dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results of the complete attack We have developed the attack with the Java OPAL library  [SSD]  which implements several protocols for smart cards (including SCP02). The communication with the smart card and the reader is managed with the javax. smartcardio package. The experiments have been done on two laptops HP EliteBook and Dell Latitude E6430 running on Windows 7. Four card readers have been used: the inner laptop readers (Broadcom Corp Contacted SmartCard, Alcor Micro USB Smart Card Reader), a contact reader (Omnikey 5321 Smart Card Reader USB), and a contactless reader (SpringCard Prox'N'Roll). Our program simulates both the legitimate server and the attacker. The kinematic is the following. First an n-byte string is pseudo-randomly generated (the secret value the attacker has to retrieve).
The users of commercial applications, such as Facebook, ever-increasing amounts of data that needs to be accessible worldwide. Ensuring the lowest possible access time for users is crucial for the user experience. A key factor impacting the performance of such applications is data locality, i. e. the location of the data relatively to the application. Accessing remote data is orders of magnitude slower than using local data. Although such remote accesses may be acceptable for rarely-accessed data (cold data), they hinder application performance for frequently-used data (hot data). For instance, in a social network application, popular profiles should be replicated at all sites whereas others can remain located at fewer locations. Finding the right balance between replication and storage is critical: replicating too many profiles wastes costly memory and bandwidth, while failing to replicate popular ones results in degraded application performance.
Large-scale, data-intensive applications In this paper we target large-scale applications serving large amounts of data to users around the world, while seeking to enable low-latency access for these users to potentially-mutable data. Examples of such applications span multiple use-cases, among which: Scientific system monitoring. Monitoring a geodistributed cluster requires collecting potentially large number of metrics from computers around the world. This is for example the case for MonALISA  [4] , monitoring thousands of servers distributed in more than 300 sites around the world. The collected data is aggregated live, and is used to provide live insights about the system performance and availability around the world. To deliver the real-time promise of MonALISA, ensuring that the monitoring data that is needed by scientists around the world is located as close as possible to them is crucial.
Our proposal in brief: outline and challenges In this paper, we demonstrate that it is possible to integrate dynamic replication with the existing architec-ture of these storage systems, which enables us to leverage their existing, built-in algorithms to efficiently handle read and writes in geo-distributed environments. Such dynamic replication seeks to place new copies of the hot data in sites as close as possible to the application clients that access it. To that end, we permit the clients to vote for dynamic object replicas to be created at a specific site. These votes are collected at each node and disseminated across the cluster so that the objects which received the most votes (or popular objects) are identified by the storage system. Dynamic replicas of such popular objects are created at sites where they are popular, and deleted when their popularity drops.
When trying to access an object, clients tentatively determine the location of its closest replica (either static or dynamic) and address requests directly to the node holding it. Such approach however raises a number of challenges. We acknowledge that using client votes has been proposed before in the context of replicated relational databases, specifically to ensure data consistency  [36]. Transposing this idea to decentralized storage systems poses a number of significant challenges that we address in this paper. Specifically, collecting client votes efficiently without using a centralized process requires us to propose a novel, fully-decentralized, loosely-coupled vote collection algorithm. While existing dynamic replication techniques leverage a centralized repository to direct client requests to the nearest available replica, we propose a technique allowing clients to tentatively locate the closest available replica without any prior request to any of such repositories.
Prototype implementation We implement our approach atop a real-world keyvalue store. Voldemort  [14]  is an open-source clone of Dynamo  [13]. It is developed by LinkedIn and is extensively used in their software stack  [46]. Voldemort is a modular Java application, making it a relevant choice for our experiments. The total of our additions account for about 2,800 lines of Java code, excluding open-source Space-Saving and Bloom Filter libraries. We modify both the server code to handle decentralized object replication (Section 9. 1) and the client code to perform close replica location (Section 9. 2).
For all our experiments, we use t2. large general-purpose instances, evenly distributed over 6 sites spanning 4 continents: California, Virginia, Ireland, Germany, Australia, and Japan. Each virtual machine has access to 2 CPU cores and 8 GB RAM. The host server is outfitted with 10 Gigabit ethernet connectivity. Our measurements show the main bottleneck of these virtual machines to be the CPU. Single-site experiments on bare-metal servers show higher throughput than Amazon EC2. Dataset and workload. The main workloads we target are dominated by reads, such as the applications described in the motivation section above. Yet, we prove that even for write-intensive workloads, our data replication system increases average read performance at the cost of a slightly increased write latency due to the added cost for dynamic replica synchronization.
To that extent, we use YCSB  [47]  to generate both our initial dataset and storage operations. YCSB is an industry-standard benchmark commonly used to evaluate the performance of key-value stores. We generate 500 million 1 KB records, that we insert in Voldemort as our initial data. The workload against the cluster is generated by two YCSB instances on each site (12 in total) running in their own virtual machine. Requests are generated according to a Zipfian distribution, with Zipf parameter ρ = 0. 99, drawing from a set of 20 million keys. Each measurement is performed 50 times at maximum throughput for a period of 5 minutes. Bar plots represent averages. To account for changing hot object set, YCSB instances are restarted in turn -one every 30 seconds. This leads to a varying request distribution over time.
We collect all measurements on a stable, non-saturated cluster to ensure that our measurements do not suffer from compute resource exhaustion on the server. Requests are locally throttled at each node to a maximum of 1,000 concurrently running queries. Experimental configuration. Our prototype uses the default time window length of 10 seconds, a grace period of 3, a k value of 10,000, and a client error threshold of 5%. Client-side site preferences are identical for every node in the same site. In the applications we target, both database clients and servers are located in a well-defined datacenter. Consequently, the preference list for each client  can be statically configured -showing no significant client overhead. Preferred sites are ordered by average measured round-trip latency, up to a maximum of 100 ms.
Acknowledgment This work is part of the  BigStorage  project, supported by the  European Commission  under the  Marie Skłodowska-Curie Actions  ( H2020-MSCA-ITN-2014-642963 ). The authors would like to thank the anonymous reviewers whose counsel and expertise greatly contributed to this research. The experiments presented in this paper were carried out on the  Amazon Web Services  infrastructure provided by  Amazon  through the  AWS Cloud Credits for Research program.
Methods LAMP primers targeting the four ESBLs CTX-M groups were designed using the Primer Explorer V4 software. The detection limit of the method was tested by serial dilution of reference DNAs. The primer specificity of the LAMP reaction was tested on DNA extracted from six strains producing various group of CTX-M and validated using DNA extracted from CTX-M-resistant clinical isolates (isolated from pus, urine, or blood). Results were compared with those of conventional PCR.
LAMP primer design Nucleotide sequences in the four groups of CTX-M β-lactamase genes were selected from the National Center for Biotechnology information (NCBI). The accession numbers of these sequences were X92506. 1, Y10278. 1, AF255298. 1, AY005110. 1, AF305837. 1, AY044436. 1, AY080894. 1, AF488377. 1 for the CTX-M group 1; AB770487. 1, Y14156, U95364, AJ005044, AJ005045. 1, AJ416344. 1 for the CTX-M group 2; AF189721. 1 for the CTX-M group 8; AF174129, AF252623, AF252622, AY029068, AY033516, AJ416346. 1, AY143430. 1, AY156923. 1 for the CTX-M group 9. These sequences were aligned using BioEdit 7. 2. 5 software  [20]. Consensus sequences were analysed with the LAMP primers design software "Primer Explorer V4" software (http://primerexplorer. jp/e/) and specific primers were automatically designed.
The four basic primers used for LAMP were a forward inner primer (FIP), a backward inner primer (BIP) and two outer primers (F3 and B3)  [14]. One or two Loop primers (LF and LB) were designed for each target. These last are optional but can be used to accelerate the reaction time  [21]. Primers were validated using BLAST software (http://www. ncbi. nlm. gov/BLAST) to ensure their specificity. The list of used primers is shown in Table  2.
Acknowledgments This work was funded by  Dedonder Clayton  ( EC/MAN/N˚305/12 ),  Pasteur Institute of Paris. The authors thank  Pr Patrice Courvalin ,  Antimicrobien Agents Unit, National Reference Center (NRC) ,  Pasteur Institute of Paris  for the kind provision of strains used for the specificity assays and  Dr Frederique Randrianirina ,  Clinical Center of Biology, Pasteur Institute of Madagascar, Antananarivo, Madagascar , for the kind provision of the clinical isolates used for the evaluation test in this study. We specially thank all staff of  Bacteriology Experimental Unit, Pasteur Institute of Madagascar ,  Antananarivo Madagascar , and  Anjanirina Rahantamalala  for technical help.
How animals interact and develop social relationships regarding, individual attributes, sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis, allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package Animal Network Toolkit (ANT) was developed with the aim of implementing in one package the many different social network analysis techniques currently used in the study of animal social networks. Hence, ANT is a toolkit for animal research allowing among other things to: 1) measure global, dyadic and nodal networks metrics; 2) perform data randomization: pre-network and network (node and link) permutations; 3) perform statistical permutation tests.
The motivation behind Animal Network Toolkit (ANT) comes from these limitations. We developed this R package in order to provide researchers in animal social networks (usually small networks under 1000 nodes) that have specific observational data protocols with a unique software to process their raw data and help them with the selection of the most appropriate metrics and statistical analyses. This paper outlines the large spectrum of analyses allowed by ANT: 1) A multilevel approach through the study of nodes, dyadic and global network metrics. 2) Appropriate randomization techniques (pre-or network permutations) depending on the data. 3) Large amount of networks metrics available, some commonly used in animal research and others lesser known.
New parameterizations of photosynthesis, carbon allocation and phenology are proposed based on a compilation of field measurements. A specific harvest module is further added to the model to simulate the rotation of bioenergy tree PFTs based on their age dynamics. The resulting ORCHIDEE-MICT-BIOENERGY model is applied at 296 locations where field measurements of harvested biomass are available for different bioenergy crops. The new model can generally reproduce the global bioenergy crop yield observations. Biases of the model results related to grid-based simulations versus the point-scale measurements and the lack of fertilization and fertilization management practices in the model are discussed. This study sheds light on the importance of properly representing bioenergy crops for simulating their yields.
The harvest index (HI, harvested biomass as a fraction of aboveground biomass) is very different for grain crops and herbaceous bioenergy crops. In addition, most vegetation models currently do not account for realistic rotations of ligneous bioenergy plants (e. g. poplar and eucalypt). Modeling the harvest of woody bioenergy crops should be based on rotation practices of typically a few years rather than on assuming annual full harvest like for herbaceous crops. This requires simulating forest age dynamics  (Yue et al. , 2017)  to accurately represent the ligneous biomass harvest. In this study, we aim to model biomass yields of four major lignocellulosic bioenergy crops in the global dynamic vegetation model ORCHIDEE. We introduce the new bioenergy crop PFTs, adjust the parameters relevant to physiology, phenology and harvest process of bioenergy crops based on observations, and evaluate the simulated biomass yields using a new global dataset of field measurements.
Model description The proposed parameterizations of lignocellulosic bioenergy crops are based on an extended version of ORHCIDEE  (Krinner et al. , 2005)  -ORCHIDEE-MICT  (Guimberteau et al. , 2017)  which contains relevant features of gross land use change, wood harvest and forest age classes dynamics  (Yue et al. , 2017). The model simulates energy exchange, water balance and vegetation carbon processes in the ecosystem and is the land surface component of the French Earth System Model (ESM) IPSL-CM  (Krinner et al. , 2005). The principal processes related to carbon cycling comprise photosynthesis, vegetation carbon allocation, autotrophic and heterotrophic respiration, plant phenology (e. g. leaf onset and senescence) and litter and soil carbon dynamics  (Krinner et al. , 2005). ORCHIDEE-MICT further includes high-latitude related processes with new parameterizations of soil carbon vertical discretization, snow processes, and the SPITFIRE fire module  (Guimberteau et al.
1a-c. Vcmax25 values generally decrease from eucalypt > poplar and willow > Miscanthus ≥ switchgrass. The interquartile range of Vcmax25 is large for eucalypt (N = 42) from 75 to 126 μmol m -2 s -1 and for poplar and willow (N = 30) from 57 to 165 μmol CO2 m -2 s -1. Miscanthus and switchgrass have a relatively smaller interquartile range of Vcmax25 (17 to 32, N = 38 and 12 to 26, N = 17, respectively). We adjusted the prescribed parameters Vcmax25 and arJV (Table  2 ) for each bioenergy crop PFT using a value close to the median value in the observation dataset (Fig. 1a, c , within a range of 10% of the median values). We also verified that Jmax25 from Equation (1) is also in the range of independent Jmax25 observations (Fig. 1b ). Importantly, the observation-based estimates of Vcmax25 and We also adjusted other parameters including θ (the convexity factor of the response of rate of electron transport to irradiance), and α(LL) (conversion efficiency of absorbed light into e-transport rate at strictly limiting light) and g0 (residual stomatal conductance when irradiance approaches zero) in the leaf-level photosynthesis equations of ORCHIDEE to match higher productivity based on field measurements or empirical data (Table  2 ).
, 2005). SLA for different bioenergy crops from our data compilation (164 entries in Table  S1 ) is shown in Fig. 1d. A factor of 2 is used to convert the SLA unit from m 2 g -1 dry matter to m 2 g -1 C. Observation derived SLA for eucalypt is lower than for the other bioenergy crops, and SLA for switchgrass is relatively larger. SLA is set to the median value of observations for PFT16 (Miscanthus) and PFT17 (switchgrass), and close to the 75 th percentile value of the data we compiled for PFT14 (eucalypt) and PFT15 (poplar and willow) (Fig. 1d  and Table  2 ). Another important plant trait for photosynthesis is the leaf orientation, which determines the radiation extinction in the canopy. Although LAI of eucalypts is generally moderate  (Anderson, 1981; Stape et al. , 2004; Whitehead and Beadle, 2004) , leaf angles are nearly close to vertical in mature eucalypts forest  (Anderson, 1981; King, 1997) , leading to a good distribution of radiation to the lower canopy layers.
, 2011). In ORCHIDEE, the rotation length for bioenergy tree PFTs is associated with the setting of age classes (see Section 2. 2). Namely, harvesting starts from the second youngest age class, thus the biomass class boundary for the second youngest forest age cohort is assumed to define the rotation length. For idealized simulations presented below, we used a rotation length of 4-6 years based on the harvest age and rotation length in the evaluation dataset (Fig. 2 ; Section 3. 2). Here, the harvest age (Fig. 2 ) represents the age when the biomass of bioenergy trees was harvested or estimated. It is directly reported by the original literature and corresponding to the reported yield. Rotation length (Fig. 2 ) is the management practice reported in the original literature, and it is the same as harvest age in most studies.
Evaluation dataset We used the global bioenergy crop yield dataset from Li et al. , (submitted, see Data availability) to evaluate the performance of the modified ORCHIDEE-MICT-BIOENERGY model. This global dataset was compiled from more than 200 field measurement based studies with five main bioenergy crop types, i. e. eucalypt, poplar, willow, Miscanthus and switchgrass  (Li et al. , submitted). About 98% of the compiled observations are reported as the aboveground biomass, and the rest are reported as the total of aboveground and belowground biomass. We thus didn't exclude the observations of the total biomass in the model-observation comparison since their fraction is very low (<2%). The biomass yield in this dataset is compiled in a unit of ton DM (dry matter) ha -1 yr -1 , corresponding to the mean annual biomass yield.
For example, if the original literature reported the total harvested biomass of poplar at a certain age, the total biomass amount is divided by age to get the mean annual biomass yield. If the original literature reported the annual harvested biomass of Miscanthus for several years, each annual yield is taken as one observation. Note that this dataset does not distinguish the utilization of the plantation (for bioenergy use or for timber / pulpwood). In order to evaluate the simulated biomass yields by ORCHIDEE at half-degree resolution, we calculated the median and range of all observations in each half-degree grid cell containing at least a site of the dataset. Each half-degree grid cell may contain observations from different sites or one site with different species, genotypes, treatments (e.
g. different irrigation or fertilization levels). Globally, the number of half-degree grid cells containing observations for PFT14 (eucalypt), PFT15 (poplar and willow), PFT16 (Miscanthus) and PFT17 (switchgrass) are 63, 120, 69 and 44 respectively (see maps in Section 3. 5), giving a total of 296 grid cells (some may be have several crops in common). data is a merged product of CRU TS climate dataset  (Harris et al. , 2014)  and NCEP reanalysis data  (Kalnay et al. , 1996).
We compared the annual harvested biomass in bioenergy harvest pool in per area unit, so the harvest area has no influence on our model evaluation. For the bioenergy trees (PFT14 and PFT15), a spin-up of 100 years without harvest was run first to derive biomass evolution in time to define the respective biomass boundaries for age classes in each grid cell (see  Yue et al. , 2017). The biomass boundaries are grid-cell specific because of the different vegetation growth rates in different grid cells. The six age classes from youngest to oldest are thus set to be corresponding to 0-4, 4-6, 6-10, 10-30, 30-50, and >50 years. We set the second youngest age class that is used in priority for bioenergy harvest (Section 2. 2) to be 4-6 years (Fig. 2 ) based on harvest age and rotation length reported by the original publications in the evaluation dataset  (Li et al.
Biomass-age relationship at site level A good representation of biomass-age curves for bioenergy trees in the model is crucial to reproduce the yields, especially in the first several years after plantation (≤ rotation length). However, most of the observations in the global evaluation dataset were only mean annual yield  (Li et al. , submitted). This precludes a detailed analysis of biomass dynamics over time for bioenergy trees. We thus selected 22 studies (Table  S2 ) from the evaluation dataset that reported biomass amount of multiple ages (at least two years) and at the same site for eucalypt, poplar or willow. We went through the original articles to derive the biomass-age curves and compared them with the same curves from the model simulations (Fig. 4  and Fig. 5 ). There is a good agreement on biomass-age relationship of eucalypt between model and observation in some sites in Australia and China (Site #2, #8-12 in Fig.
Model performance before and after bioenergy crop implementation In this study, we added four new PFTs to represent the main lignocellulosic bioenergy crops and implemented new parameterizations for each new PFT. As a first step, we evaluated the biomass production from bioenergy crops in ORCHIDEE using a global field measurement dataset. We compared the biomass yields simulated by the new ORCHIDEE-MICT-BIOENERGY with the yields from previous ORCHIDEE version (Fig. 11 ). In the previous version, bioenergy crops were all taken as herbaceous C4 crop (PFT13), and thus severe overestimation (overestimating 60% on average) occurs for tropical bioenergy trees (i. e. eucalypts, gray squares in Fig. 11a ). Although using herbaceous C4 crop generally reproduce the observed biomass yields of poplars and willows (grey squares in Fig.
Future development In ORCHIDEE-MICT-BIOENERGY, nutrient limitations and management like irrigation and fertilization are not explicitly implemented. Instead, we used parameter values in the range that favors a higher productivity (Section 2. 3, Fig. 1 ). However, there are still some regional biases of biomass yields for different bioenergy crops. For example, ORCHIDEE-MICT-BIOENERGY underestimates the biomass yields of Miscanthus in UK by 43% (Fig. 8 ) and overestimates the yields of switchgrass in eastern US by 18% (Fig. 9 ). Thus, for a regional use of modelled results, slight modifications of related parameters would be needed. In addition to the biomass yields, the carbon densities or fluxes in soil should also be systematically evaluated, which is currently impeded by lack of a global dataset of soil organic carbon for bioenergy crops to our knowledge.