dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the reference database polygons are composed of correlated instances, i. e. , with quite similar feature vectors, describing the same land cover class. In addition, the class label noise of real datasets generally affects whole polygon instances. For this reason, the used noise injection procedure corrupts all the polygon instances. On the other hand, if adding noise at polygon level results in a number of corrupted instances higher than the desired one, instances are randomly selected inside the polygon in order to respect the given noise level.
Sampling Strategy The sampling procedure, applied to both synthetic and real datasets, aims at constructing two independent set of instances: one for the training and one for assessing the quality of the produced land cover map quality. The split is made at a polygon level (50% for training and 50% for testing) in order to ensure that there are no pixels from the same polygon in the training and the test sets. For each dataset, the procedure is repeated ten times in order not to influence the result by a specific split of reference data. The ten independent training datasets are then corrupted by using the noise injection procedure described in Section 2. 2. Finally, the evaluation is done at the pixel-level with the label noise-free test datasets obtained by the split procedure.
Results and Discussions This section aims at evaluating the influence of training class label noise based on: (1) the noise level; and (2) the classifier choice. For this purpose, RF, SVM-Linear, and SVM-RBF classifiers have been tested on synthetic and real datasets. Several classification configurations are studied in the following. Firstly, the impact of the number of classes, and the use of different input feature vectors and different number of training instances are tested with the injection of random label noise. Secondly, the algorithm complexity is studied for the random label noise. Then, the influence of systematic label noise is analyzed. Finally, the differences between the three classifiers are discussed.
Influence of the Number of Classes Firstly, random class label noise influence is evaluated through different number of classes on synthetic and real datasets. NDVI features are used as input data for the classification. For 2-class experiments, the random noise can be considered as rule-based noise since it is injected between similar land cover classes. Figure  4  displays average OA over the ten runs as a function of the noise level. The first row shows the results for the three synthetic datasets (Table  2 ), whereas the second row shows the results for two real datasets (dataset number 1 and 4 from Table  4 ). Each curve color represents a different classifier: the blue for RF, the red for SVM-RBF, and the yellow for SVM-Linear. Error bars represent the standard deviation of classification accuracy over ten runs for each noise level.
Comparing the results of the three classifiers, SVM-RBF obtains the lowest performances. Except for 10-class synthetic dataset, the SVM-RBF OA values decrease almost linearly with the noise level. On the other hand, Figure  4b-d  show that RF and SVM-Linear classifiers have similar behaviors up to about 25% noise level. At these low noise levels, the emergence of flat zones corroborates the low influence of class label noise on both classifiers. For noise levels above of 25%, SVM-Linear is the most robust classifier except for the 5-class real dataset. Figure  4  highlights the consistency of the created synthetic datasets. Indeed, the results on both rows of Figure  4  are very similar. However, RF classifier outperforms SVM-Linear for 5-class real dataset, which is not the case with synthetic dataset.
Influence of Input Feature Vectors Classification performances in the presence of random label noise have been assessed in the previous section by only using NDVI feature vector as classifier input data. In real world applications, the use of more complex features may be considered with the availability of the new remote sensing data. Spectral bands and spectral features can be extracted for real datasets as described in Section 2. 1. The goal here is therefore to compare the effect of random class label noise for different input feature vectors. More specifically, real datasets 1 to 6 of Table  4  are used. Accordingly, the 2-class and 5-class problems are analyzed. Figure  5  displays average OA values as a function of the noise level for real datasets. The first row shows the results for 2-class problem, whereas the second row shows the results for 5-class problem.
In addition, adding spectral features increases RF performances compared to the use of only NDVI features. In particular, this can be seen on the second row of Figure  5  for RF and SVM-RBF on 5-class real datasets. By contrast, no significant differences can be observed between SB-NDVI or SB-SF, which confirms the previous results obtained in  [54]. To conclude, RF classifier is more robust than SVM in the presence of random class label noise when using spectral bands and spectral features.
Influence of the Number of Training Instances Previous results show that adding spectral features may help for the improvement of classification performances. However, the high dimension of the previous problem can affect the classification performances. To evaluate this effect, the same input datasets and the same noise injection procedure as previously are used, but the number of training instances is here equal to 4800 (or 12,000) for 2-(or 5-) class problem (dataset number 7 to 12 from Table  4 ). Following the same figure style than previously, Figure  6  shows classification performances for different noise levels. The first row displays the results for 2-class problem with 4800 training instances, whereas the second row displays the results for 5-class problem with 12,000 training instances.
Algorithm Complexity Algorithm complexity has also been studied in order to examine the impact of class label noise on algorithm behaviors. In the case of RF, the complexity is evaluated by computing the average path length of each training instance. A small average path length means a simple model to learn and a rapid decision phase. Concerning the SVM algorithm, the number of support vectors has been selected to represent the SVM complexity. The number of support vectors depends on the distribution of the data and the value of the C parameter. A small number of support vectors corresponds to a simple model to learn. These experiments have been carried out by using 2500 training instances for 5-class synthetic and real datasets with random label noise. NDVI, SB-NDVI, and SB-SF have been used (dataset number 4 to 6 from Table  4 ).
For RF, the model complexity is similar for the different input feature datasets. However, the SB-SF and SB-NDVI complexities are lower than NDVI complexity at a 0% noise level. By using more features, the quality of feature subsets selected at each node is improved. Thus, the split quality is also enhanced, leading to a reduction in the required number of splits. Figure  7a, c  show that the RF complexity curves follow three stages. During the first stage, Figure  4b ,e shows a slight decrease of the OA values. At these low noise levels, the RF classifier manages the mislabeled data resulted in an increase of RF complexity. Then, the RF complexity remains stable when the OA values fall steadily. In this case, the RF classifier assimilates mislabeled data as normal data. After 50% noise level, the RF complexity increases again due to the excessive presence of mislabeled training data.
In addition, the average path length for the synthetic dataset is higher than for real datasets due to the size of the input feature vectors, which is smaller for the synthetic dataset. A higher average path length involves a slower decision phase for new instances. However, the training computational time, which is much higher than decision phase time, increases with the number of features. Considering SVM-RBF complexity, the addition of more features requires the use of more support vectors. The Gaussian kernel embeds data in a higher dimension space, leading to the possibility to select more support vectors. Thus, it may explain why the number of selected support vectors is higher for SVM-RBF than for SVM-Linear. However, NDVI SVM-Linear is an exception, since the number of support vectors is higher than NDVI SVM-RBF for high noise levels.
Study of Systematic Label Noise Previous studies have been performed for the generation of a random label noise. To analyze the effect of more realistic mislabeled data, the study of systematic label noise is performed for the 5-class synthetic and real datasets with NDVI features. Figure  8  displays average OA values over the ten runs as a function of the noise level. Figure  8a  shows the results for 5-class synthetic dataset, whereas Figure  8b  shows the results for 5-class real dataset. Each curve color represents a different classifier: the blue for RF, the red for SVM-RBF, and the yellow for SVM-Linear. The results show that the systematic label noise is similar to the random label noise, but more harmful (Figure  4b, e ). Thus, a short plateau can be observed at low noise levels for the RF and SVM-Linear classifiers.
In addition, the OA values decrease more strongly. For instance, at 40% noise level, the RF OA values of the 5-class real dataset are equal to 72% and 60% for random and systematic noise respectively. As for the random label noise, the SVM-RBF OA values decrease linearly with the noise level. The SVM-RBF classifier is the most impacted classifier by the systematic label noise. The systematic label noise has an higher influence on the classification performances than the random label noise. In addition, the RF classifier is more robust to the systematic label noise than the SVM-RBF and the SVM-Linear classifiers.
Comparison between Classifiers The results obtained in the previous sections show how the performances decrease with the presence of class label noise. Comparing all classifier results, RF seem less sensitive to noise than SVM. Furthermore, RF have similar behaviors for the studied configurations: changing the number of classes, increasing the number of instances, or adding some features to the input vector. This result complies with the literature. In  [37] , eleven classifiers, including RF and SVM-Linear, are compared on different datasets. The RF classifier is the most robust among the eleven. Similarly, RF yield better performances than SVM-RBF in  [74]. However, the classifier parameters were not optimized in this study, that may be more adverse for SVM-RBF. The effect of noise in the training set has also been studied in  [14] : the RF classifier was more robust than a single classification tree.
The embedding in a space of higher dimension allows SVM-RBF to find an optimal hyperplane that perfectly follows training instances. The γ parameter sets the width of RBF kernel. When the γ value is small, the model is too constrained and cannot capture the data structure. The resulting model behaves similarly to a linear classifier. By contrast, a model learned with a large γ value is very sensitive to the input data, i. e. , it tries harder to avoid misclassifying in the training dataset. Thus, the model looses its learning generalization ability, and has difficulty to classify new instances. The γ values obtained for SVM-RBF classifier are studied to validate the SVM behavior. Table  6  displays γ values selected with cross-validation for 5-class 2500-instance real dataset for the three input feature sets.
3 are tested on training and test sets. The experiments are carried out for the 5-class real dataset with NDVI features and 2500 training instances. The purpose is to compare the optimal parameter values obtained by the cross-correlation on training set to the optimal one expected for the testing data. Figure  9  displays the cross-validation performances for SVM-RBF classifier at 0%, 20%, 40% noise levels. For each figure, the horizontal and vertical axis display C and γ values respectively in logarithmic scale. The first row shows OA obtained by the five-fold cross-validation procedure on the training set. By contrast, the second row shows OA obtained on an independent test set. The red cross highlights the selected parameter values found with the five-cross validation procedure, i. e. , the highest OA obtained with the training set.
Conclusions The effect of mislabeled training data on supervised classification has been analyzed and discussed in this work. Specifically, the robustness of three supervised methods in the presence of training class label noise is evaluated. To the authors' knowledge, the effect of such noise has not been yet analyzed for land cover mapping with satellite image time series. The three well known classifiers RF, SVM-RBF and SVM-Linear, have been compared with different noise levels and different classification configurations. For this purpose, experiments have been carried out on synthetic and real datasets. The synthetic dataset has been specifically designed for this study. It simulates vegetation profiles over one year. A new strategy to inject class label noise based on polygons has also been proposed.
The general results have shown that RF and SVM are robust classifiers for low noise levels in the presence of mislabeled training data. The impact of training random class label noise has also been studied through different classification configurations. Firstly, the influence of the number of classes has been analyzed. The 2-class datasets imply difficult problems, where accuracies decrease faster than 5-or 10-class problems. These experiments have also corroborated the consistency of the synthetic dataset, where the same trends have been observed on real datasets. However, they have been carried out only with NDVI features as input data, showing some overfitting for SVM-RBF. Therefore, a second experiment has been performed by adding more features as input data to the classification system.
The parametrization of SVM was not straightforward. More specifically, it has been shown that the cross-validation procedure is impacted by the noise presence, leading to classifier overfitting. An interesting future work may focus on realistic land cover confusions. This harmful noise would probably have more impact on both SVM and RF classifiers. Similarly, only balanced problems have been analyzed here. However, in real classification problems, there are major and minor land cover classes representing the landscapes. Therefore, the influence of noise on imbalanced datasets could also be studied in the future. Furthermore, this work focuses only on mislabeled training instances, but validation instances may also be noisy. It could be therefore interesting to characterize the classification performances as a function of validation class label noise to assess the procedure used to validate map accuracies  [17, 22].
Grid search results for SVM parameters optimization on NDVI 5-class real dataset at 0%, 20% and 40% noise levels. The first row displays cross-validation results obtained with training set, the second row displays Overall Accuracies obtained with an independent test set. The red cross highlights the best parameter configuration obtained with cross-validation procedure. (a) for 0% noise level on training set; (b) for 20% noise level on training set; (c) for 40% noise level on test set; (d) for 0% noise level on test set; (e) for 20% noise level on validation set; (f) for 40% noise level on test set.
This work is funded by the  French spatial agency (CNES)  and the  French mapping agency (IGN) , and it has been carried out at CESBIO laboratory-supported by the  University Paul Sabatier ,  CNRS  ( Centre National de la Recherche Scientifique ),  CNES ,  IRD (Institut de Recherche pour le Développement) -and MATIS laboratory supported by  IGN. The SPOT-4 imagery was obtained under the  SPOT-4/Take-5 program. The SPOT-4/Take-5 imagery is copyrighted to CNES under "CNES 2013, all rights reserved. Commercial use of the product prohibited".
2-Class Dataset 5-Class Dataset 10-Class Dataset Land cover class 0. 9 0. 8 0. 7 Corn Corn silage Sorghum NDVI 0. 6 0. 5 Sunflower Soy Wheat Rapeseed Barley Evergreen 0. 4 Deciduous 0. 3 0. 2 0 50 100 150 200 250 300 350 DoY Corn Corn silage Corn Corn silage Corn Corn silage Sorghum Sunflower Soy Sorghum Sunflower Soy Wheat Rapeseed Barley Evergreen Deciduous.
Grid search results for SVM parameters optimization on NDVI 5-class real dataset at 0%, 20% and 40% noise levels. The first row displays cross-validation results obtained with training set, the second row displays Overall Accuracies obtained with an independent test set. The red cross highlights the best parameter configuration obtained with cross-validation procedure. (a) for 0% noise level on training set; (b) for 20% noise level on training set; (c) for 40% noise level on test set; (d) for 0% noise level on test set; (e) for 20% noise level on validation set; (f) for 40% noise level on test set. 25 38. 9 52. 8 73. 7 76 78. 3 66. 8 80. 7 73. 7 66. 8 (f) 40% noise level on test Figure 9.
C q was determined using the Mx-Pro 3. 20 software with default settings. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and 18S ribosomal 1 (S18) were chosen as reference genes for normali-zation, validated using BestKeeper. 57 mRNA expression analysis, normalization and statistical analysis were performed using REST2009 software  58  using the ΔΔC q method and a pair-wise fixed reallocation randomization test.
5 W per gel until the dye front has reached the bottom of the gel. Detection was carried out by tetrathionate silver staining. 64. 9. 4. Image analysis. The gels were scanned after silver staining on a flatbed scanner (Epson perfection V750), using a 16 bit grayscale image acquisition. The gel images were then analyzed using the Delta 2D software (v 3. 6). Spots that were never expressed above 100 ppm of the total spots were first filtered out. Then, significantly-varying spots were selected on the basis of their Student's t-test p-value between the treated and the control groups. Spots showing a p-value lower than 0. 05 were selected. This strategy is used to avoid the use of arbitrary thresholds, which can result in discarding statistically-valid relevant changes and including non-valid changes.
8 kV and the cone voltage at 35 V. Mass calibration of the TOF was achieved using fragment ions from Glu-fibrino-peptide B on the [50;2000] m/z range. Online correction of this calibration was performed with Glu-fibrino-peptide B as the lock-mass. The ion (M + 2H) 2+ at m/z 785. 8426 was used to calibrate MS data and the fragment ion (M + H) + at m/z 684. 3469 was used to calibrate MS/MS data during the analysis. For tandem MS experiments, the system was operated with automatic switching between MS (0. 5 s per scan on m/z range [150;1700]) and MS/MS modes (0. 5 s per scan on m/z range [50;2000]). The two most abundant peptides (intensity threshold 20 counts per s), preferably doubly and triply charged ions, were selected on each MS spectrum for further isolation and CID fragmentation using collision energy profile.
Fragmentation was performed using argon as the collision gas. Mass data collected during analysis were processed and converted into. pkl files using ProteinLynx Global Server 2. 3 (Waters Corporation, Milford, USA). Normal background subtraction type was used for both MS and MS/MS with 5% threshold and polynomial correction of order 5. Smoothing was performed on MS/MS spectra (Savitsky-Golay, 2 iterations, window of 3 channels). Deisotoping was applied for MS (medium deisotoping) and for MS/MS (fast deisotoping). The TripleTOF 5600 (Sciex, Ontario, Canada) was operated in positive mode, with the following settings: ionspray voltage floating (ISVF) 2300 V, curtain gas (CUR) 10, interface heater temperature (IHT) 150, ion source gas 1 (GS1) 2, declustering potential (DP) 80 V. Information-dependent acquisition (IDA) mode was used with Top 10 MS/MS scans.
The MS scan had an accumulation time of 250 ms in m/z [400;1250] range and the MS/MS scans 100 ms in m/z [150;1800] range in high sensitivity mode. Switching criteria were set to ions with a charge state of 2-4 and an abundance threshold of more than 500 counts and exclusion time was set at 4 s. IDA rolling collision energy script was used for automatically adapting the CE. Mass calibration of the analyser was achieved using peptides from digested BSA. The complete system was fully controlled by AnalystTF 1. 7 (Sciex). Raw data collected were processed and converted with MSDataConverter in. mgf peak list format. For protein identification, the MS/MS data were interpreted using a local Mascot server with the MASCOT 2. 4. 1 algorithm (Matrix Science, London, UK) against UniProtKB/SwissProt (version 2016_01, 550299 sequences).
Alsace, the French National Research Program for Environmental and Occupational Health of ANSES (PNREST 2015/032, Silimmun Grant), the toxicology project of the CEA (Imaginatox grant), and the French National Research Agency (ANR-16-CE34-0011, Paipito grant). This work is a contribution to the Labex Serenade (no. ANR-11-LABX-0064) funded by the "Investissements d'Avenir" French Government program of the French National Research Agency (ANR) through the A*MIDEX project (no. ANR-11-IDEX-0001-02). This work used the platforms of the Grenoble Instruct centre (ISBG; UMS 3518 CNRS-CEA-UJF-EMBL) with support from FRISBI (ANR-10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology (PSB), as well as the platforms of the French Proteomic Infrastructure (ProFI) project (grant ANR-10-INBS-08-03).
The electron microscope facility is supported by the Rhône-Alpes Region, the Fondation Recherche Medicale (FRM), the fonds FEDER, the Centre National de la Recherche Scientifique (CNRS), the CEA, the University of Grenoble, EMBL, and the GIS-Infrastrutures en Biologie Sante et Agronomie (IBISA). We also thank the Fondation pour la Recherche Médicale for financial support of a Synapt HDMS mass spectrometer.
This paper aimed to obtain a flexible, reproducible and lightweight sensor to record the deformations of a specific structure, while respecting simple and rapid prototyping processes. Moreover, the substrate deformations were suspected to be weak (less than 10%). First, the selected polymers and the method used to develop the strain gauge piezoresistive sensor to provide electrical data over a wide range of temperatures, are fully described. In particular, the sensor was designed as a water-based thermoplastic elastomer (silicone)/intrinsically conductive polymer (PEDOT:PSS) composite. Next, sensor characterization was performed in terms of conductive, mechanical and electromechanical properties. As the developed sensor is deposited by direct coating on thin and flexible substrates, it is therefore of prime importance that the embedded sensor does not alter their mechanical behavior.
Electromechanical Characterization of the Sensor The reproducibility and the performance as a weak strain gauge of the sensor systems were studied through electromechanical tests. Furthermore, the influence of the plasma treatment and the protective layer deposited on the gauge factor were characterized. The electrical response of the sensor to strain was recorded over time by a data acquisition system with a built-in voltage source from Keithley. The internal power supply provided a voltage set at 5 V and allowed for measurements with high accuracy. This apparatus was connected to a computer to record, plot, and process the sensor electrical voltage during the tensile elongation of the substrate. A simple divider bridge was implanted between the Keithley and the MTS 2/M bench, which recorded the strain applied to the sensor over its deformation.