dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential PBMC gene expression analysis and validation PBMC-based differential gene signatures between healthy volunteers and coronavirus-infected patients were selected by performing supervised SAM 51 using R-based siggenes package  52. Differentially expressed genes with FDR < 0. 05 were chosen as described in our previous publications  [53] [54] [55]. Gene scores for signatures up or downregulated in coronavirus-infected patients in other validation datasets were derived using ssGSEA  20  using R scripts from GenePattern platform  56. Enrichment and other IS analysis for systems level understanding of the disease Briefly, the differentially expressed gene expression signatures from PBMC of patients infected with coronavirus were used to query multiple databases for metasignatures such as pathways, mechanistic processes, and their associated networks that are connected with different disease manifestations by enrichment analysis-based IS.
These gene signatures and meta-signatures were further systematically linked to wire the pathophysiology in patients, again, in a hierarchical fashion, from to whole organism level (Fig. 1A ). Hypergeometric gene enrichment analysis for most of the studies were performed using R-based hypeR tool  57. Disease conditions-based enrichment analysis was performed using R-based Dose package  58. Nearest Template Prediction 22 using R-based tool from GenePattern platform  56  was used to derive distance between two signatures-acute-like vs. recovering-like patients. Different gene set databases were from downloaded from EnrichR 59 and MSigDB  20. Immune gene sets were from Rooney et al. 34. Additional gene sets and databases used in this study are: REACTOME  28  , KEGG  60  , COMPARTMENTS database  61  , BioGPS-gene portal system  36  and DisGeNE  62.
The ATLAS physics program relies on very large samples of Geant4 simulated events, which provide a highly detailed and accurate simulation of the ATLAS detector. However, this accuracy comes with a high price in CPU, and the sensitivity of many physics analyses is already limited by the available Monte Carlo statistics and will be even more so in the future. Therefore, sophisticated fast simulation tools have been developed. In Run 3 we aim to replace the calorimeter shower simulation for most samples with a new parametrised description of longitudinal and lateral energy deposits, including machine learning approaches, to achieve a fast and accurate description. Looking further ahead, prototypes are being developed using cutting edge machine learning approaches to learn the appropriate calorimeter response, which are expected to improve modeling of correlations within showers.
Introduction The ATLAS physics program relies extensively on very large samples of Geant4 simulated events, which provide a highly detailed and accurate simulation of the ATLAS detector  [1] [2] [3]. In addition to physics, these simulated events are also used for the design and optimization of the detector and trigger. The baseline simulation strategy  [4]  for the ATLAS experiment uses the Geant4 simulation toolkit  [5] , which models the interactions of the particles with the matter in the detectors to a very high level of accuracy. However, with time, as the dataset from the LHC grows in size and complexity the computing resources needed to produce these simulated samples continues to grow. In fact, the sensitivity of many physics analyses is already limited by the available Monte Carlo statistics.
The blue points in Figure  1  show how much the total CPU could be reduced by extensively using the fast calorimeter simulation instead of Geant4 (blue down triangles), and by adding a fast method of tracking detector simulation and reconstruction (blue circles), and, finally, by increasing the speed of the generators by a factor of two (blue up triangles). Here we will outline the different fast simulation techniques currently under development by the ATLAS experiment, discuss recent improvements, and briefly review the current performance. (2) middle curve using in addition a faster version of reconstruction, which is seeded by the event generator information for the tracks; (3) bottom curve, where the time spent in event generation is halved, either by software improvements or by re-using some of the events.
Towards Even Faster Simulation While the fast calorimeter simulation shows great promise, even greater gains in the CPU time are needed to simulate and reconstruct events, requiring additional techniques and new paradigms. The FastChain  [18]  is a generic fast simulation framework that incorporates FastCaloSim2, but provides additional fast simulation modules. The main target for improved speed is through the introduction of fast simulation (and reconstruction) techniques for the inner tracking detector and, to a lesser extent, the muon spectrometer. Two possibilities are currently being explored. The first, known as Fatras  [19] [20] [21] , simulates events using a simplified description of the detector geometry and parametrisations of physics processes. The second avoids both simulation and reconstruction of tracks by producing hits based on the true particle and fitting those to determine the track parameters.
Conclusion Large samples of Geant4 simulated events play a critical role within the ATLAS physics program, but their production requires large amounts of CPU. This is why the development of fast simulation techniques is critical for maintaining, and indeed increasing, physics performance, especially when looking ahead to the large datasets of the HL-LHC. We have reviewed the fast simulation methods currently under development by the ATLAS experiment. Two methods focusing on replacing the expensive calorimeter simulation with a parametrised response were presented with FastCaloSim v2 aiming to become the default simulation method for Run 3. In addition, FastChain seeks to speed up the simulation even further through new methods for the inner detector simulation and reconstruction. The methods presented are at various levels of development and highlights of recent progress were presented.
Introduction With the application of LiDAR sensors in many areas, such as autonomous driving and augmented reality, efficient understanding of large-scale 3D point clouds is a challenging task. Many LiDAR datasets  [1, 2, 3, 4]  have been released as benchmarks for various 3D applications. Thee benchmarks usually provide cleaned 3D point clouds with well-annoated labels ignoring the problems of noises, sampling pattern difference and weak reflective strength. However, it is still difficult to develop an effective and efficient solution. There are two main reasons: One is that the raw data from LiDAR scanners consist of a tremendous amount points returned from all possible reflective objects, which brings in heavy computation cost. Another reason is that the points are usually nonuniformly distributed, unstructured and unordered, which means that it is not easy to apply mature 2D deep convolutional networks.
Further work  [10, 11, 12]  have shown their power in processing and learning from 3D point clouds. However, due to the limited main memory and possible information loss from downsampling large point clouds, it's still hard for these learning-based methods to train on large point clouds without downsampling. In this Shape Retrieval Challenge (SHREC) track on 3D Point Cloud Semantic Segmentation for Street Scenes, we compare different frameworks to segment semantic objects from large-scale 3D point clouds. Both handcrafted and learning-based methods are included for evaluation. The contributions are summaried as: • We provide a large-scale 3D street-scene point cloud dataset for 3D semantic segmentation. • We evaluate different algorithms on the dataset and help finding solutions for large-scale 3D point cloud processing.
Related Work In recent years, several annotated datsets for outdoor 3D point cloud semantic segmentation have been pub-lished. Semantic3D  [1] , recorded with a terrestrial laser scanner (TLS), is a large outdoor 3D LiDAR dataset with over a billion points and 8 class labels. However, it has only 30 samples for training and testing, and the huge amount of points per sample (because of high precision of TLS) leads to heavy memory usage when training without downsampling. KITTI  [2]  offers nearly 15 thousands of scans of outdoor objects, but has only three classes (car, pedestrian and cyclist) and is labelled with bounding boxes instead of being point-wise annotated. It concerns more on 3D object detection instead of 3D semantic segmentation in large-scale scenarios. Paris-Lille-3D  [3]  is another worth noting 3D LiDAR dataset with 50 classes from which 10 classes are used for testing.
It has over 143 million points with 7 different scans and is recorded by a mobile laser scanner (MLS). DublinCity  [4]  provide a manual annotation of over 260 million laser scanning points with 13 hierachical classes in one scene. It has 3 coarse level classes (building, ground and vegetation) and 10 refined classes (e. g. door, tree and sidewalk). It is one of the densest urban LiDAR dataset recorded with an aerial laser scanner (ALS). These datasets have either the problem of huge size of a single scan (Semantic3D and DublinCity) or few scans for training and testing (Paris-Lille-3D and DublinCity). In contrast to these datasets, our Street3D dataset provides less dense but more scenes for outdoor 3D semantic segmentation tasks. It consists of a large amount of well annoated points with 5 classes and 80 different street scenes.
Dataset The point cloud data is provided by Cyclomedia Technology, where we selected 80 3D point cloud for street  scene and manually labelled them. The point clouds are recorded using both a panoramic image capturing device that capture every 5 meter together with a Velodyne HDL-32 Lidar sensor that performs continues capturing on the car. The recorded pointcloud is meshed, where for each pixel in the panoramic images, we compute the intersection (by ray tracing) with the dense mesh from the original pointcloud. The point of intersection provides us both color from the panoramic images capturing device together with position information. Each point cloud represents a street scene and contains a group of objects. We used the open source software Cloud Compare  [13]  to manually label point clouds.
Objects are labelled into 5 meaningful classes and an extra 'undefined' class. They are: 'Building', 'Car', 'Ground', 'Pole' and 'Vegetation'. Only the five meaningful classes will be evaluated. The 80 3D point clouds are randomly divided into train and test sets with 60 for training and 20 for testing. For each point cloud, there are over two million points. Summary information of the dataset is in Tab. 1. Examples of the dataset are visualized in Fig. 1. The dataset is made publicly available at https: //kutao207. github. io.
Methods In this part, we introduce four novel methods as well as a baseline method -PointNet++ for our 3D point cloud semantic segmentation task. The architectures of these four novel methods are varied. P4UCC is a nonlearning framework of a four-staged pipline which aims to classify the 5 classes progressively. Spherical DZNet projects 3D point clouds to 2D images and train a CNN model for semantic segmentation. Then the results are back-projected to 3D world and interpolated for 3D point clouds. ResGANet adopts graphs to encode the geometric information of 3D point clouds and use a residual graph attentional network to train an end-to-end model to predict point cloud semantic labels. GRandD-Net combines multi-scale features and Unet architecture to build a powerful model for the 3D point cloud semantic segmentation task.
More details are described as follows. This method is contributed by authors Sebastián Ortega, Agustín Trujillo, José Pablo Suárez, José Miguel Santana, and Cristian Ramírez. Our proposal is a fourstaged pipeline that aims to progressively classify ground, car, pole, building, and vegetation points in vehicle-borne point clouds. Its general architecture can be seen in Fig. 2. Each stage aims to identify only a certain class. The first one is dedicated to ground point detection and also generates pixel-wise features which are useful for subsequent stages. Stage 2 looks for the detection of car points, Stage 3 filters pole points and Stage 4 splits the remaining points between building and vegetation classes. The code is available at https://github. com/ChanoOT/P4UCC.
Building Detector The remaining points are grouped as well. A recursive algorithm for extraction of vertical planes based in MLESAC  [17]  is applied to each cluster. Clusters with a majority of points in a vertical plane and with ∆Z > 2. 5m are considered to represent buildings. Otherwise, they are classified as vegetation. This leads to the final result that can be seen in Fig. 5. This method is contributed by authors David Duque-Arias, Santiago Velasco-Forero, Jean-Emmanuel Deschaud, Francois Goulette and Beatriz Marcotegui. We propose an automatic algorithm for point cloud segmentation using only geometrical data [x, y, z]. The proposed methodology is based on 2D projections. The main process relies on a spherical projection from the sensor point of view while bird eye view (BEV) projection is used for ground detection.
The FAISS library  [18]  is used in order to reduce computational cost at several steps in the process. The proposed algorithm is presented in Fig. 6  and it is mainly divided into four steps: 1) Coarse ground detection from Bird-Eye View (BEV) by means of quasi-flat zones; 2) Spherical projections and feature extraction; 3) Train Deep Learning model with 2D images; 4) Postprocessing and backprojection 2D predictions to 3D using a kNN classfier (with k = 3). The code is available at https://github. com/daduquea/sphericalDZ.
Segmentation in 2D projections In the spherical projection, each point cloud is represented by an image of 1024 × 64 pixels corresponding to the azimuthal and elevation angles, respectively. A set of five features is selected in this study: z coordinate, depth (distance to sensor) and the normal vector in 3D coordinates denoted by (N x , N y , N z ). A very simple normal estimation based on depth image gradient  [20]  is used. Only points that have not been classified as ground are projected. If several 3D points fall in the same projected pixel, the classical approach to project the closest 3D point to the scanner at each pixel of the 2D image is used. Spherical projection has been chosen because the dataset is acquired with a terrestrial scanner. The scanner position is not given. We estimate it from the BEV as the barycentre of the empty space surrounded by the highest point density.
Fig. 7  shows ground truth and computed features of '5D4KVPBP' from train set after ground extraction. We trained a Deep Learning model using a variation of Unet architecture  [21]  as presented in Fig. 8. This choice was motivated by its capability to include input features at different steps of the decoding stage of the network. Specifically, the original input is directly concatenated to the features computed by the network as a shortcut connection, as is shown in the Fig. 8. Including shortcuts in the model has demonstrated several advantages in image segmentation tasks  [22]  such as increasing convergence speed and stabilizing weight updates. Two strategies have been tested: 1) Predict labels using trained model (The code is available at https://github. com/daduquea/ sphericalDZ); 2) Remove last layer of the model and predict labels using kNN classifier.
GRanD-Net: Grid subsampling, RandLA-Net and Dilated Convolutions This method is contributed by authors Kiran Akadas and Shankar Gangisetty. We propose a deep-learning based 3D semantic segmentation of point clouds inspired from the works of RandLA-Net  [12]  known as GRanD-Net shown in Fig. 12. We improve the RandLA-Net  [12]  by considering point features in terms of (x, y, z) coordinates, subsample using Grid subsampling and adopting dilated convolutions  [27]  to gain better efficiency for semantic segmentation of large-scale 3D point clouds. The code is available at https://github. com/ KiranAkadas/GRanDNet.
Data Loading To load the data in batches, we generate the data flow for each batch. For a given batch size n and the steps in each epoch s, (n × s) point clouds are reserved for each epoch. To avoid ordered learning by the GRanD-Net model, we feed the data randomly. The k-Nearest Neighbours (kNN) algorithm is used with a pre-defined set of k neighbours being selected of all the sub-sampled points. If the sampled points are less than the given predefined k points, we pick the points with replacement. To prepare a batch of point clouds, we generate the neighbour indices for every point in a point cloud. These are used to get the relative point features. We then randomly sample 25 % of points to be reduced in the next phase while down-sampling and simultaneously track the indices for up-sampling.
These dilated convolutions make our proposed GRanD-Net faster and more efficient since we are increasing the area of filter coverage without increasing the parameters and affecting the feature learning, thus reducing the number of convolutions. The local spatial encoding in DRB uses the centre points and their kNN neighbours to encode the point cloud using relative positional information. At each step, we apply random sampling with DRB using the points we loaded earlier to reduce the size of input point cloud to 25%. The attentive pooling is used as a replacement to max-pooling in order to compute the attention score for every feature which is further aggregated to avoid loss of information and learn important local information. We use GeLU  [28]  as the activation layer in our GRanD-Net model for better learning of non-linear features.
GeLU prevents strong negative activations which may affect the model. The curvature and non-monotonicity of GeLU is used to learn complex functions much better compared to ReLU and leaky ReLU. The output of the stacked DRBs is up-sampled and passed through multi-layer perceptrons (MLP) followed by fully connected layers shown in Fig. 12. The use of skip-connections and MLP while upsampling ensures that the labelling is accurate. Our approach follows an all-inclusive up-sampling approach that refines labels gradually unlike simple interpolation, which would result in a single label for a group of points ignor-  ing the demarcation of classes. The use of interpolation would further require the use of a post-processing technique to refine the output. Finally, the predicted semantic labels for every point are obtained as the output of the model with dimensions (N, d out ), where d out is the number of classes in the dataset.
Discussion As shown in Tab. 3 and Fig. 16 , we have the following observations: • From obtained results with Spherical DZNet, we have identified some drawbacks related to spherical projections: 1) Objects size strongly depends on distance to sensor; 2) High variation of objects shape according to sensor perspective. Both of them may affect the capability of the model to learn. In addition, we identified a particularly low score in pole class. We think it is mainly due to the presence of traffic sign of point-cloud 5D4L1RW5. Few traffic signs are present in the dataset. Moreover, this one is close to the scanner and has over 150 thousand 3D points. • It is worth noting that P4UCC is the only nonlearning-based approach but still outperforms 3 learning-based methods (Baseline, Spherical DZNet and ResGANet) by using four detectors to identify point categories progressively.
It shows that handcrafted method is still reliable and may outperform learning-based methods. • GRanD-Net achieves superior performance on four of the classes, except pole. We observe that the resulting IoU of ground, vegetation, building, and car classes are segmented accurately as the dataset distribution in these classes is high and learnt better. We 16  (5). Based on the confusion matrix and visual inspection, we observe that few instances of the pole class are mislabelled as building shown in Fig. 15 , due to their proximity to the building points and also the insufficiency of the pole training points. • It is obvious that 'Building' and 'Ground' are easy to be correctly classified while 'Car' and 'Pole' are hard to be segmented. Considering the proportion of 'Car' and 'Pole' in the train/test set is 2.
Another issue worth noting is the limited classes of our Street3D dataset. For the 3D semantic segmentation task, it's sufficient to have 5 classes. However, it may not be enough for specific tasks. For example, 'road' is an important category for autonomous driving application. However, Street3D has 'ground' instead of 'ground'. It's not easy to recover road from only 3D 'ground' points. But our Street3D can still contribute to training and validating deep learning networks for autonomous driving application. The 4 learning-based methods in this paper can be easily transferred to or finetuned on a dataset with a 'road' label as what have usually been done in 2D CNN applications.
Conclusions In conclusion, this track has drawn attention on 3D point cloud semantic segmentation for street scenes using multiple approaches. We provide a street scene dataset composed of 80 scans which are well annotated with five class labels. We introduce four novel and different methodologies that outperform a state-of-theart deep learning based method (PointNet++) for the new Street3D benchmark. As there is a non-learning method (P4UCC) which also outperforms learning-based approaches, it shows that well-designed feature descriptors for the classification could have more importance in the segmentation than the learned features, specifically with unbalanced data. The four methods have focused not only on different architectures but also on different features (covariance-based features in P4UCC, multi-scale features in GRanD-Net, graph representations in Res-GANet, spherical descriptors in Spherical DZNet), which opens the possibility of combining some of the descriptors from each discussed proposal in a new architecture.
This method is contributed by authors Tao Ku, Remco C. Veltkamp and Bas Boom. We introduce the Residual Graph Attentional Networks (ResGANet) for the 3D point cloud semantic segmentation task. The ResGANet takes the semantic segmentation task as a supervised learning task. We use the 60 point clouds with semantic labels in training dataset to train and validate our model. As shown in Fig. 9, the whole pipeline is composed of three parts: preprocessing, model training & evaluation, and backprojection. As our model training is based on the proposed Residual Graph Attentional Networks (ResGANet) and the input is in a fixed point number, we need to have a voxel subsampling first and then sampling the point cloud to a fixed size. After training and evaluation, we also need to back-project the valuation results on downsampled point clouds to the original ones.
The proteins that were specifically identified in the fetal muscles (159 human orthologs) and AT (373 human orthologs) were annotated by GO terms related to specialized functions of muscular and adipose tissues. Within the muscle dataset, several GO terms were grouped in the functional category "muscle development and functioning", which annotated 45 proteins (Figure  1 ) with GO terms mainly related to muscle contraction, organization, or development; to the assembly of actin and myosin filaments; and in the acquisition of muscle contraction function. Within the adipose dataset, some enriched GO terms were grouped within the functional category "lipid metabolism", which annotated 13 proteins (Figure  2 ) mainly involved in the regulation of the balance between fatty acid synthesis and oxidation.