dataesr/scientific-paragraphs-categorization huggingface dataset:
For generalisation, we performed the data analysis on a separate 100 HCP participants. The data analysis consisted of computing the level of non-equilibrium/non-reversibility, Rሺtሻ, using the output of TENET on this generalisation set after being trained on the bulk of the data. As specified in details in the Methods, Rሺtሻ is computed as the accuracy of classification of forward and time reversal of backward trajectory of the global timeseries (across sliding windows at time t and across participants). Perfect classification of maximal non-reversibility is thus assigned a value of 1 and where 0 corresponds to full reversibility.
The differences between rest and tasks were similar to the results of the mean of the non-equilibrium in the sense that there were significant differences between all (p<0. 001, Wilcoxon rank sum; except between MOTOR vs LANGUAGE and WM and GAMBLING) but importantly for this measure, the SOCIAL task had the lowest variability over time, which was much lower than REST. On the other hand, REST is showing one of the largest levels of nonstationarity which is consistent with the idea that resting state is more intrinsically driven and less extrinsically controlled by the environment. This can also be appreciated from Figure  3B , where the two panels show time evolution of the levels of non-equilibrium/non-reversibility, Rሺtሻ, for REST (left) and the SOCIAL task (right). As mentioned above, the level of equilibrium is associated with the fluxes of transitions between different states, i.
Network-level analysis of HCP data differentiates between HCP and task We performed a network level analysis of HCP data using the TENET framework. For this analysis we used the Schaefer 500 parcellation where each parcel belongs to one of the seven Yeo networks. The TENET framework was then applied in the same manner as in the global analysis but now used on the parcels belonging to each network. Again, in order to balance the data, we used 890 HCP participants with the longest possible duration available in all conditions (176 TRs). The results are from the generalisation that was performed on a separate 100 HCP participants (see Methods). We used the same sliding window size and shifting of this window as in the global level analysis but now the input is the window size multiplied by the number of parcels for a given level analysis of HCP rest and tasks.
Analysis of the non-equilibrium/non-reversibility of the seven Yeo resting state networks showed differential responses between rest and tasks for the seven resting state networks. Figure  4A  shows boxplots of the level of non-equilibrium for each of the seven Yeo networks for rest and the seven tasks, which is also shown as spider plots in Figure  4B. Comparable to the global level analysis there is lower levels of non-equilibrium for rest compared to the seven tasks. This suggests that REST is more in equilibrium and therefore more intrinsically driven. Interestingly, the REST condition shows the highest levels of non-equilibrium in the default mode network (DMN) and Visual (VIS) network. Similarly, the sensory networks  (VIS and SOM)  are showing the highest level of non-equilibrium across the seven tasks (except for LANGUAGE), and thus most driven by the environment.
Node-level analysis of HCP data reveals distinct patterns of local non-equilibrium/non-reversibility Beyond the global and network level analyses, we were interested in studying how the node-level nonequilibrium/non-reversibility can distinguish the local level of influence on brain regions by the environment. We applied the TENET framework at the node-level using exactly the same amount of data across all rest and seven tasks, similar as above (see Methods). For the training of TENET, in order to perfectly balance the data and avoid any potential source of bias, we used 890 HCP participants with the longest possible duration available in all conditions (176 TRs). For generalisation, we performed the data analysis on a separate 100 HCP participants. The node-level rendering for non-equilibrium/non-reversibility for REST and the SOCIAL task is shown in Figure  5A.
Using the arrow of time in neuropsychiatric disease Given that the TENET framework by design measures how the environment is driving the brain, and its high level of sensitivity demonstrated above, it would appear a promising avenue for better characterising between neuropsychiatric diseases. We therefore applied the TENET framework on the large public UCLA dataset of neuropsychiatric patients with schizophrenia, bipolar and ADHD and matched control group of participants. Figure  6  shows the results of using the TENET framework to establish the reversibility on the global and local node levels for the four groups. The left panel of Figure  6A  shows boxplots of the average reversibility across time, where the control group was significantly higher than each of the neuropsychiatric groups (all p<0.
Node-level results Equally, the TENET framework can be applied at different scales. We investigated this measure at the level of resting state networks and at the node level. Similar to the findings at the global scale, the findings at the local scale reveal clear inter-regional heterogeneities between rest and tasks (see Figure  5 ). The findings show that in many cases higher associative brain regions are more driven by the environment during task performance in the HCP dataset. Specifically, Figure  5A  shows the nodelevel rendering for non-equilibrium/non-reversibility for REST and the SOCIAL task. Similar to the results for the global level, these are the two conditions with the lowest and highest levels of nonequilibrium. The power of the TENET framework is perhaps illustrated in the results for the MOTOR task, where the thresholded results show selective engagement of the somatomotor regions as expected  (Barch et al.
, 2013)  but equally the results show engagement in visual cortices and midline medial prefrontal regions. Another good example is the LANGUAGE task, where the results show broad engagement of ventral lateral prefrontal cortex, superior and inferior temporal cortex (including the anterior temporal poles bilaterally  (Barch et al. , 2013; Binder et al. , 2011). Also, as expected from this primarily auditory task, the visual regions are not in the top 15% of driven regions. Similarly, the WM task shows high levels of driving in regions including the medial prefrontal cortex, posterior cingulate, and the occipital-parietal junction, fully consistent with the literature  (Barch et al. , 2013; Drobyshevsky et al. , 2006). Many previous studies of the HCP dataset have used conventional neuroimaging categorical analyses to bring out the differences between conditions and baseline  (Barch et al.
Neuroimaging Ethics For the HCP dataset, the Washington University-University of Minnesota (WU-Minn HCP) Consortium obtained full informed consent from all participants, and research procedures and ethical guidelines were followed in accordance with Washington University institutional review board approval. For the UCLA dataset, as detailed in  (Poldrack et al. , 2016) , the Consortium for Neuropsychiatric Phenomics recruited neuropsychiatric participants and healthy controls who gave written informed consent following procedures approved by the Institutional Review Boards at UCLA and the Los Angeles County Department of Mental Health.
Neuroimaging Participants UCLA rest Consortium for Neuropsychiatric Phenomics published a dataset with neuroimaging as well as phenotypic information for 272 participants. We used the preprocessed data with a total of 261 participants, since seven of the participants were missing T1 weighted scans  (Gorgolewski et al. , 2017)  and three healthy controls and one ADHD patient were missing resting state scans. The total population analysed consists of 122 healthy controls, as well as participants with diagnoses of adult ADHD (40 patients), bipolar disorder (49 patients) and schizophrenia (50 patients).
HCP preprocessing and extraction of functional timeseries in fMRI resting state and task data The preprocessing of the HCP resting state and task datasets is described in details on the HCP website. Briefly, the data is preprocessed using the HCP pipeline which is using standardized methods using FSL (FMRIB Software Library), FreeSurfer, and the Connectome Workbench software  (Glasser et al. , 2013; Smith et al. , 2013b). This standard preprocessing included correction for spatial and gradient distortions and head motion, intensity normalization and bias field removal, registration to the T1 weighted structural image, transformation to the 2mm Montreal Neurological Institute (MNI) space, and using the FIX artefact removal procedure  (Navarro Schroder et al. , 2015; Smith et al. , 2013b). The head motion parameters were regressed out and structured artefacts were removed by ICA+FIX processing (Independent Component Analysis followed by FMRIB's ICA-based X-noiseifier  (Griffanti et al.
, 2014; Salimi-Khorshidi et al. , 2014) ). Preprocessed timeseries of all grayordinates are in HCP CIFTI grayordinates standard space and available in the surface-based CIFTI file for each participants for resting state and each of the seven tasks. We used a custom-made Matlab script using the ft_read_cifti function (Fieldtrip toolbox  (Oostenveld et al. , 2011) ) to extract the average timeseries of all the grayordinates in each region of the DK80 parcellation, which are defined in the HCP CIFTI grayordinates standard space. The BOLD timeseries were filtered using a second-order Butterworth filter in the range of 0. 008-0. 08Hz.
UCLA preprocessing and extraction of functional timeseries in fMRI resting state data The preprocessing of UCLA resting state datasets is described in details on the website and in the paper by Gorgolewski and colleagues  (Gorgolewski et al. , 2017). Briefly, the preprocessing was performed using FMRIPREP version 0. 4. 4 (http://fmriprep. readthedocs. io). This robust preprocessing pipeline is based on the Nipype workflow engine7 and aims to combine different implementations of various MR signal processing algorithms (from established software packages) to deliver a robust spatial normalization and nuisance estimation workflow.
HCP dataset For the large-scale HCP dataset, we performed the reversibility and non-equilibrium analyses at three different spatial scales: Global, network and local node-level. For each scale, we trained and analysed three different TENETs, one for each spatial scale. We used roughly 90% of the data for the training set (890 participants) and the remaining 10% for the test and validation sets (100 participants). For the global level, the input patterns consisted of the windowed timeseries across the whole brain in the DK80 parcellation, i. e. each pattern consisted of 80 windowed timeseries. For the network level, the input patterns consisted of the windowed timeseries in the Schaefer500 parcellation, i. e. which each parcel belong to one of the seven Yeo resting state networks. We performed the analysis for the Schaefer500 parcels belonging to each of the seven Yeo networks and thus obtained a measure of non-equilibrium for each Yeo network.
72 seconds) and for nodelevel analysis, we used a window size of 150 TRs. These were then shifted forward until the end of the datasets (with a length of 3 TRs for global and network level analysis and 2 TRs for node-level analysis). For each of the sliding windows, we generated two input patterns containing 1) forward and 2) time reversed backward sliding windowed timeseries and for the supervised learning, we associated each pattern with the output class label of forward and backward, respectively.
UCLA dataset For the analysis of the smaller scale UCLA dataset with coarser TR (2 seconds) and shorter duration (152 TRs), we carried out different spatial scale analyses at the level of global and node-level for the resting state data of control and three neuropsychiatric disorders (schizophrenia, bipolar disorder and ADHD). For this much smaller dataset of 261 participants, we used roughly 90% of the data for the training set (40 participants in each of the four conditions) and the remaining data for the test and validation sets (i. e. 36 participants for the global and 35 participants for node-level analysis). For the global analysis, we shuffled the data 500 times in order to perform non-parametric significance testing (for the boxplot in Figure  6A ). For the node analysis we shuffled the data 10 times and computed the average node-level of non-equilibrium/non-reversibility.
The input to TENET consisted of sliding windows from the BOLD neuroimaging data from all the participants. We generated these sliding windows with slightly different parameters for the two types of analysis: For global level analysis, we used a windows size of 10 TRs of 2 seconds and for node-level analysis, we used a window size of 20 TRs. These were then shifted forward until the end of the dataset with a length of 1 TR.
e. global ܰ ൌ 80, network ܰ ൌ 7, node level ܰ ൌ 1) and ‫ݓ‬ is the size of the sliding window. This is followed by 10 fully connected layers including 1) batch normalisation, which normalises a mini-batch of data across all observations for each channel independently and 2) a non-linearity operation implemented using reluLayer, which performs a threshold operation to each element of the input, where any value less than zero is set to zero. The dimensions of the 10 layers are  [2048;1024;512;256;128;64;32;16;8;4]. The final layer of TENET is a softmax classification layer of dimension [2], corresponding the two possible output class labels (forward and backward). For training TENET, we used the deep learning algorithm ADAM, which is an algorithm for firstorder gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments to attenuate the effects of noise as it is implemented in MATLAB with the recommended default parameters  (Kingma and Ba, 2014).
Acknowledgements G. D. is supported  Spanish national  research project (ref. PID2019-105772GB-I00 MCIU AEI ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI) ;  HBP SGA3 Human Brain Project Specific Grant Agreement 3  (grant agreement no. 945539 ), funded by the  EU   H2020 FET Flagship programme ;  SGR Research Support Group  support (ref. 2017 SGR 1545 ), funded by the  Catalan Agency for Management of University and Research Grants (AGAUR) ;  Neurotwin Digital twins for model-driven non-invasive electrical brain stimulation  (grant agreement ID:  101017716 ) funded by the  EU   H2020 FET Proactive programme ;  euSNN European School of Network Neuroscience  (grant agreement ID:  860563 ) funded by the  EU   H2020 MSCA-ITN Innovative Training Networks ;  CECH The Emerging Human Brain Cluster  (Id.
001-P-001682 ) within the framework of the  European Research Development Fund Operational Program of Catalonia 2014-2020 ;  Brain-Connects: Brain Connectivity during Stroke Recovery and Rehabilitation  (id. 201725. 33) funded by the  Fundacio La Marato TV3; Corticity ,  FLAG-ERA JTC 2017  (ref. PCI2018-092891 ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI). MLK is supported by the  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ), and  Centre for Eudaimonia and Human Flourishing  funded by the  Pettit and Carlsberg Foundations.
Visualisation and Quantification of Biofilm After 24 h, 48 h and 72 h of incubation, biofilm formation was recorded using a fluorescence inverted microscope DM IRB (Leica Biosystems, Nanterre, France) coupled with a CoolSNAP FX camera (Roper Scientific, Lisses, France). A video was recorded in real time at a rate of 60 frames per seconds. MetaVue TM software (Molecular Devices, Sunnyvale, CA, USA) was used for imaging. ImageJ ® was utilized to colour black and white images, to include scale bars and to calculate biofilm percentage. The 16-bit grayscale images were adjusted with the threshold function to fit the bacterial structure and were analysed using the "Analyze Particles" function. The percentage of biofilm was evaluated before and after automatized debridement and after 24 h of antibiotics treatment following the same protocol as previously described  [25, 26].
Results are presented as the mean ± standard deviation. Statistics were performed using a t-test on GraphPad Prism version 7. Ns, non-significant. Funding: This research was funded by CHU Nîmes, Thématique Phare. CP is supported by a PhD grant from Biofilm Pharma (Bourse CIFRE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments: A. P. : C. D. -R. , A. S. and J. -P. L. belong to the  FHU INCh (Federation Hospitalo Universitaire Infections Chroniques, Aviesan). We thank the  Nîmes University hospital  for its structural, human and financial support through the award obtained by our team during the internal call for tenders "Thématiques phares". We thank  Sarah Kabani  for her editing assistance. This study was presented orally at the 31st ECCMID (N •  1469 ).
Hence, these methods can be applied to all historic SuperDARN data, including data from radars that are no longer operational, or for which the hardware has been significantly changed over the operating lifetime (as long as interferometer data have been recorded by that radar at that time). • As the elevation angle determination involves a phase difference between two signals, estimations of  dif f by all the methods may be affected by a 2 phase ambiguity. That is, the same phase difference between the main and interferometer array signals (which relates to the elevation angle) will occur even if one of the phase values is shifted by a multiple of 2. This means that the same calibration adjustment will occur for a  dif f value that is shifted by   , where  is an integer, and  is the operational frequency of the radar.
The separation of ionospheric and ground scatter echoes using the default SuperDARN ground scatter criteria can be challenging for some radars, particularly those at mid-latitudes where low-velocity ionospheric scatter is often misinterpreted as ground scatter (e. g. ,  Ribeiro et al. , 2011; Burrell et al. , 2018). • This method assumes  dif f was properly calibrated for the SAS radar when the Chisham VHM was developed.
Longer-term trends and variations in  dif f may exist, such as solar cycle variations, or changes due to the degradation of the radar hardware over time. However, it is difficult to identify such variations clearly without longer datasets. The small point-to-point fluctuations are clearly evident in the comparison plots presented in Figs. 3, 4,  and 5. These fluctuations are likely to be the result of random errors due to the varying amounts of data being used to determine each of the estimates. In some cases, such as the 1-minute E-region method estimates presented in Fig. 3 , there are occasionally estimates that are significantly in error. This is likely due to only small amounts of E-region echoes being available on these days. These point-to-point fluctuations are unlikely to represent true changes in the actual  dif f but are likely a result of uncertainties in the  dif f estimation techniques.
Summary and recommendations Improvements to the calibration of SuperDARN interferometers can significantly improve the accuracy of the determined elevation angles, and hence, of the geolocation of SuperDARN scattering locations. This paper has shown how this calibration can be satisfactorily performed purely using existing historical SuperDARN data sets, without the need for engineering measurements or access to the radars. The paper can be summarised as follows: • Five different complementary methods for determining  dif f have been presented. • Comparisons of  dif f estimates using the different methods are roughly consistent (mostly within ∼5 ns). However, it must be stressed that there is presently no consensus as to which of the methods is the 'best' or 'most accurate'. • Errors in  dif f of ∼5 ns lead to only small ground range errors at near ranges and ∼50 km error at ranges around 2000 km.
As a result of the findings presented in the paper, the authors make the following recommendations for scientists using SuperDARN elevation angle measurements: • For high-spatial resolution analysis, the use of elevation angle measurements is recommended over virtual height models. • Do not always assume the  dif f values in the radar hardware files are appropriate for an interval of data. The above methods can be used to estimate  dif f for smaller intervals of time, to provide more accurate elevation angles. • Always assess the uncertainty in elevation angle determinations that is introduced by the uncertainty in  dif f. • Do not use elevation angles within 5 • of the maximum elevation angle for a given radar, beam and frequency. • Always include values of  dif f in any publications that use elevation angles, as well as the details of the calibration methods and any associated software used to estimate this  dif f value.
Acknowledgements The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by the national scientific funding agencies of  Australia, Canada, China, France, Italy, Japan, Norway, South Africa, UK , and United States. The raw SuperDARN data are available from the  British Antarctic Survey (BAS)  SuperDARN data mirror (https://www. bas. ac. uk/project/superdarn). The operations of the SuperDARN Canada radars are supported by the  Canada Foundation for Innovation ,  Innovation Saskatchewan ,  Canada , and the  Canadian Space Agency. GC is supported as part of the  BAS Polar Science for Planet Earth Programme , funded by the  UK Natural Environment Research Council (NERC)  as part of  United Kingdom Research and Innovation (UKRI). AGB is supported by the  Chief of Naval Research.
EGT thanks the  National Science Foundation, USA (NSF ) for support under grant  OPP-1836426. AM thanks the  Programme National Soleil-Terre de l'Institut des Sciences de l'Univers (PNST/INSU)  funded by  CNRS, France ,  CNES, France  and  CEA, France  and the  Institut Paul Emile Victor (IPEV)  for supporting French SuperDARN activities. SGS acknowledges support from  NSF ,  USA  grants  AGS-1341925  and  AGS-1934997.
Introduction Using the Internet of Things (IoT) to stimulate transformational efficiencies in several application domains among which manufacturing, automotive, health and smart cities, is an idea that holds fabulous promises. Indeed, exploiting smart and connected devices to produce real-time data and to quicker take decisions provides new ways to make businesses more efficient, and to forge links between the digital world and the real. On the other hand, after the myriad of cyberattacks on IoT systems revealed during the few past years, experts have warned that IoT could harm people if IoT is left unsecured. As many technological concepts are involved under the IoT umbrella, it is indeed not surprising to observe that IoT systems are vulnerable to a wide range of security attacks. And it is likely that this security problem will grow more complex in the future, as long as new technologies and platforms will be proposed.
In  [14] , security models are devised with data collected from an IoT system. A manual security analysis is then performed to find potential attack scenarios, evaluate security metrics and assess the effectiveness of different defence strategies. Other works introduced specialised model-based testing (MbT) methods. Some of them are said to be active, i. e. security test cases are built by hands or automatically generated from a given (formal) specification, and are later used to experiment IoT systems  [15, 1, 22, 23]. These active testing techniques could complement MLCA to get larger event logs. Other methods are said to be passive because they are based upon monitoring tools, which detect the violation of security properties by checking rule satisfiability in the long run  [32, 6, 20].