dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Results were expressed as mean ± SEM and analyzed using GraphPad Prism 8. 1 software. The Mann-Whitney and oneway ANOVA tests were used to compare all datasets. Normal distribution of the data and variance similarity were verified using GraphPad Prism. Statistical significance was determined at p < 0. 05. All experiments were performed with a minimum of N = 3 biological replicates and n = 3 technical replicates. For mice studies, sample size was determined using the methods described by Berndston et al. [52]. Pre-determined exclusion criteria included the absence of signal at the start of the experiment. *P < 0. 05, **P < 0. 01, ***P < 0. 001.
Numerical Model Description The impact problem presented were numerically modelled using the finite element commercial code ABAQUS 6. 14/Explicit  [17]. The mesh of the specimen, see Figure  7 , has 264,100 nodes and 242,500 eight-node hexahedral elements with one integration point (C3D8R in ABAQUS notation)  [17] , distributed in three zones with different refinement: • Zone A: the plate zone closes to the impact location. Requires the finest mesh, and its size is related to the radius of the projectile (34,900 elements).
Learning graph structure (W) In the sub-problem, we learn the connectivity (or similarity) between samples from the data rather than using certain existing distance measurements. Therefore, the resulting optimization problem can be formulated as = = N s W Z W W W W min 4 s. t. , 1/ 0, , k i j W 1,1 T , 1,1 (9) whose solution has been obtained with an effective ADMM as well, as presented in  Hong et al. (2019c). Please note that for those samples with labels, we construct a graph-based local discriminant analysis (LDA)  (Belkin and Niyogi, 2003)  in the place of the corresponding part in the learned graph W, as shown in Fig. 2. The LDA-like graph (W L ) can be expressed by denote the low-dimensional feature representation for the labeled and unlabeled samples, respectively. Fig. 4. Convergence analysis of the proposed IMR method on three different datasets: Indine Pines, Houston2018, and Berlin EnMap.
, 2017; Zhou et al. , 2017; Xu et al. , 2018b; Hong and Zhu, 2018). In addition, we also draw the convergence curves corresponding to the three used datasets, respectively, by recording the relative loss of objective function of Eq. (  5 ) in each iteration, as shown in Fig. (4 ). One can be seen from the figure is that our model is able to fast reach the state of convergence with more or less 20 steps. As observed in Section 2. 3: Model Learning, the computational cost in our IMR model is mainly dominated by matrix products, where the most costly step lies in solving S, yielding an overall  2 ) ) + d N M t ( ( 2 O computational cost for Eq. (  5 ).
Indian pines dataset The hyperspectral scene located in the northwestern Indiana, USA, has been widely used in various HSI-related tasks, such as dimensionality reduction  (Hong et al. , 2016b; Hong et al. , 2018)  and classification  (Dópido et al. , 2012). It consists of × 145 145 pixels with 220 spectral bands covering the wavelength from 400 nm to 2500 nm at intervals of 10 nm. There are 16 classes in the scene that are mostly vegetation, as detailed in Table  1  along with the number of training and test samples. Fig. 6  shows the false-color image of the studied scene as well as the distribution of training and test samples used in  Ghamisi et al. (2014) ,  Hong et al. (2018).
Houston2018 dataset This dataset is multi-modal data provided for the 2018 IEEE GRSS data fusion contest, where the HSI was acquired by an ITRES CASI 1500 sensor. The HSI, with dimensions of × × 601 2384 50, was collected from the wavelengths between 380 nm to 1050 nm at a ground sampling distance (GSD) of 1 m. This is a complex city scene with 20 challenging classes (see Fig. 7  and Table  1  for more details, including the specific training and test information). Note that we downsampled the ground truth map to the same GSD with the HSI by the nearestneighbor-interpolation.
Berlin EnMap dataset The EnMap HSI with a GSD of 30 m was simulated by the corresponding HyMap data  (Mueller et al. , 2002)  over a hybrid area that includes urban, rural, and vegetation in Berlin, Germany, this data is openly and freely available from the website 2. This image consists of × 797 220 pixels and 244 spectral bands in the wavelength ranging from 400 nm to 2500 nm. The ground truth in the scene is generated by the  Haklay and Weber (2008)  in the form of land cover and land use, and further refined and corrected by means of Google Earth.
The number of iterations in the proposed IMR According to the model's stopping criteria in Algorithm 1, our IMR method generally converges to a desirable solution that corresponds to a well-learned adjacency matrix (W) out of three or four iterations. To support the results more effectively, we further investigate the effects of assigning a different number of iterations in IMR for the three datasets. Fig. 5  gives both visual and quantitative results with the increase of the IMR's iterations 4. Note that the IMR with iterative 0 equivalently degrades to a version without label propagation. The OAs are clearly much lower without using an iterative strategy to update pseudo labels (iterative 0) than when using several iterations. Intuitively, this proves the superiority of the iterative strategy by gradually optimizing the pseudo-labels.
The Indian pines dataset Fig. 6  presents the classification maps for different HDR compared methods using two classifiers on the Indian Pines dataset; Table  2  correspondingly lists the quantitative results obtained under the optimal parameter combination. Using the NN classifier, there is basically the same classification performance in OSF and FSDA. Despite an improved supervised criteria, FSDA still yields poor classification accuracy, since directly projecting the original data into a discriminative subspace with the limited amount of labeled samples is very challenging, especially when dealing noisy data (e. g. , HSI) with various spectral variabilities. Overall, the classification performance by considering the unlabeled samples is better than that without considering them. It should be noted, however, that inspired by latent subspace learning, the JL model dramatically outperforms FSDA (more than 10% improvement), but also improves the OAs of around 4%, 6%, 2%, and 1%, respectively, compared to those semi-supervised HDR approaches (SELD, CDME, SL-LDA, and SSLFDA).
The Houston2018 dataset Classification performance using the different low-dimensional feature representations is evaluated on the Houston2018 dataset both visually and quantitatively, as shown in Fig. 7  and listed in Table  3 , respectively. The optimal parameters used for different compared methods are given in Table  3  as well. Likewise, due to more challenging categories in this scene and small-scale training set, the ability to classify the materials for the LSVM is limited. This might explain a phenomena in Table  3 , that is, why the NN-based classifier, to some extent, performs better than the SVM-based one for many compared methods. More specifically, OSF yields a poor classification performance, due to the highly redundant spectral information and the sensitivity to noise. Unlike OSF that directly uses the original spectral features as the input features, FSDA and JL are apt to discriminate the materials due to.
The Berlin EnMap dataset For the Berlin EnMap dataset, the visual comparison of eight different algorithms in the form of classification maps is shown in Fig. 8. Table  4  details the comparison by means of three quantitative indices: OA, AA, and. With a very high spectral dimension (244), OSF only holds a 53. 97% accuracy when using the NN classifier. The performance of supervised HDR methods (SFDA and JL) is obviously superior to that of OSF, with an increase of at least 8% using the NN classifier. This reveals the importance of HDR in the follow-up hyperspectral data analysis. Furthermore, these methods exhibit balanced accuracies using the LSVM classifier, where JL shows a better classification performance owing to its well-designed architecture in the regression-based latent subspace learning.
On the regularization parameters The quality of low-dimensional features extracted by the proposed IMR model is, to some extent, sensitive to the selection of three regularization parameters ( , , and ) as shown in Eq. (  5 ). For this reason, we experimentally investigate the effects of different parameter setting in terms of OA via the NN classifier. The resulting analysis on the three datasets is quantified in Fig. 9 , where the parameter combinations of = = = = = = 0. 8, 0. 01, 0. 1), ( 0. 9, 0. 01, 0. 01), and = ( = = 0. 8, 0. 1, 0. 01) obtain the optimal classification performance on the test set for the Indine Pines dataset, Houston2018 dataset, and Berlin EnMap dataset, respectively. The results regrading the parameter setting are basically consistent with those obtained by cross-validation on the training set (see the Section 3.
On the subspace dimension Apart from the regularization parameters, we analyze the performance gain in the different subspace dimension of our IMR method, since a proper subspace dimension tends to reach a trade-off between discrimination and redundancy of the dimension-reduced product. For this purpose, the corresponding experiments are conducted by using the NN classifier to see the classification performance with the gradually-reducing dimension. As can be seen from Fig. 10 , with the increase of subspace dimension, the IMR's performance sharply increases to around 20 for first dataset, 30 for the second dataset, and 20 for the last dataset, respectively, then starts to reach a relatively stable state, and finally decreases with a slight perturbation when the subspace dimension is approaching to that of original spectral signature.
On the training set size Although the IMR adopts the semi-supervised learning strategy by jointly accounting for the labeled and unlabeled samples, yet the HDR's performance is determined by the number of training samples to a great extent. This is, therefore, indispensable to investigate the sensitivity with an increasing size of training set. To highlight and emphasize the effectiveness and superiority of our proposed method in the HDR issue, we arrange the classification task by resetting the training set randomly selected from all labeled samples out of 10 run with the different proportions in the range of 5% to 50% at a 5% interval and the rest as the test set, and the average classification accuracies are reported by integrating the ten outputs in the end. Fig. 11  shows a similar trend in OAs with two classifiers (NN and LSVM) on the three different datasets, that is, the classification performance improves with the size of training set, faster in the early, and later basically stabilized.
This also indicates that our semi-supervised method is not heavily dependent on a largescale training set, which can hold a desirable and competitive performance in HDR, even when only small-scale labeled samples are used for training. On the other hand, we can observe an interesting conclusion on the first two datasets from the Fig. 11  that the NN classifier outperforms the LSVM one when the training samples are insufficient, e. g. , less than around 15% of total samples. This could be well explained by the fact that LSVM is a learning-based classifier depending on the adequate samples for training an effective model, which is also supported by the experimental results yielding the higher OAs using the LSVM than those using the NN while using more training samples. Furthermore, with the increasing of training samples, the performance gain is prone to gradually become slow and meet the bottleneck, probably due to the lack of the spatial information modeling.
Conclusions To facilitate the use of unlabeled samples effectively and efficiently, we propose a novel regression-based semi-supervised HDR model, called iterative multitask regression (IMR), which 1) simultaneously bridges the labeled and unlabeled samples with the labels and pseudolabels in a multitask regression framework; and 2) progressively updates the pseudo-labels in an iterative fashion. This model provides us a new insight into the solutions of HDR-related problems. We conducted extensive experiments on three convincing and challenging HSI datasets, demonstrating that our method (IMR) is capable of extracting more discriminative features by allowing for the unlabeled samples and by optimizing the pseudo-labels. It should be noted, however, that while there has been a desirable performance boost in IMR, it is still limited to working well only by linearly learning the low-dimensional feature representations for complex nonlinear cases.
Acknowledgements The authors would like to thank the  Hyperspectral Image Analysis group  and the  NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston  for providing the  CASI University of Houston  dataset. This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No [ ERC-2016-StG-714087 ]), from  Helmholtz Association  under the framework of the  Young Investigators Group "SiPEO"  ( VH-NG-1018 , www. sipeo. bgu. tum. de) and from the  German Research Foundation (DFG)  under grant  ZH 498/ 7-2. The work of N. Yokoya was supported by  Japan Society for the Promotion of Science (JSPS)   KAKENHI 15K20955.
Here we report on new and more extensive measurements of the gas compositions and fluxes of fumarolic emissions from the La Soufrière volcano in 2016-2017, while degassing unrest at its summit displayed continued expansion of thermal (>50 • C) ground areas, new fumaroles and the reactivation of several other vents since July 2014. For these measurements, we used a novel instrumental geometry consisting of an array of three MultiGAS devices operated simultaneously at various heights (1 to 3 m) above the ground. This instrumental array allowed us to accurately determine the fumarolic gases emitted from the different active vents of La Soufrière lava dome, as well as gas emission rates from the three major degassing vents. Moreover, in addition to be compared with previous gas data, our results are interpreted in light of three complementary information: (i) recent electrical conductivity imaging of the underground circulation of hydrothermal fluids inside the lava dome  [29] , (ii) ground deformation of the lava dome as revealed by extensometric survey of its main fractures since 1995  [22, 30] , and (iii) seismic data recorded by the Observatoire Volcanologique et Sismologique de Guadeloupe (OSVG-IPGP).
Otherwise, chemical data available for the other fumarolic vents unaccessible to gas sampling were obtained recently from in-situ MultiGAS survey (March 2006 and March 2012 campaigns  [12] ). In this study we report on gas compositions for all the fumarolic vents (see Figure  1c ) that were active on top of the lava dome in 2016-2017, as well as gas fluxes from the three major emitting vents (Tarissan, Cratère Sud and Gouffre-56). We also report data on fumarolic degassing at the base of the lava dome, Morne Mitam site, along the Ty fault. Our chemical and flux results are then compared to previously obtained data  [10, 12, 43]. From here in, we use the acronyms listed in the caption of Figure  1  for the fumarolic vents, whereas full names will be maintained for major structural elements such as faults, fractures and craters.
Post-processing of data was performed using the RatioCalc software  [48]. Differences in the response time of the sensors were taken into account from lag times in correlation analysis of the various time series, and potential interference between SO 2 and H 2 S sensors was calibrated and corrected. CO 2 and H 2 O contents were corrected for the ambient air composition, measured in the clean atmosphere outside the volcanic plumes. Figure  3  shows an illustration of MultiGAS recordings at the three main craters. for SO2 (sensor type 3ST/F; calibration range: 0-200 ppm, accuracy: ± 2%, resolution: 0. 1 ppmv), H2S (sensor type 2E; range: 0-100 ppm, accuracy: ±5%, resolution: 0. 7 ppmv) and H2 (sensor type EZT3HYT; range: 0-200 ppm, accuracy: ± 2%, resolution: 0. 5 ppmv), all connected at a Campbell Scientific CR6 datalogger.
Post-processing of data was performed using the RatioCalc software  [48]. Differences in the response time of the sensors were taken into account from lag times in correlation analysis of the various time series, and potential interference between SO2 and H2S sensors was calibrated and corrected. CO2 and H2O contents were corrected for the ambient air composition, Fumarolic gas fluxes were determined on six occasions in 2016 (March, May and December) and 2017 (March and October) at the three main degassing craters: CS, TAS and G56 (Figure  1c ). As detailed below, gas fluxes were derived by scaling the integrated amount of each gas species in the air-diluted plume cross-sections to the wind speed measured during the gas survey with a hand-held anemometer. During most of our measurements, the volcanic gas plumes were flattened to the ground by relatively strong trade winds (4-16 m•s -1 ) and, according to both field observations and video-camera footage, had a maximum height of ca.
Instead, 'colder' gas emissions from the TY, BLK1 and NA1, as well as NPE1 and NPE2 vents, display widely different and variable H 2 O/S tot and CO 2 /S tot ratios. Figure  5a  shows the fumarole compositions in an H2O-CO2-Stot ternary diagram. Displayed here are only our May 2016 gas samples in which the H2O molar proportion was accurately determined with an external Rh sensor. The data are compared with the 2012 MultiGAS dataset  [12] , unpublished data for Col de l'Echelle fumarolic emissions during the 1976 eruptive crisis (P. Allard, in prep. ) and high-temperature (720 °C) SO2-rich gas collected in 1996 from extruding andesite in nearby Montserrat island  [9] ; the latter is taken as a reliable proxy for the andesitic magmatic end-member at La Soufrière. The diagram reveals a relatively restricted compositional domain for gas emissions from the major fumarolic vents (TAS, SCS and G56) in 2012-2017, at least in terms of H2O/Stot ratios.
Patterns of Fracturing and Shallow Ground Deformation Among the numerous fractures dissecting La Soufrière lava dome (Figure  1b-c ), 12 have been monitored since 1995 by OSVG using extensometers  [30]. The dataset collected until 2017 reveals that some fractures displayed systematic trends in extension over the last 20 years, whereas others displayed no extension or even a contraction. Figure  6f  shows the displacement vectors computed from the extensometric data set since 2007, with particular focus on deformation that affected the fractures hosting most of the fumarolic activity. The main fractures of Napoléon (NAP1, in Figure  6 ), Faille du 30 Août (F303, in Figure  6 ), Breislack (BLK1, in Figure  6 ), 8 July 1976 (F8J1, in Figure  6 ), all show an extension pattern since 1995. For these fractures, three main temporal phases of extension can be identified (Figure  6d, e ): (i) a period of moderate extension from 1995 to 1999, coinciding with the initial seismogenic and degassing phase of the ongoing unrest; (ii) a period of no extension or minor contraction between 1999 and 2004; then (iii) a new period of extension, at a much higher rate, from 2004 to 2017.
When compared to previous data, fumarolic gas fluxes determined with the highest possible accuracy in 2016-2017 verify the persistency of an elevated degassing rate through the central conduits of La Soufrière. However, they also reveal a recent spatial shift in fumarolic degassing intensity towards the eastern and northern sectors of the lava dome where SW-NE fractures linked to the Breislack fault system, as well as the south-oriented Fracture du 30 Août 1976 (Figure  1c ), are progressively widening, whereas other fractures tend to close or remain stable. These coupled geochemical and geophysical observations at La Soufrière provide a new framework to better elucidate the actual significance of fumarolic gas changes during the current unrest phase and in the future. At present, the available dataset for gas emissions and geophysical signals at La Soufrière do not support the hypothesis of a shallow magma intrusion as being responsible for the current unrest.
Acknowledgments: Our study was supported by funding from a  CNRS-INSU  contract (Aleas-2015, S. Moune), from  University of Palermo  (ref. G. Tamburello) and from  IPGP  (equipment grant and postdoctoral contract to G. Tamburello). We thank  G. Hammouya  ( OVSG ) and  O. Crispi  ( OVSG ) for gas and acid lake sampling and measurements;  D. Gibert ,  F. Nicollin , and participants of the ANR projects DOMOSCAN (2009-2013) and  DIAPHANE  ( ANR-14-ce04-0001 ;  2014-2018 ) for their assistance with electrical resistivity measurements and TAR acid lake pH measurements;  M. Bitetto  and  A. Aiuppa  ( University of Palermo ) for their support to the development and testing of the UniPa-type MultiGAS; and  Glyn Williams-Jones  for help and calibration of the  SFU MultiGAS. The staff of OSVG-IPGP provided us with invaluable assistance in the field, for maintenance of the stations, and with unpublished data.
Acknowledgments. The authors would like to thank the  Association Nationale de la Recherche et de la Technologie (ANRT)  and the  Volvo group  for sponsoring the project. All the simulations in this study are performed on internal clusters of Volvo group and PMCS2I cluster at  Ecole Centrale de Lyon. The authors would like to express their gratitude to  Engine combustion network  and  Professor Raul Payri  from  CMT -Motores Térmicos, Universitat Politècnica de València  for allowing access to some of their experimental data base.
Ubiquitylation is a common post translational modification of eukaryotic proteins and in the human malaria parasite, Plasmodium falciparum (Pf) overall ubiquitylation increases in the transition from intracellular schizont to extracellular merozoite stages in the asexual blood stage cycle. Here, we identify specific ubiquitylation sites of protein substrates in three intracellular parasite stages and extracellular merozoites; a total of 1464 sites in 546 proteins were identified (data available via ProteomeXchange with identifier PXD014998). 469 ubiquitylated proteins were identified in merozoites compared with only 160 in the preceding intracellular schizont stage, indicating a large increase in protein ubiquitylation associated with merozoite maturation. Following merozoite invasion of erythrocytes, few ubiquitylated proteins were detected in the first intracellular ring stage but as parasites matured through trophozoite to schizont stages the extent of ubiquitylation increased.
The P. falciparum ubiquitome: distribution of ubiquitylation sites and stage specificity To identify ubiquitylation sites and substrate proteins in both intracellular and extracellular blood stage parasites, di-Gly remnant peptides produced by trypsin digestion of cell lysates were immunopurified and analysed by liquid chromatography-tandem mass spectrometry. Ubiquitylated peptides from both parasite and host proteins were identified by searching Q Exactive MSMS spectra against a combined P. falciparum and H. sapiens protein database using MaxQuant  [26]. In total, we identified 1,464 distinct ubiquitylation sites in 546 parasite proteins (1% false discovery rate [FDR], site localisation probability >0. 75), and 281 ubiquitylation sites in 115 host substrates (Figure  1 , Supplementary Tables  1  and 2 ).