dataesr/scientific-paragraphs-categorization huggingface dataset:
Shared parameters across datasets (i. e. , the focal lengths and the distance between the MLA and the sensor) are close enough to indicate that our model successfully generalizes to different focus configurations. Furthermore, the parameters obtained by our method with an other main lens, i. e. , R12-D, are coherent with the previously obtained parameters, stressing out that our model can be applied to a different camera setting. Finally, our method is the only one providing the micro-lenses focal lengths in a single unified model. The other methods calibrate either several MLA-sensor distances (RTRX), or several models, one for each type (NOUS). Note that distortion coefficients and MLA rotations are close to zero. The influence of these parameters will be analyzed in the proposed ablation study of the camera model in subsection 7.
Quantitative evaluations of the camera model Reprojection error. In the absence of ground truth, we first evaluated the intrinsic parameters by estimating the reprojection error using the previously computed intrinsics. We consider only free-hand calibration target images which are not used in the calibration process. We use the root-mean-square error (RMSE) as a metric to evaluate the reprojection error on the corner part of the features, for each dataset. For the BAP method, the corner reprojection part is reported in Table  4 , as well as the radius reprojection part within parentheses. Regarding the NOUS methods, the original error is expressed using the mean reprojection error (MRE). We converted the final error to the RMSE metric for comparison. Note that the latter method operates separately on each type of microlens, meaning that the number of features is not the same as with NOUR and BAP.
First, the reprojection error is less than 1 pixel for all methods, for each dataset, demonstrating that the computed intrinsics lead to an accurate reprojection model and can be generalized to images which are not from the calibration set. Second, even though the NOUS method provides the lowest RMSE, it shows a significant discrepancy according to the considered type. The error obtained by our method is sightly higher than the error from NOUR, but this can be explained by the fact that our optimization does not aim at minimizing only the corner reprojection error, but the radius reprojection as well. Note that the positional error ε u,v predominates in the total cost by two orders of magnitude compared to the blur radius error ε ρ , but the latter still helps to constrain our model as shown by the relatively close intrinsics between the datasets.
Controlled environment poses evaluation. With our experimental setup, we acquired several images with known relative translation between each frame. We compare the estimated displacements along the z-axis from the extrinsic parameters to the ground truth. The extrinsics are computed with the models estimated from the free-hand calibration. In the case of the RTRX method, we use the filtered depth maps obtained with the proprietary software RxLive to estimate the displacements. The translation errors along the z-axis with respect to the ground truth displacement from the closest frame are reported in Figure  8  for datasets R12-A (a), R12-B (b) and R12-C (c). The relative error ε z for a known displacement δ z is computed as the mean absolute relative difference between the estimated displacement δz and the ground truth, for each pair of frames (T T T i , T T T j ) separated by a distance δ z , i.
e. , ε z (δ z ) = η -1 ∑ (T T T i ,T T T j )|zi-zj=δz δ z -δz /δ z , (43) where δz = ẑiẑ j , and η is a normalization constant corresponding to the number of frames pair. The mean error with its standard deviation across all datasets for BAP, NOUR, NOUS, and RTRX are reported in (d). Firstly, the mean error across R12-A,B,C datasets are of the same order for the evaluated methods around 3 %: for BAP, ε z = 2. 92 ± 0. 73 %; for NOUR, ε z = 3. 50 ± 3. 08 %; for NOUS1, ε z = 1. 68 ± 1. 53 %; for NOUS2, ε z = 3. 40 ± 2. 19 %; for NOUS3, ε z = 3. 30 ± 3. 35 %; and, for RTRX, ε z = 4. 96 ± 4. 44 %. This is also the case for the dataset R12-D where our model has a mean translation error of ε z = 3. 37 %. Note that all evaluated methods outperform RTRX as the depth maps computation might not be as precise as the optimization of extrinsic parameters.
Our method ranks second in terms of relative mean error. Even though lowest error is obtained by the method NOUS for type (1), it presents a large standard deviation and the errors for the other two types are significantly higher. In real application context, there is no way to know in advance which type will produce the smallest error. Nousias et al. (2017)  suggest that when extrinsics are sufficiently close, we can use representative The error ε z is expressed in percentage of the estimated distances, and truncated to 7 % to ease the readability and the comparison. The mean error with its confidence interval across all datasets for our method (BAP), Noury et al. (  2017 ) method (NOUR),  Nousias et al. (2017)  method for each type (NOUS1, NOUS2, NOUS3), and for the proprietary software RxLive (RTRX) are reported in (d).
Please refer to the color version for better visualization. extrinsics that are calculated by averaging the extrinsics from the individual types. Our results do not match this observation as the estimated extrinsics are significantly different for each type. As shown, only the first type gives satisfactory results whereas the other two present a larger error with a significant standard deviation. Averaging the extrinsics from all types will therefore minimize the difference between poses but will not provide the best possible estimation. Secondly, the standard deviation can be seen as an indicator of the estimation precision across the datasets, and thus indicates whether the model can generalize to several configurations or not. Our model presents the lowest standard deviation as illustrated in Figure  8 (d).
This indicates a low discrepancy between datasets and thus that the model is precise and consistent for all configurations. Thirdly, we analyze the behavior of each method for each dataset across different distances. None of the methods suffered from a constant bias, as we do not observe a decreasing relative error as the distance increases. BAP and NOUR present a stable relative error for all distances, i. e. , with approximately 0. 3 % of standard deviation. This indicates that the estimation suffered only from a scale error. One could thus re-scale the poses to provide a precise and accurate esti-mation. We cannot draw any conclusion for the other methods since the variations do not follow any obvious pattern. Finally, our model differs from the model of  Noury et al. (2017)  by modeling the micro-lens focal lengths.
Ablation study of camera parameters To evaluate the influence of each parameter of the camera model, we present an ablation study of some of them. We focus the analysis on distortion coefficients (Q 1 , Q 2 , Q 3 , P 1 , and P 2 ), on some degrees of freedom of the MLA, especially its tilt with respect to the sensor (θ x , θ y ), and the pitch be-Table  5 : Ablation study of some camera parameters. For each dataset, the reprojection error ε all , computed using the RMSE along with the relative translation error ε z , expressed in %, are reported. The symbol (resp. , ×) indicates if we keep (resp. , remove) the considered parameters. × × - - - - - - 8 × × × - - - - - - tween micro-lenses (∆ µ ). All combinations of the parameters have been tested, resulting in eight configurations. For each configuration and on each dataset of R12-A,B,C: first, we calibrate the camera intrinsic parameters; second, we evaluate the model using the RMSE of the reprojection error; and finally, we quantitatively estimate the relative translation error on the evaluation dataset.
Each configuration has been initialized with the same intrinsic parameters, and used the same observations for all processes. Results are reported in Table  5. The first column is the configuration number. The Tilt column indicates if we keep ( ) or remove (×) the parameters θ x and θ y. The Pitch column stands for the parameter ∆ µ , and the column Dist for the distortion parameters Q 1 , Q 2 , Q 3 , P 1 , and P 2. The reprojection error ε all is given by its RMSE, and the relative translation error ε z is expressed in percent with respect to the ground truth displacement. The configuration 1 is our reference, corresponding to the complete model. The optimized parameters are close to the ones from Table  3 , i. e. , with less than 1 % of variation, for all converging configurations and for all datasets.
Finally, when freezing the pitch to its initial value, the positional part of the reprojection error increases. It is especially the case for dataset R12-A, where the reported errors in Table 5 are the highest of all configurations. This confirms our previous observation that the deviation of the micro-image centers and their optical centers does not satisfy an orthographic projection between the MIA and the MLA. The pitch should be taken into account, on one hand to improve the precision of the model, and on the other hand not to hinder the optimization process.
Relative blur calibration We calibrate the blur proportionality coefficient κ for the three datasets using our BAP features. Figure  9  presents two windows extracted around BAP features of different types from the same cluster, showing different amount of blur. The target image to be equally-defocused according to our model is shown before, (b), and after, (c), blur addition. The estimated PSF of the relative blur is given in (d). The optimized blur proportionality coefficients κ are reported in Table  3. Theoretically, the parameter should be the same for all three datasets. Empirically this observation is validated for R12-A and R12-B. Estimated κ for R12-C is lower. This is because the micro-lenses focal lengths in R12-C are slightly shorter than in R12-A and R12-B. Analytically, this difference generates a higher amount of relative blur, and thus a shorter estimate of κ to match the observed blur in image space.
Conclusion To calibrate a plenoptic camera, state-of-the-art methods rely on simplifying hypotheses, on reconstructed data or require separate calibration processes to take into account the multifocus configuration. Taking advantage of blur information we propose: 1) a more complete plenoptic camera model with the introduction of a new BAP feature that explicitly models the defocus blur; this new feature is exploited in our calibration process based on non-linear optimization of reprojection errors; 2) a new relative blur calibration to fill the gap between the physical and geometric blur, which enables us to fully exploit blur in image space; and 3) a way to profile the plenoptic camera and its extended depth of field (DoF). Our camera model is applicable to the multi-focus plenoptic camera (both in Galilean and Keplerian configuration), as well as to the single-focus and unfocused plenoptic camera.
In case of the Raytrix multi-focus camera, our ablation study shows that main lens distortions and MLA tilt can be omitted without hindering the calibration process nor the pose estimation. The study also indicates that explicitly including the pitch of the micro-lenses in the model improves the results. In addition, our calibration methods are validated by quantitative evaluations in controlled environment on realworld data. Our method provides strong initial intrinsics during the pre-calibration step, and coherent optimized camera parameters for all evaluated configurations. It shows a low and stable relative translation error across all the datasets. In the future, we plan to use blur information in complement to disparity to improve metric depth estimation.
An algorithmic decision is explained differently, depending on the person to whom it is explained, the reason why the explanation is needed, the place and time of the explanation, the ergonomics of human-machine interaction, and so on. To solve this context dependency problem, engineers and researchers increasingly tend to directly test different kinds of explanations on the end user of the AI algorithm, in order to establish the best one. Interpretability is then defined as a property of an algorithm that empirically reaches at best some end-user desiderata. There are several possible desiderata for an AI explanation: it must be actionable, understandable, increase satisfaction and confidence, and so on  (Sokol and Flach, 2020). In the same way that Machine Learning integrates the decision context through a trial-and-error procedure that tests on a historical database, the production of AI explanations also becomes a trial-and-error procedure that tests several explanation devices according to well-defined end-user desiderata  (Doshi-Velez and Kim, 2017; Miller et al.
Data and Methods We designed a randomized experiment on 800 people with 2x4 scenarios, using a consumer panel collected over 2 weeks in June 2020 by Kantar. Each scenario was tested on a random sample of 100 individuals representative of the population, using statistical quotas for gender and age. We also verified that none of the 8 scenarios was statistically distributed differently in terms of education level and socio-professional category, compared to the total population. For each scenario, we asked the participants to imagine that they applied to a bank for a loan and that the bank was using an artificial intelligence algorithm to accept or refuse the application (see Appendix A). To build the AI algorithm we used the German Credit Scoring [39] database from the UC Irvine Machine Learning Repository as a training dataset for our ML algorithm.
This dataset consisted of 1000 individual profiles, each with 20 features and categorized as either "good" or "bad" credit risks. From the 1000 individuals in the German Credit Scoring database, we built two Machine Learning algorithms to decide whether to accept or refuse the credit application: a logistic regression with integer coefficients (for the transparent explanation scenarios, see below) and a 2-layer feedforward neural network of 10 neurons in each layer (for the post-hoc explanation scenarios, see below). Using these two Machine Learning algorithms, we constructed 2 "ethical" configurations on 4 explanation configurations, making a total of 8 scenarios (2x4). For each scenario, we started by introducing the case, then gave the input variables used as input by the AI algorithm, and informed the participant that his or her credit had been refused by the algorithm (see Appendix A).
The two "ethical" configurations These two configurations corresponded respectively to cases where the algorithmic decision was "sexist" or not. To simulate the sexist algorithmic decision, we identified, within the German Credit scoring database, the case of a woman whose loan application had been refused by our algorithm and who would have been accepted if she had not been a woman 2. Once this case was identified, we asked the respondents to imagine that they applied to a bank for a loan, and that they had the same characteristics as this woman identified in German Credit Scoring. We then build two scenarios with these characteristics: one where the gender variable was integrated into our learning model, and one where the gender variable was not integrated into the learning model. We refer to this first scenario as the "sexist decision" scenario because it simulates the decision of an algorithm that denies bank credit to a woman because of her sex.
General Information (1S,2S)-1,2-cyclohexanediamine mono (hydrogen chloride)[13] were synthesized as described in previous published work. Dichloromethane, methanol, ethanol, diethyl ether  were dried before use. Copper bromide was purified by treating it with glacial acetic acid and washed several times with ethanol absolute and diethyl ether. Diphenyl ether was dried on molecular sieves. The AZMA synthesis was performed according to a described procedure  [12]. 1 H and 13 C NMR was recorded on Bruker AV360 spectrometer, operating at 360 MHz for 1 H NMR, and 90 MHz for 13 C NMR. Chemical shifts are reported downfield from CDCl3 (δ: 7. 26 ppm, for 1 H NMR and δ: 77. 0 ppm for 13 C NMR) used as an internal reference. Data are represented as follows: chemical shift, multiplicity (br = broad, s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet), coupling constants in Hertz (Hz), integration.
With the availability of low-cost and efficient digital cameras, ecologists can now survey the world's biodiversity through image sensors, especially in the previously rather inaccessible marine realm. However, the data rapidly accumulates, and ecologists face a data processing bottleneck. While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the revolution in the automatic assessment of biodiversity by video recording can be considered. However, current applications of DL models to biodiversity monitoring do not consider some universal rules of biodiversity, especially rules on the distribution of species abundance, species rarity and ecosystem openness. Yet, these rules imply three issues for deep learning applications: the imbalance of long-tail datasets biases the training of DL models; scarce data greatly lessens the performances of DL models for classes with few data.
Finally, the open-world issue implies that objects that are absent from the training dataset are incorrectly classified in the application dataset. Promising solutions to these issues are discussed, including data augmentation, data generation, cross-entropy modification, few-shot learning and open set recognition. At a time when biodiversity faces the immense challenges of climate change and the Anthropocene defaunation, stronger collaboration between computer scientists and ecologists is urgently needed to unlock the automatic monitoring of biodiversity.
Indeed, the current work of deep learning applications to automatically detect and classify animals in imagery is based on two premises: (1) an important database of each class of interest (thereafter "species"), and (2) a balanced dataset. These hypotheses are not verified for unconstrained wildlife video census. In particular, we point out three issues inherent to biodiversity video census, as well as the state of the art of DL approaches to answer them. The aim of this work is (1) to point out ecological questions that can or cannot yet be tackled through DL applications by understanding its possibilities and limitations, and (2) to highlight recent advances of DL to unclog unconstrained wildlife video census.
Deep Learning for Biodiversity Monitoring In recent years, a number of studies have examined the use of deep learning applied to ecological questions  [9, 10]. As shown in the most recent papers, the application of DL for species identification or detection relied on a limited number of species to process, as well as an important and balanced dataset. For instance,  [11]  discriminated 20 mammals species with an accuracy of 87. 5% thanks to a dataset composed of 111,467 images;  [12]  detected and counted one species in videos with an accuracy of 93. 4% with a dataset composed of 4020 images;  [13]  identified eight moth species with an F-measure (a common metric combining recall and precision) of 93% with a dataset of 1800 images, artificially augmented to 57,600 during the training of the model;  [14]  discriminated 109 plant species with an accuracy of 93.
9% thanks to a dataset of 28,046 images. In the rare applications of deep learning on underwater videos  [15] , discriminated 20 coral reef fish species with an accuracy of 78% using a dataset of 69,169 images. Most proposals were adding information to DL models in order to reinforce the identification/detection, such as image enhancement, object tracking and class hierarchy. Historically, DL models were trained on benchmarks, composed of hundreds to thousands of images per class, and applied to a closed testing dataset  [16]  A closed dataset is defined as classes in the testing dataset that are the same as classes in the training dataset. Thus, deep learning models trained for biodiversity monitoring still rely on closed, relatively large and balanced collection of images, following the framework developed in the field of computer science without considering intrinsic properties of biodiversity.
Biodiversity Rules and Deep Learning Limits Species are no simple objects to classify. Their distribution and abundance follow a few universal rules that need to be accounted for in order to unlock the automatic assessment of biodiversity in underwater videos. Since the early work of  [17] [18] [19] , ample evidence across world ecosystems show that in nearly every community in which species have been counted, the distribution of species abundance is highly skewed, such that a few species are very abundant, and many species are present in relatively low numbers. For deep learning applications, this first universal rule of ecology implies heavily unbalanced training datasets, while balanced datasets are a crucial part of robust and accurate models. This issue, hereafter referenced as the "long-tail dataset issue", is especially acute for speciose communities, such as coral reef fishes, where several hundred species, of which only a few dozen are abundant, can co-occur at a single site and at a single time in a video or another sampling station  [20, 21].
The third rule of ecology that seems relevant for deep learning stems from the openness of ecosystems  [25, 26]. The flow of energy, material, individuals and species across ecosystem boundaries is ubiquitous and plays a key role in ecosystem functioning. The degree of openness of marine ecosystems is particularly high because of the aquatic environment that facilitates the movement of species and the existence of most marine species of a planktonic larval stage favouring dispersal  [27]. Ecosystem openness implies the issue of applying a deep learning model to an "open world problem". According to  [28] ,this issue is intrinsic to DL and is defined by a greater number of classes (in our case, species or conditions) in the application dataset than in the training dataset. In the context of biodiversity monitoring, the application dataset is composed of unconstrained recordings of wildlife and ecosystems, and due to ecosystem openness and the limits of sampling and annotation efforts, it cannot be considered as a closed-world application.
Long-Tail Datasets Long-tail datasets are problematic for deep learning model training. Classes with more samples in the training dataset have more impact on the final model. As a result, a model trained with an imbalanced dataset will have more success in predicting classes with more data, while predicting classes with fewer data will be hampered  [29] [30] [31]. Furthermore, it was shown [32] that the degradation of predictions due to imbalanced datasets increases as the complexity of the task increases, which makes data imbalance of great impact in the case of complex ecosystems studies. Although a few studies suggested that training quality (e. g. , sufficient data of all classes in datasets) can decrease the impact of imbalances  [31, 33] , it is impossible to gather enough data for the large number of "rare" species composing marine ecosystems.