dataesr/scientific-paragraphs-categorization huggingface dataset:
cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/ gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Part of this research was carried out at the Jet Propulsion Laboratory, Caltech, under a contract with NASA. BGM simulations were executed on computers from the Utinam Institute of the Université de Franche-Comté, supported by the Région de Franche-Comté and Institut des Sciences de l'Univers (INSU). EEM acknowledges support from the NASA NExSS program. JG designed BANYAN Σ, compiled the bona fide members, wrote the IDL codes, wrote the manuscript, generated figures and led all analyses;EEM shared parts of the young association literature data, characteristics and bona fide members, and provided general comments;ORL wrote the initial Python translation of BANYAN Σ; LM, AR and DR performed the BANYAN I, LACEwING and convergent point tool calculations used in Section 8, respectively; AR also provided help with the bona fide members compilation; DL and JKF shared ideas and comments; LP shared ideas and provided comments especially for the ROC curves analysis; AR performed custom Besançon Galactic simulations and wrote the second pagraph of Section 6; and RD shared comments and supervized ORL.
A. Overview In a mobile VSN tasked with mapping a region using RGB-D sensors, it is highly possible that multiple sensors will observe the same scene from different viewpoints. Because of this, scenery captured by the sensors with overlapping FoVs will have a significant level of correlated and redundant information. Here, our goal is to efficiently extract and encode the uncorrelated RGB-D information, and avoid transmitting the same surface geometry and color information repeatedly. Consider the two sensors, a and b, of this VSN with overlapping FoVs. Let Z a and Z b denote a pair of depth images returned by these sensors, and C a and C b are the corresponding color images. In the encoding procedure, we first estimate the location and orientation of one sensor relative to the other. Then, correlated and redundant information in color and depth images are identified to minimize unnecessary data transmissions to the central monitoring station.
A. Performance Evaluation of the Relative Pose Estimation In order to quantitatively evaluate the performance of the relative pose estimation algorithm, we used two groups of datasets with varying of occlusions. We first generated our own datasets by using a turntable setup to obtain the imagery viewed from accurately measured angular positions. A number of objects were placed on the center of the turntable, and the images were captured with a tripod mounted Kinect sensor. In the experiments with the first dataset group, the ground truth is known exactly at every precisely controlled 5 • interval. We used this setup to compare our algorithm (ICP-BD)  [24]  with the standard ICP  [67]  and ICP in inverse depth coordinates (ICP-IVD)  [56]. The performance of the algorithms was evaluated based on the rotational and translational RMS errors.
Our experiments show that, as one can expect, the number of iterations increases as the angular difference between two views increases. Two representative results are plotted in Fig. 9. In order to gain further insight into the number of iterations required by our algorithm in densely cluttered scenes, we used a second group of datasets which were selected from the Technical University of Munich Computer Vision Group's RGB-D SLAM dataset and benchmark collection [68],  [69]. Each dataset is a sequence of Kinect video frames capturing one scene from different angles of view. For emulating the situations including varying amounts of occlusion between two sensor views, we extracted four new sequences from each dataset by taking one frame out of every 5, 10, 20, and 30 frames. For each trial we treated two consecutive frames in the new sequence as the depth images captured by two separate sensors.
A USB WiFi adapter was connected to the BeagleBoard to provide communication between robots. We ran an ARM-processoroptimized Linux kernel. OpenKinect  [73] , OpenCV  [74]  and libCVD  [75]  libraries were installed to capture and process image information. The default RGB video stream provided by the Kinect uses 8 bits for each color at VGA resolution (640 × 480 pixels, 24 bits/pixel). The monochrome depth video stream is also in VGA resolution. The value of each depth pixel represents the distance information in millimeters. Invalid depth pixel values are recorded as zero, indicating that the RGB-D sensor is not able to estimate the depth information of that point in the 3-D world. Color and depth images were captured in six different scenes, as shown in Fig. 11. In this set-up, sensor a transmits entire captured color and depth images to the central monitoring station.
In this section, we present our experimental measurements and evaluation regarding the overall energy consumption and amount of transmitted data of Fig. 12 : eyeBug  [70] ,  [71] , the mobile RGB-D sensor we use in our experiments. The color and depth data generated by the Kinect sensor is processed on a BeagleBoard-xM  [72]  computer running the GNU/Linux operating system. the RPRR framework collected on our eyeBug mobile visual sensors to demonstrate this aspect. The overall energy consumption of the RPRR framework can be measured by E R overall = E processing + E encoding + E sending = V o I p t p + V o I e t e + V o I s t s (15) in which V o denotes the sensor's operating voltage, and I p , I e , and I s represent the current drawn from the battery during processing, encoding, and sending operations.
Data acquisition and analysis All videos were recorded with a high-speed digital camera (Phantom Miro M310, Vision Research). A Hough circled detection algorithm  51  was used to roughly localize droplets centers and sizes. This rough detection was then refined using Virtual Image Correlation  52  based on a graylevel distribution along one cross-section of the droplet shape. This refinement allowed for a sub-pixel estimation of the 2D droplet section imaged by the high-speed camera. Such analysis allowed to monitor droplet trajectories and volumes. The data acquisition and control procedure of optical and triggering is similar to the one we previously described in ref  47. The PMT signal was recorded and converted to 8 bits through the Analog to Digital Converter of a LabVIEW FPGA module.
The sampling frequency was adjusted to 200 kHz. At each acquisition step, 8-bit PMT values were joined to form a 64-bit word queued in a LabVIEW direct memory access (DMA) first in, first out (FIFO). DMA FIFO enables high bandwidth transfer of data from the field-programmable gate array (FPGA) to the host computer. Three 8-bit signals were joined in these 64-bit words, such that PMT acquisition could be transferred together with the signals of high-voltage signal triggers. On the host computer, FIFO elements were dequeued in the main LabVIEW vi. Each 64-bit word was decoded to the three original 8-bit values. Data were then directly streamed to the disk in a Waveform Audio FileFormat (WAV) 8-bit PCM. An open-source digital audio editor (Audacity) was used to convert the signal into 16 bits format, and a home-made MATLAB script was further used to analyze the signals.
Acknowledgements This work was supported by the  Ministère de l'Enseignement Supérieur et de la Recherche , the  Université Paris-Descartes , the  Centre National de la Recherche Scientifique (CNRS)  and the  Institut National de la Santé et de la Recherche Médicale (INSERM). V. Taly acknowledges the financial support of  Agence Nationale de la Recherche  ( ANR Chipset , no. ANR-15-CE11-0008-04 ),  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences and Health)  within the framework of the  Cancer Plan , the ligue nationale contre le cancer ( LNCC, Program "Equipe labelisée LIGUE " no. EL2016. LNCC/VaT ) and  HTE Program-HetColi network ,.
5 (RCP8. 5) emissions were assumed. WRF-CHIMERE simulated levels of aerosol species under BAU2030 and RED2030 for the modeling domain using the base year meteorology and 2030 boundary conditions from LMDZ-INCA. The extended aerosol optical depth module (AODEM) calculated the total columnar AOD and BC AOD for all scenarios with an assumption on the internal mixing state. Under RED2030, the health benefits were analyzed in terms of the number of avoided premature deaths associated with ambient PM 2. 5 reduction along with BC DRF reduction. Under BAU2030, the average number of the premature deaths per 100 000 people in the SEA domain would increase by 30 from BY2007 while under RED2030 the premature deaths would be cut down (avoided) by 63 from RED2030. In 2007, the maximum annual average BC DRF in the SEA countries was 0.
Nevertheless, if the vehicle age limit is not strictly enforced, more aged engines will have higher EFs in the future. For other countries and territories in the modeling domain, due to the limitation of historical activity data, the BAU emissions were projected using their GDP growth trends over the period 2000-2007. The BY2007 emission data for those countries were taken from the international databases that were provided at a different resolution (i. e. , 0. 5 • × 0. 5 • ). Therefore, the spatial analysis of "grid conversion" in geographic information system (GIS) was used to convert the emission data in to the same grid resolution 30 × 30 km 2 for the whole domain. The land mask map that was generated from the WRF model with a resolution of 30 × 30 km 2 was used to regrid the emissions on the border between land and sea (assigning zero emissions to the sea area).
, 2009). The PM 2. 5 annual concentration was used rather than BC alone following the recommendations of the WHO Task Force (WHO, 2012) that PM 2. 5 should continue to be used as the primary metric in quantifying the human exposure to PM and resulting health effects. Concentration response (CR) data were obtained from  Smith et al. (2009)  who indicated that every increase in PM 2. 5 by 1 µg m -3 is approximately associated with a 1. 006 % increase in the risk of all-cause adult mortality for individuals of 20 years and older. The I ref data representing the adult mortality rate for all causes and both sexes (per 1000 people) for every nation included in the study domain were obtained from the United Nations database (http://data. un. org/Data. aspx? d=WHO&f=MEASURE_CODE:WHOSIS_000004) for the base year of 2007.
The 0. 5 • resolution gridded population (POP) data were obtained from the Potsdam Institute for Climate Impact Research (PICIR) (http://clima-dods. ictp. it/ Users/fcolon_g/ISI-MIP/UNWPP/) for both 2007 and 2030, and the data were further gridded to a finer resolution of 0. 25 • of the modeling domain. The proportions of adult population (age ≥ 20 years) for the countries were taken from the World Bank database (http://data. worldbank. org/indicator/ SP. POP. 0014. TO. ZS). This study assumed that the baseline mortality and age structure were distributed following the population density and were not changed between 2007 and 2030.
We observed some increasing trends of SO 2 emissions from the residential sector when LPG was used to replace wood as cooking fuel because of its higher sulfur content. As for BC species, the emission ratios of RED2030 / BAU2030 for biomass open burning and residential sector in Indonesia were 0. 48 and 0. 49, respectively, and in Thailand were 0. 37 and 0. 41, respectively. For the industry and transport sectors, the corresponding emission ratios for BC were 0. 87 (Indonesia) and 0. 6 (Thailand). Under BAU2030, the emissions of BC was rising steadily with a high rate from the BY2007 (Fig. S2 ). For other countries in the modeling domain, the IPCC RCP8. 5 was used to project the emissions under RED2030 and this was done using the regional (Asia) emission ratios between 2030 and 2007 emissions (using the IPCC emission database) for every species.
Acknowledgements. We acknowledged  Didier Hauglustaine  for sharing the simulation results of the LMDZ-INCA used for the boundary conditions in this work. This research was financially supported by the  French government  under the  Asian Institute of Technology (AIT)/Sustainable Development in the Context of Climate Change (SDCC) -France Network cooperation  and by the  United States Agency for International Development (USAID)  under the  PEER-SEA (Partnerships for Enhanced Engagement in Research for Southeast Asia ) research project. Edited by: Toshihiko Takemura Reviewed by: two anonymous referees.
Accelerators need a central processing unit (CPU) to process the main code and off-load the specific kernels to them. To exploit the massive parallelism available within the GPUs, the application kernels must be rewritten. The dominant programming language is OpenCL that is cross-platform, while other alternatives are vendor dependent (e. g. , CUDA  [2] ). Node: A computational node can include one or several sockets and accelerators along with main memory and I/O. A computational node is, therefore, the minimum autonomous computation unit as it includes cores to compute, memory to store data, and network interface to communicate. The main classification of shared memory nodes is based on the kind of memory access they have: uniform memory access (UMA) or  [3]  nonuniform memory access (NUMA).
Software We have seen an overview of the hardware available within a supercomputer and the different levels of parallelism that it exposes. The different levels of the HPC software stack are designed to help applications exploit the resources of a supercomputer (i. e. , operating system, compiler, runtime libraries, and job scheduler). We focus on the parallel programming models because they are close to the application and specifically on OpenMP and MPI because they are the standard "de facto" at the moment in HPC environments. OpenMP: It is a parallel programming model that supports C, C++, and Fortran  [4]. It is based on compiler directives that are added to the code to enable shared memory parallelism. These directives are translated by the compiler supporting OpenMP into calls to the corresponding parallel runtime library.
OpenMP is based on a fork-join model, meaning that just one thread will be executing the code until it reaches a parallel region; at this point, the additional threads will be created (fork) to compute in parallel and at the end of the parallel region all the threads will join. The communication in OpenMP is done implicit through the shared memory between the different threads, and the user must annotate for the different variables the kind of data sharing they need (i. e. , private, shared). OpenMP is a standard defined by a nonprofit organization: OpenMP Architecture Review Board (ARB). Based on this definition, different commercial or open source compilers and runtime libraries offer their own implementation of the standard. The loop parallelism in OpenMP had been the most popular in scientific applications.
Indeed, "the easiest way of making software scalable is to make it sequentially inefficient"  [7]. CFD is considered a memory-bounded application, and this means that sequential performance is limited by the cost of fetching data from the main to the cache memory, rather than by the cost of performing the computations on the CPU registers. The reason is that the sparse operations, which dominate CFD computations, have a low arithmetic intensity (flops/byte), that is, few operations are performed for each byte moved from the main memory to the cache memory. Therefore, the sequential performance of CFD codes is mostly determined by the management of the memory transfers. A strategy to reduce data transfers is to maximize the data locality. This means to maximize the reuse of the data uploaded to the cache by: (1) using the maximum percentage of the bytes contained in each block (referred as cache line) uploaded to the cache, known as spatial locality, or (2) reuse data that have been previously used and kept into the cache, known as temporal locality.
This explains, in part, the reluctance of CFD code developers to rely on the loop parallelism offered by OpenMP. There exists another reason, which lies in the difficulty in maintaining large codes using this parallel programming, as any new loop introduced in a code should be parallelized to circumvent the so-true Amdahl's law. As an example, Alya code  [24]  has more than 1000 element loops. However, the situation is changing, for two main reasons. First, nowadays, supercomputers offer a great variety of architectures, with many cores on nodes (e. g. , Xeon Phi). Thus, shared memory parallelism is gaining more and more attention as OpenMP offers more flexibility to parallel programming. In fact, sequential kernels can be parallelized at the shared memory level using OpenMP: one example is once more the coarse solve of iterative solvers; another example is the possibility of using dynamic load balance on shared memory nodes, as explained in  [25]  and introduced in Section 4.
Another option consists in repartitioning the mesh, to achieve a better load distribution. In order for this to be efficient, a parallel partitioner (e. g. , using the space filling curve-based partitioning presented in Section 3) is necessary in order to circumvent Amdahl's law. In addition, this method is an expensive process so that imbalance should be high to be an interesting option. In the second group, several parallel runtime libraries offer support to solve load imbalance at the MPI level, Charm++  [34] , StarPU  [35] , or Adaptive MPI (AMPI). In general, these libraries will detect the load imbalance and migrate objects or specific data structures between processes. They usually require to use a concrete programming language, programming model, or data structures, thus requiring high levels of code rewriting in the application.
I/O and visualization Scientific visualization focuses on the creation of images to provide important information about underlying data and processes. In recent decades, the unprecedented growth in computing and sensor performance has led to the ability to capture the physical world in unprecedented levels of detail and to model and simulate complex physical phenomena. Visualization plays a decisive role in the extraction of knowledge from these data-as the mathematician Richard Hamming famously said, "The purpose of computing is insight, not numbers [. ]. "  [61]  It allows you to understand large and complex data in two, three, or more dimensions from different applications. Especially for CFD data, the visualization is of great importance, as its results can be well represented in the three-dimensional representation known to us.
Traditionally, I/O and visualization are closely related, as in most workflows, data used for visualization are written to disk and then read by a separate visualization tool. This is also called "postmortem" visualization, since the visualization may be done after the CFD solver has finished running. Other modes of interaction with visualization are becoming more common, such as "in situ" visualization (the CFD solver also directly produces visualization images, using the same nodes and partitioning), or "in-transit" visualization (the CFD solver is coupled to a visualization program, possibly running on other nodes and with a different partitioning scheme). I/O. Output of files for postmortem visualization usually represents the highest volume of output from a CFD code, as well as some possibly separate operations, especially explicit checkpointing a restart, requiring writing and reading of large datasets.
Logging or output of data subsets also requires I/O, often with a smaller volume but higher frequency. As CFD computations can be quite costly, codes usually have a "checkpoint/restart" feature, allowing the code to output its state (whether converging for a steady computation or unsteady state reached for unsteady cases) to disk, for example, before running out of allocated computer time. This is called checkpointing. The computation may be restarted from the state reached by reading the checkpoint from a previous run. This incurs both writing and reading. Some codes use the same file format for visualization output and checkpointing, but this assumes data required are sufficiently similar and often that the code has a privileged output format. When restarting requires additional data (such as field values at locations not exactly matching those of the visualization, or multiple time steps for smooth restart of higher order time schemes), code-specific formats are used.
Some libraries, such as Berkeley Lab Checkpoint/Restart (BLCR)  [62] , try to provide a checkpointing mechanism at the runtime level, including for parallel codes. This may require less programming on the solver side, at the expense of larger checkpoint sizes. BLCR's target is mostly making the checkpoint/restart sufficiently transparent to the code that it may be checkpointed, stopped, and then restarted based on resource manager job priorities, not I/O size and performance. In practice, BLCR does not seem to have evolved in recent years, and support in some MPI libraries has been dropped; so it seems the increasing complexity of systems has made this approach more difficult. As datasets used by CFD tools are often large, it is recommended to use mostly binary representations rather than text representations.
Reassembling data for visualization (or partitioning on disk) require additional I/O, so it is best to avoid them if possible. Another approach is to use "shared" or "flat" files, which are read and written collectively by all tasks. MPI I/O provides functions for this (for example MPI_File_write_at_all using MPI), so the low-level aspects are quite simple, but the calling code must provide the logic by which data are transformed from a flat, partitionindependent representation in the file to partition-dependent portions in memory. This approach provides the benefit of allowing checkpointing and restarting on different numbers of nodes and making parallelism more transparent for the user, though it requires additional work for the developers. Parallel I/O features of libraries such as HDF5 and NetCFD seek to make this easier (and libraries build on them such as CGNS and MED can exploit those too).
Performance of parallel I/O is often highly dependent on the combination of approach used by a code and the underlying file system. Even on machines with similar systems but different file system tuning parameters, performance may vary. In any case, for good performance on parallel file systems (which should be all shared file systems on modern clusters), it is recommended to avoid funneling all data through a single node except possibly as a fail-safe mode. In any case, keeping data fully distributed extending to the I/O level is a key to handling very large datasets which do not fit in the memory of a single node. Given the difficulty of obtaining portable I/O performance, some libraries like adaptable I/O system (ADIOS)  [67]  seek to provide an adaptable approach, allowing hybrid approaches between flat or separate files, with groups of file for process subsets, based on easily tunable XML metadata.