dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUDING REMARKS In this article, we presented the Yvis platform that was developed to facilitate the analysis of large numbers of antibody structures and sequences. The Yvis platform includes a very convenient, innovative and robust high-density visualization (Collier de Diamants) of antibody sequence alignment that allows the analysis of hundreds of thousands of sequences in a single representation. Moreover, the Yvis integrated database is updated weekly and stores data on antibody PDB structures obtained from SAbDab and PDB files (e. g. PDB and chain identifications, antibody/antigen producing-organism names, molecule description), processed data from IMGT/DomainGapAlign (e. g. gapped sequences, V and J germline allele assignment and the corresponding identity values), and antibody-antigen putative contacts obtained by processing the structure coordinates.
The producing-organism names are stored following Uniprot Taxonomy that facilitates database searches based on these names. In addition, there are various database search and filter options. The Yvis platform can also process user files that contain sequences obtained by different methods (e. g. FASTA, IMGT/DomainGapAlign and IMGT/HighV-QUEST files). The Yvis platform can be used in different types of antibody analysis. For example, the quick visualization of the most conserved or divergent positions in a set of related antibodies can guide antibody engineering and mutagenesis experiments. In antibody repertoire studies, the Collier de Diamants visualization, coupled with the sequence comparison feature, can be used to compare thousands of antibody sequences with a specific germline sequence.
16 µM) performed in duplicates. Image acquisition and analysis. Images of macrophage cultures were acquired using the automatic Opera QEHS confocal imaging system (Perkin Elmer Technology). For the L. donovani HCA, a 40x water objective and two lasers (488 nm for immuno-stained parasites; 405 nm for Hoechst-stained parasite and macrophage nuclei, and HCS-stained macrophage cytoplasm) were used for acquiring 285 fields for the entire well or 30 fields per well in routine HCA conditions. For L. amazonensis HCA, a 10x air objective and three lasers (561 nm for mCherry-expressing parasites; 488 nm for LysoTracker-containing PVs; 405 nm for Hoechst-stained macrophage nuclei) were used to acquire images from entire wells (15 fields). Images were transferred to the Columbus Conductor ™ Database (Perkin Elmer Technologies) and analyzed using the integrated Image analysis building blocks.
For L. donovani HCA, we developed a new script based on object segmentation subroutines detecting successively the macrophage nucleus, the macrophage cytoplasm, the parasites and their nuclei, with their associated features (number, size and object intensity). All methods used for segmentation are described in the Columbus Manuel. Analysis was simplified by only taking into account the number of macrophages and the number of amastigotes per macrophage, which was the common way to estimate the anti-leishmanial activity in screening campaigns. For the sake of clarity, both toxicity against host cells and activity against the parasites of tested compounds were normalized to DMSO-treated controls and expressed as percent of DMSO controls. The viability index is calculated by establishing a ratio of macrophage numbers between compound-and DMSO-treated cells, while the anti-leishmanial activity is represented by the number of amastigotes per macrophage.
Compounds causing a 50% or higher reduction of macrophage density were considered toxic. Depending on their parasite viability values expressed as percentages of DMSO control (POC), non-toxic compounds were classified as inactive, weakly active or active for POC above 60%, between 60 and 50% or less than 50%, respectively. Finally, compounds that presented POC values lower than 10% but that also reduced the macrophage density by 40 to 50% were considered as active toxic. Analysis of our biological data showed low standard deviations for all non-toxic, replicate compounds, with standard deviation medians of 9,43% and 15,50% for macrophage and amastigote per macrophage numbers, respectively. For the L. amazonensis HCA, we applied the method we described previously  19. Quantitative values obtained for host cell and parasite numbers were exported to Excel and SigmaPlot for further analysis and graphical representations.
Acknowledgements The authors would like to thank  Dr. Julio Martin , head of  Parasite Chemotherapy at GSK Diseases of the Developing World (GLAXOSMITHKLINE INVESTIGACIÓN Y DESARROLLO, S. L. )  for providing the  Leish-Box compounds. The UtechS PBI is grateful for support from the  French Government (L' Agence nationale de la recherche (ANR) ) programmes:  Investissements d' Avenir programme ('Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases' ; grant  ANR-10-LABX-62-IBEID  and ' Laboratoire Revive' ; grant  ANR 10-LBX-73-REVIVE );  France BioImaging  ( FBI ; grant  ANR-10-INSB-04-01 ), the  Région Ile de France  ( Domaine d'intêret majeur Innovative technologies for life sciences ,  DIM 1HEALTH ) and the  GIS IBiSA (Infrastructures en biologie santé et agronomie ) and the  Institut Pasteur.
S. L. , G. F. S. and E. P. are funded by  Institut Pasteur  and  INSERM U1201  and were also supported by the  FP7 A-ParaDDisE program  funded under the  European Union's Seventh Framework Programme  (grant agreement  602080 ) and the  Agence Natioinale de Recherche (ANR)   Pathomethylome  project ( ANR-15-CE12-0020-02 ). SL is a PhD student from the  Université Paris Diderot ,  Sorbonne Paris Cité. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Authors like Cacko (2008) ,  Bouchard et al. (2008)  and  Fayolle (2009)   For most traditional joining process simulations, the main concern is to properly model metal flow inside the dies without considerations on damage. In these cases an uncoupled fracture criterion was sufficient to account for crack occurrence. But in the new generation of joining processes, such as High-Speed Nailing, Spin-Blind Riveting or even Flow-Drilling Screw, every materials layers have to be perforated before being joined. Then sheets failure modelling becomes mandatory to get realistic sheets reaction as well as final interface geometry and therefore it has to be assessed by an appropriate damage evolution law. Wiethop (2010)  discussed the importance of the fracture criterion in punch riveting simulations using Simufact software.
Constitutive law calibration To properly calibrate the models according to the characterization tests performed on our materials, finite element simulation of our tests have been built with FORGE® software. The virtual extensometers displayed in Figure  4  and Figure  6  are used to consider the same gauge area in both numerical and experimental configurations. Law parameters are then optimized to obtain a mechanical response in simulation as close as possible to the one from experiment. This optimization is based on a metamodel-assisted evolutionary algorithm developped in the works of  Roux (2011) , coupled with FORGE®, and called MOOPI. This evaluation is done through a cost function which quantifies discrepancy between simulation and experimental curves in the least square sense.
 Nail-shaft material As mentionned in section 3. 2, a compression set-up has been used to test nail-shaft mechanical behavior. Former described procedure were also used to simulate shaft compression phaseusing MOOPI softwareuntil a satisfying set of parameters is obtained. Friction coefficient has been set to ensure that -barrel effect‖ is captured by simulation independantly from material parameters identification. A Coulomb-limited Tresca model was.
A wide range of stress-states is covered by the tested specimen geometries, from for SH's to for NT2's. Hence it becomes possible to assign a mean stress-state to fractureobtained from simulation -to the measurement of effective plastic strain at the specimen surface -obtained from experimentand therefore calibrate the fracture loci on these data-points. Table  5  presents what are the data-points for every specimen and material. Since Matlab® optimization algorithm requires as many data-points as parameters to identify, it has been necessary for some criteria to add -optimization bounds‖ to the actual set of data-points given by experiments. It consists in additional fracture points, acting like -virtual experiments‖, considered in High Stress Triaxiality (HST) conditions, i. e. when the stress-state is.
Ideally, it is even better if the DDOS attacks can be filtered out and the IVC-U availability is restored so that the vehicle can access desired off-board services. Skilled network administrators protect their networks and Internet devices against DDOS attacks by monitoring the incoming data traffic and comparing some traffic patterns with prior known attack signatures (  [9] ) or simulated attack signatures (as explained in  [10] ). But the attacks evolve at every day (zero-day attack) and attack signature databases should be continuously updated. It is not an acceptable solution for an IVC-U. The only viable solution is an automated mechanism with unsupervised learning. Some detectors of suspect incoming traffic have recently emerged using unsupervised learning (  [3] ,  [4] ,  [6] ,  [8] ,  [9] ).
5s and with an Intel core i5 @2GHz. The CPU of an IVC-U is much less powerful than an Intel core i5 and the IVC-U can't assign so many CPU resources for monitoring DDOS attacks. That is the reason why the algorithm can't be used as is for an IVC-U. In addition, the internet traffic in network equipment has statistical properties and data patterns which are very different than those of an IVC-U. The number of open IVC-U connections at a given time is small but can change quite significantly in a short period of time whereas the relative variability of the number of connections in network equipment does not change so fast. As well, the incoming data traffic in an IVC-U can be very bursty with periods of no data. A consequence of that is the feature normalization: we can't know à-priori the min-max value of a feature because its range can change quite significantly between 2 acquisitions, depending on the nature of open connections.
In our implementation, the grid of each subspace is a 10x10 grid. The grid resolution is a trade-off between 2 conflicting objectives: -If the grid resolution is too coarse, some outliers (which would be due to data traffic anomalies) could vanish in cells of legitimate data traffic. -If the grid resolution is too high, the density threshold (as specified later in this document) is more complex to set. As well, the data points of legitimate traffic may be scattered in too many cells. This might result in frequent clustering steps and thus it might impact the CPU load. As a rule of thumb, we set the rule that the data points of the legitimate incoming data traffic on an IVC-U are scattered in less than 10 cells per subspace. We found that a 10x10 grid fulfills this requirement for typical IVC-U incoming data traffic.
(E6) A cell k is dense if d (k) is higher than a density threshold called Thdens. In our implementation, the density threshold is set at 5. Like the grid resolution, the density threshold is a trade-off between 2 conflicting objectives: -If the threshold is too high, many cells with data points of legitimate traffic are not dense. As a consequence, the range of the abnormality score between a data point of legitimate traffic and a data point of corrupted traffic is reduced. -If the threshold is too low, most of the cells with data points are dense and a new data point of corrupted data traffic might be projected in a dense cell for some sub-spaces. It decreases the probability of detecting a DDOS attack. -As a rule of thumb, we set the rule that 90% the data points of the legitimate incoming data traffic on an IVC-U are projected in dense cells.
(E10) The scaling is needed for two reasons. It allows to fairly balance the scores per subspace when assessing the global abnormality score and to compensate the fact that the features are not adequately scaled when the vector Yn is built. It also better measures the abnormality level in a given subspace: if all the data traffic during the observation window is inside clusters for this subspace, and if the new projected vector is out the clusters for this subspace, the input vector is very abnormal in this subspace (even if the distance to the closest cluster is not so large). In the other way, if many points of the observation window are out of clusters for this subspace, and if the new projected vector is out the clusters for this subspace, the input vector is not so abnormal. When the input vector is inserted in the observation window, the average anomaly level has to be recomputed for the subspaces where the new vector and the old vector do not project in the same cell.
Setup and Used data set A MATLAB model is developed to validate the proposed algorithm. The data set consists of a Mawilab network trace as proposed in  [4] , captured on the 30 th January 2015. We searched an IP destination which suffers from SYN flood attacks. The trace is shrunk to the traffic towards this IP address. It is our initial data set. It is then split into 2 data sets: a 1 st data set without any SYN flood attack and a 2 nd data set with SYN flood attack.
Simulation results Figure  3  shows what the subspaces look like when there is no DDOS attack. Each subspace is a 10x10 grid. The indexes on the x and y axis show the centers of the cells. The color code is the following: -Yellow indicates a dense cell, -Blue indicates a non-dense cell with at least one projected vector: the blue color becomes darker with a higher number of data points, -Very dark blue indicates a cell without any projected data point. Note that the simulations are done with = Time To Live (TTL) instead of number of the IP subnets. Partitioning of 10 subspaces when there is no DDOS in the incoming data traffic These simulations results are based on a real data set and validate the assumption that the legitimate incoming traffic converges towards dense clusters. The simulations also show that clusters of a few subspaces (the subspaces with the or features) breathe: a few cells alternate between dense and non-dense status so that the shape of the cluster looks breathing.
The simulation also identifies the subspace (y1, y2) (TCP SYN ratio vs ICMP ratio) as the most discriminant subspace for this data set. It also finds the source addresses of DDOS flows. We have checked in the Wireshark logs that these flows are rogue flows (they all are unfinished TCP connection requests). This algorithm has been implemented and fine-tuned on a prototype. Most of the algorithm functions are encoded from the MATLAB with MATLAB encoder.
Setup description The experimental set-up (figure  6 ) consists of 2 Linux PCs. The CPU of these PCs is an Intel core i5 chipset at 2. 3GHz. The 1 st PC simulates the IVC-U and the In-Vehicle Infotainment (IVI) system. The 2 nd PC simulates the Internet services or the server where car data are uploaded and the DDOS attack is hosted. are inserted in a local network. The 1 st PC hosts a VLC server and the algorithm implementation. Its IP address is 192. 168. 0. 1. The 2 nd PC reads the video which is streamed by the 1 st PC. The IP address of the VLC client is 192. 168. 0. 2. The DDOS attack is a TCP SYN flood attack: it simply sends a TCP SYN (which is never acknowledged) on IP address 192. 168. 0. 3. The TCP port index is incremented after each sent TCP SYN packet so that the TCP ports are scanned until the port index wraps around.
Measured abnormality score The parameters of the algorithm must be tuned so that the abnormality score is as low as possible when there is no DDOS attack. The key parameter is the observation window size. If it is too small, it can't grab a wide variety of incoming data traffic. A new type of legitimate data traffic could result in a peak of abnormality score and it could result in a false positive. Figure  7  illustrates this. The brown curve plots the abnormality score. A SSH application is randomly triggered in parallel with the VLC server. The SSH features are different from the features of the VLC traffic. When the observation window size = 200, the SSH application causes some peaks of abnormality score. When it is 300, there is a higher probability that features vectors of the previous SSH are present in the observation window.
Measured CPU load of the proposed algorithm and performance discussions The CPU load has been measured with the Linux Top command. The 1 st measurement has been done when there is no DDOS attack: it measures the CPU load penalty of the algorithm to monitor the incoming traffic and search a DDOS pattern. The CPU is an Intel core i5 chipset at 2. 3GHz. Thus, the penalty of the algorithm in terms of incremental CPU load when there is no DDOS attack (which is the case in 99% of the IVC-U lifetime) is very low. The incremental CPU load is less than 1% for this scenario. Such a result is consistent with the fact that the clustering step itself occurs very rarely and only for very few subspaces. The CPU load of the algorithm is also much less than the CPU load of the applications (in about a 20x ratio for this test).
They do not really care about the best trade-off cost vs performances. So far, we have not found any paper which is focused on finding this optimal trade-off and which published some data about the incremental CPU load which is needed to detect DDOS attacks and filter these attacks. Regarding the performances themselves, the automotive car manufacturers do not transmit safetycritical or privacy-sensitive data over the public Internet network. These data are carried over the private car network. Therefore, the key performance which matters here is the false alarm rate. If a DDOS attack is not detected, it is only impacting the infotainment applications. In this context, we have not detected any false alarms after several hours of testing where the incoming data traffic is a mix of multimedia flow (which is the typical data traffic over an IVC-U).
All the TCP SYN Flood attacks we triggered have been detected. However, in the near future, with the deployment of 5G Edge Computing, more and more services for connected and cooperative mobility will be proposed to enhance vehicle traffic efficiency and driver safety. These services will be even mandatory for the high level of autonomous driving (AD level 4 or more). It means that multiple cellular connections which carry safety-critical data will be open and these connections can suffer of the DDOS attack. Thus, the True Positive Rate KPI becomes very crucial. But an IVC-U is not network equipment. The True Positive Rate (TPR) and False Positive Rate (FPR) performances shall be assessed while considering the specific features of the received data traffic of these future services.
The TPR and FPR performances would likely improve if these thresholds are adjusted depending on the type of applications involved in the received data traffic. But it has been demonstrated. The last next step should consist in building a data test set which reflects the features of the received data traffic over the public Internet network and from providers of new cooperative and connected mobility services. Once done, we should be able to assess the True Positive Rate and False Positive Rate performances with a relevant data test bed.
g. , OnyxTree, Xfrog, and many others summarized at http://vterrain. org/Plants/plantsw. html). For instance, Widlowski et al. [34]  used different virtual forests generated with Xforg and Arbaro software for the fourth phase of the radiative transfer model intercomparison (RAMI-IV) exercise. Nevertheless, most software tools create trees suitable only for exterior visualization and simulation of virtual landscapes (e. g. , for computer games). They are less suitable for ecosystem studies requiring "biologically" correct 3D plant representations that account also for irregularities caused by natural and anthropogenic factors (e. g. , spruce needle defoliation and regeneration;  [35] ). Another group of methods for constructing 3D tree representations uses the rapidly developing technology of terrestrial laser scanning (TLS, also known as terrestrial LiDAR), which captures well the explicit 3D vegetation architecture  [36] [37] [38].
Study Sites and Input Data Computer reconstruction of trees, parameterization of DART, and the final assessment of the simulated canopy reflectance using airborne and satellite RS images required multiple data sources. All required data for this study were collected at two Norway spruce (Picea abies, L. Karst) forest sites located in the Czech Republic. The primary study site is located at the experimental research station Bílý Kříž in the Moravian-Silesian Beskydy Mountains (49°30 N, 18°32 E, 850-900 m a. s. l. ). The site facilitates long-term research of tree ecophysiology and carbon fluxes. It is part of the national Czech Carbon Observation System (CzeCOS) and the international Integrated Carbon Observation System (ICOS) research networks e. g. ,  [51, 52]. The targeted stand is a spruce plantation growing on a southerly oriented slope (mean slope of 12.
Forest Structure Data Three-dimensional reconstruction of virtual spruce trees required two types of forest structural data: (i) TLS data for reconstruction of woody skeleton and spatial distribution of foliage (clumping) and (ii) data on geometrical and angular distribution of spruce needle shoots. The TLS data were acquired using an OPTECH Ilris-36D (Teledyne Optech, Vaughan, ON, Canada) scanner at the Černá hora site in October 2009. The Ilris-36D TLS emits laser beams at the wavelength of 1. 5 µm with a maximum field-of-view window of 40°× 40°. It records the amount and intensity of returned laser pulses at up to 2500 points per second. The laser scanning data were acquired for 15 mature spruce trees that had been left standing after clearing trees damaged by multiple bark beetle outbreaks.
Each tree was scanned from two opposed positions, recording the first and the last echo return with the point sampling distance of 2 cm. The point clouds of the first and last return from the two scanning positions were aligned and co-registered in a single geometric coordinate system using the PolyWorks IMAlign module (InnovMetric Software Inc. , Quebec City, QC, Canada). The PolyWorks IMSurvey module was used to filter out individual tree point clouds and to separate wooden structures from foliage points based on the intensity of the reflected photon signals. Considering that green foliage reflects about 10% and woody elements up to 50% of the laser signal emitted at 1. 5 µm, we determined manually the intensity thresholds between foliage and wooden elements for each scan (first and last returns from two opposite scanning positions) separately.
Remote Sensing Data Two types of RS data-airborne hyperspectral images and Sentinel-2 satellite data-were used for verification of DART-simulated canopy reflectance. Both RS images were acquired over the Bílý Kříž study site on 31 August 2016, close to the solar noon. Airborne hyperspectral images were acquired with a CASI sensor (Itres Research, Calgary, AB, Canada) in the range of 372-1044 nm (72 bands with spectral sampling distance of 10 nm and pixel size of 1. 0 m) and with a SASI sensor (Itres, Research, Calgary, AB, Canada) in the range of 957-2442 nm (100 bands with spectral sampling distance of 15 nm and pixel size of 2. 5 m). Corrections of the hyperspectral images were carried out in a data processing chain established at the CzechGlobe institute  [59]. Radiometric corrections were performed using the factory calibration coefficients in the post-processing software developed by the sensors' producer.