dataesr/scientific-paragraphs-categorization huggingface dataset:
updating users profile using the detected new habits. The process of activity recognition analyses the sensors readings from the database to determine an activity that is occurring in the home. The raw database is analyzed also to detect the potential preferences represented by a set of (situation, action, feedback) triples. A potential preference is registered when: 1. an automated action by the smart home followed by a user explicit feedback is encountered, and 2. when implicit feedback is encountered (a change of an actuator value by the user him/herself). Those mentioned processes will fill two databases called "potential preferences" and "detected activities". These databases will be the entry for the data mining and learning processes that are responsible for updating the learned reward function of the Markov Decision Process.
During this learning process, the learned reward function is generalized to cover unknown situations and new users by determining the key attributes for each possible action. The detected important attributes and reward functions are saved in two eponymous databases. The last process is responsible for deciding and controlling the actuators to change the environment state using the action chosen by the MDP policy. The components and their functionality are described in detail in the following part of this section. Potential preferences are registered in a database and used to learn or update the generalized reward function (generalized adaptive rules). We use, in this work, methods of generalization that are based on detecting important attributes for each decision (see Section 3. 6). The decision making process uses the generalized reward function to plan optimal decisions that are adaptive to the current situation.
Data Processing and Acquisition The data of the different sensors and the results of the actuators' actions are acquired wirelessly (all the sensors are connected in a Zigbee  [34]  network). A Java-based software (Cleobee, V5. 10, CLEODE, Lannion, France)  [35]  is responsible for the acquisition of the sensors and the integration of all the data into a MySQL database that is kept on a computer in a technical room near the living lab. The acquisitions are centralized on only one computer by this software. The software is responsible for receiving, ordering and time-stamping these readings; therefore, no synchronization is needed. We construct, from these readings of sensors, what we call a Raw Data Element (RDE), which is defined as follows.
Definition 1. A non-exhaustive description of an RDE can be represented in the tuple id, type, value, timestamp , where: • id is the identifier of the RDE in the database, • type is the description of the type of the RDE, • value describes the value of the RDE, • timestamp is the timestamp describing the time that the changing of the RDE value took place. Example 1. An RDE of the type "level of brightness" can have a value ∈ [0, 1000], an RDE of the type "feedback" can have a value ∈ [-1, 1] and an RDE of the type "Kitchen light switch" can have a value ∈ {on, o f f }. The interactive table (Figure  4 ) has a web interface that can allow the person to perform the following actions: • Check the state of the different sensors of the room and have information on the environment, • Perform actions on the room such as turn on/off lights, power outlets, etc.
Activity Recognition In a previous work  [19] , we obtained some results on two different databases  [37, 38]  with different methods. The results are summarized in Table  1. Table  1. Overall accuracy (%) results on the two datasets with and without the Unknown class. Table  3  of  [19]. the (diff. ) column represent the different between both conditions (with and without unknown class). Considering the available information and the different sensors that we have, we are closer to the HIS (French Health Smarthome) dataset than the SH (Sweet-Home) dataset presented in this publication. As a consequence, the right part of Table  1  shows us that the results between all the methods are relatively close to each other. As we have, since this work, improved our results with SVM, and as the implementation and the learning are largely faster than the other, we will focus on this method for our activity recognition part.
An attribute at ∈ AT is considered important for an action a if the feedback value towards a depends on the value of at. The generalization of the learned rewards allows the system to adapt to unknown situations (new users or un-encountered states). The architecture backs up all potential preferences in the associated database, so there is no loss of information because of the generalization. The potential preferences database is continuously used in the process of detecting important attributes. The detection of important attributes is mainly triggered by the detection of a contradiction between a newly received potential preference and the generalized rewards. The main drawback of such algorithms is the fact that they are risky until they converge to a correct generalization. The gain in the number of needed interactions and the risk in performance before converging are evaluated in our simulated experiments in Section 4.
Convergence Results We present a convergence analysis of the GVS algorithm compared with a simple one with no generalization algorithm. First, we analyze some results of simulated data using an epoch greedy mechanism with = 10. The upper schema of Figure  5  shows that the algorithm with generalization converges optimally after nine epochs (900 simulated potential preferences). However, the simple algorithm does not converge before 1000 epochs. It is important to mention that the randomly chosen parameters of this experiment led to a state space of 1944 possible states. The figure shows the number of negative actions (actions chosen by the algorithm and leads to a negative feedback given by the predefined rules of preferences). The number of negative actions is counted after generating the n potential preferences (step 3 in the procedure) where the learned rewards from the previous loop are used to generate the actions.
It will also be the moment at which we will be able to create the models of activities adapted to the environment that we have. In a second phase, the potential preferences from the raw data collected in the first phase will be used to learn a generalized reward function (using the GVS algorithm), and the smart home will be more active while observing the users' implicit feedback and explicit actions to update the learned reward function. Users of the second phase will be asked to fill out a satisfaction questionnaire based on a Likert scale that analyzes their level of satisfaction towards the automatic behavior of the smart home and their opinion towards the way they interact with the smart home.
, 2013). Whole genome sequencing was performed at GATC Biotech (Germany) using an Illumina HiSeq system and analysed as described previously  (Holt et al. , 2013). Although sequence reads for most other data sets are publically available in the NCBI Short Read Archive (SRA), we obtained the original alignments from the authors wherever possible. With this approach we take advantage of domain knowledge of the original studies for choice of reference sequence and identification of repetitive or horizontally transferred sequences, which are important for the accurate generation of SNP.
We also find that the sampling times appear to play an important role in determining their rate. Our results provide the first genomic perspective of bacterial rates of evolution, thereby improving our understanding of the timescale over which they diversify. alignments. We removed outgroup taxa and samples that were distantly related to the majority of sequences to limit our analyses to the taxonomic group of interest and to minimize the artificial inflation of among-lineage rate variation due to the presence of very long branches in the phylogenetic trees. For the Neisseria meningitidis and M. tuberculosis Lineages 2 and 4 data sets we obtained sequence reads from the Short Read Archive and called SNPs using the RedDog pipeline [as described previously  (Schultz et al. , 2016) ; available at https://github.
, 1995) , Geneconv  (Padidam et al. , 1999) , Maxchi  (Smith, 1992) , Siscan  (Gibbs et al. , 2000) , RDP  (Martin & Rybicki, 2000)  and 3seq  (Boni et al. , 2007). Accordingly, we discarded an entire data set of N. meningitidis in which nearly half of the sequences displayed recombination (Table  S1 ). We also visually checked that the alignments were free of additional obviously recombining regions. Our final data set comprised 16 bacterial species from 13 genera, with genome sizes ranging from 1. 4 Mbp (Streptococcus pyogenes) to 6. 2 Mbp (Pseudomonas aeruginosa). The data set sizes ranged from 189 (Streptococcus pneumonia and M. tuberculosis Lineage 4) to 15 (Mycobacterium leprae ancient DNA) sequences, and alignment lengths from 15 394 SNPs (Acinetobacter baumannii) to 402 (M. tuberculosis Lineage 4) SNPs.
All the data sets analysed here included sampling times associated with each sequence in the form of year of isolation. Individual data sets spanned sampling ranges of approximately 2000 years in the case of M. leprae to 2. 5 years in Staphylococcus aureus ST8:USA300. Four data sets included ancient DNA samples: M. leprae  (Schuenemann et al. , 2013) , two Yersinia pestis data sets  (Wagner et al. , 2014) , and one M. tuberculosis data set comprising strains sampled from New World mummies, animal strains and human Lineage 6  (Bos et al. , 2014). In M. leprae, the oldest sample had an estimated age of approximately 2000 years  (Schuenemann et al. , 2013) , while in the Y. pestis data sets the oldest samples had ages of approximately 600 and 1500 years  (Wagner et al. , 2014). The New World mummy M.
tuberculosis samples had ages of approximately 900 years  (Bos et al. , 2014). Notably, one of the Y. pestis data sets included samples from both contemporary strains and those from the first, second and third plague pandemics, while the other is a subset that only included sequences from the second pandemic that began with the Black Death and contemporary strains. The remaining data sets had sampling times from a few years to several decades (Table  S1 ). All the data sets used here are available online (http://zenodo. org/record/ 45951#. Vr1M-JN95E4). Maximum-likelihood (ML) analysis and root-to-tip regression. We estimated ML trees using PhyML v3. 1  (Guindon et al. , 2010) , employing the GTR+G substitution model with four categories for the G distribution of amongsite rate heterogeneity and sub-tree pruning regrafting branch-swapping.
To test for this pattern we conducted a least squares linear regression for the genome-wide rate as a function of sampling time. Importantly, only rate estimates with strong and moderate temporal structure were included in this analysis. We used a log 10 transformation because our rate estimates span several orders of magnitude. A potential shortcoming of this analysis is that the data do not necessarily represent independent samples because some correspond to closely related lineages. This can be addressed by using phylogenetic independent contrasts, or phylogenetic generalized least squares. However, these methods require a phylogenetic tree of the evolutionary relationships of all data points that cannot be estimated for our data because the sites in different SNP alignments are not necessarily homologous.
For example, our N. meningitidis data set exhibited extensive recombination, precluding standard phylogenetic analyses. The lowest rate estimates obtained here, at ~10 À8 substitutions per site year À1 , were also comparable to previous studies of slowly evolving bacteria, notably M. tuberculosis  (Kay et al. , 2015). Importantly, however, even though they were relatively low, these rates were sufficiently rapid to be accurately estimated using genomic-scale data. In contrast, rate estimates lower than about 1. 5Â10 À8 substitutions per site year À1 could not be validated using our methods and hence may require data sampled over far longer time periods. However, it was also noteworthy that the M. leprae and one of the Y. pestis data sets did not have sufficient temporal structure for rate estimation despite the availability of ancient DNA.
Conversely, the longer-term (and lower) evolutionary rates of the type inferred here would tend to over-estimate the time-scale of bacterial transmission when applied to outbreak data. The relationship between evolutionary rate and sampling time can be used to assess the extent to which extrapolating rates of evolution over different time-scales will lead to a bias in estimates of divergence times. To this end, future studies should compare bacterial data sets of the same species across different sampling time-frames to more precisely determine the nature of the time-dependent curve. Strikingly, in some cases the time-dependent pattern appears to hold within closely related bacterial lineages, such as our A. baumanii and Salmonella enterica Paratyphi A data sets. However, it is notable that both reliable rate estimates for M.
Acknowledgements This research was funded by the  NHMRC (Australia Fellowship # AF30  to E. C. H. , Career Development Fellowship # 1061409  to K. E. H. ). S. D. was supported by a  McKenzie fellowship  from the  University of Melbourne. The following researchers kindly provided us with sequence alignments:  Johannes Krause  (M. leprae),  Alexander Herbig  (M. leprae),  Zhemin Zhou  ( Salmonella Paratyphi  A),  Simon Harris  (B. pertussis),  Julian Parkhill  (B. pertussis),  Paul McAdam  (M. tuberculosis Lineage 2, and Staphylococcus aureus  CC398 ),  Stephano Garasia  (K. pneumoniae),  Sarah Baines  (Staphylococcus aureus  ST239 ),  Anne-Catrin Uhlemann  (Staphylococcus aureus  ST-USA300 ),  Mark Davies  (Streptococcus pyogenes),  Mark Schultz  (A. baumannii),  Melissa Ward  (Staphylococcus aureus  CC398 ) and  Iñaki Comas  (M.
Data-dependent analysis was carried out, using a resolution of 30,000 for the full MS spectrum, followed by 10 MS/MS spectra in the linear ion trap. MS spectra were collected over an m/z range of 300-2,000. MS/MS scans were collected using a threshold energy of 35 for collision induced dissociation. LC-MS/MS data were then searched against a protein database (UniProt KB) using the Mascot search engine programme (Matrix Science, UK)  61. Database search parameters were set with a precursor tolerance of 5 p. p. m. and a fragment ion mass tolerance of 0. 8 Da. Two missed enzyme cleavages were allowed and variable modifications for oxidized methionine, carbamidomethyl cysteine, pyroglutamic acid, phosphorylated serine, threonine, tyrosine, tert-butyloxycarbonyllysine, norbornene-lysine and prop-2-yn-1-yloxycarbonyl-lysine were included.
MS/MS data were validated using the Scaffold programme (Proteome Software Inc. , USA). All data were additionally interrogated manually. Peptide identifications were accepted if they could be established at 480. 0% probability as specified by the Peptide Prophet algorithm  62. Protein probabilities were assigned by the Protein Prophet algorithm  62. Using these stringent identification parameters, the rate of false-positive identifications is o1%. All non-mouse proteins that were detected but could not be matched to a mouse protein (in Swissprot-TrEMBL) were not included in the analysis below (foe example, human keratins). The composition of B1. 5 MDa complexes from TAP-GluN1, PSD95-TAP and WT control were compared (Supplementary Table  1 ) using a stringent threshold of detection (Z2 unique peptides and Z1 unique peptide in Z2 replicate samples).
This level of stringency was sufficient to detect 55 proteins in TAP-GluN1, 89 proteins in PSD95-TAP and only one protein in the WT control (IgG). Since it expected that the 'tails' of the bands may partially overlap, the differential composition of proteins in the B0. 8 and B1. 5 MDa TAP-GluN1 bands were validated by BNP immunoblot of TAP-purified complexes (Supplementary Fig. 2d ). All MS data (raw data, mascot files and scaffold file) were processed with the PRIDE converter  63  and deposited in the PRIDE database (http://www. ebi. ac. uk/ pride/)  64  with ProteomeXchange accession number: PXD000011.
Peptides were recovered and desalted using uC18 ZipTip (MIllipore) and eluted to a MALDI target plate using 2 ml a-cyano-4-hydroxycinnamic acid matrix (Sigma) in 50% acetonitrile/0. 1% trifluoroacetic acid. Peptide mass were determined using a MALDI micro MX mass spectrometer (Waters) in reflection mode and analysed with Masslynx software. Database searches of the mass fingerprint data were performed using Mascot (http://www. matrixscience. com). Generation of Glun2b 2BdV knock-in mice. The Glun2b 2BdV knock-in mouse was engineered by standard mouse genetic protocols in which the GluN2B terminal valine (1,482) was deleted, leaving the last codon for Asp1471 before the stop codon (Supplementary Fig. 4a ). In brief, 5. 9 kb 5 0 and 1. 8 kb 3 0 homology arms were used to construct the gene targeting vector for GluN2B terminal valine (1,482) deletion mutation.
The targeting vector also contained a neomycin resistance cassette (pgk-Neo-pA) flanked by loxP sites at the 3 0 of the mutation and diphtheria toxin A cassette for negative selection. A linearized targeting vector was electroporated into E14TG2a ES cells and positive clones were identified and validated by genomic southern blots with 5 0 and 3 0 flanking probes (data not shown). Positive ES clones were injected into C57/BL6 blastocysts to generate mice chimeric for the Glun2b 2BdV mutation. After germline transmission, the Glun2b 2BdV mutants were crossed with a Cre-deleter line and the neo cassette was removed. Inter-crossing heterozygous mice produced viable homozygous Glun2b 2BdV/2BdV knock-in mutants. The detailed phenotypic characterization of this mutant will be described elsewhere.
Conjugated antibodies: mouse Flag-HRP (1:40,000, Sigma, A8592), goat mouse-HRP (1:20,000, Millipore, 12-349) and goat rabbit-HRP (1:20,000, Millipore, 12-348). Antibodies used for Fig. 7  are listed in Supplementary Data 1. SDS-PAGE immunoblot. Images in Figs 2b, 4a,b,d and 6h have been cropped for presentation. Full size images are presented in Supplementary Figs  6 7 8 , respectively. Human brain samples. Three cortical samples were obtained from neurosurgical operations. Tissue was immediately chilled in ice for 30-40 min after which they were frozen at À 70 °C. Tissue was not defrosted until used extraction and BNP. In all cases prior written informed consent had been obtained, and the study approved by the local regional ethics committee (Lothian Region Ethics Committee /2004/4/16). Animals.
Acknowledgements Kathryn Elsegood  ( University of Edinburgh ),  David Fricker  (Synome Ltd. ),  Eleanor Tuck  ( Synome Ltd. ) for technical support in ES culture, mouse genotyping and colony management. Rolf Sprengel  and  Peter Seeburg  ( MPI Heidelberg ) for generously providing the Glun2a dC knock-in mouse. Leonard Packman  ( PNAC, Dept Biochemistry, University of Cambridge ),  Douglas Lamont ,  Kenneth Beattie  ( Fingerprint proteomic facility, University of Dundee ),  Mark Skehel  and  Sarah Maslen  ( MRC Laboratory of Molecular Biology, University of Cambridge ) for mass spectrometry services. Ian Whittle  ( University of Edinburgh ) for human neocortical biopsy material. Nushan Gunawardana  and  Rohit Mittal  for help with cell culture ( MRC Laboratory of Molecular Biology ). Nigel Unwin  ( MRC Laboratory of Molecular Biology, Cambridge ) for advice, and  Esperanza Fernandez  ( Katholieke Universiteit, Leuven ) for providing Psd95 TAP mice.
Given the lack of a French database reporting the frequencies of the different meanings of polysemous words, and given the insufficient number of polysemous words in French free-association databases, we generated one W1 and one Tgt for each of the two meanings of W2. Then each polysemous W2 was paired with another semantically unrelated W2 in order to build all trial types. This counterbalanced design ensured that each target word was equally presented in the five semantic conditions (see Fig. 1a  and Supplementary Tables  1a  and 3 ): Congruent (Cg): W1 and Tgt are associated with the same meaning of polysemous W2 (e. g. : HAND-PALM-WRIST). Incongruent (Icg): W1 and Tgt are associated with different meanings of polysemous W2 (e. g. : HAND-PALM-TREE). Initial (Init): W1 only is associated with one meaning of the polysemous W2 (e.
In order to avoid an overly long experiment we only used masked trials. In order to avoid ERPs visual artifact, Target presentation was fixed to 200 ms. In order to limit the time of the all experiment the inter-trial jitter was slightly decreased (1000 1300, or 1600 ms). EEG recording and processing. EEG was sampled at 250 Hz with a 256-electrode geodesic sensor net connected to a high impendence amplifier (EGI, Oregon, USA) referenced to the vertex. Impedances were controlled inferior to 100 kX. Data were filtered from 0. 5 Hz to 30 Hz. Trials were segmented from À300 ms to 900 ms relative to the onset of the target stimulus (word/ pseudo-word). Trials with more than 10 channels containing voltage exceeding 6100 lV were rejected. For the remaining trials, bad channels were interpolated from contiguous electrodes.
Acknowledgements We thank  Christophe Pallier  and  Tal Seidel Malkinson  for their help on statistical issues. This work was supported by the  Fondation pour la Recherche Medicale (FRM ; "Equipe FRM 2015" grant to LN), by  INSERM  (" Poste Accueil INSERM " from November 2010 to October 2012 to BR), and by the program " Investissements d'avenir "  ANR-10-IAIHU-06. We thank the editor,  Prof. Anil K. Seth , as well as the two anonymous reviewers for their constructive feedback. Data are available on request.
The main challenge of this work is therefore to re-interpret the old well data to provide precise and detailed chronolithostratigraphic logs for GPK-1 and GPK-2, especially in their sedimentary portions. These new investigations of the GPK wells have been possible due to the new data collected in the recent geothermal wells at Rittershoffen (GRT-1 and GRT-2, located 6. 5 km from Soultz-sous-Forêts), which are characterized by a quite complete stratigraphic succession. Both sites have been explored by deep drilling operations aiming to exploit the heat extracted from a deep granitic basement (Palaeozoic) covered by a stack of 1. 4-and 2. 2-km-thick sedimentary rocks (Mesozoic to Cenozoic) at Soultz-sous-Forêts and Rittershoffen, respectively. Thus, the Rittershoffen chrono-lithostratigraphic logs have been used as a baseline to interpret the sedimentary succession in GPK-1 and GPK-2.
Well logs Some old wells (e. g. , GPK-2) do not have a standard masterlog with a detailed description of the cuttings. These wells only have an internal technical report with the lithostratigraphic interpretation, which cannot be taken as granted. Therefore, if a borehole is well described, like in Rittershoffen, and has a GR log, it is possible to make wellto-well correlations with the other wells due to the radioactive log. A well log  (Howell and Frosh 1939)  is a continuous record of the variation of a parameter as a function of depth and presents significant advantages, such as providing continuous geological data in addition to cuttings or cores  (Desbrandes 1985). Logs are therefore very useful when making multi-well spatial correlations and can provide precise information on sedimentary deposits and lithologic variations.