dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure7: Comparison of detector performance on the HVGA ATIS Corner dataset. Our method is able to track a corner longer than the baselines while keeping a low reprojection error. Evaluation on the Event-Camera Dataset We repeat similar experiments for the Event-Camera Dataset. Since the resolution of the camera is lower for this dataset, we use a larger temporal window, equal to 50ms, for the tracking. Because of the lower resolution and the higher level of noise, this dataset is more challenging. The reprojection error and the percentage of valid tracks are given in Tab. 2. Detailed values are reported in the supplementary material. We notice that applying the model trained on the ATIS camera (SILC ATIS) generalizes well on the DAVIS, reaching similar performance than the baseline. Since the DAVIS camera provides gray-level images, we also retrained a Random Forest on the DAVIS data (SILC DAVIS).
Search of the model parameters The model functioning depends on seven important parameters, indicated in Tab 1. We searched the best values of those parameters by fitting the model behaviour to the corresponding data of the human participants. In particular, we randomly sampled and 0. 01 0. 2 0. 12 evaluated 100,000 parameter combinations. For each combination, we recorded and averaged the behaviour of 20 'simulated participants', in particular their performance in the 20 trials with the stimuli S1, S2, and S3, and the average reaction times over colours on the same trials (this because the original data on the reaction times of humans were not separated). Such three performance datasets and one reaction-time dataset were compared with the corresponding average data from 14 human participants through a Pearson correlation coefficient R d,m computed as: R d,m = C d,m √ V d * V m ( 13 ) where C d,m is the covariance between the data from humans, d, and data from the model, m; V d and V m is their respective variance.
Acknowledgements  R. B. : Idea  of the model, specification of the model and tests, implementation of the model, tests, data analysis, analysis of results, writing-up. G. B. : Idea of the model, specification of the model and tests, analysis of results, writing-up. E. C. and A. B. : Specification of the model and tests, analysis of results, writing-up. R. B. carried out this work thanks to the support of the A*MIDEX grant (n°ANR- 11-IDEX-0001-02 ) funded by the  French Government "Investissements d'Avenir" program  (https://anr. fr/en/investments-for-the-future/investments-for-the-future/). G. B. and E. C. received funding from the  European Union ,  Horizon 2020 Research and Innovation Program , under Grant Agreement n°713010 Project " GOAL-Robots -Goal-based Open-ended Autonomous Learning Robots " (https://cordis.
g. they may lead to poor performances when the images are corrupted by blur, distortion, or illumination which are common artifacts in remote sensing). Furthermore, they cannot overcome the issues raised by big data such as image variabilities (i. e. ships of same type may have different shape, color, size, etc. ) and data volume. Recently, following the emergence of deep learning, an autoencoder-based deep neural network combined with extreme learning machine was proposed  [7]  and outperformed some other methods using SPOT-5 spaceborne optical images for ship detection. Compared with optical remote sensing, satellite SAR imaging appears more suited for maritime traffic surveillance in operational contexts as it is not critically affected by weather conditions and day-night cycles. In this context, open-source Sentinel-1 SAR data are particularly appealing.
Almost all coastal zones and shipping routes are covered by Interferometric Wide Swath Mode (IW), while the Extra-Wide Swath Mode (EW) acquires data over open oceans, providing a global coverage for sea-oriented applications. Such images, combined with the Automatic Identification System (AIS), represent a large amount of data that can be employed for training deep learning models  [8]. AIS provides meaningful and relevant information about ships (such as position, type, length, rate of turn, speed over ground, etc. ). The combination of these two data sources could leverage new applications to the detection and estimation of ship parameters from SAR images, which remains a very challenging task. Indeed, detecting inshore and offshore ships is critical in both military and civilian fields (e.
g. for monitoring of fisheries, management of maritime traffics, safety of coast and sea, etc). In operational contexts, the approaches used so far still rely on manual visual interpretations that are time-consuming, possibly error-prone, and definitely irrelevant to scale up to the available data streams. On the contrary, the availability of satellite data such as Sentinel-1 SAR makes possible the exploration of efficient and accurate learning-based schemes. One may however consider with care AIS data as they involve specific features. AIS is mandatory for large vessels (e. g. , >500GT, passenger vessels). As such, it provides representative vessel datasets for international maritime traffic, but may not cover some maritime activities (e. g. , small fishing vessels). Though not authorized, ships can easily turn off their AIS and/or spoof their identity.
While AIS tracking strategies  [9]  may be considered to address missing track segments, the evaluation of spoofing behaviour is a complex task. Iphar et al. [10]  evaluate that amongst ships with AIS, about 6% have no specified type, 3% are only described as "vessels". Besides, respectively 47% and 18% of the vessels may involve uncertain length and beam data. These points should be considered with care in the analysis of AIS datasets, especially when considering learning strategies as addressed in this work. Among existing methods for ship detection in SAR images, Constant False Alarm Rate (CFAR)-based methods have been widely used  [11, 12]. The advantage of such methods is their reliability and high efficiency. Using AIS information along with SAR images significantly improves ship detection performance  [13].
Material and Methods The proposed framework combines the creation of a reference groundtruthed dataset using AIS-SAR synergies and the design of a multi-task deep learning model. In this section, we first introduce the proposed multi-task neural network architecture, which jointly addresses ship detection, classification and length estimation. Second, we describe the training framework in terms of the considered training losses and of the implemented optimization scheme. Third, we detail the creation of the considered reference datasets, including how we tackled data augmentation and class imbalance issues, which have been shown to be critical for the learning process.
Creation of reference datasets With a view to implementing deep learning strategies, we first address the creation of reference datasets from the synergy between AIS data and Sentinel-1 SAR data. AIS transceiver sends data every 2 to 10 seconds. These data mainly consist in a positional accuracy (up to 0. 0001 minutes precision), and the course over ground (relative to true north to 0. 1 o ). For a given SAR image, one can interpolate AIS data from the associated acquisition time. Thus it is possible to know the precise location of the ships in the SAR image and the related information (in our case, length and type). The footprint of the ship is obtained by thresholding the SAR image in the area where it is located (the brightest pixel of the image). Since the database is very unbalanced in terms of class distribution, a strategy is also proposed in order to enlarge the training set with translations and rotations, which is a standard procedure for database enlargement (a.
k. a. data augmentation). Concurrently to our work, a similar database has been proposed in  [29]. We also evaluate our framework with this dataset (see Section 3. 5). In our experiments, we consider a dataset composed of 18,894 raw SAR images of size 400×400 pixels with a 10 m resolution. The polarization of the images are either HH (proportion of horizontally transmitted waves which return horizontally) or VV (proportion of vertically transmitted waves which return vertically). Polarization has a significant effect on SAR backscatter. However, our goal is to allow us to process any Sentinel-1 SAR images. We thus consider any HH and VV polarized image without prior information on the type of polarization. Each image is accompanied with the incidence angle since it impacts the backscatter intensity of the signal.
For the proposed architecture, the input is a 2-band image (backscatter intensity and incidence angle). Thus we did not use any pre-trained network since we assume that they can not handle such input data. We rely on Automatic Identification System (AIS) to extract images that contain a ship in their center. AIS also provides us with information about the ship type and length. As stated before, AIS may have been corrupted (e. g. with spoofing), when creating the database, we only consider ships that responds to the two following criteria; (i) their type is clearly defined (i. e. they belongs to the retained classes), (ii) their length is greater than 0 and smaller than 400 meters (the largest ship in the world). Besides, the SAR images we selected were acquired over European waters, where we expect AIS data to be of higher-quality compared with other maritime areas.
The dataset is strongly imbalanced, amongst the 5 classes (Tanker, Cargo, Fishing, Passenger and Tug), the Cargo is the most represented (10,196 instances), while the Tug is the less represented (only 444 instances). The class distribution is detailed in Figure  2  and Table  1. The length distribution shows that Tanker, Cargo, and Passenger ships have similar length distributions. Fishing ships have relatively small lengths, while Tug ship length are intermediate. To account for class imbalance  [30] , we apply data augmentation with translations and rotations. We first perform a rotation of a random angle centered on the brightest pixel of the SAR image (the center of the ship), and then perform a random translation. The same transformation is applied to the incidence angle image. The images employed to train the networks are of size 80×80 pixels.
They contain ships (not necessarily in their center, see Figure  4 ). The ship footprint groundtruth is generated by thresholding the SAR image since we precisely know the location of the ship (i. e. it is the brightest pixel of the SAR image, see Figure  3 ). The obtained footprint is not perfect (see Figure  3b ) but was shown to be sufficient to train the network. Let us note that a CFAR approach could have been employed in order to extract more precisely the ship footprint  [11]. But since our goal is not to detect ships, a coarse ship footprint is sufficient. We considered 2 configurations for the databases; a 4-classes database, employed to compare our baseline to other state-of-the-art approaches (namely MLP and R-CNN), and 5-classes database in order to evaluate how our network responds with more classes.
Each database is composed of 20,000 images of 80×80 pixels, with the same amount of samples per class (5,000 per class for the 4-classes database, and 4,000 per class for the 5-classes database). The networks are trained with 16,000 images and the remaining 4,000 are used for validation. Throughout the data augmentation process, we ensure that images can be seen either in the training or validation set, but not in both. Ships with no AIS signal are not considered in our dataset (neither to train or evaluate our model), since our strategy to build the dataset relies on matching AIS signal with SAR imagery. However, once a model has been trained, it can perform in an operational settings to detect ships with no AIS (it is indeed one of our long-term goals).
Thus it is worth being compared with our proposed model. The R-CNN has a very simple architecture presented in Figure  5. The networks are trained using 16,000 images from the augmented dataset and the remaining 4,000 images are used for validation. The evaluation of the models is performed using several metrics. The classification task is assessed through the confusion matrix, giving, for each class and overall, several metrics. The Intersection over Union (IoU or Jaccard index)  [34]  measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets. It has been designed for the evaluation of object detection. The F-score is the harmonic mean of precision and recall, it reaches its best value at 1 and worst at 0.
Our network For a 80×80 image, our method can run at 250 frames per second. The whole training takes about 9 hours and the testing about a minute. With an overall accuracy and a mean F-score of 97. 2%, the proposed multi-task architecture significantly outperforms the benchmarked MLP and R-CNN models. We report in Table  4  the confusion matrix and additional accuracy metrics. Interestingly, classification performances are relatively homogeneous across ship types (mean accuracy above 92% for all classes). Tankers involve the greater misclassification rate with some confusion with cargo. Regarding length estimation performance, our framework achieves very promising results. The length is slightly over-estimated (mean error: 4. 65 m ± 8. 55 m), which is very good regarding the spatial resolution of the Sentinel-1 SAR data (10m/pixel).
1. 93m when using jointly detection, classification and length estimation losses). As an illustration of the detection component of the proposed architecture, we illustrate in Figure  7  a detection result. As mentioned above, the thorough evaluation of this detection model is not the main objective of this study. Furthermore, without any precise ship footprint groundtruth, is is impossible to quantitatively evaluate the performance of the network for this specific task. Let us recall that the detection task has been widely addressed in the literature  [14] [15] [16]. Overall, this complementary evaluation supports the idea that Neural Network architectures for SAR image analysis may share some low-level task-independent layers, whose training can highly benefit from the existence of multi-task datasets.
Ship classification performance is in line with the performance reported above. Regarding length estimation, the mean error 14. 56 m ± 39. 98 m is larger than that reported for the groudtruthed dataset. Still, this error level is satisfactory given the pixel resolution of 10 m of the SAR image. Let us note that, given the limited samples available, the standard deviation is not fully relevant here. While a special care was undertaken for the creation of our SAR-AIS dataset, this application to a single SAR image exploits the raw AIS data. AIS data may be significanty corrupted, which may partially explain these differences.
Application to the OpenSARShip dataset The OpenSARShip dataset  [29]  has recently been made available to the community. We report here the results obtained with our framework when applied on this dataset. This dataset comprises SAR data with different polarization characteristics and also includes ship categories. With a view to ease the comparison with the previous results, we focus on SAR images that are in VV polarization and the ship categories considered, which leads to considering the following four categories; tanker, cargo, fishing and passengers. Overall, we considered a dataset of 5,225 ships (80% were employed for training and 20% for testing). In the OpenSARShip dataset, the classes are not equally represented. We report classification and length estimation performance in Table  9.
We also evaluate the performance of the model trained on our dataset and applied on OpenSARShip dataset and conversely. The results show that our model produces good results when trained and tested on the same database. However, the results do not transfer from one dataset to an other. We suggest that this  may relate to differences in the maritime traffic and environment between Europe (our dataset) and Asia (OpenSARShip dataset). The comparison to previous work on OpenSARShip dataset is not straightforward. For instance,  [36]  considers only a three-class dataset (Tanker, Cargo and Other). The reported accuracy score (76%) is lower than our 87. 7% accuracy score for the considered 5-class dataset. We may also emphasize that  [36]  does not address ship length estimation.
Discussion The reported results show that a dedicated architecture is necessary for ship classification and length estimation, while state-of-the art architectures failed to achieve satisfying performances. The MLP is sufficient for ship detection on SAR images (from a visual assessment). But this should not be considered as a good result since we only have (positive) examples of ships in our database (no negative samples, so we can not assess the false positives). Thus, the network only learns a thresholding and can not discard a ship from other floating objects (e. g. icebergs). Indeed, iceberg detection and discrimination between iceberg and ship are specific research questions  [37, 38]. Overall, the performance of the MLP stresses the complexity of the classification and length estimation tasks.
Conclusion In this paper, a multi-task neural network approach was introduced. It jointly addresses the detection, classification and length estimation of ships in Sentinel-1 SAR images. We exploit synergies between AIS and Sentinel-1 to automatically build reference datasets for training and evaluation purposes, with the ultimate goal of relying solely on SAR imagery to counter lack or corruption of AIS information that correspond to illegal activities. While the polarization type has a significant effect on SAR backscatter, we were able to train a model which jointly processes HH or VV polarisation without prior information on the type of polarisation. Our results support the assumption that HH and VV polarizations share common image features and that differences in backscatter distributions can be handled through an appropriate parameterization of the network.
Future work may further investigate the training and evaluation of the detection stage. The automation of the matching process between AIS data and SAR images has the potential for significantly increasing the size and diversity of the training and evaluation datasets. This may provide new avenues to address generalization and transfer issues between geographic areas pointed out in our results. Furthermore, while SAR imagery less affected by weather conditions than optical imagery, a specific analysis of the impact of weather conditions onto identification performance would also be of interest. Finally, the specificity of the SAR imagery would call for dedicated operations, while our network relies on standard techniques issued from computer vision.
77Illustration of DVCbased particle fracture detection. Top: vertical slice though the labelled LR dataset before creep phase (im1) with each grain coloured by. Sets of three grains illustrate the greyscales corresponding to the mask in im1, in im2 and the field. Grain 15,647 is another grain with high which is not present in the labelled slice presented. Grain 4218 is an example of having a high which is unfortunately due to to an error in particle tracking Tec 21 that enabled the collaboration that lead to these experiments is acknowledged. Laboratoire 3SR is part of the LabEx Tec 21 (Investissements d'Avenir-grant Agreement No. ANR-11-LABX-0030).
Intensity profiles as defect markers Figure  2  shows the dislocation structure for all the selected rotation angles. Most dislocations were visible for the = 11 1 condition, with some dislocations disappearing for the = 1 1 1 and = 01 0 conditions. The use of the intensity profiles to automatically detect the different defects instead of independent ECCI images was then considered. All the mini-series images were gathered to create a new stack of images. Then, the stack was aligned in Fiji software using affine transformations. The complete alignment procedure is detailed in  [22]. Thanks to the use of a 10 rotation correction during image acquisition, a precise enough alignment can be achieved, considering the pixel size ( 5. 8nm). Therefore, an intensity profile as a function of the image number was generated for each pixel.
X-ray crystallography The single-crystal Xray diffraction studies were carried out at 183(1) K on a Rigaku OD SuperNova diffractometer (CCD Atlas detector) for 3a and on a Rigaku OD XtaLAB Synergy Dualflex diffractometer (Pilatus 200K detector) for 3b, using Oxford liquid-nitrogen cryostream coolers. Single wavelength X-ray sources from micro-focus sealed X-ray tubes were used with the Mo Kα radiation (λ = 0. 71073 Å)  [74]  for 3a and with the Cu Kα radiation (λ = 1. 54184 Å)  [74]  for 3b. The selected single crystals were mounted on a flexible loop using polybutene oil fixed on a goniometer head and transferred to the diffractometer. Preexperiments, data collections, data reductions and analytical absorption corrections  [75]  were performed with the program suite CrysAlisPro. [76]  Using Olex2,  [77]  the structures were solved with the SHELXT  [78]  small molecule structure solution program and refined with the SHELXL program package  [79]  by full-matrix least-squares minimization on F 2.
Computational Details All calculations were performed using the Gaussian 09 software package  [81]. All calculations were performed using Density Functional Theory (DFT) and Time Dependent DFT (TD-DFT) to characterize ground or excited states, respectively. Solvent effects were included using an implicit model (i. e. the Polarizable Continuum Model -PCM  [82] ). In line with available experimental data, acetonitrile was considered as solvent. All calculations were performed using the global hybrid functional PBE0  [83]  in conjunction with the Los Alamos LANL2  [84]  effective core potential and the corresponding double-zeta basis set for the Ruthenium atom, with all other atoms treated with the Pople  [85]  double-zeta basis set with a single set of polarisation  [86, 87]  functions on non-hydrogen atoms (6-31G(d)).
) has been used. The singlet oxygen luminesce at 1270 nm was measured by recording spectra from 1100 to 1400 nm. For the data analysis, the singlet oxygen luminescence peaks at different irradiation intensities were integrated. The resulting areas were plotted against the percentage of the irradiation intensity and the slope of the linear regression calculated. The absorbance of the sample was corrected with an absorbance correction factor. As reference for the measurement in an CH3CN solution phenalenone (Φphenaleone=95%)  [52]  and for the measurement in a D2O solution [Ru(bipy)3]Cl2 (ΦRu(bipy)3Cl2=22%)  [47]  was used and the singlet oxygen quantum yields were calculated using the following formula: -.
The cells were grown in the incubator for additional 44 h. For the determination of the dark cytotoxicity, the cells were not irradiated and after the medium exchange directly incubated for 44 h. After this time, the medium was replaced with fresh medium containing resazurin with a final concentration of 0. 2 mg/mL. After 4 h incubation, the amount of the fluorescent product resorufin was determined upon excitation at 540 nm and measurement its emission at 590 nm using a SpectraMax M2 Microplate Reader (Molecular Devices). The obtained data was analyzed with the GraphPad Prism software.
ACKNOWLEDGEMENTS This work was financially supported by an  ERC   Consolidator Grant PhotoMedMet  to  G. G. ( GA 681679 ) and has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL  (G. G. ). FH, OB and BS thank for support by the  University of Zurich. We thank  Dr. Philippe Goldner  for access to state-of-the-art laser apparatus. I. C and F. M. gratefully acknowledge the  European Research Council (ERC)  for funding ( ERC Consolidator Grant STRIGES  to I. C. , GA No  648558 ).