dataesr/scientific-paragraphs-categorization huggingface dataset:
2% OTU abundance threshold with minimum barcode length per primer set. Filtering of chimeric reads is performed using the default settings of the UPARSE-REF algorithm implemented in the cluster otus command of USEARCH. To assign OTUs to taxonomy, standalone BLASTn megablast searches (BLASTN, RRID:SCR 001598)  [23]  of representative OTUs are performed on the National Centre for Biotechnology Information (NCBI) GenBank nucleotide database using an E-value threshold of 0. 001 and a maximum of 20 aligned sequences. OTUs are assigned to the database sequence to which they align, based on bit score, having at least 98% sequence identity and a minimum of 90% query coverage. To identify putative CITES-listed taxa, the taxon ID first was matched against the NCBI taxonomy database using Entrez Direct (edirect) functions (available at  [38] ) to retrieve the scientific name (species, genus, family, order, and synonym name).
The scientific, synonym, and/or family names are then matched against a local CITES database that is retrieved from Species+  [39]. The final results are presented as a tab-separated values file containing the BLAST hit metadata (i. e. , bit-score, e-value, accession numbers, etc. ), the scientific name, synonym name, and in case a CITESlisted taxon was found, also the CITES appendix listing and taxonomic group (i. e. , species, genus, family, or order name) under which the taxon is listed by CITES. The BLAST output was interpreted by following guidelines: first, to minimize the chance of erroneous species identifications, the same species should have at least 3 top hits, i. e. , highest bit scores. Second, if multiple hits were obtained with identical quality results, but with different assigned species or with less than 3 top hits with same species designation, the OTU fragment was considered to lack the discriminatory power to refer the hit to species level.
Pre-validation in-house of the CITESspeciesDetect pipeline A parameter scan was performed in order to assess the effect of software settings on the ability to identify species. This evaluation allowed for identification of important parameters and their effects on the sensitivity, specificity, and robustness of the procedure. This in turn resulted in specified, recommended (default) parameters values for analysing DNA metabarcoding datasets using the CITESspeciesDetect pipeline. The effects of the following parameters were assessed: base quality scores, error tolerance for primer selection, OTU radius, and OTU abundance threshold, expect E-value and query coverage threshold, percentage identity threshold, minimum DNA barcode length, and BLAST database. The parameters scan was performed on experimental mixture 11 of the pilot study (Table  2 ).
2 M NaCl solution, 12 universal plant and animal barcode and mini-barcode primer sets (Table  1 ), Qiagen HotStarTaq master mix, and Qiagen PCR purification kits. All reagents and samples were provided in quantities corresponding to ×2. 5 the amounts required for the planned experiments. After following the SOP from DNA isolation to purification of the amplified products, all the purified samples from all the laboratories (n = 160) were collected and sequenced using Illumina MiSeq paired-end 300 technology (at BaseClear, Leiden, NL, USA). The Index PCR and Illumina library preparation were performed according to the guideline, and all 160 samples were sequenced on 2 Illumina flow cells. After the Illumina MiSeq run, the raw NGS data were processed using the default settings of the CITESspeciesDetect pipeline.
Availability of supporting data All the sequence data obtained from the pilot study and the international interlaboratory validation trial, the CITESspeciesDetect pipeline, and access to the web-interface are freely available. The generated barcode sequence information for some animal and plant species was deposited in GenBank under the accession numbers LT009695-LT009705 and LT718651 (Additional file 1; Table  S1 ). The Illumina PE300 MiSeq data obtained from the pilot study and the international interlaboratory validation trial (n = 177) were deposited to ENA with study ID PRJEB18620. The script for the CITESspeciesDetect pipeline is available at GitHub. The web-interface for the CITESspeciesDetect pipeline  [17]. The access to analysis via the web-interface will be provided on request. SOP protocols are available from protocols.
11 98. 21 87. 01 EM12 355 634 92. 55 97. 54 76. 54 EM13 405 742 89. 46 96. 49 77. 31 EM14 480 772 85. 74 95. 98 81. 91 EM15 554 602 87. 05 88. 78 82. 98 Average b 464 648 88. 27 96. 44 82. 26 a( Pseudo-)reads are the combined QC pseudo-reads and the QC processed unmerged forward and reverse reads. b Averaged across the 17 Illumina MiSeq datasets.
A Qualia representation of the issue head concepts This section shows how the concepts informally presented in Sections 4. 1 to 4. 3 can be represented in Qualias and what extensions are required. The goal is to show how concepts can be specified in a concrete manner. This section introduces (1) the extensions to the GL that are required for argument mining and (2) the elements of a method to describe the type of data that is needed for knowledge driven argument mining. The Qualias are a relatively open representations where roles may be more or less developed and constrained: a method to describe Qualia roles is crucial to develop homogeneous and coherent sets of Qualias. Some role sub-typing have been introduced in the past in the EEC Parole-Simple project (http://catalog. elra. info/product_info.
This Qualia is written manually and the concepts it contains are derived from the arguments found in texts. Additional purposes or functions can be found using e. g. bootstrapping methods on the web via patterns such as 'a vaccine allows to'. If quite a lot of data can be acquired automatically, we feel that some parts of the overall organization needs to be done manually, as in most knowledge representation systems, to guarantee coherent and well-structured representations. Next, the Qualia structure of Ebola (and more generally, of a virus) is: Ebola: P represents the patient that gets the disease. The purpose of Ebola is to infect people (P) who get sick and may die. There is no agentive role since there is no volition in the Ebola virus. Ebola is a constant in this representation. To characterize the sequence of events, a sequence of causal chains from 'infect' to 'may die' is introduced in the telic role.
Stoichiometry of extrasynaptic GlyR and gephyrin complexes. Recent cryo-EM and crystallographic data have shown that GlyRα1 and α3 assemble into homopentameric complexes  19, 20. There remains controversy regarding the stoichiometry of heteromeric GlyRs, where both α 3 :β 2 and α 2 :β 3 models have been proposed (discussed in  21  ). Given the implication of the β subunit in gephyrin binding and synaptic clustering, we probed for possible differences of the assembly of α1 and α3 containing heteromeric GlyRs that could explain their different diffusion properties. To this aim we applied PALM based single molecule counting as first described by Durisic and colleagues  22. In this approach the number of molecules within a complex is determined by counting spatially and temporally separated bursts of detections arising from single fluorophores.
A difficulty lies in the fact that only a fraction of the fluorophores is functional  21. We therefore initially determined the probability of detection p det of Dendra2-tagged GlyRs with a known homopentameric subunit stoichiometry. Dendra2-GlyRα1 or α3 were first expressed in COS-7 cells that do not contain endogenous GlyRs or gephyrin. PALM imaging was done in fixed cells with low levels of recombinant receptors, using fiducial markers to correct for x/y drift during acquisition (Fig. 2A ). Clusters of detections within a diameter of 120 nm were selected with a homemade plugin in Matlab (CountMol), applying a density threshold to remove the background of single detections. Bursts of detections were considered to stem from the same fluorophore when they occurred in a time window of less than 250 frames ( < 12.
Fluorescence image acquisition and analysis. Images were acquired with NIS software on an inverted Nikon Eclipse Ti microscope with a 100x oil-immersion objective (NA 1. 49), an additional 1. 5x lens, a mercury lamp and an Andor iXon EMCCD camera (16 bit, image pixel size 107 nm), using specific emission filters (525 nm with 30 nm bandwidth for non-converted Dendra2 and mEos2; 607/36 for mRFP and Cy3; 684/24 for Alexa Fluor 647). Cluster analysis was done in selected regions of interest by creating binary masks of gephyrin clusters using image segmentation  45  in Metamorph (Molecular Devices). Background corrected, integrated GlyR and gephyrin fluorescence intensities were measured in the clusters of the mask. Intensity correlation analysis (ICA) was used to quantify the correlation between Dendra2-GlyR and gephyrin labelling at gephyrin clusters  17.
High intensity correlation quotient values (0 < ICQ ≤ 0. 5) indicate positive correlation between the fluorescence in the two channels. Single-particle tracking. The diffusion of Dendra2-tagged GlyRs in COS-7 cells or spinal cord neurons was measured by single-particle tracking (SPT) using quantum dots (QDs) as described  5. Cells were labelled with Dendra2 antibody (Antibodies-online ABIN361314), biotinylated anti-rabbit F(ab') 2 and streptavidin coated QDs emitting at 655 nm, and imaged for up to 30 min. QD trajectories (500 frames at 13 Hz) were analysed using homemade software in Matlab (Mathworks). Diffusion coefficients D were calculated by fitting the first five points of the mean square displacement (MSD) plot against time.
PALM-based molecule counting. To automatically select and analyse the intensity-time traces of photoconverted fluorophores in the pointillist image, we developed a software written in MATLAB (CountMol). First, drift corrected pointillist images were rendered by representing each detection with a Gaussian distribution (σ = 10 nm). A density-based threshold was applied to remove single detection noise. Clusters of detections of up to 100 nm in diameter with at least 9 detections were automatically selected, a circular mask of 120 nm (Dendra2) or 150 nm (mEos2) in diameter was superimposed to these pre-selected clusters, and the detections within the mask were automatically counted. In pulsed photoconversion experiments we determined a maximal bleaching time τ bleach of 12. 5 s (250 frames) for Dendra2 fluorophores and of 60 s (1200 frames) for mEos2.
Acknowledgements We would like to thank  Sabrina Colasse ,  Xavier Marques  and  Matteo Fossati  for technical assistance,  Joseph Specht  for his help with binomial fitting,  Volker Eulenberg  for cDNA clones, and  Nadine Schibille  for comments on the manuscript. This research has been funded by the  European Research Council  ( ERC, Plastinhib) ,  Agence Nationale de la Recherche  ( ANR, Synaptune ),  Labex (Memolife) and France-BioImaging (FBI). AP was supported by a  Marie-Curie ITN  network grant (NPlast) and Memolife.
Among 11 configurations, an efficient NUMA policy reduces the completion time of at least one virtual machine by more than 2 in 9 cases. • With a single virtual machine spanning 48 cores, only 4 applications remain degraded by more than 50% when we compare Xen with its best NUMA policy and Linux with its best NUMA policy. This result shows that most of the large overhead observed with Xen on large multicore was actually caused by an inefficient NUMA policy. • Because of a design choice at the hardware level, it is impossible to use an IOMMU  [6]  with the first-touch policy in the hypervisor without deeply modifying the I/O stack of the hypervisor. The remainder of the paper is organized as follows. Section 2 gives some background on Xen. Section 3 describes the NUMA policies studied in the paper.
Para-virtualization technique Xen implements two complementary techniques to intercept an access to a virtual device of a domU virtual machine. The first technique is the full-virtualization technique: the guest operating system executes a legacy driver, and Xen intercepts the low level I/O operations performed by the driver. The second technique is the para-virtualization technique: the guest operating system executes a modified driver, which calls the hypervisor to perform the I/O. In our experiments, we use the para-virtualized drivers implemented in Linux because it yields better performance.
the 48-core machine we used for the experiments, we have measured that reading a 4 KiB block (with the O DIRECT flag to avoid caching) takes 307 µs, while it only takes 74 µs in Linux. Instead of letting the hypervisor perform the address translation, modern processors provide an IOMMU component  [6]. This component is a memory management unit that can be directly used by devices to translate a physical address into a machine address. With an IOMMU component, a device accesses the physical memory without involving the hypervisor, thus significantly reducing the large overhead mentioned above. Xen is able to use an IOMMU with the PCI passthrough driver. However, the PCI passthrough driver is relatively restrictive. While the AMD IOMMU can associate devices to virtual machines at the device granularity, the PCI passthrough driver associates devices to virtual machines at the PCI express bus granularity.
Fortunately, the machine used in our experiments has two PCI express buses. For this reason, we can reserve a PCI express bus for a domU virtual machine. Dom0 can then use the other PCI express bus. With this setting, we have measured that reading a 4 KiB block takes 186 µs (still 74 µs in Linux). Note that the larger the amount of bytes read, the lower the overhead caused by virtualization. This is explained by the fact that when the number of bytes to read increases, the time it takes to start a DMA transfer becomes negligible compared to the time it takes to perform the transfer itself.
As highlighted by several research studies  [12, 17, 26] , a NUMA placement policy should both (i) balance the load on all the memory controllers in order to avoid overloaded memory controllers, and (ii) enforce memory access locality in order to avoid the saturation of the interconnect. We propose to study four NUMA policies. Three policies (round-1G, first-touch and round-4K) are static policies: they initially map a virtual address to a NUMA node, and they do not dynamically change the initial mapping. The last policy (Carrefour) is a dynamic policy: Carrefour can dynamically remap a virtual address to a new NUMA node in order to improve the average latency of memory accesses. The round-1G policy is the only (and thus default) NUMA policy implemented in Xen. The first-touch, round-4K and Carrefour policies are NUMA policies that have been implemented for the Linux operating system.
The Carrefour policy Carrefour  [12]  is a dynamic memory placement policy for NUMA architectures executing the Linux operating system. Carrefour dynamically migrates pages in order to increase the memory access locality, while avoiding contention on memory controllers and interconnect links. Carrefour monitors the memory access patterns of the threads using hardware counters. It implements three heuristics to migrate or replicate the hottest physical pages, i. e. , the most accessed physical pages. The first heuristic implemented by Carrefour is the interleaved heuristic. Carrefour executes this heuristic when it detects that memory controllers are overloaded. It randomly migrates hot pages from overloaded nodes to underloaded nodes. Carrefour also implements two heuristics that it triggers when it detects that the interconnect saturates.
Evaluation of the NUMA policies in Linux In order to verify that all the NUMA policies are useful, Figure  2  reports the improvement in terms of completion time relative to the default first-touch policy when using the different NUMA policies in Linux. The Figure presents the evaluation of 29 applications on AMD48, a 48-core machine with 8 NUMA nodes, running a native Linux operating system (see Section 5 for a detailed description of the hardware and software setting). We exhaustively evaluate all the possible combinations of static and dynamic policies implemented in Linux: first-touch, first-touch/Carrefour, round-4K and round-4K/Carrefour. We can first observe that the NUMA policy has a huge impact on performance for many applications. 17 of the 29 applications are improved by more than 25% when we compare the best against the worst NUMA policy (12 applications by more than 50% and 5 by more than 100%).
We can also observe that each possible combination is for some applications the one yielding the best possible performance (e. g. , first-touch for cg. C, first-touch/Carrefour for sp. C, round-4K for kmeans or round-4K/Carrefour for facesim). This result answers the above question: it is probably worth implementing within Xen the NUMA policies that are available within Linux. �� ���� �� ���� �� ���� �� ���� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �������� ������������������ �������� ������������������ Figure 2.
Analysis of the behaviors In order to understand why all the policies are useful, Table 1 reports two metrics measured with the first-touch and the round-4K policies. The table does not report the metrics when executing Carrefour because Carrefour already uses all the available performance counter registers. The imbalance is defined as the relative standard deviation around the average number of accesses per node. The interconnect load is defined as the average of the percentage of the bandwidth used on the most loaded interconnect links during each second. 3 e can classify the applications in three groups (column imbalance level of Table  1 ). The 11 "low" applications exhibit a low memory access imbalance of less than 85% with the first-touch policy in Linux. As presented in Section 3. 1, for these applications, the first-touch policy is perfect, because each thread tends to mostly access data structures that is has allocated.
C. At the opposite, the 13 "high" applications exhibit a high memory access imbalance of more than 130% with the firsttouch policy in Linux. As presented in Section 3. 2, a single thread tends to allocate the memory for the other threads. The round-4K policy prevents the large imbalance of the first-touch policy (see columns imbalance in Table  1 ). Carrefour tends to improve their performance because it improves their memory access locality (the interconnect load is large for 5 applications). As a result, we have measured that the round-4K/Carrefour policy is only 2% slower in average than the best NUMA policy for these applications, with a worst case of 8% for wrmem. Finally, the 5 remaining "moderate" applications exhibit a moderate memory access imbalance between 85% and 130% with the first-touch policy in Linux.
Implementing NUMA policies inside Xen As all the NUMA policies evaluated in the previous section are useful in Linux, we have implemented all these policies in Xen. In order to implement the NUMA policies inside Xen, we have identified three important needs. First, each of the policies needs a mechanism to map a virtual page of a process to a machine page of any NUMA node. Second, the Carrefour policy needs a mechanism to dynamically migrate a virtual page of a process to a new NUMA node. Third, the first-touch policy needs a mechanism to trap the first access of a process to a virtual page. These needs have driven the definition of the interface we propose in this paper. As presented in Figure  3 , the interface can be split into two parts: the external interface is used by a NUMA policy to communicate with the guest operating system, whereas the internal interface is used to communicate with the hypervisor.
In our case, the guest operating system has to be able to reallocate the free page to a new process at any time, which precludes using the ballooning driver. As the ballooning driver is inadequate, we have chosen to use a para-virtualization technique by introducing a new hypercall. The guest operating system triggers this hypercall when it adds a page to its free list, in order to inform Xen that the page is not used anymore. However, calling the hypervisor for each page release is inefficient for an application that releases physical pages often. This is for example the case of several Mosbench applications that we evaluate. These applications rely on the Streamflow allocator  [34]  because it better scales than the default glibc allocator when the number of cores increases. Unfortunately, the streamflow allocator continuously calls the Linux mmap/munmap functions to manage the memory.
Implementation of the NUMA policies With the interfaces provided to NUMA policies, implementing the first-touch, the round-4K and the Carrefour policies is straightforward. We have implemented the round-4K policy in Xen by statically allocating 4 KiB pages in a roundrobin fashion from the virtual machine's home nodes when the virtual machine is created. This implementation relies on the internal interface (see Section 4. 1). For the first-touch policy, we rely on the hypercall provided by the external interface (see Section 4. 2): when the guest operating system releases a page, the NUMA policy simply invalidates the entry in the guest page table. In order to implement the Carrefour policy, we have ported the code of Carrefour in Xen  4. The original Carrefour implementation defines two components: the system component and the user component.
The system component runs in the Linux kernel. It gathers the low-level hardware counters and associates metrics to the hot pages. It also defines a system interface to migrate a page from one node to another and to read the metrics. The user component uses these metrics to choose which page to migrate and to select the destination node. In Xen, the user component executes as a process in the dom0 virtual machine. The system component executes inside Xen. Instead of using the operating system interface to communicate with the system component, the user component performs an hypercall, which is trapped by Linux and forwarded to Xen. Then, instead of observing the memory accesses performed by the threads of the processes, the system component observes the memory accesses performed by the vCPUs of the virtual machines.
Performance evaluation This section presents an evaluation of the NUMA policies implemented inside Xen. We first present the hardware and then the software setting. We also present Xen+, an improved version of Xen used in the evaluation, along with its evaluation. Then, we compare the different NUMA policies in Xen+ with both single and multiple virtual machines. Finally, we present a comparison of Linux and Xen+ when we use the best possible NUMA policy for each application in both Linux and Xen+.
The bandwidth between nodes is asymmetric, with a maximum bandwidth of 6 GiB/s. Two of the nodes (nodes 0 and 6) are connected to a PCI bus. The network and the disk of the dom0 virtual machine are connected to the bus of node 0. The disk that contains all the benchmarks and the datasets is connected to the bus of node 6. Table  3  reports the time to access the caches and the memory on AMD48. In order to measure the memory access latency, we present two results. With 1 thread, we measure an uncontended case, in which a single thread accesses a single NUMA node. With 48 threads, we measure a contended case, in which 48 threads access the same NUMA node. This result shows that the diameter has only a small impact on performance (276 cycles for a 1-hop access versus 383 cycles for a 2-hop access in the uncontended case).