dataesr/scientific-paragraphs-categorization huggingface dataset:
gNB The gNB incorporates three main functional modules: the CU implementing the RRC and PDCP layers, the DU implementing PHY, MAC and RLC, and the Radio Unit (RU) -(RRH) (see Figure  2 ). The interface between CU and DU has been standardized by 3GPP as the F1 interface whereas for the interface between the DU and the RU the O-RAN 7. 2 interface  [40]  is becoming the de-facto standard. There is also an interface within the DU between the MAC/RLC and the PHY specified by the Small Cell Forum  [44]. Today, the OAI gNB only supports a "monolithic" mode, meaning that all the different components of the gNB are part of a single application. Inside this application, the 5G FAPI interface is used to implement the interface between PHY and MAC, while the F1 and the O-RAN 7. 2 interfaces are under development to allow for a physical separation of the different elements of the gNB.
5G Core As a part of the OAI project, the basic components for 5GC are under development. At this stage, the interfaces between the main components (e. g. , AMF, SMF, UPF, and NRF) are implemented. However, the functional logic of these components is still un- to be able to contribute to the OAI source code and still be able to keep their patent rights. Such a license will allow close collaboration with 3GPP member companies while at the same allowing commercial exploitation of the code. OAI also follows 3GPP FRAND policy on licensing. For non-commercial/academic research purposes the license guarantees the code is free to use. Some parts of the OAI code, such as the MME and the HSS still use the Apache V2. 0 license directly. This has been decided in order to ease integration with other open-source software communities, such as OpenStack  [12] , and because revenues from the EPC are less dependent on intellectual property agreements.
11 systems with 3GPP standards and the efficient use of unlicensed spectrum. • Large-Scale Emulation: refers to the use of OAI software to deploy mock networks in a simulated non-real-time or emulated real-time environment and aims to look for synergy with other network simulation tools such as Network Simulator 3 (NS3)  [8]. • Test and Measurement: to refers the use of OAI software in real-time testing systems to facilitate the diverse 5G network elements (radio units, server units, core networking elements) which will predominantly become integrated into a cloud computing environment. • RF Platform: refers to the interfacing and support of various COTS and commercial RF platforms by the OAI tool suite. The work presented in  [41]  reviews the basics of OAI along with the tools, protocol stack and applications and brought out the importance of having it as a open source for a better 5G technology and experience.
The need for a structured and statistically sound experiment management, with the goal of reducing errors in both planning and measurement collection is described in  [33]. The authors use OAI to solve the problem by integrating a sound, validated software, namely ns2-measure, with OAI, to enable harvesting samples of arbitrary metrics in a structured way, and they also describe scripts that allow structured experi-ment management, such as launching a parametric simulation campaign and harvesting its results in a plot-ready format. OAI has implemented all the necessary functions to support basic downlink functionality at both gNB and UE showing the feasibility to run 5G NR in real-time on a software defined radio platform. The roadmap describing a fully standard compliant implementation of 5G NR that is inter-operable with commercial equipment is discussed in  [34].
PhantomNet infrastructure  [2]  has been using OAI to create an end-to-end LTE/EPC network. A tutorial that describes this implementation is available in  [16]. The radio part is a SDR eNodeB with OAI software running on a USRP B210 connected to a intel-NUC unit via USB. The EPC core network is an OpenEPC  [10]  instance with SGW, PGW, MME and HSS. The UE used is a Nexus 5 with pre-install and pre-configured SIM card that works with the SDR (i. e. , LTE FDD band 4). The Nexus 5's SIM card is registered as a subscriber in the OpenEPC instance. The radio link between the Nexus 5 and the SDR eNodeB is an attenuated link. An implementation of a virtualized LTE testbed environment of C-RAN by integrating the OAI with Docker is presented in  [46]. Using the testbed, the authors conducted a workload study to understand the computation resource demand of C-RAN software.
The main goal of this procedure was to create a central EPC that all GENI sites may connect to using their own base stations (eNBs) and UEs without having to configure individual EPCs. An OAI LTE implementation emphasizing on the UP data flow is reported in  [49]. The authors emulated Over-The-Air (OTA) transmission for 1 UE and 1 eNB LTE network supporting both FDD Band 5 and TDD Band 38. The work have also performed a thorough profiling of OAI, in terms of execution time, on the UP data flow. An implementation of calibration algorithms through the OAI platform is discussed in  [36]. It proposes to implement and evaluate a less computationally intensive Multiple-Input Multiple-Output (MIMO) calibration algorithm, according to a realistic scenario based on LTE-TDD specification. The result from OAI simulator shows that it is feasible to restore the reciprocity after calibration in a non reciprocal channel, and provides an overview of the challenges in the channel estimation in a real time case.
Acknowledgements OpenAirInterface is a large open-source project with many contributors from different institutions. The work presented here would not have been possible without them. Please see the contributors list in the code repository for a detailed list. This paper emerged from the first OAI workshop on the North American continent 18 which was sponsored by the  NSF   PAWR Program  (https://advancedwireless. org/) award number  1719547. The work has further been supported by the  EU H2020 CSA EMPOWER  under grant agreement  824994.
Translation of mRNAs at specific sites may also be important for the assembly of protein complexes  (Pichon et al. , 2016) , or to avoid the deleterious effects of releasing free proteins at inappropriate places  (Mueller et al. , 2013). RNA localization can be accomplished through several mechanisms. In the case of secreted proteins, the nascent peptide serves as a targeting signal, via the signal recognition particle  (SRP)  and its receptor on the ER  (Aviram and Schuldiner, 2017). In most other cases, targeting is an RNA-driven process  (Blower, 2013; Jung et al. , 2014; Eliscovich and Singer, 2017; Bovaird et al. , 2018 for reviews). Localized mRNAs often contain a zip-code sequence, frequently located whitin their 3'-UTR, which is necessary and sufficient to transport them to their destination.
Likewise, DYNC1H1 mRNAs formed foci in all examined cell lines (Figure  S2A ). MYH3 mRNA was not expressed in HeLa, 293HEK and 3T3, but localized in cellular protrusions in C2C12 cells, as well as in dendritic-like processes of SH-SY5Y neuronal cells. In contrast, KIF5B mRNA localization varied with the cell line. This mRNA was present in neuronal dendrite-like processes of SH-SY5Y cells, but localized only weakly in HeLa or C2C12 lines (Figure  S2 , data not shown). To provide a quantitative view of mRNA localization, we developed tools to identify the cytoplasmic protrusions and we measured both the fraction of cytoplasmic mRNAs located in this compartment and their enrichment as compared to a uniform distribution of molecules (see STAR Methods). This revealed a high heterogeneity of mRNA localization in protrusions, with values varying from 0 to 60% of the molecules depending on the cell (Figure  S3A ).
These features are mostly invariant to RNA concentration and they allow to use unsupervised or supervised approaches to classify each cell in a series of patterns. For this analysis, we first generated a manually annotated dataset. We selected several localized mRNAs together with several genes annotated as random (KIF20B, MYO18A, SYNE2, PLEC), and we then manually annotated individual cells falling into the different patterns (810 cells in total). We then investigated the distributions of these annotated cells in the multi-dimensional feature space. After reducing dimensionality with a t-SNE plot (Figure  3B ), cells with different patterns were situated in different regions of the embedded feature space. Moreover, cells with the same annotations clustered in the same region whether they corresponded to the same gene or not.
Imaging of live cells Live imaging was done using a spinning disk confocal microsope (Nikon Ti with a Yokogawa CSU-X1 head) operated by the Andor iQ3 software. Acquisitions were performed using a 100X objective (Nikon CF1 PlanApo  1. 45 NA oil), and an EMCCD iXon897 camera (Andor). Samples were sequentially excited at 488 and 640nm. Cells were maintained in anti-bleaching live cell visualization medium (DMEM gfp ; Evrogen), supplemented with 10% fetal bovine serum at 37°C in 5% CO2. SiR-DNA (Spirochrome) was kept at 100 nM throughout the experiments to label DNA.
Deconvolution For Figure  S9A  and S9B, deconvolution was performed using the Huygens Professional software (Scientific Volume Imaging, Hilversum, Netherlands). The point spread function (PSF) was a theoretical one, and background values were manually estimated. The deconvolution was done using the CMLE deconvolution algorithm, with 50 iterations and a quality threshold of 0. 01, and without bleaching correction.
Image analysis and quantifications Nuclear segmentation was performed from the DAPI channel, cell segmentation from the autofluorescence of the actual smFISH image. Prior to segmentation, 3D images were projected to 2D images using maximal local focus values  (Tsanov et al. , 2016). Nuclei were segmented with a Deep Neural Network (Matterport implementation of the Mask R-CNN,  He et al. , 2017) , trained on the Data Science Bowl 2018. Cells were segmented with a custom algorithm, based on prefiltering the 2D projected smiFISH image, followed by a watershed based method using nuclear regions as seeds. In case of poor results, segmentation was corrected manually. RNAs were detected with a Python implementation of FISH-quant  (Mueller et al. , 2013) , by applying a local maximum detection on Laplacian of Gaussian (LoG) filtered images, and by decomposing larger agglomerations of spots using gaussian mixture models  (Samacoits et al.
This strategy is known as "one vs. all". Importantly, the classifiers were independent, and for this reason allowed to assign several patterns to the same cell. Training parameters were 100 trees with a maximum depth of 3 and a minimum number of samples per splitter node of 2. For each split, we consider a subset of 10 features and entropy criterion. Percentages were then calculated and visualized in a heatmap. P-values were calculated with Fisher's exact test (see code in GitHub for details). For the single-gene heatmap visualizations, we assigned to each cell a vector of classification probabilities. Note that the vectors do not sum to one because classifiers are independent. The classification vectors were then hierarchically clustered using Average method with Euclidean distance. All visualizations were done with Matplotlib.
Acknowledgements We thank  Florence Rage  and  Susanne Schmidt  for the preparation of neuronal hippocampus primary cells. We also thank the staff of MRI imaging facility for their technical support. R. C. was supported by a scholarship from the  Lebanese university  and the  Lebanese National Council for Scientific Research (LNCSR). AdS was supported by an  FRM  fellowship. This project was supported by  France BioImaging  ( ANR-10-INBS-04 ), the  Agence Nationale de la Recherche  ( ANR-11-BSV8-018-02  and  ANR-14-CE10-0018-01 ), the  Institut Pasteur , the  Ligue Nationale Contre le Cancer  and the  Fondation pour la Recherche Médicale  ( FRM grant  to EB). This work was supported by the  Labex EpiGenMed , from the framework " Investissements d'avenir ".
(Van der Walt et al. , 2014)sis scikit-image(Van der Walt et al. , 2014). All the pattern quantifications were done using the random forest classifiers except for Figures 4B, 5D, 6E and 7E-F, where cells were manually annotated to assign them a pattern type. Statistical details are given in the Figure legends (Figures 4B, 5D, 6E and 7E-F) or with the code at GitHub.
, 2013 S7239 https://code. google. 115-096-006 com/archive/p/fish- quant/ Anti-APC mouse monoclonal (F3) Deposited Data Santa Cruz sc-9998 Biotechnology, INC Experimental Models: Cell Lines Anti-β-catenin mouse monoclonal Software, Methods, Pipeline of analyze, Python libraries, Cell Signaling This paper #9581 https://github. com/ Anti-β-catenin mouse monoclonal (L87A12) Human: HeLa-Kyoto BACs (GFP-tagged) Results plots Cell Signaling Poser at al. , 2008 Maliga et al. , 2013 #2698 http://www. mitochec Henley13/paper_tra k. org/cgi-nslation_factories_2 bin/BACfinder. 020 Anti-S33/S37/Thr41 phospho β-catenin rabbit Human: RPE1-Centrin2-GFP Cell Signaling Mikule et al. , 2007 #9561 N/A Anti-ubiquitin mouse monoclonal antibody FK2 Human: HeLa ASPM Nter-Puro-P2A-STx32 Other Viva Bioscience This paper N/A VB2500 Anti-Axin1 (C76H11) rabbit Human: HeLa BUB1 Nter-Puro-P2A-STx32 Cell signaling This paper #2087 N/A Anti-FGFR10P mouse monoclonal Experimental Models: Organisms/Strains Abnova H000111116-M01 Anti-eIF4E (FL-217) Oligonucleotides Santa Cruz sc-13963 Biotechnology, INC Guide RNA seq for This paper N/A Anti-Phospho-RPS6 ASPM:AAGTGAGCCCGACCGAGCGGAGG Santa Cruz sc-54279 Biotechnology, INC Guide RNA seq for BUB1: This paper N/A Secondary goat anti-mouse Cy3 CGGGGTATTCGAATCGGCGGCGG Jackson 115-165-003 ImmunoReseach Genotyping ASPM WT forward primer: This paper N/A Secondary goat anti-mouse Cy5 TGTTCCTGGAAACCGCAATG Jackson 115-176-003 ImmunoReseach Genotyping ASPM WT reverse primer: This paper N/A Secondary goat anti-rabbit Cy5 GTTTATGTGTTGTCCCCGCC Jackson 115-175-144 ImmunoReseach Genotyping SunTag reverse primer: This paper N/A Bacterial and Virus Strains TACCCTTCTTCAGTCTGGCG Biological Samples For all smFISH and smiFISH sequences probes, see This paper N/A Table S3 Chemicals, Peptides, and Recombinant Proteins siRNA APC-1: 5'-GCACAAAGCUGUUGAAUUUdTdT-3' This paper N/A.
Ecosim EwE is a commonly-used software for developing food web models. The Ecopath module of EwE provides a snapshot of the food web through two master equations that balance the energy input and output of the food web (see  Subramaniam et al. , 2020). The balanced Ecopath model can then be calibrated to time-series data in Ecosim. Biomass dynamics in Ecosim are described through a series of coupled simultaneous equations: δB i δt = g i j Q ji - j Q ij + I i -(M i + F i + e i ) * B i (1) where δB i δt represents the growth rate of group i during the time period t, g i is net growth efficiency (production/consumption), F i is fishing mortality rate, M i is other mortality rate, e i is emigration rate, I i is immigration rate and B i is biomass. The consumption rates Q ij and Q ji are calculated based on a simple "foraging arena" model while biomasses (B i ) are split into components that are vulnerable and invulnerable to predation.
Consultation with fisheries scientists was used to inform an estimated allocation of catch to juveniles. Trawl fishing for Patagonian toothfish catches mostly immature fish while longline fishing catches mostly mature fish. To account for this difference in selection, time-series of nominal catch per unit effort (CPUE, kg per hook) and total catch for each season was allocated 28% to juveniles and the remainder to adults for longlines and trawl CPUE and catch was allocated to juveniles (Philippe Ziegler, Australian Antarctic Division, pers. comm). Fishing mortality was used to drive fishing activity in Ecosim using model estimated biomass for Patagonian toothfish (Table  A3 ), this was calculated as: Fishing mortality = Catch Biomass Calibration of the model with time-series data was performed using the step-wise fitting procedure described in the EwE best practices guideline  (Heymans et al.
Model 1 Time period 2006-2018 where there was most data coverage and each of the three CPUE inputs were used to fit the model to juvenile and adult toothfish biomass. Fishing mortality and catch were included for each fishing gear for scenarios including fishing activity as a driver. Model 2 Time period 2006-2018 where there was most data coverage and mean CPUE was used to fit the model juvenile and adult toothfish biomass (see methods). Mean fishing mortality and catch were used for scenarios including fishing activity as a driver. Model 3 Time period 1986-2018 where each of the three CPUE inputs were used to fit the model to juvenile and adult toothfish biomass. Fishing mortality and catch were included for each fishing gear for scenarios including fishing activity as a driver. Model 4 Time period 1986-2018 where an mean CPUE was used to fit the model juvenile and adult toothfish biomass (see methods).
Ecosim models are fitted to the data by tuning parameters (vulnerabilities and PP anomaly trends) to show which values could explain observed historical trends  (Alexander et al. , 2014). Consequently, the ability of the model to fit to observed data is improved with good quality data sets. In situations where data quality is poor, the model may struggle to converge on an accurate solution. The inability of the preferred model to completely represent observed trends for marine predators could be a result of data quality and the inability to incorporate all possible drivers of change. Data on population trends for baleen whales on the Kerguelen Plateau are lacking  (Dréo et al. , 2019). The data used to represent baleen whales was taken from an aerial survey of Australian southern right whale population.
ACKNOWLEDGMENTS The authors would like to thank the  Muséum national d'Histoire naturelle  for providing the fisheries data for the French EEZ and the  Australian Fisheries Management Association  for providing fisheries data for the Australian EEZ. We would also like to thank the reviewers for their helpful comments on the development of this manuscript and  Ben Raymond  for his help with R. The following packages were used to generate the figures presented in this paper: ggplot2 (Wickham, 2016), RColorBrewer (Neuwirth, 2014), and gridExtra (Auguie, 2017). This study was supported by  Australian Government's Cooperative Research Centres Programme  through the  Antarctic Climate and Ecosystems Cooperative Research Centre , the  Institute for Marine and Antarctic Studies (IMAS)  through the  Australian Antarctic Science Programme  ( AAS 4347 ) and through the  IMAS  and  University of Tasmania (UTAS) Tasmanian Graduate Research Scholarship.
DATA AVAILABILITY STATEMENT The data analyzed in this study is subject to the following licenses/restrictions: All data that were not provided under a confidentiality agreement are provided in the article. The fisheries data (CPUE, catch and model estimated biomass) were provided under a confidentiality agreement and therefore cannot be made publicly available. The data are available upon request and the fishing bodies that provided the data are cited in the article. Requests to access these datasets should be directed to Clara Péron, clara. peron@mnhn. fr, Philippe Ziegler, Philippe. Ziegler@awe. gov. au.
A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection.
To provide a safe autonomous driving service, the system should be able to detect the different road agents and predict their future paths in order to have a complete environment perception  [6]  and make the right navigation decision  [7] [8] [9]. Therefore, obstacle detection is considered as a crucial task for autonomous driving in complex urban traffic. To achieve this, diverse machine-learning-based algorithms have been developed  [10]. Due to its impressive effectiveness in representing hierarchical features, deep learning is one of the most widely used tools in this domain. In fact, training such algorithms requires absolutely huge datasets. In this context, there exist various multimodal datasets dedicated to intelligent transportation systems (ITS) such as Kitti et al. [11] , Cityscapes  [12] , Kaist Multi-Spectral dataset  [13] , nuScene  [14] , etc.
These datasets gained a particular attention over mono-modal datasets, as an individual sensor is insufficient to provide a complete perception of the environment. Furthermore, the most exploited sensors in this field such as camera, lidar, radar, etc. , offer complementary data and their collaborationn can guarantee a better scene understanding  [15]. The camera is the most widely used sensor for obstacle detection. It provides detailed information about the vehicle's surroundings such as edges, colors, etc. Lidar is more accurate in terms of depth information. With regard to radar, it is robust to weather and lighting conditions. The latter cited sensors are the most exploited in the field of autonomous vehicle, and based on the literature, the combination of the captured data from each sensor separately can improve performances  [16].
Over the last decade, various autonomous driving datasets have been published in order to enhance reasarch for environment perception. Most of these datasets are multimodal, combining different heterogeneous modalities. While some of the existing datasets use narrow-band radar, the UWB radar carries richer information. The UWB radar provides a signal that results from the reflection of a transmitted UWB pulse on the object. The deformation of the initial wave represents the signature of the object. This signature contains information about the distance, the material and the shape of the object. Moreover, different objects have distinguishable acoustic signatures that may help recognize each of them. In spite of the usefulness of the acoustic data, we notice that none of the state of the art ITS benchmarks uses acoustic modality.
• We propose a new fusion framework that combines data acquired from the different sensors used in our dataset to achieve better performances for obstcle detection task. This fusion framework highlights the potential improvement that could be acquired by the community using our dataset. The paper is organized as follows: In Section 2 we review existing multimodal environment perception datasets. In Section 3, we focus on detailing some related works especially on data fusion methods. We introduce and detail our proposed dataset in Section 4. In Section 5, we present the fusion framework and show experimental results to generate baselines for our new dataset. Section 6 provides a discussion. Finally, we conclude the paper in Section 7.