,  Maxime Corbin ,  Elia Dardevet ,  Alexandre De Haldat Du Lys ,  Emilien Fort ,  Frederic Fung Fong You ,  Eva Gril ,  Thomas Saint-Germain , and  Ruth Tchana Wandji. We would like to thank the  Paracou Research Station  managed by  CIRAD  for tree sampling. We also would like to thank the  SILVATECH (Silvatech, INRAE , 2018. Structural and functional analysis of tree and wood Facility, https:// doi. org/ 10. 15454/1. 55724 00113 62785 4E12 ) from UMR 1434 SILVA, 1136 IAM, 1138  BEF , and 4370 EA LERMAB EEF research centre  INRAE Nancy-Lorraine  for support with anatomical sectioning.
Funding This study was funded by the  GFclim  project (FEDER 20142020, Project  GY0006894 ). This work has benefited from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). S. L. was supported by a doctoral fellowship from CEBA.
Data were analyzed by FlowJo (BD Biosciences). Antibodies were purchased from BD Biosciences-PharMingen unless otherwise stated. Whole-blood culture with SARS-CoV-2 peptide pools 320 µl of whole blood drawn on the same day was mixed with 80 µl RPMI and stimulated with pools of SARS-CoV-2 peptides (M, NP1, NP2, or S; 2 µg/ml) or a DMSO control. After 15 h of culture, the culture supernatant (plasma) was collected and stored at -80°C until quantification of cytokines.
Acknowledgments We thank  Xin Mei Ong ,  Wan Rong Sia ,  Madeline Kwek , and  Charles Tiu  for performing the sVNT in this study. This work was supported by  Special National University Health System  COVID-19 Seed Grant Call, Project  NUHSRO/ 2020/052/RO5+5/NUHS-COVID/6 ; emergency COVID-19 funds from the  Saw Swee Hock School of Public Health  and the  National University of Singapore ; the  National Research Foundation Singapore  ( NRF2016NRF-NSFC002-013 ); and the  National Medical Research Council of the Ministry of Health -Singapore  ( STPRG-FY19-001 ,  COVID19RF-003 , and  COVID19RF3-0060 ).
Acknowledgments The authors would like to thank all the members of the  Association Francaise de Chirurgie (AFC) , the  National College of French Gynecologists and Obstetricians (CNGOF) , the  National College of Midwives (CNSF) , the  Research Group In Proctology (GREP)  and the  French National Society of Colo-Proctology (SNFCP)  who participated in this study.
Acknowledgements We acknowledge financial support from the  Helmholtz Research Program STN (''Science and Technology of Nanosystems'')  and  DFG Grants  ( MA 2605/6-1 ) and ( GE 2989/2-1 ). T. F. acknowledges support from  DFG Grant  ( WU349/13-1 ) and the  Landesstiftung Baden-Wu  ¨rttemberg. M. M. acknowledges support from the  111  project ( 90002-18011002 ).
Soft computing techniques were developed as databased solutions to complex problems. Owing to the tolerance of these techniques to imprecision and uncertainty, the techniques help in achieving robust and low-cost solutions to complex problems. However, engineers interested in these techniques face difficulties in the optimal choice of a soft computing technique, as well as its application in solving complex problems related to TBM tunneling. Soft tunneling techniques were successfully applied in significant scientific and engineering problems. In the tunneling industry, advances in monitoring offered new opportunities to collect extensive data related to tunnel construction, including TBM operating conditions, excavation processes, and response of the surrounding soil and built environment to tunneling.
The input geological conditions included the soil type at the tunnel crown, soil type at the tunnel invert, and groundwater level. The EPB operation parameters included the face pressure, penetration rate, pitching angle, grouting pressure, and grouting filling. The results indicated the high performance of the ANN method when employed within the range of data used in the training phase. Bouayad and Emeriault (2017) applied the ANFIS method to predict EPB-induced ground settlements during the construction of the Toulouse subway. The input parameters included the soil characteristics and ten operating parameters: TBM advance rate, face pressure, annular void grouting pressure, volume of injected grout, pressure of the cutting wheel, total jack thrust, time for excavation and installation of a lining ring, horizontal and vertical guidance parameters, and energy required for the excavation of a cubic meter of soil.
2017 ) used a FL model to predict TBM performance. The findings indicated that the rock properties could significantly affect the TBM penetration rate. Salimi et al. (2016)  showed that the ANFIS method could be applied to model the complicated relationship between the parameters involved in the tunneling parameters. Elbaz et al. (2019)  used a multi-objective ANFIS-GA model to predict the performance of EPB shield. Analyses were carried out with seven parameters, which were the tunnel depth, thrust force, cutter torque, earth pressure, head rotation speed, screw rotational speed, and grouting pressure and advancing rate. The principal component analysis reduced the input parameters to three critical parameters: thrust force, cutter head torque, and earth pressure. Comparison with field data demonstrated that the multi-objective optimization model well predicted the machine advance rate.
The results also showed that the ANFIS-GA model outperformed the ANFIS model. Gao et al. (2019)  used three classes of recurrent neural networks, which were the traditional RNNs, LSTM networks, and GRU networks to predict TBM operating parameters: torque, velocity, thrust, and chamber pressures at top left, bottom left, and bottom right. The results showed that these methods provide an excellent forecast of the main operating parameters, such as velocity, torque, chamber pressure, and thrust. It was found that the LSTM and GRU methods presented a better prediction of TBM operating parameters than the traditional RNN method. Xu et al. (2019)  used five supervised machine learning techniques to analyze the effect of input parameters in predicting TBM penetration rate. An analysis was conducted on 209 datasets related to a tunnel project in Malaysia.
In the near future, we expect a ∼ 40% improvement in cosmological constraints for 4×2pt+N from the analysis of the first three years of data from the Dark Energy Survey, mostly due to the increased survey area. This improvement will be followed by significant additional improvements from upcoming wide imaging surveys in the 2020s  [55] [56] [57]. The analysis presented in this letter is an important step towards fully realizing the potential of these richer and larger datasets. f Tinker (ν) is the Tinker fitting formula  [18] , and P cdm+b,lin (k) is the linear CDM + baryon power spectrum as a function of wavenumber k. W (kR) is the Fourier transform of the real space top-hat window function of radius R, which is defined as R = 3M 4πρ cdm+b 1 3. (4) In this analysis, the bias model is the Tinker bias  [18]  using ν defined in equation 2.
Acknowledgements. -This paper has gone through internal review by the DES collaboration. This work was supported in part by the  U. S. Department of Energy  contract to  SLAC National Accelerator Laboratory , under contract no. DE-AC02-76SF00515  (CH, DG, RW) including a  Panofsky Fellowship  awarded to DG. EK is supported by the  Department of Energy  grant  DE-SC0020247. ER is supported by  DOE  grants  DE-SC0015975  and  DE-SC0009913 , and by  NSF  grant  2009401. ER also acknowledges funding from the  Cottrell Scholar program  of the  Research Corporation for Science Advancement. HW is supported by  NSF  Grant  AST-1516997. Some of the computing for this project was performed on the Sherlock cluster. We would like to thank  KIPAC ,  Stanford University , and the  Stanford Research Computing Center  for providing computational resources and support that contributed to these research results.
Based in part on observations at  Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID  2012B-0001 ; PI:  J. Frieman ), which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES Data Management System is supported by the  NSF  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MICINN  under grants  ESP2017-89838 ,  PGC2018-094773 ,  PGC2018-102021 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and.
The authors gratefully acknowledge  C. L'Henoret  and  T. Cohen  for their technical support at the mechanics workshop. This paper was financially supported by the  network GaNeX  ( ANR-11-LABX-424 0014 ), the  BONASPES  project ( ANR-19-CE30 0007 ), the  ZEOLIGHT  project ( ANR-19-CE08-0016 ), and the  Universit e de Montpellier. S. S. and L. S. acknowledge financial support from the  Swedish Research Council  Contract No. 2017-04164 , and the  Swedish Foundation for Strategic Research (SSF) , Contract No. IS14-0027 , and  Carl Trygger's Foundation for Scientific Research , Contract No. CTS 14:189, respectively. H. P. and H. H. acknowledge the  Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Link€ oping University  (Faculty Grant  SFO-Mat-LiU No.