dataesr/scientific-paragraphs-categorization huggingface dataset:
A noise threshold is obtained by computing the 95th percentile of the monogenic amplitudes of noise distribution in each cube. Thus, if a voxel of the monogenic amplitude of the mean tomogram has an energy higher than the respective threshold (hypothesis test), that voxel can be measured at the filtering resolution. To avoid possible discontinuities among cubes, the set of thresholds was regressed by B-splines. B-Splines are very robust under the existence of outliers providing smooth functions that fit the dataset  (Jonic and Sorzano, 2011).
Experimental results MonoTomo was tested with experimental data sets from EMPIAR (10110  (Chang et al. , 2017) , 10115  (Swulius and Jensen, 2012) , 10027  (Jiko et al. , 2015)  and 10164  (Schur et al. , 2016) ). For each EMPIAR entry, a tilt series was selected that was then aligned with IMOD  (Kremer et al. , 1996)  based on fiducial markers. The tilt series were split in odd and even images (either on a micrograph or frames sense). That is, images corresponding to different tilt angles were alternatively distributed in two groups of images in the case of splitting micrographs, or the set of frames at each tilt angle was split in two groups in the approach of splitting frames; the frame splitting was only used with the first data set (EMPIAR 10164) because it was the only one with deposited frames in EMPIAR.
Experimental case 1 The first experimental data set was taken from EMPIAR entry 10164 and contains a sample of HIV-1 virus  (Schur et al. , 2016). The tilt series was acquired with an angular sampling of 3 degrees from -60 to +60 degrees of tilt. The original movies had a pixel size of 0. 675 Å and they were aligned with MotionCor2  (Zheng et al. , 2017). Note that this sample had a lower angular sampling, which implied a considerable loss of information by splitting the images tilt series in two sets (odd and even) because in that case, the odd/even set presented an angular sampling of 6 degrees. Thus, as an alternative for experimental acquisition with low angular sampling, the set of frames corresponding to each movie was split into even/odd sets to produce the even/odd tilt series at binning 2.
Experimental case 2 MonoTomo was also applied to the data set from EMPIAR 10110  (Chang et al. , 2017)  in which Vibrio cholerae cells were reconstructed. This data set had an angular sampling of 1 degree with tilt angles from -60 to +60 degrees and a pixel size of 4. 04 Å. To reduce the computational burden, the images were binned by a factor 4 getting a final pixel size of 16. 16 Å/pixel. Then, the odd and even tomograms were reconstructed with dimensions of × × 928 960 400 voxel, and the local resolution was computed. MonoTomo results can be observed in Fig. 3 , where several slices on the local resolution map are shown and compared with the corresponding slices of the density map. The resolution regions with clear structure is in the range between -40 120 Å. Note how these latter values present a smooth transition between the very detailed regions and the background.
Experimental case 3 The third experimental case was from EMPIAR entry 10115  (Swulius and Jensen, 2012)  and contained Escherichia coli cells. Images of this dataset were acquired from -64 to + 64 degrees of tilt angles with an angular interval of 1 degree and had a pixel size of 9. 46 Å. As with the previous example, the micrographs were binned by a factor 2 to speed up the local resolution calculation, resulting in a pixel size of 18.
Experimental case 4 This last test made use of the experimental data set from EMPIAR 10027  (Jiko et al. , 2015)  in which the structure of intact bovine F F o 1 ATP synthase in 2D membrane crystals was reconstructed. The tilt range was from -60 to +60 at an interval of 1. 5 degrees, and the pixel size was 3. 3 Å. Micrographs were downsampled by a factor 4 getting a pixel size of 13. 2 Å and split in odd and even sets to reconstruct the required tomograms with dimensions of × × 960 928 200 voxels. The results (Fig. 5 ) show that the resolution was in the range of 40-110 Åin regions with biological structure. In Fig. 6  a histogram of local resolution values is shown. For this experimental case, the FSCe/o was computed, getting a value of 53. 2 Å (at a threshold of 0. 5). The result of MonoTomo seems to be in agreement with the standard global resolutions.
Acknowledgements The author would like to express their gratitude to  Dr. J. J. Fernandez  for his interest, following and advice on this work. The authors would like to acknowledge economical support from The  Spanish Ministry of Economy and Competitiveness  through Grants  BIO2016-76400-R ( AEI/FEDER, UE) , the  "Comunidad Autónoma de Madrid " through Grant:  S2017/BMD-3817 , and the  European Union (EU)  and  Horizon 2020  through grant EOSCpilot ( INFRADEV-04-2016 , Proposal:  739563 ),  INSTRUCT -ULTRA  ( INFRADEV-03-2016-2017 , Proposal:  731005 ),  EOSC-LIFE (INFRAEOSC-04-2018 , Proposal:  824087 ) and HighResCells (ERC-2018-SyG, Proposal:  810057 ). The authors acknowledge the support and the use of resources of Instruct-ERIC.
Context adaptation  [28] , harnessing frequent geometry  [32]   [50]  or environment patterns  [48]   [64] , also successfully received attention from the community, but this algorithm adaptation is often application or even dataset dependent. However, less progress has been stated concerning other areas such as feature data, threshold estimation, or nonmaximum suppression. Training sets are still generated by applying the same techniques than a decade ago: Increasing the data variability is done by blindly mixing datasets  [5] , and Bootstrapping  [16]   [25]  remains among the preferred choices for training set generation. To the best of our knowledge, alternatives to greedy non-maximum suppression are never considered, and the majority of training parameters are overlooked in short-length papers.
More specifically, our contributions are manyfold. We provide extensive experiments and analysis in the domains of model size, instance selection, data cleaning, data generation, as well as near real-time classifier choice and thresholding, soft-cascading, and non-maximum suppression. In order to study underexplored state-of-the-art areas and assess parameters impact independently, we first define a baseline that performs on par with common pedestrian detectors while remaining generic enough to test on the most common competitors. To validate our experiments, we provide tests with two types of features: HoG  [16]  and LBP  [46] ; and on two major datasets, INRIA  [16]  and Caltech USA  [20]. The rest of this paper is organized as follows. Section 2 reviews the related state-of-the-art. Section 3 describes the reference classifier training methodology along with the variants that we will utilize throughout this study.
II. RELATED WORK Classifier optimization always has been a major concern in computer vision. Techniques ranging from active learning  [39]  to feature augmentation  [11]   [40]  have been employed to fine-tune the classifiers on tasks as various as person reidentification  [42] [62]  [68]  or event recognition  [12]   [41]. More specifically, data selection from imbalanced datasets has been a concern for pedestrian detection since the birth of the field. [34]  proved that disproportioned datasets degrade SVMs prediction accuracy, especially for non-linearly separable data. Subsequent research on these experiments  [65]  showed that best performance was obtained for approximately comparable class cardinalities when over-sampling the minority set. Limited work carries out these issues. Bootstrapping  [16] [25] is probably the most common adaptation to the problem.
A. Minority class oversampling As the studies on the topic prove that class imbalance leads to deteriorated performance, one may seek class balance before any training. Two options exist. The first one consists in undersampling the majority class. This solution doesn't satisfy our needs as a large training set is preferable to fully represent the data variability and avoid overfitting during the training process. The remaining solution oversamples the minority class. This process implies the creation of extra data that will not match any existing instance in the dataset images. Nevertheless, these newly created instances have to credibly represented a hypothetical data. The risk of introducing spurious data in the dataset should be minimized, especially in our case, where we may generate over 90% of one class instances.
Consequently, the large majority of data generation algorithms create new data by fusing the values of existing samples. In order to respect the credibility constraint, the parent data often are feature space neighbors, leading to the new data to be created in their feature space local vicinity. This is the overgeneralization issue that cripples most generative techniques, like SMOTE  [58] : Instead of exploring the emptiest areas of the feature space, these algorithms keep populating the denser areas, thus reinforcing the dataset biases. In order to free the process from this issue,  [55]  takes advantage of the spatial localization of most pedestrian feature values. Indeed, due to their block or region decomposition, geometrically structured descriptors, such as HoG  [16] , LBP  [1] , DPM  [24] , or Haar-inspired strategies  [18] , each of their feature values have a precise localization within the proposal window.
C. Data cleaning The purpose of data cleaning is to spruce the dataset off data that could potentially mislead the classifier training. In this paper, we experimented with two data cleaning methods with distinct spirits: Tomek links  [54]  focusses on mixed classes neighborhoods while DBSCAN cleans overpopulated areas  [23]. Tomek links are defined by pairs of opposite classes data that are respective nearest neighbors. More formally, given an instance pair (x i , x j ), where x i and x j are respectively positive and negative instances, and d(x i , x j ) is the distance between x i and x j , the d(x i , x j ) pair is called a Tomek link if there is no instance x k , such that d(x i , x k ) < d(x i , x j ) or d(x j , x k ) < d(x i , x j ). Therefore, Tomek links are drawn either from noise, or on classes border.
F. Cascade-of-Rejectors The cascade-of-rejectors is an efficient strategy for sieving out a large proportion of false positives, and has been applied several times in the context of pedestrian detection  [15] [69][7][52]  [56]. However, tuning this cascade of classifiers remains problematic. First, the classifiers are prone to  errors that propagate along the cascade. Second, most authors employing this technique solely reject negative data after each classification step to avoid the creation of classifiers likely to reject positive data during the subsequent stages. However, this leads to progressively more imbalanced datasets. Fine tuning of the original positive versus negative instances ratio is usually necessary to obtain satisfactory results (a 2 to 4 times bigger negative set is the common practice).
But this strategy is still under-optimal as balanced datasets are proved  [65]  to be the best performing ones. To deal with these issues, we propose a new cascade-ofrejectors. Our approach borrows  [33] 's main idea with a twostage classification: The first one aims for a high recall whereas the second one focuses on precision. The first stage embodies a cascade of n -1 rejectors. To avoid increasing the false negative rate, these n -1 initial classifiers enforce a high recall, moderately optimizing the performance on the precision. The metric formalizes as: M = (T N × W T N + T P )/(P + N ) (4) with P, N, T P, T N , and W T N being respectively positive, negative, true positive, true negative instances, and the weight associated to T N. W T N enforces the recall. Note that W T N = 0 comes back to optimizing for recall.
When W T N ≤ 0. 5, its parametrization has little impact on the results. We used W T N = 0. 5 for all our experiments. Negative data, with a confidence below 0. 5, are rejected at each stage. Negative data over-sampling is then undertaken to avoid emptying the validation set. When insufficient to match the positive instances cardinality, random positive data under-sampling is then considered. We employ Adaboost as classifiers for this part. These decision trees have demonstrated competitive performance whilst retaining computational efficiency  [61][44]. During testing, typically over 95% of the data are safely discarded after 4-5 iterations. This cascade-of-rejectors guaranties a high recall, increased performance induced by balancing the datasets at each iteration, and few parameters to tune.
The second stage performs a finer classification, aiming for high precision results. Since the bulk of the data have been removed, more demanding computation can be performed at this stage without slowing down the detector. We employ a dense forest classifier, that has shown  [5]  giving slightly better performance than its counterparts on pedestrian detection. We optimize for the log-average miss rate  [49]. Figure  5  illustrates the cascade effect on a toy dataset. The only parameters that require careful tuning are the rejection thresholds t(C i ), with C i the i t h classifier of the cascade. During i t h stage of the testing cascade, any data d j with confidence C i (d j ) < t(C i ) will be rejected. Since the method optimizes for the recall, the large majority of data around the separating border between the two classes are negative ones.
A. Datasets We experimented on the INRIA and Caltech-USA datasets. The INRIA  [16]  features 1832 training and 741 testing high resolution pictures. This is among the most widely used dataset for person detection. Despite its small size compared to more recent benchmarks, the INRIA dataset boasts high quality annotations and a large variety of angles, scenes, and backgrounds. The Caltech-USA  [20]  dataset totals 350000 images. Despite some annotation errors  [67] , its large size along with crowded environments, tiny pedestrians, and numerous occlusions probably make the Caltech-USA dataset the most widely used one. We experimented on the "reasonable set" that restricts algorithms to pedestrians over 50 pixels in size and a maximum of 35% occlusion. With respectively a large variability and tiny occluded detections, everyday pictures and automotive application, these two datasets offer complementary settings for our experiments.
B. Experimental setup We experimented on the INRIA  [16]  and Caltech-USA (reasonable set)  [20]  datasets with HoG  [16]  and Haar-LBP  [14]  features. HoGs are configurated with 12 × 6 cells, 2 × 2 blocks and 12 angle orientations, for a total of 2640 values. Block as well as full histogram normalisation are performed. The LBP descriptor follows the same structure, features a value per channel for each cell, and non-uniform patterns are pruned out  [45]. Candidates are selected using a multi-scale sliding window approach  [69]  with a stride of 4 pixels. Approximately 60% are loosely filtered according to "edgeness" and symmetry. No background subtraction or motion features are employed on the Caltech dataset. The training set size is up-bounded at 12K samples. Increasing this threshold doesn't improve performance.
No generative resampling is performed. The model height is set to 100 pixels for the INRIA dataset, 50 pixels for Caltech. We also implemented a cascade-of-rejectors baseline with the commonly used settings: Rejector optimisation is done according to the log-average miss rate metric. We tested this baseline with a typical initial validation set containing 4 times more negative samples than positive ones. To compare with the common multi-dataset training technique, we also used an augmented version with PETS2009  [26]  positive samples. This set gathers 50K positive instances from both datasets and 110K    I  summarizes the default methods and parameters that will be utilized for the different components alternatives. We utilised the openCV implementation for the Adaboost and random forest classifiers.
C. Model size state-of-the-art detectors empirically set the model size  [49]. In this subsection, we conducted thorough experimentation to determine what should be the optimal model size given the initial conditions and what is its actual impact on performance. We varied the model height size in relation to the pedestrian minimal size observed in the dataset. We identified the smallest detections to be 50 and 60 pixels in height for respectively the Caltech (reasonable set) and INRIA datasets. And, as shown in table II, the best corresponding model sizes for these 2 benchmarks are 70 and 100 pixels in height. The overall rule should be "the bigger, the better" as implicitly stated in  [49]. Indeed, a bigger model will harness more information. However, the quality of the detection deteriorates when the model height size exceeds 150% of the smallest dataset pedestrian.
The other two strategies, exploring parts of the feature space further away from positive instances lead to less discriminative classifiers. We explain the lower amount of data preselected for the Caltech dataset by the lower data variability of this dataset. Indeed, the angle and commonly encountered background objects are similar over the frames. lesson learned: data selection and adaptation is an important area that deserves more attention from the community. As shown in these experiments, careful initial instances selection is crucial, and can lead to major performance boost. The selected negatives similarity with positive data must be set in accordance to the dataset variability.
E. Data cleaning Data cleaning is a rarely used pre-processing step. Is it overlooked or just dispensable? To answer this question, we evaluated its true importance for pedestrian training through the 2 proposed data cleaning methods. Since our training set generation method is not deterministic, each result is averaged over a total of 6 runs on the two datasets. Approximately 0. 01 to 0. 2% of the negative data are removed. No significant dataset discrepancy has been observed. Results are detailed in table  III. The use of data cleaning shows a small but steady mean improvement. Tomek links outperform DBSCAN cleaning on this experiment. We assume that the DBSCAN cleaning may remove important data on the fuzzy class borders that fine tuned training sets tend to densely populate in order to improve the classifier discriminative power.
3) Random cuts in 2 parts of equal areas. This implies the cut passing through the centre of the bounding box. Each new data will be generated according to a different cut. 4) Random walks. The first half of the blocks are selected with the random walk algorithm  [21]  from the first sample. The other half of the blocks are selected from the second sample. Each new data will be generated according to a different walk. Using random walks guaranties that, at least, one of the 2 sets will be fully connected. 5) Random walks and horizontal cut combination. This strategy generates negative data utilizing the random walks and positive data with horizontal cuts. The methods are ranked according to their generative power and the loss of credibility of the newly generated data. For instance, with the same initial set, random walks will generate a much greater variety of new data than horizontal cuts, leading to a better exploration of the feature space.
G. Classifier type The general predominance of Random forest classifiers over Adaboost for pedestrian recognition have already been observed in the past  [5]. In this study, We aim to push the experiment further by evaluating its actual impact within a cascade-of-rejectors. For this purpose, we tune the number of Adaboost and Random forest classifiers, keeping to 6 their total amount. Adaboost classifiers always come first in the cascade in order to benefit from their higher speed-up. This experiment is only presented for HoG features and the INRIA dataset as a similar behaviour is observed with other datasets and features. Results are reported in table V. The only noticeable improvement is observed when the 4 or 5 last classifiers are random forests. This is sensible as over 90% of the data have been removed after these stages and the remaining ones are mostly grouped around the pedestrians.
I. Classifiers threshold setting Training various classifiers on different data, with varying features of a classifier cascade invariably leads to a set of disparate classifiers. In addition to that, variations between train and test sets may induce more discrepancies. The use of any classifier cascade is pointless without a mechanism to cope with these variations. To deal with it, the actual strategy estimates and sets a different threshold t(C i ) for each classifier C i. Setting up a set of cascade thresholds is not an easy task and the methods that tackle this particular pedestrian detection sub-problem are, at best, empirical. During testing, the optimal threshold setting is supposed to replicate the training phase dataset reduction across the cascade. In other words, each step should progressively eliminate harder negative examples while the recall should remain close or equal to 1.
K. Comparison with the state-of-the-art Table  VI  shows our method's ranking compared to the stateof-the-art, in terms of performance and speed. Table  I  parameters and random forest were employed for this experiment. T =0. 58 is used for these runs. This work compares favorably to the state-of-the-art. We used using Adaboost classifiers and soft coding reverse engineering for this experiment. While not being the best detector on the market, with respectively 17% and 46% log-average miss rate on the INRIA and Caltech datasets, our approach still provides a very competitive performance/speed ratio. For instance, it scores better than the famous DPM  [24]  and similarly to the integral channel features  [17]  while running 7 times faster. VeryFast  [3]  and the crosstalk cascades provide a viable alternative in terms of speed while displaying lower performance.
LBP is the best descriptor on the INRIA dataset, while HoGs show the best performance on Caltech. We assume that the numerous little pedestrians on the latter impact the texture descriptor more strongly than the gradients. The HoG and LBP fusion yields little improvement. Packed crowds, teensy pedestrians and awkward poses remain the main failure cases. Finally, Deep learning technique  [36] ,  [51]  perform worse and are slower,  [37]  performs much better but is slower, while  [38]  is slightly more efficient and faster. It is worth stating that careful machine learning choices and parametrisation can bring standard features close to state-of-the-art deep learning performance. The end-to-end system is trained in 4-14hours on one core for 10-200K data and processes 3-4 fps on an Intel Xeon 2.
V. CONCLUSION This paper studied through extensive experimentation underexplored mechanisms at various stages of a pedestrian classifier training, with an emphasis on data selection, dataset generation at the initial stage and during the classifier cascade. Our study highlighted data selection and non-maximum suppression as underexploited components for high performance, and soft-cascade thresholding assessment as potential research direction. We also disproved a few empirically received ideas, namely the predominance of the random forest classifier (with high performing detectors) or the use of the DBSCAN algorithm for data cleaning. Besides extra experiments in domains such as active learning or part-based models, the application of the optimized Fair-Train pipeline to deep learning is an obvious possible next step for this work.