dataesr/scientific-paragraphs-categorization huggingface dataset:
It will also output the result of an RAO's spacing test, which assesses whether the angles are uniformly distributed (statistically no preferential angle orientations), or if there is a significant angular bias. 3. The script also offers the possibility to compare different type of samples. You can compare the total area of the cracks in two sets of samples (e. g. , 10 images of mutants 1 compared with 10 images from mutants 2) (see Step D5, "Global_Output_Size = True"). This will run statistical tests on the compared samples to determine if the average area of cracks in images of one sample series is different from that of another sample series. The choice of statistical test depends on the normality and the variance of the data. First, a Shapiro's test for population normality is run on both sample series.
Conclusions In the present research, a variable contact pressure trend was determined by data mining an existing data base of verified FE simulation results, collected by the cloudbased finite element (FE) simulation platform Smart Forming, in order to satisfy the contact pressure characteristics of hot stamping processes. Following the data driven approach, a dedicated, novel testing scheme was generated and applied to determine the IHTC values between a hot AA7075 aluminium alloy specimen and cold cast-iron tools, which were subsequently applied to the calibration of the experimentally verified IHTC model to predict the IHTC evolution as a function of contact pressure. Therefore, the effect of contact pressure on the IHTC was obtained in a single IHTC test by using the dedicated novel testing scheme, which has significantly improved testing efficiency.
The authors thank the  European Commission  for their support on the  H2020 project 'Low Cost Materials Processing Technologies for Mass Production of Lightweight Vehicles (LoCoMaTech)' , Grant No:  H2020-NMBP-GV-2016(723517 ). This research was also supported by the  China Scholarship Council  (Grant CSC no. 201608060261): A non-profit institution enabling talented Chinese students to undertake a PhD programme overseas.
Motivation: Analysis toolkits for shotgun metagenomic data achieve strain-level characterization of complex microbial communities by capturing intra-species gene content variation. Yet, these tools are hampered by the extent of reference genomes that are far from covering all microbial variability, as many species are still not sequenced or have only few strains available. Binning co-abundant genes obtained from de novo assembly is a powerful reference-free technique to discover and reconstitute gene repertoire of microbial species. While current methods accurately identify species core parts, they miss many accessory genes or split them into small gene groups that remain unassociated to core clusters. Results: We introduce MSPminer, a computationally efficient software tool that reconstitutes Metagenomic Species Pan-genomes (MSPs) by binning co-abundant genes across metagenomic samples.
MSPminer relies on a new robust measure of proportionality coupled with an empirical classifier to group and distinguish not only species core genes but accessory genes also. Applied to a large scale metagenomic dataset, MSPminer successfully delineates in a few hours the gene repertoires of 1661 microbial species with similar specificity and higher sensitivity than existing tools. The taxonomic annotation of MSPs reveals microorganisms hitherto unknown and brings coherence in the nomenclature of the species of the human gut microbiota. The provided MSPs can be readily used for taxonomic profiling and biomarkers discovery in human gut metagenomic samples. In addition, MSPminer can be applied on gene count tables from other ecosystems to perform similar analyses.
Second, although species of public health interest (e. g. Escherichia coli, Salmonella enterica or Clostridium difficile) are represented by hundreds or even thousands of genomes in public databases, only few strains are available for the great majority of commensal species. Consequently, accessory genes associated with microbial phenotypic traits may be missing in gene repertoires constructed from reference genomes. De novo metagenomic assembly where overlapping reads are merged into longer sequences called contigs is a powerful referencefree technique for overcoming the limitations of reference-based methods. However, assembly remains a computationally challenging task and despite the many dedicated tools proposed, the process only recovers incomplete genomes scattered in multiple contigs  (Sczyrba et al.
In addition, it is not suitable for detecting genes shared between several species. To overcome these limitations, we developed MSPminer, the first tool that discovers, delineates and structures Metagenomic Species Pan-genomes (MSPs) from large-scale shotgun metagenomics datasets without referring to genomes from isolated strains. MSPminer presents several significant improvements over existing methods. First, it relies on a robust measure of proportionality for the detection of co-abundant but not necessarily co-occurring genes as expected for non-core genes. Second, genes grouped in a MSP are empirically classified as core, accessory or shared. We applied MSPminer to the largest publicly available gene abundance table which is composed of 9. 9 M genes quantified in 1267 human stool samples  (Li et al.
Simulated dataset For evaluation purposes, we generated abundance tables simulating the counts of genes from a single virtual species. The pan-genome of this species consisted in 1000 core genes detected in all strains and 6000 accessory genes present only in some of them. Gene lengths were randomly drawn between 100 bp and 5000 bp. The prevalence of accessory genes was randomly drawn between 2. 5% and 99. 5%. In a first simulation used to evaluate MSPminer ability to recover a species pan-genome, 200 samples containing each a single strain of the species were generated. The sequencing coverage of a strain in a sample was drawn from a uniform law (min ¼ 0. 6, max ¼ 20) and read length was set to 100 bp. In a given sample, the theoretical number of reads mapped on a gene was calculated according to the gene length, the strain coverage and the presence or not of the gene in the strain.
Comparison to the canopy clustering algorithm MSPminer was compared to the Canopy clustering algorithm  (Nielsen et al. , 2014)  which is the only gene binning tool publicly available. Both tools were applied to the metagenomic dataset described above using default parameters (Supplementary Methods). In total, MSPminer grouped 17. 8% more genes than Canopy (3 288 928 versus 2 704 552) although MSPminer had a more stringent gene selection criterion (6 971 229 versus 7 304 439 genes processed). Both tools had a very high precision (mean > 98%) but MSPminer brought a significant gain in sensitivity (median: 77% versus 62%) (Supplementary Table  S4 ). Remarkably, Canopy produced more objects with at least 150 genes than MSPminer (2010 CAGs versus 1661 MSPs) as it splits some species (e. g. E. coli) into multiple clusters.
Parameters impacting the quality of the MSPs The quality of the MSPs is impacted by the upstream steps required for generating the gene counts table, as well as by the biological and ecological characteristics of the dataset. At the sequencing level, the number of reads (sequencing depth) generated for each sample impacts the detection and coverage of subdominant species, while read length affects the quality of the assembly and the ability to assign a read to a gene without ambiguity. At the bioinformatics level, assembly, gene prediction, gene redundancy removal, mapping and counting require expertise to select the most appropriate strategies, tools and parameters. Indeed, assemblers returning chimeric contigs which combine sequences from highly related species, inaccurate predictors generating truncated or merged genes, redundancy removal with a common threshold for all genes (95% of nucleotide identity) lead to genes of variable quality in catalogues.
When quantifying genes, keeping only uniquely mapped reads underestimates the abundance of some genes whereas considering shared reads can generate false positives. As shown on simulated data and verified on a real metagenomic dataset, longer genes are more likely to be clustered in MSPs because they have greater and less dispersed counts. Finally, at the biology level, a high number of samples with varied phenotypes will improve the comprehensiveness and quality of MSPs. Indeed, as the number of samples grows, MSPminer will be able to identify rare species and assign rarer accessory genes to their respective MSPs. In addition, highly prevalent accessory genes will be reclassified from core to accessory as observed while sequencing an increasing number of strains of a species  (Touchon et al.
NoSQL databases are considered as a serious alternative for processing data whose volume reaches limits that are difficult to manage by relational DBMS. So far, they are praised for the capability to scale, replication and their capability to deal with new flexible data models. Most of these systems are compared to read/write throughput and their ability to scale. However, there is a need to get more in depth to monitor more precise metrics related to RAM, CPU and disk usage. In this paper, we propose a benchmark suite tools that enables data generation, monitoring and comparison. It supports several NoSQL systems including: column-oriented, document-oriented as well as multistores. We present some experimental results that show its utility.
, 2016; Xiang et al. , 2010). The read mechanism is tightly related to the writing mechanism configuration  (Xiang et al. , 2010). A maximum optimization of the writing performances affects the reading performances and vice versa  (Cattel, 2011). Therefore, many systems allow performance tunings that enable database administrators/data architects to tune the performance of the NoSQL system with respect to read/write or querying workloads. Tuning is usually done and tested before deployment of solutions. This step stands to evaluate major of NoSQL system performances; system resources utilization i. e. adjusting the memory and the resources usage, in order to achieve the expected performance. It becomes important to have benchmarks that can enable comparison and monitoring on different NoSQL systems.
e. repair process. In this paper, we present a benchmarking tool that enable DBA to tune and monitor the NoSQL performances with guarantee to best resources usages. Our suite tools is built to offer for both academia and industrial a useful benchmarking tools with a reference dataset and a set of exploratory queries. To the best of our knowledge, we introduce our benchmarking suite as one of the fewer solutions that supports multistores systems. More precisely the benchmark provides: performance tuning interface: setting configuration parameters monitoring tool: displaying metrics in realtime fashion benchmarking tool: data loading and exploratory queries execution. Figure  1 : Functionalities Supported.
Also, there are several plugins that allow the integration of MongoDB in most of the popular performances monitoring tools, to mention Munin, Ganglia, Cacti, Zabbix or Nagios. These solutions are not specific to MongoDB but they are adapted to be compatible with. However, despite the fact that all of the above mentioned tools offer important performance monitoring interfaces for major of NoSQL systems', they were built to run exclusively on a specific NoSQL system or there is a need to run them separately on each of the desired systems. Moreover, in the context of multistore, it is necessary to have a monitoring tool able to support several DBMS at once. To our knowledge, a tool capable of monitoring so-called multistore solutions has not been proposed yet (Table  1 ). Moreover, by studying performance tuning, we must also considerer cumbersome NoSQL issues such as data synchronization.
MongoDB MongoDB is a document-oriented database with a high horizontal scalability. The main idea of MongoDB is to freely evolve and make shards to improve read/write performances. Unlike Cassandra, MongoDB is based on a master-slave architecture and the OS takes in charge the cache writing. Indeed, the memory usage of MongoDB is under the control of the operating system's virtual memory. In other words, MongoDB will use memory as much as it is available, and it will switch to disk if needed. Deployments with enough RAM memory for the application's data will achieve best performances. Since version 3, MongoDB has introduced a new storage engine called WiredTiger, which allows the specification of a maximum cache size, which did not exist in the old storage engines (MMAPv1, TokuMX, Rocks). Like Cassandra, the write process is not instantaneous.
e. performance and system resource usage tuning, including commit log, compaction, memory, disk I/O, CPU, reads, and writes. MPT is able to distinguish between common parameters for both systems and specific ones. More precisely, regarding the common parameters, we are concerned with the following aspects. Cache Memory. There are two types of cache: row_cache and key_cache. Row cache temporarily keeps the data written into the Heap memory and therefore, during a reading operation, it allows a quick response without accessing the disk. However, as the cache size is limited, i. e. in Cassandra 2. 2 and later, it is fully stored into off-heap memory using a new implementation that relieves on garbage collection pressure in the JVM. The subset stored in the row cache use a configurable amount of memory for a specified period of time.
It is necessary to determine the cycle for flushing data to disks. The related parameters are: -key_cache_keys_to_save -key_cache_size_in_mb -key_cache_save_period For the specific parameters for each system, we focus on the following aspects. We start by the parameters related to Cassandra configuration. Compaction and Compression Strategies. These two aspects are the main resource usage regulators in Cassandra and they can often cause a fail if the following parameters are not properly set: MemTable allocation type -MemTable cleanup threshold -File cache size -MemTable flush writer -MemTable heap/offheap space -Memtable_heap_space_in_mb -Compaction_throughput_mb_per_sec -Concurrent_compactors After defining the different specific configuration parameters for Cassandra, we introduce now the ones related to MongoDB.
GUI The home interface of the GUI allows choosing the system to be evaluated, Cassandra, MongoDB or the multistore. Thereafter, there are two tabs, a tab for configuration and a tab for benchmarking monitoring. The configurations tab allows the user to set the parameter values in the configuration subsection. The second tab contains the memory usage graphs, which are structured as follows: -A graph for "table creation" phase, which displays the memory in MB, used for each created table. -A graph for "loading" phase that displays the memory used for the data inserted onto each table. -A graph for "reading" phase that displays the memory used for each queried table, in MB. -A Simulation graph, which combines the results of the three previous graphs. -A graph to track the performance tuning behaviour.
Automatic Synchronization (Repair) Process In order to synchronize t-he data, NoSQL systems integrate a synchronization tool generally called Repair. It consists of manually executing a repair operation at each node. This repair process is based on Mercle tree, i. e. comparing each partition of table /document with other partitions of other replicas. This process significantly increases the CPU usage due to the important number of comparisons. This CPU usage is more important if other read/write processes are running at the same time. In our solution, we have built a script to automatize the repair process. It is executed at Cassandra start and it is based on the following rules: -A repair operation is launched in continuous each ten days. This interval is used to do not impact the others process as the compaction.
Comparing MPT with JConsole In this experiment, we compare memory usage using another monitoring tool JConsole. As a reminder, JConsole is a monitoring tool known in Java environments since several years. We compare results provided by JConsole tool and the results provided by our tool. The results are reported in the Figure  5  and Figure  6. In this section, all the experiments were conducted using a single node server that is a !5 3. 4"#$ processor coupled with 8"% of RAM and two 2&% '(&( disks that runs under )*+,-' 7, used to evaluate. /+0/1% 23. 4. 2. we use anther server with the same characteristics to evaluate Cassandra v3. 1. 0. We used the data loading component benchmark of the tool suite (1) to create table and documents, (2) to load generated data into MongoDB/Cassandra and (3) to query data.
The data is generated using csv format for Cassandra and json format for MongoDB. This is done for illustrative purposes to show that we can generate, load and query data with our benchmark. Also, we show that we can monitor each benchmark phase and all metrics related to performance tuning. Precisely, we evaluate: Memory usage with ¼ th heap size and ½ th heap size in both systems Cassandra and MongoDB. Comparing our solution with another monitoring tool, Jconsole. We conduct the same experiment i. e. evaluation of three phases with ¼ of heap and ½ of heap. The Figure  6  shows that the max is 3. 1GB for ½th heap i. e. 4 GB and 1. 6 GB for ¼ of heap i. e. 2GB. Unlike our solution, distinguishing between results of each phase can be extremely difficult. In simulation Figure  3 , we can see that it is possible to monitor separately the three phases.
CPU Usage Performances In this experiment we evaluate and compare CPU utilization. In Figure  9 , we report the CPU utilization with ¼ th of heap size. We can see that we exceed 70 % of CPU usage during table creation phase. The utilization of CPU becomes less important to reach a maximum of 35% in loading phase and 12% at reading phase. In Table  3 , we report all results in both configuration (1/4 th and ½ th heap). We note that the CPU usage decreases while increasing the heap memory. This paper presents a suite of tools that enable benchmarking and monitoring for NoSQL systems by considering the columns-oriented model, Cassandra and documents-oriented model, MongoDB. We build a complete suite of tools integrating a configuration tool, a benchmarking tool as well as a monitoring tool. We also proposed an automation process of data synchronization.
Then, for a chosen city, the process would be to use people, companies and authorities' expectations and requirements for elaborating the best solution for the corresponding city. This tool is essentially composed of modules which are being developed by using Agile methodology. The global architecture of the collaborative tool (DIGICCOLDT as shown in Fig. 4. ) is composed of: • HMI module manager: it manages the liaison with the users: the site, the apps, the information to the users and the ones that the users return. • Expert data analysis system: it performs data processing and analysis. It includes rules of management, decision, design, modeling. • Knowledge capitalization module: it allows the registration of a case (here rather the package of information related to a user). The idea is to be able to propose to another user designs that have been made by a previous user to facilitate its design.
• Modeling tools module: a graphical representation tool that will make it possible to represent a model • Dashboard Performance Measurement Module: a kind of dashboard to measure the level of each indicator that has been defined, this will evaluate for example the impact of proposals, models etc. • Database: it contains all the information retrieved as part of the project. • Coordination module: it manages the interactions between the different modules. It serves as a link between them and could allow to make the final synthesis. • Simulation tools module: it includes the simulation tools used in the project. PTV Vissim is one of them. We can add others, such as FlexSim or Anylogic for the internal flow part of the company. • Expert system it is a tool that from all the changes and proposals, builds a kind of ideal model as the project progresses.
Modelling of the existing system flows For realizing modelling of the existing system, two simulation tools were chosen: • Flexsim for representing process flows in companies and in platforms of the area • And PTV VISSIM for representing traffic in the area. These tools are related to the collaborative design tool being developed by Icam Paris Sénart. PTV designs, sells and make the support of these software solutions for traffic and transport, mobility and logistics. And one tool of this company is named Vissim and is able to show in real time traffic flow in a city. Vissim could compare crossroads geometries, analyze priorities given to public transport or identify effects of signals. The real existing system model simulation will be presented during Faim 2018 conference. The goal here was to model road networks of highways (130km/h and 110km/h), express roads (90km/h), logistics hub roads (50km/h) and roads allowing access to them (between 50km/h and 90km/h) in the area of Sénart.
This modeling must be perfectly realized in order to measure exactly increasing due to changes in future models. Technically, an error in a junction modeling, (for instance, the number of lanes or speeds on the network) could radically change the traffic simulation result. OpenStreetMap base map was used for defining route location, during the network elaboration. Data were acquired by making company interviews but also by measuring input and output flows directly on main axes of the area. This is the most critical and complex point of this model. Indeed, the traffic is composed of several types of users: • Travelers: vehicles crossing through the territory (example: Melun -Orly road). • Temporary residents: vehicles coming in the area for the day (example: Fontainebleau -Villaroche road (platform)).
Firing Rate Recording of Metabosensitive Muscle Afferents The recording of afferent discharge was performed at the dorsal root. It allowed us to acutely detect sensory signal than on entire nerve, which include both sensory and motor fibers. A pair of bipolar tungsten electrodes was thus positioned on C7 dorsal root before being immersed in paraffin oil. Electrode position on dorsal root was never changed throughout recordings. The neural signal was amplified (10-100 K) and filtered (30-10 kHz) with a differential amplifier and was also referred to a ground electrode implanted in a nearby tissue. The nervous activity was recorded and fed into pulse window discriminators (P2MP R ; SARL), from which the firing rate was extracted. The output of these discriminators providing noise-free tracings (discriminated units) was displayed on a computer on separate tracings using a data acquisition system (Biopac MP150 R and AcqKnowledge software, Paris, France).
Consequently, the recorded reflex amplitude was related to the activation of metabosensitive muscle afferents (Figure  1B ). The post-injection firing rate was compared with the corresponding baseline. The temporal profile of firing rate variation and the extraction of the peak firing rate after each injection were performed by a specific MATLAB program (The MathWorks, version 8. 0. 0. 783 R2012b) that was described elsewhere  (Laurin et al. , 2011). Briefly, the firing rate was measured on 10 s periods all along the recording period, in which the firing rate was calculated on different time interval lasting from 1 to 10 s and averaged between them. The variations were expressed in percentage of the corresponding baseline discharge (i. e. , baseline firing correspond to 0%) as well as in the AFF-POST/AFF-PRE injection ratio.