dataesr/scientific-paragraphs-categorization huggingface dataset:
5, ISAM, JSBACH, JULES, LPJ, LPJ-GUESS, LPX, OCN, ORCHIDEE, VEGAS, and VISIT, see Materials and Methods). The weight of each model was determined by how well its GPP matches the spatial patterns of its proxy, SIF, within each biome type, i. e. , higher weights were given to more realistic models and vice versa (see Methods). Our second objective is to better understand what factors control the spatial patterns of GPP IAV using the resultant SIF-constrained grid-based GPP dataset. The IAV of GPP and Evapotranspiration (ET) were analyzed together since they are closely coupled. We also adopted the Budyko framework  28  to explain the IAV pattern along the aridity index (hotspot in semi-arid regions).
We further attribute the IAV of GPP to different climatic factors by calculating the partial correlation coefficients between GPP and precipitation, temperature, and shortwave downward radiation. Similar to the results from a previous analysis using the MTE approach  1  , we find that for most land surfaces, annual GPP is controlled by precipitation, except for some tropical and boreal regions. Contributions from temperature and radiation are less significant (Fig. 3a-c ). In addition, for most drylands (aridity index < 0. 65), GPP and ET are closely coupled, as represented by a high Pearson correlation coefficient (significant at the 0. 05 level for 65. 4% of areas in Fig. 3d ). This tight coupling between GPP and ET in dry regions is robust to the choice of a particular ET dataset, as it is present for 12 different ET datasets (Supporting Information, Text S2).
Materials and Methods GPP from 14 models. GPP dataset from 14 models for the period 2000 to 2011 can be grouped into two categories, i. e. , three data driven models (VPM, MTE, and MOD17) and 11 process based models from TRENDY-v4 project. The VPM model and MOD17 are both light use efficiency models while using different driving data and parameters  26, 49. MTE uses machine learning algorithms which combine the observation from flux tower, satellite imagery, and gridded climate datasets  25, 50. The TRENDY-v4 datasets are the latest release for the TRENDY project which compared the simulation results from dynamic global vegetation models (DGVMs)  51. All these models are driven by the same climate forcing data and monthly GPP estimates are used. (see Supporting Information, Text S1). Solar-induced chlorophyll fluorescence from GOME-2.
The solar-induced chlorophyll fluorescence (SIF) measurements are retrieved from GOME-2 onboard the MetOp-A satellite. The GOME-2 instrument captures the Earth radiation from ~240 to 790 nm at a nominal nadir footprint of 40 × 80 km 2 (and 40 × 40 km 2 since 15 July 2013) with a spectral resolution of ~0. 5 nm. Compared with SIF measurements from other satellites (GOSAT, OCO-2, etc. ), GOME-2 data has the longest observation history (2007-2015). We used the version 26 SIF from Joiner et al. 14, 24  available at http://avdc. gsfc. nasa. gov. This product is retrieved at wavelengths surrounding 740 nm, where the far-red peak of SIF emission is located, based on a principal component analysis approach to account for atmospheric absorption. The initial results are quality controlled and aggregated to produce a level 3 monthly mean product at 0.
Hence, we can use SIF as a proxy for GPP within each biome type both spatially and temporally. To get the ensemble of all GPP estimates, the weight of each GPP product should reflect how well the GPP product matches its respective SIF spatial or temporal pattern within each biome type. Considering that SIF has relatively low values (0-4. 0 mW m -2 sr -1 nm -1 ) compared to its uncertainties (0. 1-0. 4 mW m -2 sr -1 nm -1 ) at monthly scales, SIF can be averaged temporally (from monthly to yearly) or spatially (within-biome for each month) to reduce those uncertainties and compare with GPP. The land cover data used to identify different biome types came from MCD12C1  53. This dataset has a spatial resolution of 0. 05 degree with a proportion of each biome type for each grid cell. We aggregated it to 0.
Acknowledgements We thank the  Numerical Terradynamic Simulation Group at the University of Montana  for providing the improved MOD17 GPP and MOD16 ET datasets. We thank the TRENDY-v4 modelers for contributing model outputs. We thank  Dr. Kaiyu Guan  for discussion on the early version of the manuscript. This study by Y. Z. , X. X. , X. W. , and J. D. is partially supported by a research grant (Project No. 2013-69002 ) through the  USDA National Institute for Food and Agriculture (NIFA)'s Agriculture and Food Research Initiative (AFRI) ,  Regional Approaches for Adaptation to and Mitigation of Climate Variability and Change , and a research grant ( IIA-1301789 ) from the  National Science Foundation EPSCoR. We thank  Ms. Sarah Xiao  at  Yale University  for the English editing of the manuscript.
Similarly, the maneuverability was evaluated by means of a subjective perception on approachability of all targeted areas of the collecting system, both expressed by each surgeon as a preference at the end of every procedure. Data are presented as mean and standard deviation. Betweengroups comparisons were made using Kruskal-Wallis test. A p value <0. 05 was considered significant. Statistical analyses were performed using GraphPad Prism version 6. 00 for Mac OS X, GraphPad Software.
Finally, the proposed approach is illustrated at device and circuit level, considering four different test vehicles in 65 nm technologies: a stand-alone transistor, a CMOS inverter, a SRAM cell and a flip-flop circuit. RWDD results are compared with data obtained from a full three-dimensional (3D) numerical approach (TCAD simulations) at transistor level. The importance of the circuit feedback on the charge-collection process is also demonstrated for devices connected to other circuit nodes.
This chapter describes in-depth the RWDD approach and illustrates all of these points in the three following sections. Section 2 details the physical insights of the model. The practical implementation of the method using an object-oriented programming language and its parallelization on GPUs is presented in Section 3. Besides, the capability of the approach to treat multiple-node charge collection is presented. Section 4 details the coupling of the model either with an internal routine or with SPICE program for circuit solving. Finally, Section 5 illustrates this proposed approach at device and circuit-level, considering four different test vehicles in 65 nm technologies: a stand-alone transistor, a CMOS inverter, a SRAM cell and a flip-flop circuit. RWDD results are compared with data obtained from a full three-dimensional (3D) numerical approach (TCAD simulations) at transistor level.
(  8 ) for the sole and isolated inverter #1 or Eqs. (  8 ) and (  9 ) for the full SRAM cell can be easily solved in the time domain, using a fourth-order Runge-Kutta method  [19]  with a time step Δt identical to the one used in the RWDD algorithm for charge transport and collection. Using notations introduced in Eq. (  8 ), the incremental results for t n+1 = t n + Δt are: L 2L 2L L V V 6 n 1 n 1 2 3 4 1 1 K 2K 2K K V V 6 + + + + = + (10) + + + + = + (11) with: Eqs. (  10 )-(  19 ) constitute the core model implemented in the code RWDDCPP for CMOS inverter and SRAM cell solving. In Eq. (  9 ), NMOS and PMOS source-to-drain currents are analytically modeled using the EPFL-EKV model 2. 6  [20, 21] , here implemented in C++ as numerical functions (I DN and I DP ) having two arguments (see Figure  5 ).
The EKV 2. 6 MOSFET model is a predictive (scalable) compact model for the simulation of submicron CMOS technologies. It was built taking into account fundamental physical characteristics of the MOS structure. The model offers a continuous of the different regimes of the transistor operation (weak, moderate and strong inversion), which is mandatory for circuit modeling. The version 2. 6 takes into account numerous essential issues for the transistor modeling such as the effects of the doping profile, substrate effects, process-related aspects (oxide thickness, effective channel length and width and junction depth), mobility effects due to vertical and lateral electric fields, the velocity saturation, short-channel effects, etc. Section 5 details several examples of simulations using this code RWDDCPP.
SPICE simulator For more complex circuit architectures than a single inverter or a SRAM cell, an external SPICE circuit simulator can be otherwise used in the place of the internal subroutine for circuit solving. In this case, the circuit simulator is instantiated in interactive mode by the executable code with a circuit netlist corresponding to the simulated structure. Current source(s) emulating the transient collected current(s) at the different circuit nodes is (are) automatically added to the netlist by the RWDDCPP program. At each time step of the simulation, the magnitude of each current source is updated as a function of the number of charge packets collected by the corresponding biased junction, following Eq. (  5 ). Then, the SPICE simulator computes the new voltages on the circuit nodes and finally, from Eqs.
Model validation-isolated NMOS This simplest case corresponds to an isolated transistor subjected to single event irradiation. The radiation-induced collected current and charge have been separately computed using the RWDDCPP code and the commercial Synopsys TCAD simulation platform  [24]. particle striking the center of the drain perpendicularly to the device. A good agreement between results obtained with RWDD and TCAD are shown in this graph. One can observe a few differences between the two curves, especially in the early stages of the transient, mainly due to: (i) the arrival of discrete charge packets at the level of the collecting drain contact in the RWDD approach which induces inherent granularities; and (ii) the characteristic time (2 ps) of the Gaussian time distribution used in the TCAD simulation.
Thus, complex architectures, such as flip-flops, can be easily modeled and charge-sharing mechanisms are accurately simulated. This chapter mainly focused on the model implementation and the way to solve the circuit response in the time domain, taking into account the circuit feedback on the charge collection process. Four simulation test cases have been explored and compared to radiation experiments or TCAD simulations in order to validate the proposed model. These test cases show good quantitative agreement between measurements and simulated data over a large range of LETs up to 60 MeV. cm 2 /mg and structure complexity. This first implementation remains therefore not self-consistent with the electrostatic potential (in other words, Poisson's equation is not solved during the transient computation) and does not take into account the possible interac-tions between charge packets.
In silico analysis of the genomes Protein-encoding open reading frames (ORFs) were predicted using Glimmer  [30]. Initial functional annotation of the ORFs and percentage amino acid identities were determined using BLASTP  [31]  and RAST server  [32]. Transfer RNAs (tRNAs) were screened in the genomes using ARAGORN  [33]. The BRIG software  [34]  was used for genome comparison of phage genomes which gave the best hit scores using BLASTP. Consequently, ɸAPCEc01 genome was compared to E. coli phages T4, vB_EcoM_JS09, RB27, vB_EcoM-VR7, Av-05, HX01, vB_EcoM_PhAPEC2, and RB69 (GenBank numbers NC_000866, NC_024124, NC_025448, NC_014792, KM190144, NC_018855, NC_024794, and NC_004928, respectively), and to Shigella phage Shf125875 (GenBank KM407600). ɸAPCEc02 genome was compared to E. coli phages rv5, 2 JES-2013, vB_EcoM_FFH2, and vB_EcoM-FV3 (GenBank numbers NC_011041, NC_022323, NC_024134, and NC_019517, respectively).
ɸAPCEc03 genome was compared to E. coli phages T5, bV_EcoS_AKFV33, DT57C, DT571/2, and vB_EcoS_FFH1 (GenBank numbers NC_005859, NC_017969, KM979354, KM979355, and KJ190157, respectively), to Salmonella phages SPC35, Shivani, and Stitch (GenBank numbers HQ406778, KP143763, and KM236244, respectively), and to Yersinia phage phiR201 (GenBank HE956708).
Acknowledgments This publication has emanated from research conducted with the financial support of  Science Foundation Ireland (SFI)  under Grant Number  SFI/12/RC/2273. The authors would like to thank  Pr. Paul O'Toole  for giving access to samples from the  ELDERMET  project, and  Dr David Clarke  for providing some E. coli strains. The authors are also grateful to  Dr Jennifer Mahony ,  Dr Eoghan Casey  and  Dr Brian McDonnell  for their help and advice. The authors thank  Juliette Bougnon  and  Tracey Leonard  for their help. The authors also acknowledge the technical help of  Angela Back  in sample preparation for TEM.
A quadrivalent, inactivated, split-virion influenza vaccine containing a strain from both B lineages (IIV4) has been developed, but its safety and immunogenicity in young children has not been described. This was a phase III, randomized, double-blind, active-controlled, multi-center study to examine the immunogenicity and safety of IIV4 in children 3-8 y of age (EudraCT no. 2011-005374-33). Participants were randomized 5:1:1 to receive the 2013/2014 Northern Hemisphere formulation of IIV4, an investigational trivalent comparator (IIV3) containing the B/Victoria lineage strain, or the licensed Northern Hemisphere IIV3 containing the B/Yamagata lineage strain. Participants who had not previously received a full influenza vaccination schedule received 2 doses of vaccine 28 d apart; all others received a single dose.
Solicited reactions Solicited injection-site reactions were reported by 62. 4% of participants in both the IIV4 and pooled IIV3 groups (Table  4 ). Solicited systemic reactions were reported by 48. 9% of participants in the IIV4 group and 45. 5% in the pooled IIV3 group. At the site of injection, pain was the most frequently reported solicited reaction (56. 5% for IIV4, 55. 4% for IIV3) (Table  5 ). The most common solicited systemic reactions were malaise (30. 7% for IIV4, 28. 2% for IIV3), myalgia (28. 5% for IIV4, 26. 8% for IIV3), and headache (25. 7% for IIV4, 19. 2% for IIV3). Overall, there were no major differences in the rates, types, or severity of solicited reactions between the IIV4 and pooled IIV3 groups (Table  5 ). In almost all cases, solicited reactions started within 3 d after injection lasted 3 d or less, and were grade 1 (Figure  S1  and data not shown).
Study design and objectives This was a phase III, randomized, double-blind, active-controlled, multi-center study performed at 4 centers in Poland, 11 in Finland, 4 in Mexico, and 3 in Taiwan during the 2013/2014 Northern Hemisphere influenza season (EudraCT no. 2011-005374-33). The primary objective of the study was to demonstrate, for each strain, non-inferiority of specific antibody responses induced by IIV4 compared with IIV3-1 and IIV3-2. Secondary objectives were to assess the safety profile of each vaccine during the 28 d following each vaccination, to collect SAEs, including AESIs throughout the study; to assess humoral immune response induced by IIV4; and, for the B lineage strains, to demonstrate superiority of antibody responses induced by IIV4 compared to the antibody responses induced by the IIV3 containing the alternate B strain lineage.
Safety Parents or legal guardians of each child recorded solicited injection-site (pain, erythema, swelling, induration, and ecchymosis) and systemic reactions (fever, headache, malaise, myalgia, and shivering) up to 7 d after each vaccination in a diary. Severity of solicited reactions were graded 1 for mild, 2 for moderate, and 3 for severe (Table  S4 ). Investigators recorded safety according to the ICH E2A Guideline for Clinical Safety Data Management: Definitions and Standards for Expedited Reporting. Parents or guardians of children participating in the study received a final telephone call 6 months after the last vaccination for the collection of SAEs, including AESIs (anaphylaxis, Guillain-Barr e syndrome, encephalitis/myelitis, neuritis, febrile and non-febrile convulsions, thrombocytopenia, and vasculitis).
Statistical analysis Statistical analysis was performed using SAS Ò version 9. 2 (SAS Institute, Cary, NC, USA). The primary analysis (non-inferiority) was performed in the per-protocol analysis set, defined as all randomized participants who completed the vaccination schedule, had a post-last vaccination blood sample drawn, and completed the study according to protocol. For each strain, non-inferiority was demonstrated if the 2-sided 95% CI of the ratio of the GMT between IIV4 and that of the comparator IIV3, calculated using a normal approximation of log-transformed titres, was > 2/3. Secondary immunogenicity analysis (superiority) was performed in the full analysis set, defined as all randomized participants who received at least one dose of the study vaccine and had a post-last vaccination blood sample drawn.
Incorrect PDB code In the original article  (Urzhumtsev et al. , 2015) , we used several atomic models in order to test the algorithms and provide examples. Unfortunately, the incorrect PDB code had been reported for one of them. Everywhere in the text (x6. 2, Tables  2  and 3 ), 1rge should be used instead of 1dqv and ribonuclease S should be used instead of synaptotagmin. We apologize for this confusion. The diffraction data set used for test refinement of ribonuclease S was obtained from the CCP4  (Winn et al. , 2011)  distribution (http://www. ccp4. ac. uk/ examples/rnase/rnase25. mtz).
, 2015). As discussed previously (x2. 3 in  Urzhumtsev et al. , 2013, and references therein) , the discrepancy between two corresponding matrices is significant when the libration amplitude becomes larger than approximately 0. 10-0. 15 rad (6-9 ). To investigate the number of models sufficient to reproduce U TLS by U ensemble , we took the C A atoms from fragment A6-A61 of protein G IgG-binding domain III model (PDB code 2igd) and fitted TLS matrices to individual anisotropic U n of this model using the phenix. tls tool. Then we calculated U TLS for each atom of the model and independently generated a set of random models from which we calculated U ensemble to compare them with U TLS. Fig. 1  shows the mean relative difference between the sets of the U elements as a function of the number of generated models.
Introduction With the ongoing proliferation of increasingly cheaper devices to capture, amongst others, visual information by means of digital cameras, developing technologies for the storage of this ever growing body of information and providing means to access these huge databases has been and will be an important requirement. As part of this development, it is important to organise campaigns for evaluating the emerging problems and for comparing the proposed techniques for solving the problems based on the exact same scenario in a reproducible way. Motivated by this, ImageCLEF has for many years been an ongoing initiative that aims at evaluating multilingual or language independent annotation and retrieval of images  [20]. The main goal of ImageCLEF is to support the advancement of the field of visual media analysis, classification, annotation, indexing and retrieval, by proposing novel challenges and developing the necessary infrastructure for the evaluation of visual systems operating in different contexts and providing reusable resources for benchmarking.
Many research groups have participated over the years in its evaluation campaigns and even more have acquired its datasets for experimentation. The impact of ImageCLEF can also be seen by its significant scholarly impact indicated by the substantial numbers of its publications and their received citations  [30]. There are other evaluation initiatives that have had a close relation with ImageCLEF. LifeCLEF  [16]  was formerly an ImageCLEF task. However, due to the need to assess technologies for automated identification and understanding of living organisms using data not only restricted to images, but also videos and sound, it was decided to be organised independently from ImageCLEF. Other CLEF labs linked to ImageCLEF, in particular the medical task, are: CLEFeHealth  [11]  that deals with processing methods and resources to enrich difficult-to-understand eHealth text and the BioASQ  [2]  tasks from the Question Answering lab that targets biomedical semantic indexing and question answering.
Overview of Tasks and Participation The 2016 edition of ImageCLEF consisted of three main tasks that covered challenges in diverse fields and usage scenarios. In 2015  [31]  all the tasks addressed topics related to processing the images in order to automatically assign metadata to them, not directly evaluating retrieval, but techniques that produce valuable annotations that can be used for subsequent image database indexing, mining or analysis. This year there was also a new task that evaluated retrieval of small segments from images containing handwritten text. The three tasks organised were the following: -Medical Classification: addresses the identification, multi-label classification, caption prediction and separation of compound figures commonly found in the biomedical literature. -Image Annotation: aims at developing systems for automatic annotation of concepts, their localization within the image, and generation of sentences describing the image content in a natural language.
In order to participate in the evaluation campaign, the groups first had to register either on the CLEF website or from the ImageCLEF website. To actually get access to the datasets, the participants were required to submit a signed End User Agreement (EUA) by email. Table  1  presents a table that summarize the participation in ImageCLEF 2016, including the number of registrations and number of signed EUAs, indicated both per task and for the overall lab. The table also shows the number of groups that submitted results (a. k. a. runs) and the ones that submitted a working notes paper describing the techniques used. The number of registrations could be interpreted as the initial interest that the community has for the evaluation. However, it is a bit misleading because several people from the same institution might register, even though in the end they count as a single group participation.
The EUA explicitly requires all groups that get access to the data to participate. Unfortunately, the percentage of groups that submit results is often relatively small. Nevertheless, as observed in studies of scholarly impact  [30] , in subsequent years the datasets and challenges provided by ImageCLEF do get used quite often, which in part is due to the researchers that for some reason were unable to participate in the original event. Although for the 2015 edition of ImageCLEF the participation increased considerably with respect to previous years, this was no longer the case for the current 2016 edition. This could be in part due to a CLEF restriction that required to reduce the number of tasks from four to three. However, the number of registrations and signed EUAs for the continuing tasks also decreased.
Participation and Results Table  1  shows the participation in this task. In 2016, there were slightly fewer registrations than in 2015, however the same number of groups submitted runs and the total number of submitted runs increased to 69. Three groups participated in the compound figure detection subtask. The DUTIR group obtained the best results achieving a 92. 7% of accuracy (see Table  2 ). Multi-label classification had two participants BMET and MLKD. BMET has achieved the best combined result of an Hamming loss of 1. 35% and an fmeasure of 32% (see Table  3 ). Only one participant, UDEL CIS  [26] , submitted submitted. BCSG  [18]  achieved the best results with an accuracy of 88. 43%, a good increase compared to past years. Unfortunately, there were no participants in the caption prediction task, however the data are made available and will hopefully be used in the future.