dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the total number of processing units in a layer is the sum of all neurons independently of the type. Also, observe that the two depthwise convolutions of a same layer share the kernel size, differing only in the stride and padding. These both parameters are presented as follows: the value related to the first depthwise convolution is reported separated by a comma of the value related to the second depthwise convolution. Although not visually represented, the pointwise convolutions explored in the morphological layers always use the same configuration: kernel 1 × 1, stride 1, and no padding. Fig. 7 : In the first (top) part, false-color and ground-truth images of the explored hyperspectral datasets. In the second (bottom) part, the classes, the number of pixels used for training the models, and the total amount of labeled pixels.
baseline, referenced as PreMorph-ConvNet and exploited only for the (non-synthetic) image classification datasets, uses predefined morphological operations as pre-processing to extract the first set of discriminative features  [20] ,  [28]. Such data is then used as input to a ConvNet (in this case, the previously described one), which is responsible for performing the final classification. The third baseline, used only for the (non-synthetic) image classification datasets and referenced hereafter Depth-ConvNet, is exactly the DeepMorphNet architecture but without using binary weights and depthwise pooling. This baseline reproduces the same DeepMorphNet architecture (also using depthwise and pointwise convolutions) and, consequently, has the same number of parameters, except for the binary weights.
Other baselines, used only for the (non-synthetic) image classification datasets, are the deep morphological frameworks proposed by  [23] ,  [24]. In these cases, the DeepMorphNet architecture was recreated, as far as possible, using both techniques. Finally, the last baseline, referenced with the suffix "SK" and explored only for the (non-synthetic) image classification datasets, is the convolutional and morphological networks, but with Selective Kernels (SK)  [32] , which allows the network to give more importance to certain feature maps than to others. Differently from the morphological networks, all baselines use batch normalization  [1]  (after each convolution) and Rectified Linear Units (ReLUs)  [30]  as activation functions. It is important to note that, despite the differences, all baselines have the exact same number of layers and feature maps of the base DeepMorphNet.
C. Experimental Protocol For the synthetic datasets, a train/validation/test protocol was employed. In this case, 60% of the instances were used as training, 20% as validation, and another 20% as test. Results of this protocol are reported in terms of the average accuracy of the test set. For the other image classification datasets, fivefold cross-validation was conducted to assess the accuracy of the proposed algorithm. The final results are the mean of the average accuracy (for the test set) of the five runs followed by its corresponding standard deviation. Finally, for the pixel classification datasets, following previous works  [38] -  [40] , we performed a random sampling to select 1,000 training samples/pixels from all classes, as presented in (the bottom part of) Figure  7. All remaining pixels are used to assess the tested architectures.
V. RESULTS AND DISCUSSION In this section, we present and discuss the outcomes. Section V-A presents the results of the synthetic datasets while Section V-B discusses the results of the other datasets. Finally, Section V-C presents the results of the pixel classification datasets. Please remember that we present a proof of concept of a new paradigm: using non-linear morphological operations instead of standard linear convolutions for a deep network. To demonstrate the benefits of this proposal, similar architectures must be considered, including in terms of the number of neurons, layers, etc. In order not to confuse the benefits of proposal with the advantages of complex networks, as introduced, the proposed models were inspired by the simple AlexNet  [2]  architecture.
A. Synthetic Image Classification Datasets As explained in Section IV-A1, two synthetic datasets were proposed in this work to validate the feature learning of the deep morphological networks. Furthermore, as introduced, both datasets can be perfectly classified using one opening with specific SEs. Supported by this, the proposed DeepMorphSynNet, composed of one opening neuron, can be used to validate the feature learning process of the proposed technique, given that this network has the capacity of perfectly classifying the datasets as long as it successfully learns the SE. Given the simplicity of these datasets, aside from the first method describe in Section IV-B, we also employed as baseline a basic architecture composed uniquely of a classification layer. Specifically, this network has one layer that receives a linearized version of the input data and outputs the classification.
The proposed morphological network and baselines were tested for both synthetic datasets using the same configuration, i. e. , learning rate, weight decay, momentum, and number of epochs of 0. 01, 0. 0005, 0. 9, and 10, respectively. Results for the synthetic square dataset are presented in Table  I. Among the baselines, the worst results were generated by the ConvNets while the best outcome was produced by the network composed of a single classification layer (86. 50%). A reason for this is that the proposed dataset does not have much visual information to be extracted by the convolution layer. Hence, in this case, the raw pixels themselves are able to provide relevant information for the classification. However, the proposed morphological network was able to outperform all baselines. Precisely, the DeepMorphSynNet yielded a 100% of average accuracy, perfectly classifying the entire test set of this synthetic dataset.
As introduced in Section IV-A1, in order to achieve this perfect classification, the opening would require a square SE larger than 5 × 5 but smaller than 9 × 9 pixels. As presented in Figure  8a , this was the SE learned by the network. Moreover, as introduced, with this SE, the opening would erode the small 5 × 5 squares while keeping the larger 9 × 9 ones, the exact outcome of the morphological network, as presented in Figures  8b  and 8c. Results for the synthetic rectangle dataset are presented in Table  I. In this case, the proposed DeepMorphSynNet and the ConvNet baseline produced perfect results, with the classification layer producing the worst result. Such results may be justified based on properties of this dataset: the distinct orientation of the rectangles may help both DeepMorphSynNet and ConvNet, while the exact same number of pixels for both rectangle classes may hinder the classification layer.
B. Image Classification Datasets For the image classification datasets, all networks were tested using essentially the same configuration, i. e. , batch size, learning rate, weight decay, momentum, and number of epochs of 16, 0. 01, 0. 0005, 0. 9, and 2,000 respectively. Aside from those approaches, we also employed as baseline an approach, called hereafter Static SEs, that reproduces the exactly morphological architecture but with static (nonoptimized) SEs. In this case, each neuron has the configuration based on the most common SEs (mentioned in Section II-A). The features extracted by these static neurons are classified by a Support Vector Machine (SVM). The idea behind this baseline is to have a lower bound for the morphological network, given that this proposed approach should be able to learn better SEs and produce superior results.
1) UCMerced Land-use Dataset: Results for the UCMerced Land-use dataset are reported in Table  II. In this case, all networks outperformed the lower bound result, generated by the Static SEs. Additionally, the MorphoN  [23]  and DMNN  [24]  networks were outperformed by all other approaches. In fact, all other experimented techniques, including the DeepMorphNets and the networks with Selective Kernels  [32] , yielded very similar results. This indicates that the proposed method is capable of optimizing the morphological filters to extract salient and relevant features, generating satisfactory outcomes. In order to grasp the difference between the information captured by DeepMorphNets and ConvNets, we performed a comparison, presented in Figure  10 , between the feature maps of such networks.
As can be observed, there is a clear difference between the characteristics learned by the distinct networks. In general, the DeepMorphNet is able to preserve different features when compared to the ConvNets, which corroborates with our initial analysis. To better evaluate the proposed morphological network, a convergence analysis of the architectures in the UCMerced    11 : Convergence (fold 1) of the architectures for both datasets. Note that, for simplicity, only the networks without Selective Kernels  [32]  were reported.
2) WHU-RS19 Dataset: The second part of Table  II  presents the results related to the WHU-RS19 dataset. Again, as expected, all architectures outperformed the lower bound result, generated by the Static SEs. As for the previous dataset, all experimented techniques, including the DeepMor-phNets and the networks with selective kernels  [32] , achieved very similar outcomes, outperforming the MorphoN  [23]  and DMNN  [24]  approaches. These results reaffirm the previous conclusions related to the ability of the morphological networks to capture interesting features. Figure  10  presents the same comparison, as before, between the feature maps extracted from DeepMorphNets and ConvNets. Again, it is remarkable the difference between the features extracted by the distinct architectures, which corroborates with previous analysis and results.
C. Pixel Classification Datasets For the pixel classification datasets, all networks and baselines were assessed using the same configuration, i. e. , batch size, learning rate, weight decay, momentum, and number of epochs of 10, 0. 01, 0. 0005, 0. 9, and 1,000 respectively. 1) Pavia Centre Dataset: Results for the Pavia Centre dataset are reported in (the first part of) Table  III  In order to allow a visual comparison, Figure  12  presents the false-color image, the ground-truth, and the prediction maps generated by the proposed approach and the baseline. From this image, it is possible to observe that the DeepMorphNet produced a more consistent prediction map than the ConvNet baseline. We argue that this is mainly due to the fact that the proposed method can learn distinct and relevant patterns of specific classes, such as Tiles and Bare Soil (cyan and dark blue colors, respectively), an outcome that can be better perceived in the normalized confusion matrices presented in Figure  13.
2) Pavia University Dataset: Results for the Pavia University dataset are reported in (the second part of) Table  III. Again, the proposed DeepMorphNet outperformed the Con-vNet model by approximately 5 percentage points of average accuracy (86. 52% versus 82. 44%, respectively). This gain can be also seen in the prediction maps (Figure  12 ). Overall, the proposed method was capable of producing better results for some specific classes, such as Trees and Shadows (yellow and dark blue colors, respectively), an outcome that can be better observed in Figure  13.
VI. CONCLUSION In this proof of concept work, we proposed a new paradigm for deep networks where linear convolutions are replaced by non-linear morphological operations. The proposed method, called Deep Morphological Network (DeepMorphNet), is able Input Image Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Fig. 10 : Input images and some produced (upsampled) feature maps extracted from all layers of the networks for the UCMerced Land-use (top example) and RS19 Datasets (bottom example). Again, for simplicity, only the feature maps of the ConvNet (first row), Depth-ConvNet (second row), and DeepMorphNet (last row) models, without Selective Kernels  [32] , were reported. to perform morphological operations while optimizing their structuring elements. Technically, the proposed approach is composed of morphological layers, which consist of morphological neurons.
Such processing units are built upon a framework that is essentially based on depthwise convolution and pooling layers. In fact, this framework provides support for the creation of the basic morphological neurons that perform erosion and dilation. These, in turn, allow the creation of other more complex ones that perform opening, closing, tophats, and (an approximation of) reconstructions. The proposed approach is trained end-to-end using standard algorithms employed in deep networks. Experiments were conducted using six datasets: two synthetic, two image, and two pixel classification ones. The first two were only employed to analyze the feature learning of the proposed technique whereas the other datasets were employed to assess the efficiency of the DeepMorphNets. Results over the synthetic datasets have shown that the proposed DeepMor-phNets are able to learn relevant structuring elements perfectly classifying them.
Considering the image classification datasets, the proposed DeepMorphNets outperform other similar deep morphological frameworks  [23] ,  [24]  whereas producing competitive results when compared to ConvNets with equivalent architectures. Finally, for the pixel classification datasets,   the proposed method outperformed (in approximately 5 percentage points of average accuracy) standard convolutional networks. In general, a conclusion common to all datasets is that the morphological networks are capable of learning relevant filters and extracting salient features. Although the presented conclusions may open opportunities towards a better use and optimization of non-linear morphological operations, learning the optimal shapes of the structuring elements used in each morphological neuron raises multiple scientific and technical challenges, including the high number of trainable parameters (which increase the complexity of the network), and so on.
[32]lts, in terms of accuracy, of the proposed method and the baselines for the image classification datasets. Land-use dataset is presented in Figure11a. For simplicity, only the networks that do not use selective kernels[32]were reported. Note that the proposed model is slower to converge when compared to the other networks. A reason for that is the large number of trainable parameters of DeepMorphNets, as presented in TableII. However, given enough training time, all networks converge very similarly, which confirms that the proposed DeepMorphNets are able to effectively learn interesting SEs and converge to a suitable solution.
1 : 9 Fig. 8 : 198 Fig. 8: Visual results for the square synthetic dataset. (a) The learned SE. (b)-(c) Examples of the output of the opening neuron for two classes of the square synthetic dataset. The first column represents the input image, the second one is the output of the erosion, and the last one is the output of the dilation. Since erosion and dilation have tied weights (i. e. , the same SE), they implement an opening.
7 Fig. 9 : 79 Fig. 9: Visual results for the rectangle synthetic dataset. (a) The learned SE. (b)-(c) Examples of the output of the opening neuron for two classes of the square synthetic dataset. The first column represents the input image, the second one is the output of the erosion, and the last one is the output of the dilation. Since erosion and dilation have tied weights (i. e. , the same SE), they implement an opening.
To reduce risk, it is therefore vital to know how a new system will respond to radiation before deployment. In this paper, we measured the effect of radiation and categorized the SEEs observed in the sensor. We also tested how radiation affects pure event-based computation in the context of optical flow estimation, which is known to be sensitive to noise and temporal imprecision, under both radiation and non-radiation conditions. Finally, we also used this preliminary data to develop a simulator that makes it possible to inject events with radiation-noise effects into any data stream. We call this simulator the "Event-based Radiation-Induced Noise Simulation Environment," (Event-RINSE). Event-RINSE allows realistic neutron beaming effects to be added to any event based data sequence. These simulated radiation effects enable designers to test developed algorithms prior to mission deployment.
C. DATA COLLECTION AND ANALYSIS The sensor was connected to a computer running software which interfaced with the sensor to record events. Events were later processed offline. Data was taken with the beam on and off in order to observe the increase in noise caused by irradiation. Radiation-induced noise can be seen in the form of clustered noise-like patterns and line streaks of moving particles in the focal plane, as will be detailed in the following sections. The recorded data was parsed to get an event rate to measure the number of events generated by the sensor per second. The counted events were then separated into ON and OFF events. The average events per second were calculated for each experiment with standard deviation as error. Data was collected with the sensor facing the beam source and at 90 °, to observe how the angle of incidence affected the incoming radiation noise.
A. INDUCED-EVENT RATE Data was collected with the lens cap on the sensor to minimize environmental influence from external lighting. First, the mean number of radiation-induced ON and OFF events per second was measured. The average number of events can be seen in Figure  3. A significant bias towards ON events was observed. The induced-event probability density was plotted against the pixel coordinates of the sensor to observe any location preferences for upsets. To measure this, the pixel location of each induced event was divided by the total number of events measured for both angles of incidence. These measurements can be seen in Figures  4(A ) and 4(B). In both cases, the induced events are quite uniform across the sensor, with the 0°angle of incidence tending to bias towards the location of the neutron beam's 1 inch diameter.
C. SIMULATION ENVIRONMENT USAGE The Event-RINSE simulation environment is written in Python with many supporting parameter flags that can be used to modify the simulation model. Normal Python data analysis modules are needed for the simulator, namely SciPy  [45]  and NumPy  [46] , while OpenCV  [47]  is used to display videos of the event data. The simulator is run using Python3 environment with runtime flags for campaign customization. Currently available flags and descriptions can be seen in Table  1. The input data file is the only input that is necessary to run the simulator. Input files are assumed to be plain text files in < x > < y > < timestamp (µs) > < polarity > format.
ACKNOWLEDGMENTS This research was supported by  SHREC  industry and agency members and by the  IUCRC Program  of the  National Science Foundation  under Grant No. CNS-1738783. This work was performed, in part, at the  Los Alamos Neutron Science Center (LANSCE) , a  NNSA User Facility  operated for the  U. S. Department of Energy (DOE)  by  Los Alamos National Laboratory  (Contract  89233218CNA000001 ). We would also like to thank  M. Lozano ,  M. Ullán ,  S. Hidalgo ,  C. Fleta , and  G. Pellegrini  from the " Instituto de Microelectrónica de Barcelona" (IMB-CSIC)  for insightful discussions.
This research was supported by  SHREC  industry and agency members and by the  IUCRC Program  of the  National Science Foundation  under Grant No. CNS-1738783. This work was performed, in part, at the  Los Alamos Neutron Science Center (LANSCE) , a  NNSA User Facility  operated for the  U. S. Department of Energy (DOE)  by  Los Alamos National Laboratory  (Contract  89233218CNA000001 ).
Additionally, tidal stream environments are challenging to survey due to strong tidal flows and inherent turbulence, adding extra logistical, financial and risk considerations to any in situ data collection effort  [12, 23]. Hence, there is an urgent need for suitable platforms that can safely, and cost-effectively, survey tidal stream environments at the fine-scale spatio-temporal resolutions required  [24]. Investigations into remote sensing techniques, as a means to remedy this, have highlighted the viability of quantifiable measurements through imagery of the surface  [22, 25]. This study used unmanned aerial vehicles (UAVs), launched from a moving ship during four days of continuous surveys in both 2016 and 2018. The aim was to characterise the spatial distribution of kolk-boils within a tidal stream environment, and then relate key physical environmental properties (current velocity, current direction, tidal phase, depth, seabed roughness, slope angle and aspect) to boil presence and area at the surface to understand the drivers and predictability of these features.
Data Collection Twenty-five UAV surveys were conducted between 23 and 25 June 2016 and th eight between 21 and 24 July 2018 from a moving vessel (MRV Scotia) transiting con ously around the Island of Stroma. A multirotor UAV was used in both instances du the capability to hover, meaning increased stability and positional control and thus hi accuracy for fine-scale survey work such as this  [35]. UAV surveys were focused on Inner Sound with flights occurring near-continuously, weather permitting, during light hours and across differing phases of the tidal cycle (ebb/flood) (Figure  4 ). This minated in 15 hours and 13 minutes of total flight time. All flights were undertaken cording to Civil Aviation Authority regulations for UAV operation, as well as adherin recommended best practice guidelines for UAV usage within environmental rese  [36, 37].
Data Collection Twenty-five UAV surveys were conducted between 23 and 25 June 2016 and thirtyeight between 21 and 24 July 2018 from a moving vessel (MRV Scotia) transiting continuously around the Island of Stroma. A multirotor UAV was used in both instances due to the capability to hover, meaning increased stability and positional control and thus higher accuracy for fine-scale survey work such as this  [35]. UAV surveys were focused on the Inner Sound with flights occurring near-continuously, weather permitting, during daylight hours and across differing phases of the tidal cycle (ebb/flood) (Figure  4 ). This culminated in 15 h and 13 min of total flight time. All flights were undertaken according to Civil Aviation Authority regulations for UAV operation, as well as adhering to recommended best practice guidelines for UAV usage within environmental research  [36, 37].
Data Processing Images were registered in space and time using onboard telemetry data, in this case altitude, heading and the coordinates (of the central point) of each image. In combination with the calculation of camera specific calibration values, using an equation detailed in UAV take-off occurred from a consistent location on the deck of the MRV Scotia of a known height above sea level (10. 6 m). During operation, all surveys were manually flown at a consistent speed over ground. This was kept consistent by monitoring onboard GPS speed readouts. Flights were carried out at windspeeds <5 m/s for safety, which was well within the capability specified by the UAV manufacturer  [38]. All flights were conducted against the prevailing current direction to avoid miscounting objects of interest.