dataesr/scientific-paragraphs-categorization huggingface dataset:
Event sendRequest = any proc q where grd1: proc ∈ P grd2: q ∈ network(proc) grd3: pc(proc) = sr grd4: sent(channels → (proc → q) → request) = 0 then act1: channels := send(channels → (proc → q) → request) end Event stopSending = any proc where grd1: proc ∈ P grd2: pc(proc) = sr grd3: ∀q•(q ∈ network(proc) ⇒ sent(channels → (proc → q) → request) > 0) The action for the sending of a message is translated using the DISTALGO function send: T-→ x (channels := send(channels → (proc → dest) → msg)) = send(T-→ x (msg), to=T-→ x (dest)) Note that channels and proc are not present in the resulting code since channels is implicit in DISTALGO and proc corresponds to the process executing the send statement. The sent and received events defined in EVENT-B are translated as DISTALGO queries on message history.
Generation of the Main Function The main function of the generated DISTALGO program defines different local constants as well as the different processes to execute, and starts the local algorithms of all the processes. This function is generated using exclusively the context CONTEXT-CM and more precisely, only the axioms of the context. The (identifiers of these) axioms should thus respect the rules given in Section 3. 1 and the names of the variables and constants are inferred correspondingly. The code of the main function contains a fixed part independent of the algorithm and specifying, for example, the behaviour of the communication channels. We omit here the fixed part and the various imports that might be needed and focus on the part generated from the EVENT-B model. The axiom Nodes allows us to infer the set {PCl 1 ,.
RNAs were then converted to full-length double-stranded cDNA and normalized with the Mint-2 and Trimmer-2 kits (Evrogen, Moscow, Russia), respectively. Later, q-RT-PCR assays were developed on a set of genes of different abundances to assess the efficiency of normalization. Normalized double-stranded cDNAs were sheared into 450 bp fragment size using the Covaris E220 system (Covaris Inc. , Massachusetts, USA). Individual indexed NGS libraries were then produced with the SPRIworks HT reagent kit (Beckman Coulter, Indianapolis, USA). An equimolar pool of the four libraries was sequenced on the Illumina MiSeq platform (V2 chemistry, PE 2 × 250 nt, 12 million of clusters) (Illumina, California, USA). FastQC was used to check raw data quality. Then, sequencing adaptor removal and quality trimming were performed using trim_galore and SMART oligos (normalization primers) that were masked in the raw sequence readings using an in house script.
MIRA 4. 0 (http://mira-assembler. sourceforge. net) software was used to perform a de novo assembly of all samples at once. Reads of parental genotypes were remapped on the assembly using BWA  36  and only contigs with more than 10x coverage were kept for further analysis. Transdecoder 37 was used to predict open reading frames in contigs. BUSCO v3. 0. 1  38  was used on viridiplantae odb10 database to assess completeness of the dataset. Functional annotation was performed using eggNOG-mapper  39  on eggNOG database 5. 0  40. The SNP discovery was then performed with SAMtools mpileup  36  and BCFtools call  41. Only homozygous SNPs were retained. Genotyping f 3 populations. DNA samples were extracted from leaf tissues of the parents and the individuals of the three populations using the NucleoSpin Plant II Mini kit (https://www.
The captured samples were sequenced on HiSeq sequencing platform (Illumina, California, USA) with a Paired End sequencing strategy of 2 reads of 100 bases. The objective was to produce around 3 Million sequencing clusters per sample. Raw reads were trimmed for adaptor sequence using cutadapt 1. 8. 3  42  and then aligned on the targeted regions with Novoalign (http://www. novocraft. com, Selangor, Malaysia). Genotype at each position of interest was determined using SAMtools mpileup  36  and in house Perl scripts to filter out low quality positions, call the SNPs, and to produce a genotyping matrix for all 2000 selected markers.
Genetic maps construction for each population. Markers were first filtered for deviation from Mendelian segregation (37. 5:25:37. 5) using the following index = ⋅ ∑ - - = ( ) Distortion f f min f 1 2 1 i N genotypicclasses i observed iexpected expected 1 , , with threshold of 0. 8. Then, markers were assigned to linkage groups by estimating the pairwise recombination frequencies within each population using the maximum likelihood procedure with the forward-backward algorithm and a LOD score of 5. 0 as the threshold for significant linkage in the software JoinMap V5. 0  43. In a second stage, markers were ordered and assigned to their positions in each linkage group by likelihood maximization. Candidate orders' likelihoods were computed as for F 2 populations using Spell-QTL Bayesian inference  44  and heuristic local maximization was performed using custom R scripts implementing the algorithm described earlier in Ganal et al.
Acknowledgements Authors thank the field and laboratory staff for technical assistance, especially  Philippe Declerck  and  Dominique Notteau  ( RAGT Semences ),  Gilles Furet and Corie Dekker (Limagrain)  and  Matthieu Floriot  ( Agri Obtentions ) for their participation in the production of the recombinant populations. In addition, authors are grateful to  Nathalie Rivière  ( Biogemma ) for useful advice in the choice of the genotyping technology. This work was supported by the  Project Investissements d' Avenir PeaMUST  under the grant number  ANR-11-BTBR-0002. GQE -Le Moulon benefited from the support of Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication does not allow anonymity-preserving access to services. Third, users have multiple online accounts that often reflect distinct identity aspects. This makes proving combinations of identity attributes hard on the users. In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attributebased authentication based on: 1) the seamless integration between usable/strong device-centric authentication methods and federated login solutions; 2) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels; and 3) a novel centralized component that allows end-users to perform identity profile and consent management, to prove combinations of fragmented identity aspects, and to perform account recovery in case of device loss.
Introduction Authentication on the Web relies on the password paradigm, which was developed during the 60s for accessing monolithic mainframe computers. We admit that a 128-bit very complex and long (∼20 characters) password used for a specific service is highly secure when it is only stored in the brain of the user and it is computationally hard to guess. However, as the number of Web services increases, the password paradigm entails an inextricable tension between security and usability as users become burdened with memorizing and managing multiple pass-words. At the same time, passwords can be shoulder-surfed, key-logged, replayed, eavesdropped, brute-forced and phished. In addition, password databases can be leaked and even if the service follows security good practices (i. e. , hashing and salting the passwords) the attacker can guess the password by performing a dictionary-based brute-force attack.
Terminology User Device. This is the main gateway to get to DCA. In this work, we assume a user device that is able to utilize recent advances in the field of Trusted Execution Environments (TEE)  [25]. This enables the device to securely safeguard cryptographic credentials within its software stack. Identity Providers (IdP). IdPs are trusted entities that are responsible for securely maintaining and transferring endusers' identity attributes. They incorporate strong authentication mechanisms so that they can regulate end-users access. In the context of Privacy-Preserving Attribute-based Access Control, IdPs are responsible for issuing and verifying the endusers' cryptographic credentials. Identity Consolidator (IDC). This is a centralized trusted entity that acts as the main IdP and manages all the access control needs of the user.
Identity Consolidator (IDC) The IDC is an integral component in our architecture that fullfils the needs of requirement R3. It is a centralized fully trusted entity that can be considered as a special instance of an IdP, which offers identity federation, identity and privacy management, and is required for failure recovery. The IDC collects identity attributes from various IdPs upon a user's request. The collected attributes are securely stored in a repository within the IDC. We describe below all the modules that comprise the IDC. Authentication Management Module (AuthMM). It encapsulates a FIDO-enhanced federated login protocol, which allows the IDC to act as an OIDC IdP for undertaking FIDO authentication. This module also allows the IDC to run federated login protocols for transferring identity attributes between distinct IdPs.
Apart from these, the AuthMM also offers the appropriate failure recovery mechanisms in cases where the user loses access to his device. Account Management Module (AMM). The AMM is responsible to keep track of all the BAAs, SPs, and IdPs of a user and it also allows BAA, SP, and IdP admins to register their entities with the IDC. Using this knowledge, the AMM acts as a BAA discovery service for SPs that may require a second-factor authentication. In addition, the AMM enables the user to: 1) man-age his IDC account, for example to set his preferred degree of privacy within the IDC (e. g. , only certain attribute are stored on the IDC) or completely delete his account; 2) manage the status of his accounts in various SPs and IdPs; and 3) protect his accounts by locking access to them in case of device loss.
Identity Integration module (IIM). The main responsibility of this module is the standardization and normalization of the users' identity information. We acquire this information via physical means (e. g. , using Near Field Communication (NFC) to read the user's e-Passport information), and we also perform online identity acquisition where the IDC acts as an SP to receive the users' identity attributes from other IdPs through OIDC. The IIM encapsulates the required logic for combining, fusing, inferring and validating identity attributes.
Federated Privacy-preserving Attributebased Authentication Our architecture was carefully designed to provide a PABAC solution on top of OIDC, while also addressing requirement R5. More precisely, we propose a custom authentication module within the OIDC Provider that acts as an Idemix/U-Prove verifier allowing IdPs to issue and verify cryptographic credentials. In fact, we modify the OIDC Provider so that it uses onetime pseudonyms instead of persistent unique identifiers. In this way, we enable SPs that are not aware of any cryptographic credentials stacks to allow end-users use cryptographic credentials and get access to their resources. Federated PABAC offers two concepts of anonymity, namely untraceability and unlinkability. Untraceability is the security property that precludes the IdP that issued an attribute credential from tracking to which SP the credential has been shown.
Multi-device Support We modify the FIDO UAF client and server software so that it allows the user to register multiple FIDO cryptographic keys, one for each device they use, for each account they maintain. This modification enables the users to maintain multiple devices. Besides this, a user is able to authenticate to an SP through his desktop computer. To achieve this, we integrate a Quick Response (QR) authentication server within the IdP's OIDC software to enable authentication from desktop computers to SPs using FIDO. Therefore, there is no need for the users to run any user device components on their desktop computers. We acknowledge that the availability of the mobile device of the user is crucial since a mobile device is required for authentication. However, this is also a limitation for FIDO and DCA in general.
Implementation In this section we provide the details of our prototype implementation. We implemented all the architecture components as well as all the protocol extensions and integrations that we describe in Section 5. OIDC/FIDO UAF. To exploit the OIDC Provider features, we make use of the OpenAM software  4. We implemented, within the OIDC Provider, a custom authentication module, which is responsible for undertaking the authentication of the users according to the FIDO UAF specification. To achieve this, our custom authentication module communicates with a FIDO UAF Server using a REST interface. The FIDO UAF server handles the authentication of the user by communicating with the FIDO UAF client that runs on users' devices. OIDC/PABAC. PABAC is realized through the deployment of Idemix/U-Prove credential stacks.
OIDC/FIDO UAF. As described in Section 6, we implemented a custom authentication module by deploying a FIDO UAF server to the IdP's software stack to enable IdPs authenticate end-users using FIDO. Here, we evaluate this deployment in terms of performance to identify: 1) how it performs under high authentication demands; and 2) the overhead that our custom authentication module introduces compared to the vanilla OIDC and FIDO protocols. We note that, in our measurements we do not count any user-induced delays (i. e. , the time the user needs to enter his password) and we use a 20 characters long password for authentication in the vanilla OIDC case. We evaluate the performance of a vanilla OIDC deployment by employing the standard OIDC authentication process. A similar evaluation was also conducted for the vanilla FIDO UAF authentication process.
All the simulations were performed by porting an Android client on a desktop, which implements the required functionality for standard OIDC and FIDO authentication, and we simulate the parallel authentication processes using different threads. Following the same approach, we also evaluate our OIDC/FIDO UAF authentication module. Again, the simulations are performed by running our Android OIDC/FIDO client implementation on a desktop. We repeat each experiment 10 times and we calculate the 95% confidence interval of the average response time of each deployment as the number of authentication requests increases. Figures  5a  and 5b  present the results of the evaluation of our OIDC/FIDO custom authentication module as well as those of vanilla OIDC and FIDO. We observe that our custom authentication module scales along with the number of authentication requests, with the server's average response time not being drastically impacted.
Compared with the vanilla OIDC and FIDO, our implementation does not introduce any substantial delay in the authentication process: when the number of simultaneous authentication processes is 4K, the average response time of an OIDC/FIDO authentication request is 4. 5 sec and 2. 4 sec more than the average response time of the vanilla OIDC and FIDO protocols, respectively. Considering the advantages of our proposed solution, we consider the additional delays as negligible. We note that all the experiments were conducted using an OpenAM software stack and a FIDO UAF server that both run on the IdP and the client requests are executed using an Internet connection. OIDC/PABAC. Next, we evaluate our OIDC/PABAC custom authentication module. For this evaluation we implemented a custom OIDC/PABAC authentication module by utilizing the Idemix credentials stack.
We deployed the implemented module to the IdP's software stack to enable the IdP to act as an Idemix credential issuer and verifier. The purpose of this evaluation is to identify: 1) how our PABAC-enabled IdP performs under high authentication demands; and 2) the overhead that our implementation introduces compared to the vanilla OIDC and Idemix protocols. We note that in our measurements we do not count the time required for the issuance of the Idemix credential. We evaluate both a vanilla Idemix deployment and our OIDC/PABAC implementation. Similar to the OIDC/FIDO evaluation, we repeat each experiment 10 times and we calculate the 95% confidence interval of the average response time of each deployment as the number of authentication requests increases. Figures  6a  and 6b  present the results of the evaluation of our OIDC/PABAC implementation compared with those of the vanilla Idemix and OIDC protocols, respectively.
Next, we also examine the case where the attacker is able to perform software attacks. If the behavior capturing protocols run in the Normal World (Rich OS), a skilled software attacker can intervene and modify the contents of the memory while also modify the information captured from the device's sensors. However, since all the local measurements are immediately sent to the BAA and are not stored locally on the device then we can prevent such an attacker from bypassing the behavioral authentication. On the other hand, if the protocols run in the Secure World (aka Trust-Zone, or Trusted Execution Environment-TEE), no software attacker can compromise the memory and information paths. However, we do not have the ability to develop protocols for TEE as the trusted computing base has to be approved by vendors, such as Intel, Samsung, etc.
We can just invoke specific services of it, such as storing cryptographic keys and performing secure cryptographic operations. For example, the activation of the on-demand behavioral authentication with a verifier is triggered through TEE-enabled secure biometrics (e. g. , fingerprint). This is supplied by FIDO and can protect the user in case the device has been recently stolen and the behavioral signature has yet to change. Last, when an attacker is able to perform software and hardware attacks then he can bypass the trusted execution and present himself as the legitimate user only if the device is recently stolen, but again the owner of the device can lock access to his online accounts on that device using the AMM. Service and Identity Providers. SPs and IdPs face various threats. Initially, by establishing protected sessions between the SPs/IdPs and the users, our solution guarantees that the access tokens are never exposed to unauthorized parties during an authentication.
Be-havioSec  [3]  offers continuous behavioral authentication software as a service. It uses real-time behavioral and statistical analysis tools to resist attacks like account fraud, sharing, and takeover. These solutions are typically deployed on the SP and are application-domain-specific. In our architecture, BAAs are offered as independent entities that can harvest user behavior data from a user's device in a non-intrusive and battery efficient way. More precisely, we propose an open architecture under which any entity able to capture behavior can offer behavioral authentication via OIDC, while also offering enhanced protection against attackers that manage to compromise the device.
Detection of chromatin structure differences Sequence quality was checked with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). For D. pulex, reference genome was downloaded from ftp://ftp. ensemblgenomes. org/pub/metazoa/release-40/fasta/daphnia_pulex/dna/Daphnia_pulex. V1. 0. dna. toplevel. fa. gz , corresponding to GenBank assembly accession GCA_000187875. 1. For S. mansoni, v5 reference genome was downloaded from ftp://ftp. sanger. ac. uk/pub/pathogens/Schistosoma/mansoni/genome/Assembly-v5/. For both, alignment was done with Bowtie2 evoking the following parameters: bowtie2-align-s basic-0 -p 6 -x genome -N 1 -L 32 -i S,1,1. 15 --n-ceil L,0,0. 15 --dpad 15 --gbar 4 --end-to-end --score-min L,-0. 6,-0. 6. Uniquely aligned reads were retained by filtering the tag "XS:i:" that is absent in their alignement annotations.
Acknowledgments We thank  J. F. Allienne  at the Bio-environment platform ( University Perpignan Via Domitia ) for support in NGS library preparation and sequencing, and the  Environmental Epigenomics platform  for support with ATAC-Seq procedure. This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ).
Grant information This work was performed with the support of  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ) and the  RTP Epigenetics in Ecology and Evolution  ( RTP3E ). R. A. and C. G. were supported by the  Wellcome Trust   Strategic award   107475/Z/15/Z. C. G. and A. M. received support from  JSPS  grant  S17087.
X-ray diffraction was applied to investigate five fatigue stages, namely OL-1, OL, OL+1, OL+1100 and OL+3100. In this notation OL represents the cycle count at which the overload event occurred, while the number of cycles, X, before or after the overload is described as OL-X or OL+X respectively. Within each stage, 2D strain/stress maps were acquired at P max and P min while line profiles were obtained at the intermediate loading levels so as to collect data in a time efficient manner. The details for the map and line scans are summarised in the following section. Due to the large grain size of CG Ni, and hence bad grain sampling statistics within the small gauge volume, synchrotron X-ray diffraction measurement could not be performed on the CG sample. Nevertheless the lab. fatigue experiment monitored by DIC combined with postmortem fractography and roughness analysis was able to give good insight into the overload effect on CG Ni.
In this study, d 0 is determined at a location that is far away from the crack (close to the corner on the tested samples) with no externally applied load. For FCC structured Ni, (311) is one of the lattice planes that are least affected by the intergranular stresses  [32]  and was therefore chosen to calculate the strain  [33]. The strain estimation and the merging of the fine and coarse maps were then performed using the pyXe strain analysis package  [34]. The stress distribution around the crack tip in the mid-thickness of the sample under plane strain conditions can then be evaluated by σ yy ¼ E hkl 1 þ ν hkl À Á 1-2ν hkl À Á ν hkl ε hkl xx þ 1-ν hkl ε hkl yy ð2Þ where the elastic modulus E 311 = 185 GPa and the Poisson's ratio ν 311 = 0. 33 following the Kroner polycrystal modelling scheme  [32].
Digital image correlation Random and fine scratch patterns were generated on the sample surfaces with grinding paper from 600 up to 2000. For the DIC measurements on the CG sample, one teleconverter was used giving a field of view of 6. 61 × 6. 61 mm 2 and a pixel size of 3 μm. For the NC sample, two teleconverters were used to give a field of view of 3. 28 × 3. 28 mm 2 and a pixel size of 1. 6 μm. The commercial software LaVision DaVis was used to process the images. For each fatigue stage, all the images were correlated to the one at P min. An interrogation window of 32 × 32 pixels with 75% overlap (8 pixels spacing between neighbouring windows) was used to obtain the displacement fields for both samples. The ΔCOD at various distances from the crack tip was calculated by the subtracting the displacements (in the crack opening direction) of the upper and lower crack flanks (as shown in Fig.
3 ). Another important fracture mechanics parameter, the J-integral  [35] , can also be extracted from the elastic region of the displacement field (as shown in Fig. 3 ): The J-integral is path independent and can be related to the SIF in an elastic field (linear elastic fracture mechanics conditions)  [30] : J ¼ Z Γ wdy-T i * ∂ u i * ∂x ds ! ð3Þ K I ¼ ffiffiffiffiffiffi JE 0 q ð4Þ where E ' = E for plane stress and E ' = E/(1-ν 2 ) for plane strain. A routine for calculating the J-integral from the displacement field was implemented using Python code. The procedure and verification of this routine can be found in Appendix A.