dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, there are significant differences between molecular databases in the broadening parameters of CO, as shown in Figure  9. The difference in the gamma parameter between HITEMP2019 and ExoMol are as follows: γ L = 0. 0146-0. 025 for HITEMP2019, γ L = 0. 0326 for ExoMol (default values in the definition file), and γ L = 0. 026-0. 033 in the observing band. We note that the broadening parameters in HITEMP2019 are for "air" and not for the H 2 atmosphere as explained in Section 2. 2. 1 and that the broadening parameters given in the broadening file of ExoMol (labeled as ". broad" in the caption) were produced by averaging the values of other molecules  (Wilzewski et al. 2016). Despite the incompleteness of the broadening parameters, we attempted to estimate the brown dwarf's  mass using ExoMol broadening parameters (.
ACKNOWLEDGMENTS We greatly appreciate the constructive comments from an anonymous reviewer. We also thank  Stevanus Nugroho ,  Takayuki Kotani ,  Masayuki Kuzuhara ,  Hiroyuki Ishikawa ,  Michael Gully ,  Brett M. Morris ,  Daniel Kitzmann , and the  REACH collaboration  for fruitful discussions. This study was supported by  JSPS KAKENHI  grant nos. JP18H04577 ,  JP18H01247 ,  JP20H00170 , and  21H04998  (H. K. ). In addition, this study was supported by the  JSPS Core- to-Core Program Planet2 and SATELLITE Research from the Astrobiology center ( AB022006 ). Y. K. is supported by  Special Postdoctoral Researcher Program at RIKEN.
Numerous long-read assembly and post-assembly tools are available, relying on a large variety of approaches and algorithms, and many of them are frequently updated. However, even with this plethora of tools, there is no single silver bullet to genome assembly for all taxonomic groups. The systematic assessment of these assembly tools is needed to properly exploit data  (Murigneux, Rai, et al. , 2020). But performing benchmarks to find the best combination of tools for a given dataset and application is a highly complex task (see for example Ryan R. Wick and Holt, 2021 and Chen et al. , 2020) , even if the computer code used for such studies is increasingly being made available (e. g. Ryan R  Wick et al. , 2021 or Latorre-Pérez et al. , 2020). This endeavor would presumably benefit from streamlined generalist data processing workflows that are accessible, scalable, traceable and reproducible.
A few solutions, such as Katuali  (Katuali, Accessed 25th July 2022)  or CCBGpipe  (Liao et al. , 2019) , were previously developed to tackle these issues, but they are dedicated to either eukaryotic or prokaryotic genomes only, or provide a restricted choice of assemblers. Some of these are also difficult to parallelize in a HPC environment, or to update with the latest versions of software components. In addition, for the subsequent evaluation of the assemblies, it is critical to organize and aggregate the numerous Quality Control (QC) metrics generated by various tools, in order to facilitate comparisons. As an example, microPIPE  (Murigneux, Roberts, et al. , 2021) , a recently released Nextflow-based bacterial ONT reads assembly pipeline, fills some of these gaps but does not incorporate QC analysis apart from QUAST.
Implementation CulebrONT assembles, circularises, polishes and corrects genome sequences from raw read sequences in fastq format, and provides QC metrics. To provide more flexibility to the user, individual tools are optional and include the most popular ones. Thus, CulebrONT can be used either to prototype and explore assemblies on new organisms or in assembly production. While originally developed primarily for working on ONT data, CulebrONT can also be used on PB data with generalist tools. Six recent community-validated assemblers are currently included (Canu  (Koren et al. , 2017) , Flye  (Kolmogorov et al. , 2019) , Raven  (Vaser and Šikić, 2020) , MiniAsm (Heng Li, 2016) (coupled with minipolish (Ryan R. Wick and Holt, 2021)  for an initial polishing), Shasta  (Shafin et al. , 2020)  and smartDeNovo  (Liu et al.
The pipeline code is written following the Snakemake syntax, and include a dedicated Python module which should facilitate readability for developers. The Snakemake system implements scalability at the level of jobs that are scheduled independently to run, in parallel or sequentially depending on the available resources and the computing infrastructure. In addition, for rules that run programs with internal parallel processing capabilities, it is possible to configure the number of cores allocated to the corresponding individual jobs in CulebrONT. Snakemake handles job failures and a restarted instance of the pipeline will compute only missing output. CulebrONT relies on conda (Anaconda Software Distribution 2021) and singularity  (Kurtzer et al. , 2017)  that simplify installation of specific versions of the software, secure environments and greatly improve reproducibility.
CulebrONT is available as a Python Package in PyPi to ease its installation https://pypi. org/project/culebrONT/. In addition, the CulebrONT API adapts the installation to local or distributed/HPC environment. In terms of modularity, relevant tools or processing steps can be selected or omitted in the user setup file config. yaml, and CulebrONT builds a dedicated instance of the workflow. Data can come from a single sample as well as from multiple ones (one fastq file per sample in the data folder). Tools paths are imported from tools_path. yaml, and cluster resources are managed using the cluster_config. yaml file (Figure  1. 1). The dedicated CulebrONT class checks configuration files, controls if data and software environments exist and ensures the global coherence of the requested steps.
These information can be useful for users that want to benchmark and identify suitable workflows for their data. In order to provide an illustration of how CulebrONT can help benchmark assembly pipelines, ONT genome sequencing datasets for four highly contrasted species were tested : i. a strain of Acinetobacter baumannii (Ryan R  Wick et al. , 2021)   For bacterial sequences, all available assemblers included in CulebrONT were tested using reads for samples ABJ9 and HM1C1321 from A. baumannii and H. haemolyticus. For ABJ9, good results were obtained by Canu and Miniasm, but the best (the longest N50 and lowest L50) were found using Miniasm + 2 minipolish rounds + Medaka correction with only 2 circularised contig (N50 3,798,675 bp and L50 1) obtaining and a Busco score of 98. 4%. Busco results obtained on this sample can be found on the figure 2.
Discussion Obtaining a good assembly is a complex task, and the software ecosystem for this is evolving continually. While CulebrONT is currently proposing the state-of-the-art tools from today, we expect evolution in the next months or years for the next releases in different ways: On a short term, we will integrate the possibility of adding an external fasta sequence for using the QC part (for instance coming from an assembler not included in CulebrONT), re-entry at any steps, and also optimize the disk space usage. On a medium term, we will re-evaluate the available tools (adding hifiAsm  (Cheng et al. , 2021) , removing tools not updated for years), restructure the final output structure for more readability, and better integrate the outputs from diverse QC tools (in particular BlobTools or RAM usage per tool).
Conclusion In summary, CulebrONT simplifies the analysis of large-scale assemblies by allowing fast and reproducible processing of a single dataset or on a collection of samples simultaneously. The output facilitates the comparative evaluation of the assembly workflows while keeping a traceable record of the analyses. Table  1  -Results obtained on ONT data using CulebrONT on various biological models and assemblers. The "Organisms" row provides information about the samples, the "Reads" subsection describes input reads features, the "Activated tools" section lists the tools activated ('yes') in the corresponding CulebrONT run, and a summary of the best results is presented in the "Best workflow" row (considering best by the longest N50 and lowest L50 in this study).
Acknowledgements Data processing was performed at the  High-Performance Computing Cluster i-Trop of IRD. Authors thank  N. Tando  for administration support and the  South Green Plateform. We also thanks the  French Bioinformatics Infrastructure IFB  for feedback on CulebrONT installation. Version 5 of this preprint has been peer-reviewed and recommended by Peer Community In Genomics (https://doi. org/10. 24072/pci. genomics. 100018 )  Julie Orjuela  et al.
(ABJ9: reads N50 15,130 bp; median 7,621 bp; 741 Mb), ii. a strain of Haemophilus haemolyticus (Ryan R Wick et al. , 2021) (HM1C1321: reads N50 10,569 bp; median 7,027 bp; 1,6 Gb) iii. Meloidogyne graminicola nematoda (Phan et al. , 2020) (VN18: reads N50 9,372 bp; median 2,853 bp; 3. 2 Gb) and iv. a Oryza sativa dataset (unpublished data) (DJ123: N50 22,328 bp, median 13 544 bp, total 14,6 Gb).
To validate our CNN model, we also introduce a new virtual dataset for both road and rail environments by leveraging the video game Grand Theft Auto V (GTAV). We provide extensive results of our different models on both KITTI and our own GTAV datasets. Through our results, we show that our method is the fastest available 3D object detection with accuracy results close to state-of-the-art methods on the KITTI road dataset. We further demonstrate that the pre-training process on our GTAV virtual dataset improves the accuracy on real datasets such as KITTI, thus allowing our method to obtain an even greater accuracy than state-of-the-art approaches with 16. 16% 3D average precision on hard car detection with inference time of 11. 1 ms/image on an RTX 3080 GPU.
Introduction Multiple object detection consists of detecting and identifying objects in scenes. In smart mobility applications, vehicles must have a better perception of their environment. This perception must guarantee not only a good detection of the objects, but also a good interpretation of the scenes. In multimodal road and railway smart mobility, the objects of interest to be detected are vehicles, pedestrians, bus, cyclists, trees, etc. If the road sector is widely covered in the scientific literature, this is less the case for the railway smart mobility. This is in part due to the lack of specific railway datasets with ground truth data dedicated to 3D object detection. This poses a real challenge in that it requires the 3D object detection algorithm to train under dataset with ground truth data.
This need for environmental perception has led to the development of 3D detection methods, using either a LiDAR-type depth sensor or images from cameras, to improve safety. Although 3D object detection is a much sought-after field, few methods focus on the real-time aspect that is essential for a real-world application. Of course, in road and railway multimodal smart mobility, it is desired that 3D multi-object detection is performed in real time. This represents another challenge. In this paper, we propose to fill these gaps, lack of railway dataset with ground truth data and real-time 3D object detection, by proposing a new virtual dataset (GTAV) dedicated to both road and railway environments for 3D object detection, which includes images taken from the point of view of both cars and trains.
With this dataset, we propose a new method dedicated to real-time 3D object detection based on the well-known 2D object detector YOLOv5  [3]. With this new method, we introduce 3D anchor boxes which makes our method the fastest method available for 3D object detection with accuracy comparable to state-of-the-art methods. Using a pre-trained model on our GTAV dataset, our approach outperforms state-of-the-art methods. Through our experiments, we also demonstrated the validity of our method for real-time embedded applications for autonomous vehicles (cars and trains). The main contributions put forward in this paper are the following: 1. A new Lightweight CNN (L-CNN) method for real-time 3D object detection based on the YOLOv5 2D object detector. Our L-CNN allows the following object predictions: -2D detection -Distance from the camera -3D centers projected on the image plane -3D dimensions and orientation 2.
A new virtual dataset (GTAV) dedicated to both road and railway environments for 3D object detection. The GTAV dataset includes images taken from the point of view of both cars and trains. The remainder of this paper is organized as follows. Section 1 introduces the paper. In Sect. 2, we review the related work for autonomous navigation datasets, 3D object detection approaches in which we will present methods based on depth sensors as well as methods based on monocular images. In Sect. 3, we describe in more details our multimodal road/railway GTAV dataset. Our new L-CNN model about the 3D multi-class object detection is presented in Sect. 4. Analysis and experimental results are described in Sect. 5. Finally, the conclusion and future directions are outlined in Sect. 6.
Autonomous driving datasets Any approach based on Deep Learning requires annotations (also called "ground-truth") to perform a training. One of the main factors in the performance of any of these approaches in terms of precision is the quality and quantity of the data in the dataset. Among the datasets dedicated to autonomous vehicles, the vast majority are intended to autonomous driving in road environments and focus on object detection and scene segmentation. Some of them are mono-modal (in general, with images as a single modality), and others are multi-modal. Even though the building of mono-modal datasets is less time-consuming and expensive than multimodal datasets. The latter represent a crucial advantage in the use of supervised artificial intelligence approaches because they offer a wider variety of ground truths from different sensors.
Although these data are expensive because they require the use of expensive sensors such as LiDAR or radar, they are indispensable for environmental perception applications such as object detection/localization, distance estimation, tracking. We can also find many other road datasets for similar tasks such as CityScape  [4] , PascalVOC  [5] , etc. The best-known road dataset is KITTI  [6]  (Karlsruhe Institute of Technology and Toyota Technological Institute), which includes a rich ground truth from both distance sensors (LiDAR), a GPS/IMU unit, and stereoscopic cameras (color or grayscale). It comprises different information captured and synchronized at 10Hz. The vehicle is equipped with different sensors: cameras, 3D Velodyne LiDAR (100k points per frame), 3D GPS/IMU data (location, speed, acceleration).
The camera is calibrated and synchronized with Velodyne LiDAR and with a GPS/IMU sensor  [6]. KITTI includes 3D object track labels for different object classes (cars, trucks, trams, pedestrians, cyclists). It is a reference base for both training and evaluation of methods for stereo, optical flow, visual odometry, 2D object detection, 3D object detection, depth estimation, 2D tracking, etc. Among the road datasets, we can also find the NuScenes  [7]  dataset which is a public large-scale dataset for autonomous driving. NuScenes is more recent than the KITTI dataset which inspired it. It includes 1000 driving scenes (of 20 s in length each) in Boston and Singapore, cities known for their dense traffic. It includes 23 object classes annotated with accurate 3D bounding boxes at 2 Hz which is a good annotation for common autonomous driving tasks such as object detection, distance estimation, and tracking.
NuScenes includes approximately 1. 4 M camera images, 390 k LIDAR sweeps, 1. 4 M RADAR sweeps, and 1. 4 M object bounding boxes. As with KITTI, this dataset incorporates one LiDAR sensor, 5 radar sensors, 6 cameras to provide 360 • coverage around the vehicle, and one GPS/IMU unit  [7]. This dataset contains 100x more images than KITTI and allows training methods for 2D and 3D object tracking, 2D and 3D object detection, depth estimation. ROad  [8]  event Awareness Dataset for Autonomous Driving (ROAD), includes 22 videos with 122 K annotated video frames, and 560 K detection bounding boxes with 1. 7 M individual labels. ROAD is a dataset that was designed to test a robot-car's situation awareness capabilities. The annotation process follows a multi-label approach in which road agents (vehicles, pedestrians, etc.
), their locations as well as the action they  [8]. We can find many other road datasets for similar tasks which are also multimodal large-scale autonomous vehicles datasets. Argoverse  [9]  dataset images are captured under different weather conditions. It includes annotations for RGB images, LiDAR/radar, and 3D boxes. It includes more sensors than both KITTI  [6]  and NuScenes  [7]  datasets. Argoverse includes 3D bounding boxes with tracking information for 15 objects of interest  [9]. Another multimodal dataset, Lyft  [10]  uses cameras and LiDAR to provide 2D annotation and 3D bounding boxes for 4 object classes. All these datasets provide contextual knowledge through humanannotation which is very important for scene understanding applications. Waymo open dataset  [11]  is a new large-scale, high-quality LiDAR and camera data that includes 1150 scenes (each with the 20s).
Sensors produce annotated data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. To our knowledge, there are only a few image datasets dedicated to the railway  [12, 13]. However, these datasets are not dedicated to 3D detection and do not have ground truth information on depth estimation. Therefore, these datasets cannot be used for the training or evaluation of 3D object detection approaches. Finally, there are databases of virtual images created with simulators such as CARLA  [14]  or SYNTHIA  [15]. The advantage of these databases is that they are simpler to acquire, the ground truth data are acquired automatically by the API simulator, which allows the acquisition of mass data. It also allows acquiring a database adapted to specific needs such as the understanding of scenes in the railway environment.
However, one of the disadvantages of this kind of dataset is that they have dated graphics, which makes it difficult to use trained dataset in real conditions. To overcome this problem, recent work has been done on the acquisition of a database via a video game  [16]. Indeed, video games make graphic quality their priority, which makes using of video games particularly interesting. The video game GTAV is used to acquire road databases similar to KITTI or City-Scape. Although there are many virtual road datasets, to our knowledge, there is no similar state-of-the-art work for the railway.
In  [19] , 2D detection is done by the multi-scale regionproposal MS-CNN  [20] , extended to regress the orientation and the dimensions of the 3D bounding boxes. Estimation of orientation uses a MultiBin principle, in which angle is turned to a hybrid classification and regression task, in which values are separated into bins representing the classes. DeepMANTA  [21]  is a coarse-to-fine model for simultaneous vehicle detection, localization, visibility characterization, and 3D dimension estimation. It is based on a twostep process, the first one of which outputs bounding boxes associated with vehicle information, and the second one uses these outputs and a 3D vehicle virtual dataset to recover 3D orientations and locations. In  [22] , a depth map obtained by means of a fully-convolutional network (FCN) is fused with the input RGB image before feeding a region-proposal network (RPN).
In MonoGRNet  [25] , 3D object detection is decomposed into four sub-tasks: 2D object detection, object center depth estimation, projected 3D center estimation and local corner regression. Those four pieces of information are obtained in parallel through a single-pass process, and then combined. Anchor-based methods have also been proposed in M3D-RPN  [26]  and GAM3D  [27]. M3D-RPN leverages depthaware convolution for locating the specific features and improve the 3D scene understanding. The approach consists only of a single-stage using anchor boxes for predicting both 2D and 3D object positions. GAM3D also uses 3D/2D anchor boxes to predict the objects' 3D bounding box and introduces ground-aware convolution module to provide additional 3D cues to the network, thus improving the accuracy of the network.
Another important aspect of our needs is to be able to perform well in road environment as well as railway environment. The lack of existing railway dataset has led us to develop our own dataset, made of virtual images from GTA-V game. We will use this custom dataset in complement to KITTI. In addition to our virtual railway dataset, we are developing a real railway dataset with ground truth allowing us to go further in the development of railway e-Advanced Driver Assistance Systems (e-ADAS).
GTAV virtual multi-modal dataset presentation To compensate for lack of real dataset in the railway environment, we turn to a virtual dataset. The main advantage of a virtual dataset is the simplification of its acquisition and annotation. However, this is done at the cost of the fidelity to reality. Indeed, most of these datasets (such as CARLA) do not have sufficient graphical fidelity to allow the methods trained on them to offer good performances once applied in real world conditions. To overcome this problem, we turn to video games to acquire a railway database for 3D object detection. Indeed, where simulators lack graphical fidelity, video games try to offer the best graphical fidelity to offer users a feeling of authenticity. This is why several works have already been carried out in this field to extract databases.
One of the most interesting games is the video game Grand Theft Auto V, which allows the user to drive road vehicles and presents realistic road scenes with, for example, level crossings, crosswalks, heavy traffic, etc. A lot of work has been done in this direction to build databases for the road environment  [16, 23]. Our railway database has been acquired with a data acquisition pipeline based on the work of  [31]. To do this we used a modified version of the game  [32]  code to allow the user to drive a subway or a train, allowing us to build a hybrid database with both road and rail scenes that can then be used for training and evaluation of our 3D detection approach.
GTAV virtual multi-modal dataset architecture The ground truth is generated automatically by the game's API and includes among other things the 3D and 2D bounding boxes, the object class, the depth map and the semantic map. All these annotations are taken from the point of view of a stereoscopic camera with an acquisition frequency of 10 Hz. Some statistics for our dataset can be found in Figure  1. During the acquisition of the dataset, several classes of objects were annotated. However, to combine our virtual dataset with the 3D detection KITTI dataset, we selected only the classes that are also present on KITTI (Car, Person, Cyclist) dataset. Our dataset is composed of 140 K samples from the stereoscopic camera. Each of these samples has a depth map and a ground truth for 3D detection (position in the camera coordinates, dimensions, orientation, etc.
3D multi-class object detection Our method is a single-stage 3D object detector based on the YOLOv5 2D detector neural network. Indeed, it is a light convolutional network that offers good accuracy, allowing real-time use even on embedded systems such as Nvidia Jetson TX2. YOLOv5 offers a significant performance gain compared to YOLOv5 thanks to an improved network architecture as well as innovations on data augmentation during training. The single-stage design of our network allows for better computational times compared to multiple stage methods  [19, 21, 23]  and it can be trained end-to-end. Another problem posed by multiple-stage methods was the degradation in accuracy for the 3D parameters regression when the predicted RoIs used for feature alignment vary from the ones used in the training  [33].