dataesr/scientific-paragraphs-categorization huggingface dataset:
This information allows analysing the effects of environmental change on interaction patterns. For example, it has been noted that the Huh7. 5 cell line differs from the 7. 0 parent cells by a single mutation (p. Thr55Ile) in Retinoic acid-inducible gene I (RIG-I) protein, which impairs interferon signaling  69. Analyses by the MacCarthy group using data uniquely provided by IMEx suggest notable differences in the pattern of host proteins interacting with HCV proteins from these two different cell lines  67. Huh7 proteins interacting with proteins from HCV are involved in cancer, apoptosis, immune defence response and cell cycle functions, whilst the equivalent Huh7. 5 proteins are enriched for protein folding, localisation, and transport. D A C 1 K A T 2 B P IN 1 M E D 1 V R K 1 N F Y A N F Y B K M T 2 E V D R DA XX B C L 2 L 1 S M A D 2 Recent changes to both the database model and PSI-MI XML3.
Future and sustainability Since first described in 2012, the IMEx Consortium has gained new member data resources and curation groups; these include the UniProt Consortium and the Functional Gene Annotation group at University College London. Some previous member resourcessuch as MPIDBare no longer in existence, but the data remain in the IntAct database and are updated with each release of UniProt. The IMEx Consortium has released almost 900,000 interaction evidences to IMEx curation standards and continues to provide access to another 100,000 legacy binary evidences. It has expanded the IMEx dataset to include new interactor types, new methodologies and new data types such as dynamic interactions. The Consortium remains open to the participation of new partners, and will make access to the IntAct editorial tool, curation training and data quality control available on request.
For detailed information on both IMEx membership and on data deposition, please see https://www. imexconsortium. org. Data producers can contribute to the IMEx project in three key ways. First, by depositing interaction data with one of the Consortium partners as an integral part of the publication process. Second, by always clearly identifying all the constructs used in any interaction experiment  71  , ideally by the addition of an accession number from a database such as UniProtKB or by making the species of origin of any clone very clear (for example "Human hemagglutinin (HA)-tagged RRP1B (Q14684)…. "). Additional sequence detail will enable mapping to the correct isoform, when relevant. Third, data producers can request the curation of papers, particularly when these supply interactions missing from an interactome, or bring additional details to an interaction that is not already present in the dataset.
Researchers seeking assistance with these requirements may contact intacthelp@ebi. ac. uk. The IMEx Consortium received EC funding to establish itself and has more recently received UK BBSRC and NIH grants. Currently, the consortium relies on localised, national funding and research grants to maintain resources. IMEx is the first Consortium to be recognised as an ELIXIR core resource, highlighting the importance this organisation places on database collaboration and data sharing. ELIXIR has supplied local funding to support member databases and to fund Consortia meetings. It is hoped that the recognition by ELIXIR will result in longer term funding for Consortium-wide activities. The Global Biodata Coalition  72  is currently looking to extend this model of sustainable funding for core-data resources in the life sciences giving hope for the long-term future of key resources such as the IMEx Consortium.
Acknowledgements The IntAct team at EMBL-EBI received funding from  EMBL core funding, Open Targets  (grant agreements  OTAR-044  and  OTAR02-048 ) and the  Wellcome Trust (Biomedical Resources  grant INVAR # 3367 ). This work by UniProt was supported by the  National Eye Institute (NEI) ,  National Human Genome Research Institute (NHGRI) ,  National Heart, Lung, and Blood Institute (NHLBI) ,  National Institute on Aging (NIA) ,  National Institute of Allergy and Infectious Diseases (NIAID) ,  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, National Institute of General Medical Sciences (NIGMS) ,  National Cancer Institute (NCI)  and  National Institute of Mental Health (NIMH)  of the  National Institutes of Health  under Award Number  [U24HG007822 ] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health ) and EMBL core funding and  Open Targets  (grant agreements  OTAR02-048 ).
40MI. The recently published JAMI library has been developed to import and export molecular interaction data in a variety of formats and versions, providing a single change-resilient software component40. Software and tools developed on top of the JAMI framework are able to integrate and support all versions of MITAB, PSI-MI XML, MI-JSON and XGMML. JAMI's model interfaces are abstracted from the underlying format, masking the requirements of each data format from developers. JAMI is capable of serving the full richness of IMEx data to tools and resources built on this framework. It has been implemented by the IntAct and Complex Portal databases in addition to the InterMine data warehouse. RpsiXML. RpsiXML 44 provides an R interface to PSI-MI XML2. 5 files and can therefore be used to query the IMEx data.
Workload model In high-level design, software components are used to manage complexity. This model is in providing whole system functions, which are sharply enlarging  (Niang et 2017). Once the gathering of components structure is realized, it is necessary to build the workload behaviour of the system. The latter describes end-toend flow of the executed actions during a certain system mode  (Naija el al. , 2015). When the system is in a given mode, it provides a subset of system features  (Naija al. , 2016 ) (Mansour el al. , 2019). In this work, we study the behaviour of autonomous vehicle in normal mode (NM) and degraded mode (DM) as shown in Figure  2. The end-to-end scenarios, stereotyped «saEndtoEndFlow», are activated by external event. However, the event Localization_act actives the GPS component every 30 milliseconds and is annotated with MARTE «GaWorkloadEvent» stereotype.
17 minutes for 50% of the passengers in the worst case. This is tolerable in our project where the safety of passengers comes first. Compared to classical AMoD approaches, only 50% of passengers will reach their destination and 50% of passengers will be blocked or in danger. Thereby, the adaptive architecture allows the vehicle to continue its intended mission, when abrupt threats appear, at a reduced level of QoS, rather than failing completely. This contribution facilitates complex autonomous vehicles modelling and checking, reduces the development time, cost, and improves software process quality.
STATE OF THE ART In the past decades, the robotics community has extensively studied verification of safety requirements in intelligent transport. We will discuss in the following the methodologies that particularly use formal verification. In  (Althoff and Dolan, 2014)  the authors propose an online verification approach using reachability analysis in order to capture all future possible scenarios. Safety is guaranteed with respect to the modelled uncertainties and behaviours if the occupancy of the autonomous vehicle does not intersect that of other traffic participants for all times. To allow a faster verification in an emergency situation, specific maneuvers can be stored in a database. Nevertheless, online verification cannot predict safety for infinitely many states such as offline verification  (Bohrer, 2019).
Implementation Cost of Pyjamask-96 As our upcoming analysis is a key recovery attack where the time complexity is dominated by binary operations, we estimate the costs of a software implementation of Pyjamask-96 for comparison. • Costs of SubBytes: SubBytes uses a 3-bit S-box of degree two. Hence, we assume that a software implementation of this S-box needs at least 3 nonlinear operations to compute the degree two terms of the S-box in each output bit and 3 linear operations to add the linear parts to the output. Needing a total of 6 32-bit instructions for SubBytes. • Costs of MixRows: MixRows consists of three matrix multiplications. In the following we only want to count the number of XOR operations needed to apply a matrix, neglecting the necessary storing or rotations. We assume that an implementation of one matrix multiplication needs at least 4 32-bit XOR operations since the hamming weight of the circular matrices involved is at least 11.
Application on Pyjamask-96-AEAD So far, we have focused our analysis solely on the 96-bit block cipher Pyjamask-96. As the NIST candidate Pyjamask-96-AEAD uses Pyjamask-96 in OCB3 [KR14], we discuss how our attack translates to this use. The first restriction lies in the data limit that we have to use less than 2 48 blocks. We can then apply our attacks needing less than 2 48 data to Pyjamask-96-AEAD, but with a loss of 2 rounds. In the following, we explain why.
2010; Staniczenko et al. 2013; Poisot & Gravel 2014; Astegiano et al. 2015). Moreover, few studies have compared interaction networks along environmental gradients  (Devoto et al. 2005; Schleuning et al. 2012; Sebastián-González et al. 2015; Pellissier et al. 2017). In order to compare networks of different sizes, a better alternative is to switch from network-derived metrics to the comparison of the probability of interaction given by regression models, which can consider multiple factors and latent variables and assume that the sampled data are just part of a larger unobserved dataset  (Grace et al. 2010). Calcareous grasslands are characterized by highly diverse plant communities with a high proportion of entomophilous species  (Baude et al. 2016) , thus they are a convenient model for such studies.
Plant -hoverfly networks For each site, we constructed an interaction network consisting of all pairs of interacting plant and insect species, pooling data from all months. A pair of species (i,j) was connected with intensity vij when we recorded vij visits of insect species i on plant species j in the site. We calculated the network specialization index, H2'  (Blüthgen et al. 2006 ) using the H2fun function implemented in the bipartite package  (Dormann et al. 2009; R Core Team 2018). We obtained the d-value (Kullback-Leibler divergence between the interactions of the focal species and the interactions predicted by the weight of potential partner species in the overall network) and the dmax-value (maximum d-value theoretically possible given the observed number of interactions in the network) using the dfun function in the bipartite package  (Dormann et al.
2009 ). We did not use the d' values provided by this package as they sometimes yielded spurious results based on the computation of the minimal d value (e. g. reporting low d' for species with only one partner in the network). We then manually calculated the standardized specialization index d'  (Blüthgen et al. 2006)  for each plant and insect species as the ratio of the d-value to its corresponding dmaxvalue. We calculated the modularity of the network and the associated partition of species into modules using the cluster_leading_eigen method for modularity optimization implemented in the igraph package  (Csardi & Nepusz 2006; Newman 2006). Modularity optimization can help identify strong, simple divisions of a network into relatively independent sub-networks by looking for highly interconnected subnetworks.
However, modules are not meant to inform about more subtle groupings among the species, e. g. particular avoidance of interactions between insects of group A and plants of group 1. In order to detect such groups, we implemented latent block models (LBM) using the BM_poisson method for Poisson probability distribution implemented in the blockmodels package  (Leger et al. 2015). Blocks are calculated separately for the two groups (insect and plant) based on the number of visits (i. e. a weighted network). The algorithm finds the best divisions of insects and plants through fitting one Poisson parameter in each block of the visit matrix, thus essentially maximizing the ICL (Integrated Completed Likelihood;  Biernacki et al. 2000; Daudin et al. 2008). The LBM script is given in Supplementary Information (Appendix S3).
We considered only flowering plants which had been visited by pollinators. For the pollinators, we considered only hoverflies which were found in interaction. To build the species phenology tables for both plants and hoverflies, we merged the information provided by two sources of data (field data and the literature): we used the observed phenology of both plants and insects during the field session as the only source of information for plants (plants visited by insects and plants found in the botanic inventory in the site at that date), and we complemented the hoverfly phenology with information provided by the Syrph the Net Database  (Speight et al. 2016). We then built the phenology overlap (PO) matrix based on the species phenology tables by calculating the number of phenologically active months that are shared by each pair of insect and plant species along the season.
Model and parameter comparison We estimated the 16 models that included all combinations of 0 and 4 of the abovementioned effects to understand which effects were more likely to play a role in the structuring of the network. The goodness-of-fit of these models were compared using the leave-one-out cross-validation criterion (LOO) calculated using the R package loo using Pareto smoothed importance sampling for regularizing importance weights  (Vehtari et al. 2017). The LOO criterion is a fully Bayesian method to compare models of different complexities and to estimate prediction accuracy using the log-likelihood evaluated at the posterior simulations of the parameter values  (Vehtari et al. 2017). Models can thus be ranked according to their LOO scores, with the best model being the one with the lowest LOO value.
Discussion Latitude affects the seasonality, with advancing species phenologies at higher latitudes, and thus, can be a limiting factor for the phenological coupling of interacting species  (Post et al. 2018). In this study we explored the effect of phenology overlap on a large network of species interactions in calcareous grasslands and how this effect could vary along a latitudinal gradient in France using empirical data on six planthoverfly networks. We identified plants and insects at the species level to build detailed interaction networks and hence avoid spurious generalisation levels. In order to better understand the determinants of variation in species interactions in space and time, we used the latitudinal gradient to consider variations linked to environmental cues and the entire flowering period to allow for seasonal variation  (Valverde et al.
The first column shows the Braun-Blanquet coefficients of, the second column, their percentages, and the third column, the transformed abundances used as the plant abundances in the model. Table  S3. The following generic script was applied to all the study sites using all 16 models. The script is separated in three blocks which communicate among them: the script options, the model definitions and the execution (model inference). We defined three options to set (i) the name of the directory (-d), (ii) the site (-s) and (iii) the type of model (-m). We used, as an example, the information for the site of Bois de Fontaret (BF). Exemple: Rscript (name) "script-SEMLOO_generique. R" "-d o-BFs-2016" "-s BFs" In order to calculate the standardised coefficients for each parameters used, at the end of the third block, we added the functions to get the parameter values for each site and each model.
Acknowledgements Financial support was provided by the  ANR   ARSENIC  project (grant no. 14-CE02-0012 ), the  Region Nord-Pas-de-Calais  and the  CNRS. We also thank  Martin Speight  for insect identification,  Clément Mazoyer  for informatic support and all the students who took part in the field campaign. This work is a contribution to the CPER research project CLIMIBIO. The authors thank the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts-de-France Region  and the  European Funds for Regional Economical Development  for their financial support. Version 4 of this preprint has been peer-reviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100034).
For example, in Fig. 3 , Y has two rays (0, 1) T and (1, 2) T , but no lines. A polyhedral cone is called pointed if it does not contain lines. Now we show that for any space group Y is a pointed polyhedral cone. Choosing p = 0 and arbitrary q, due to Eq. (  16 ) we have LΛ :,1:r Line • q ≥ 0 as well as LΛ :,1:r Line • q ≤ 0 since we can replace q with -q, and thus ∀q ∈ R n , LΛ :,1:r Line • q = 0. As LΛ :,1:r is a rank-r matrix, there must be Line = 0. In this paper, Eq. (  16 ) is called the H-representation of polyhedron and Eq. (  17 ) is called the V-representation. The algorithm to determine the V-representation from the H-representation and vice versa is available in many mathematics packages such as the SageMath package  [75]. The trivial B-vectors, which decompose into positive sums of EBRs, are given as X (Eq.
( B4 ) For case-(ii), we can rewrite the y-vector as y = y 1 y 2 y 3 = y 2 1 1 1 + (y 3 -y 2 ) 1 0 1 + (y 1 -y 3 ) 1 0 0. ( B5 ) Therefore there are four Hilbert bases There are two commonly used algorithms to calculate the Hilbert bases of positive affine monoid, i. e. , the Normaliz algorithm  [79]  and the Hemmecke algorithm  [80] , which are available in the Normaliz package and the 4ti2 package, respectively. In this work, we mainly use the 4ti2 package to solve the Hilbert bases. Applying this algorithm for each Y , we are able to calculate all the fragile roots in all SGs, as tabulated in Table  S3  of  [74]. In Table  1  in the main text, we summarized the numbers of fragile roots in all the SGs. b 1 = 1 1 1 , b 2 = 1 0 0 , b 3 = 1 1 0 , b 4 = 1 0 1. ( B6.
ACKNOWLEDGMENTS We acknowledge  Maia G. Vergniory ,  Zhijun Wang , and  Claudia Felser  for collaboration on previous related works. Z. S. and B. B. are supported by the  Department of Energy  Grant No. desc-0016239 , the  National Science Foundation  EAGER Grant No. DMR 1643312 , Simons Investigator Grants No. 404513 , No. ONR N00014-14-1-0330 , No. NSF-MRSECDMR DMR 1420541 , the  Packard Foundation  No. 2016-65128 , the  Schmidt Fund for Development of Majorama Fermions  funded by the  Eric and Wendy Schmidt Transformative Technology Fund. L. E. is supported by the  Government of the Basque Country  (project  IT779-13 ), the  Spanish Ministry of Economy and Competitiveness (MINECO) , and the  European Fund for Economic and Regional Development (FEDER ; project  MAT2015-66441-P ). We are mostly grateful to the  MPG computing center  for allowing us access to the Cobra and  Draco Supercomputers.
Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called "brain states" corresponding to "functional configurations" of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graphconvolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data.
Briefly, a set of k Brain States {BS} k, corresponding to functional configurations, is obtained from rsfMRI in non-human primates in the awake state and under different anesthetics. Then each {BS} k is ranked according to its similarity with an anatomical connectivity matrix defined in  [5]. In  [1, 12]  the authors demonstrated that dynamic long-range functional correlation matrices reduce to the underlying structural anatomical connectivity matrix during anesthesia (Fig. 1 ). In other words, the brain states {BS} k are directly related to the level of consciousness. The methodology described above includes the entire dataset for the extraction and annotation of {BS} k. Although the set of {BS} k is robust and stays stable when a new rsfMRI sequence is added to the database, the analysis process resumes the whole clustering, with potentially an appropriate initialization.
Material and Method Material: This study uses the dataset described in  [1]. Data were acquired either in the awake state or under different anesthetic conditions (ketamine, propofol, or sevoflurane anesthesia) in five macaque monkeys. Levels of anesthesia were defined by a clinical score (the monkey sedation scale) and continuous electroencephalography monitoring. 156 resting state functional MRI runs with corresponding structural MRI were acquired on a Siemens 3-Tesla with a customized single transmit-receiver surface coil. The spatial prepossessing was performed with the NSM pipeline described in  [3, 13] , with specific timeseries denoising operations  [1, 12]. Specifically, voxel time-series were filtered with low-pass (0. 05-Hz cutoff) and high-pass (0. 0025-Hz cutoff) filters and a zero-phase fast-Fourier notch filter (0.
The corresponding dynamic connectivity matrices led to a data array of shape 156x464x82×82 which was Fisher transformed to form a set of 156x464 samples. Each sample is more than a list of independent numbers and encodes the functional connectivity patterns. Dominant recurrent patterns of brain correlations (the {BS} k set) were determined using an unsupervised k-means clustering with k states (or classes), k in  [3, 9]. To avoid double dipping, ie. the use of the same dataset for the training and the testing , the k-means was performed on a subset of data, referred in the following as the train set. The labels associated to the remaining data, referred in the following as the test set, were recovered by selecting the closest brain state {BS} k. To this end, the correlation coefficients between the test set dynamic functional connectivity matrices and each {BS} k were computed.
Data partitioning: As the dataset is composed of 5 monkeys, with 156 rsfMRI runs in different arousal conditions (31 runs for the awake state, 25 runs for ketamine anesthesia, 25 runs for moderate propofol sedation, 30 runs for deep propofol anesthesia, 25 runs for moderate sevoflurane sedation, and 20 runs for deep sevoflurane anesthesia), we propose to leave one monkey out as the test set (35 runs). The four other monkeys form the train set (121 runs). We further checked that both train and test sets have matching proportions of arousal conditions and brain states labels. The test set was set aside and not used during training. The train set was further split into validation and training folds using a 3fold stratified cross validation approach. Model architecture: The BrainNetCNN  [2]  is designed specifically for network data input, and in our application will enforce the functional connectivity patterns.