dataesr/scientific-paragraphs-categorization huggingface dataset:
The final data set contained 1787 high-quality single cells from 1982 sequenced and 5156 genes out of 5245 genes with annotated transcripts. Transcriptomes were normalized with the weighted Trimmed Mean of M-values (TMM) method  ( 37 ). Cells were normalized either all together or in five groups containing biologically similar stages: groups included IDC, liver-stage, gametocytes, ookinetes/oocysts, and sporozoites. Visual inspection of the relative expression plot (fig. S2 ) showed little difference between normalization by biological group versus all together. Unless otherwise specified, further analysis was done on cells normalized by biological group.
10X filtering and normalisation For P. berghei , the output filtered matrix from Cell Ranger was read into Seurat (v 2. 3. 4) (  17 ). Low quality P. berghei cells with fewer than 230 detected genes were removed from further analysis. Initial inspection of filtered cells in the P. knowlesi and P. falciparum data sets showed that early-stage and late-stage IDC parasites were missing. These stages express fewer genes per cell relative to later stages based on our Smart-seq2 data and we have previously observed a lower detection of genes per cell in P. falciparum  ( 5 )  , suggesting these cells may have been removed by Cell Ranger's default thresholding. Using the raw output matrices for these species, we adjusted thresholds to retain cells with >100 genes per cell for P. falciparum and >150 genes per cell for P.
Motif Discovery Motif discovery was performed using DREME which searches for short (8 bp) motifs expressed as regular expressions (consensus sequences allowing for wildcards but not variable length gaps) in a given set of sequences  ( 45 ). The 1000 bp upstream of the start codon for each gene detected in the Smart-seq2 data set was used in the analysis. For each cluster the input data set was the upstream regions of each gene within that cluster, and the negative set was the upstream region of genes that were not in that cluster.
Illumina reads from these libraries were mapped to the P. berghei v3 genome sequence using HISAT2 v2. 0. 0 ( 34 ) , with --rna-strandness RF --max-intronlen 5000. The data were viewed using Artemis v18. 0. 0 ( 47 ). 5' UTRs were defined as the region between the start codon and where RNA-seq coverage dropped to zero in at least two of the three samples. Upstream intergenic regions were defined from the start of the 5' UTR to the next, upstream increase in coverage from one or more RNA-seq libraries. The upstream intergenic regions were BLASTed against each other (blastall 2. 2. 25, -p blastn -e 1e-20). The sequences involved in each hit were extracted, excluding those overlapping others with lower E-values. These sequences were then BLASTed against each other (blastall 2. 2. 25, -p blastn -e 0.
SCmap We used scmap (version 1. 1. 5) ( 18 )  to compare data sets. We built three sets of cell indices that could be queried with the scmapCell() function that would allow each individual cell in the query data set to be mapped to a reference index  ( 18 ). To compare the Smart-seq2 and 10X data we built an index of the blood-stage Smart-seq2 data (including gametocytes) and mapped the full P. berghei 10X data set (including gametocytes) onto it. Because the IDC consists of a continuous set of cell-stages and not discrete clusters, we modified the cell assignment method in scmap: cells were assigned based on the top nearest neighbor. If the top cell had a cosine similarity of greater than 0. 5, the query cell was assigned to that indexed cell along with its supporting metadata (cluster assignment, bulk prediction, pseudotime value).
Using this cosine similarity threshold, 94% of 10X P. berghei cells were assigned to a cell in the SS2 P. berghei reference data set. To align the IDC trajectories across the three 10X datasets, we first compiled a set of one-to-one orthologs between ten Plasmodium species ( P. berghei , P. knowlesi , P. falciparum , P. malariae , P. ovale , P. vivax , P. gallinaceum , P. yoelii , P. chabaudi , P. cynomolgi ) from OrthoMCL (  52 ) (file S1). Using these orthologs, we built an scmap reference index that contained all P. berghei 10X IDC cells (gametocytes removed). We mapped both the P. falciparum and P. knowlesi data to this ortholog reference index. In addition to identifying the top nearest neighbor cell, we were able to incorporate information from the top three nearest neighbors to assign each cell based on the principal component space.
To do this we took a mean of the first two principal components of the top three nearest neighbors. Given this coordinate assignment, we located the nearest cell on the PCA and assigned the query cell to this index cell. If all three of the nearest neighbors had a cosine similarity of 0. 3, then the query cell was given an assignment. With this lower cosine similarity threshold to account for cross species differences, we were able to assign over 96% of P. falciparum and 99% of P. knowlesi cells to a P. berghe i index cell. Finally, to map single cell samples from the field, we built a 1:1 ortholog index of the complete 10X P. berghei data set, including the gametocytes that were excluded for the IDC evaluations. We used this reference because (i) of its full representation of the IDC and mature gametocytes (ii) it originates from an in vivo system like the volunteer cells.
'Clock' pseudotime For the three 10X data sets , pseudotime around the IDC was calculated by fitting an ellipse to the data projected into the first two principal components using direct least squares (Fig. 3C , fig. S14 ). Angles around the centre of this ellipse were calculated for each cell and oriented to a starting cell which was defined using known markers. In order to align the three species in pseudotime, 10X data from P. knowlesi and P. falciparum were projected directly onto the P. berghei reference using scmap  ( 18 )  and cells were given the pseudotime of their P. berghei assigned cell. This 'clock' pseudotime was aligned to real-time progression through the IDC using two methods. First, bulk RNA-seq data (  21 ) , from synchronized P. berghei parasites across twelve equally spaced time points around the IDC was projected onto our single-cell reference and their position in the first two principal component space was estimated from the average of their three nearest neighbours.
23. H. J. Lee et al. , Integrated pathogen load and dual transcriptome analysis of systemic host-pathogen interactions in severe malaria. Sci. Transl. Med. 10 (2018), doi: 10. 1126/scitranslmed. aar3619. The Wellcome Sanger Institute is funded by the Wellcome Trust (grant 206194/Z/17/Z), which supports MKNL, MH, OB, MB, and JR. MKNL is supported by an MRC Career Development Award (G1100339). KNN received funding from Danish institute of Advanced Study (D-IAS) and University of Southern Denmark (SDU). JKH and HB were supported by the Wellcome Trust (107372), the UK Department for International Development (DFID), the Swedish International Development Cooperation Agency (Sida), the Swiss Agency for Development and Cooperation (SDC), the German Academic Exchange Service (DAAD) and the Kenyan government.
The fact that our denoising method produces an estimation of each point denoised position coherent with the raw data allows us to propose a denoised position for almost all the elements of the point cloud. As a consequence, the output point cloud has the same density as the input one (where most popular denoising methods are removing points from the dataset). For applications that require to have a perfect match between the raw data and the point position, our tool is also capable to produce a denoised point cloud by suppressing points that are far from the estimation of their denoised position. By applying this second type of filter, the denoised point cloud has a lower density but the remaining points has not been moved at all.
The GEOWN denoising workflows GEOWN developed two workflows that can adapt themselves to any point cloud: 1. For a general point cloud (from LiDAR or Photogrammetry), without LiDAR raw data: we apply a series of geometric filters that produce a denoised point cloud in the same coordinate system than the original one. The lack of knowledge about the points acquisition history does not allow our probabilistic tool to be used, but the combination of successive geometrical tools allows the computation of a denoised point cloud preserving sharp edges and sparse elements. 2. For LiDAR only and with raw data information, we first compute a CSMU model that feeds a series of geometric/probabilistic filters. This workflow is recommended for low precision LiDAR systems as the characteristics of each sensor are taken into account during the process.
To do this, we used a point cloud acquired on a car with a low cost UAV LiDAR system. We see that the car's structure has been recovered without artifacts. We remark that the interface between the different parts of the car are still smooth. In Figure (  8 ), we show the result of our denoising algorithm on a dataset produced by a Velodyne VLP16 coupled to an Applanix APX15. This kind of system exhibits a relatively high level of measurement noise that can be significantly reduced thanks to a GEOWN algorithm.
Comparison for simulated point clouds To compare the two denoising methods, we simulated several point clouds containing elements that can be challenging for a denoising method and thus they can enlighten performance differences (sharp edges, curves, multiple surface orientations. ). Figure (  9 ) shows the denoising results for SOR-OD and for GEOWN's method on one of these artificial datasets. It can be seen that the trueness of GEOWN denoising is better than the trueness of the point cloud created by SOR-OD, when the two methods are applied with their optimal parametrization. The edge of the bi-plane is smoothed by the SOR-OD and the resulting denoised point cloud exhibits a delimitation reflecting the fact that the noisy points has been deleted with respect to their orthogonal distance to the scanned surface.
RESULT FOR POINT CLOUD FROM PHOTOGRAMMETRY The behavior of a denoising algorithm is naturally improved in the case of high density point clouds, such as those produced by a photogrammetry process. Indeed, with a greater density, the local geometry of the point cloud can be estimated at lower scale, resulting in a better local geometric description of the point cloud. To illustrate this fact, we tested the GEOWN denoising on a dense data set, produced by Fast ORtho Mapping (FORMap), a photogrammetry tool developed by GEOWN. The Ground Sampling Distance (GSD) of the dataset was 1cm. In Figure (  14 ) top, a colorized point cloud from FORMap is shown. The car roof and hood are subject to typical errors of a photogrammetry process on sleek surfaces (lack of correlation between pixels) producing significant errors and noise.
ACKNOWLEDGEMENTS We would like to thanks the  Company Microdrones Canada Inc. for providing us the point clouds we analysed in this paper, and in particular  Stefanie Van-Wierts  for her support and relevant comments on this paper. The comparison between the low-cost SICK-LiDAR and the Riegl VUX-UAV was possible thnaks to Microdrones ability to operate the two systems on the same day and flight configuration, using the mdLiDAR 1000 and mdLiDAR 3000 UAV systems.
Here we use a recently developed version of the PARODY code including a particle-incell method  (Bouffard et al. , 2017)  to conduct a first exploratory study of rapidly-rotating pure chemical convection in the endmember case of infinite Schmidt number. Note that we consider Sc = +∞ by setting κ ξ = 0 and that our approach thus differs from that of  Zhang and Busse (1990) ,  Zhang (1991 Zhang ( , 1992) )  who achieved infinite Prandtl number by using an infinite viscosity (ν = +∞). As will be shown, the phenomena involved in rotating pure compositional convection are rich and numerous. In this exploratory study, we aim at giving a first descriptive and qualitative insight into this particular physics. We also assess whether the stably stratified layer at the top of the core can be formed by the accumulation of chemically buoyant parcels of fluids emanating from the inner core boundary.
, 1988; Huang et al. , 1992; Tackley and King, 2003; Tskhakaya et al. , 2007; Sironi and Spitkovsky, 2009  for a non-exhaustive list of examples). In this work, we use a recently developed version of the dynamo code PARODY-JA  (Aubert et al. , 2008)  which includes a PIC method to treat the compositional field. Details on the numerical method can be found in  Bouffard et al. (2017) , together with the results of two benchmarks that validate the implementation of the PIC method. We performed a total of 11 numerical simulations summarized in Table  1  to conduct a first exploratory study of non-magnetic pure chemical convection in a rapidly-rotating spherical shell in the end-member case of infinite Schmidt number (κ ξ = 0). For one simulation (case 9 * ), the homogeneous sink term σ ξ in Equation (  17 ) is replaced by a sink term near the top boundary equivalent to σ ξ i that equilibrates the light elements injected at the bottom.
DISCUSSION AND CONCLUSION Using a new version of the PARODY code including a particlein-cell method, we ran a first series of exploratory simulations of pure chemical convection at infinite Schmidt number in a rotating spherical shell. In these simulations, light elements are injected within a thin layer above the bottom boundary. This preliminary work remains descriptive and qualitative, but reveals that chemical convection in the Earth's outer core is a rich and vast topic that warrants several more focused and quantitative studies. We showed that, principally as a result of their lower diffusivity, chemical plumes have a complex dynamics that may be influenced by several distinct instabilities in the different regions of the shell. We observed secondary instabilities such as undulations, plumes precession, fragmentation into chemical "blobs" that comply qualitatively well with some previous predictions and laboratory experiments.
FUNDING This study is partly funded by the  French Agence Nationale de la Recherche  (grant  446  number  ANR-15-CE31-0018-01 , MaCoMaOc). The simulations were performed on the ADA machine of the  IDRIS center (Orsay, Paris) , with grant number  i2016047436  and on the Hydra cluster of the  Max Planck Society (Garching, Germany ).
In this paper, we study the capacitated single allocation Hub Location-Routing Problem (CSAHLRP) with independent collection and delivery processes. We focus on the design of less-than-truck load (LTL) transport networks for the transport of goods between many shippers and their clients using flow concentration hubs. We seek to integrate the hub location and vehicle routing strategies such that the location of the hubs, the allocation of supplier/client nodes to hubs, the design of routes between nodes allocated to the same hub, as well as the inter-hub freight transportations, can be determined efficiently. We propose a mixed integer linear programming model for the problem with the aim of minimizing the total fixed and variable costs. Computational experiments based on the Australian Post (AP) data set geographical network are conducted with the CPLEX solver.
Furthermore, they allowed pick-ups and deliveries to occur simultaneously and imposed a maximum tour duration but no capacity on vehicles. Their formulation led to a decomposition into two sub-problems, handled by an efficient Bender's decomposition algorithm. Computational experiments were carried out based on the Australian Post (AP) standard data set. The approach was compared to solving the MILP model with CPLEX, and succeeded in solving problems with up to 100 nodes. In the study of  Setak et al. (2013) , a comprehensive MILP model of the HLRP was proposed. The aim was to build a general hub network topology to minimize the total cost, establishing hub nodes and inter-hub links, and connecting hub nodes and non-hub nodes. Paths containing only one hub node or a direct link from origin to destination were allowed.
Their model was solved with the CPLEX solver. In their experiments, data from the Australian Post (AP) and Civil Aeronautics Board (CAB) were used for problem sizes of up to 20 nodes. Mohammadi et al. (2013)  focused on a stochastic green HLRP (SGHLRP) and tried to minimize the total cost along with the environmental effect. A multi-objective MILP formulation was proposed and a Multi-Objective Invasive Weed Optimization algorithm (MOIWO) was applied to obtain Pareto optimal solutions. Computational results on randomly generated instances of up to 100 nodes showed that the proposed algorithm outperformed other multi-objective algorithms in the literature, such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Pareto Archived Evolution Strategy (PAES) and the Strength Pareto Evolutionary Algorithm (SPEA).
Mokhtari and Abbasi (2014)  used a Variable Neighbourhood Particle Swarm Optimization Algorithm (VNPSO) to solve the many-to-many HLRP with uncapacitated hubs. The efficiency of the proposed algorithm was compared to a Bender's decomposition algorithm using numerous samples of up to 300 nodes created randomly. Numerical results indicated that it performed better for large problems. Bostel et al. (2015)  studied the HLRP applied to the special features of postal service systems in which collections and deliveries may occur simultaneously within the same routes. A MILP model was solved using CPLEX and a memetic algorithm based on a genetic algorithm and a local search was developed to solve the problem for problems instances from the AP data set of up to 100 nodes. In the following, we now describe articles focusing on problems with predetermined open hub numbers (p hubs problems).
We indeed assume that the size/weight of freight to be transported from suppliers and to clients is significantly smaller than the vehicle capacities to require vehicle routing collection or delivery processes, which is generally the case in retail distribution. We propose a new MILP model by integrating efficient flow variables and constraints of the VRP into the classic HLP formulations. Solutions are obtained by solving the model with the CPLEX solver, and developing a memetic algorithm. Instances adapted from the network of the AP data set have been used as well as logistics and cost parameters from professional data of freight transport. Instances up to 100 nodes have been solved. As can be seen from this description and Table  1 , our problem presents one or more specificities which differ from other works.
Data and parameters Since there are no published benchmark instances corresponding to our problem, we have generated the problem instance networks on the basis of the geographic network of the Australian Post (AP) standard data set  (Ernst and Krishnamoorthy (1999) ). According to our hypothesis, it is assumed that the processes of picking up the freight from the suppliers and delivering it to the clients are distinct, and that the demand flows between each supplier-client pair are predetermined. Hubs may be located at the same geographical position as suppliers or clients or not. The distances between two nodes have been extracted from the AP data set. The flows (in tons) for each supplierclient pair have been generated uniformly within the interval [0. 15, 1. 0] in order to be consistent with the capacity of the vehicles (15 tons, see Table  6 ) and the possibility of loading the freight of several suppliers (resp.
Introduction The Internet of Things (IoT) refers to the inter connection and exchange of data among devices/sensors. Currently, with the explosive growth of the IoT technologies, an increasing number of practical applications can be found in many fields including security, asset tracking, agriculture, smart metering, smart cities, and smart homes  [1]. IoT applications have specific requirements such as long range, low data rate, low energy consumption, and cost effectiveness. The widely used short-range radio technologies (e. g. , ZigBee, Bluetooth) are not adapted for scenarios that require long range transmission. Solutions based on cellular communications (e. g. , 2G, 3G, and 4G) can provide larger coverage, but they consume excessive device energy. Therefore, IoT applications' requirements have driven the emergence of a new wireless communication technology: low power wide area network (LPWAN).
LPWAN is increasingly gaining popularity in industrial and research communities because of its low power, long range, and low-cost communication characteristics. It provides longrange communication up to 10-40 km in rural zones and 1-5 km in urban zones  [2]. In addition, it is highly energy efficient (i. e. 10+ years of battery lifetime  [3] ) and inexpensive, with the cost of a radio chipset being less than 2e and an operating cost of 1e per device per year  [4]. These promising aspects of LPWAN have prompted recent experimental studies on the performance of LPWAN in outdoor and indoor environments  [5] [6] [7]. In summary, LPWAN is highly suitable for IoT applications that only need to transmit tiny amounts of data in long range, as shown in Fig. 1. As recently as early 2013, the term "LPWAN" did not even exist  [8].
Sigfox Sigfox is an LPWAN network operator that offers an endto-end IoT connectivity solution based on its patented technologies. Sigfox deploys its proprietary base stations equipped with cognitive software-defined radios and connect them to the back end servers using an IP-based network. The end devices connected to these base stations using binary phase-shift keying (BPSK) modulation in an ultra-narrow band (100 Hz) sub-GHZ ISM band carrier. Sigfox uses unlicensed ISM bands, for example, 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia. By employing the ultra-narrow band, Sigfox uses the frequency bandwidth efficiently and experiences very low noise levels, leading to very low power consumption, high receiver sensitivity, and low-cost antenna design at the expense of maximum throughput of only 100 bps.
Electric metering In the electric metering market, companies typically require frequent communication, low latency, and high data rate. Typically, they require neither low energy consumption nor long battery life time as electric meters have continuous power source. Moreover, companies need real-time grid monitoring to make immediate decisions, e. g. , load, outages, and interruptions. Thus, Sigfox is inappropriate for this application as it does not handle low latency. On the contrary, electric meters can be setup using class-C LoRa to ensure very low latency. However, NB-IoT is a better fit for this application because of the required high data rate and frequent communication. Moreover, electric meters are typically found in stationary locations in densely populated areas. Therefore, it is easy to ensure NB-IoT coverage by cellular operators (LTE).
Smart farming In agriculture, the long battery lifetime of sensor devices are required. Temperature, humidity, and alkalinity sensors could significantly reduce water consumption and improve yield. The devices update sensed data a few times per hour as the environment conditions have not radically changed. Thus, Sigfox and LoRa are ideal for this application. Moreover, many farms today do not have LTE cellular coverage; thus, NB-IoT is not the solution for agriculture in the near future.