dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset overview The studied ICP signals came from 10 adult patients suffering from traumatic brain injury, admitted to the ICU of the University Hospital of Saint-Etienne (France), between March 2022 and March 2023. For each of them, ICP was invasively monitored with an intraparenchymal sensor (Pressio, Sophysa, Orsay, France) for a duration of 8. 3 ± 5 days (min = 3. 8, max = 15) at a sampling frequency of 100Hz. The dataset used in this study was constituted by randomly sampling 5 one-hour sections for each record. 4 of them were affected to the training dataset, whereas the last one was affected to the testing dataset. After the pulses were preprocessed and individualized as described in section 3. 1, 1 of out 15 was selected to be part of the final datasets. Those pulses were labellized with the positions of P1 and P2 if both of them were visible, [0, 0] otherwise.
The training dataset was finally composed of 13,127 pulses, including 12,308 with a calculable P2/P1 ratio. Its testing counterpart was composed of 4,344 pulses, including with 3847 a calculable P2/P1 ratio. This proportions are in accordance with  Rashidinejad et al. ( [?] ) who estimated a missing subpeak probability at less than 10% based on their 700-hour dataset. To assess the performances of the final dataset, an additional 10-minute segment was randomly sampled from each of the 10 patients. This second testing dataset, hence divided into 10 contiguous segments, was composed of 7,399 pulses, among which 6,815 had a calculable P2/P1 ratio.
LSTM-FCN network The two above-mentioned architectures process the input data with different objectives. Whereas CNN focus on the neighborhood of each point, recurrent neural networks are meant to exploit the causalities inherent to sequential data. LSTM-FCN networks attempt to combine both strategies, and were specifically designed for time series classification [?]. Moreover, Mataczynski et al. ( [?]) obtained good results with such an architecture for pulse shape index calculation. The LSTM-FCN network we implemented contains a three-block encoder, put in parallel with an LSTM cell. Their respective dimensions are identical to those used for the CNN and for the LSTM-based recurrent network. Both the computations are performed in parallel. The outputs are then concatenated and processed by two dense layers.
Results Experiments were performed separately on the pulse selection and on the peaks detection tasks, in order to select a single neural network for each of them. The same training and testing datasets of labelled pre-processed pulses were used for both tasks, with 10% of the training set used for validation. After having our framework completed with two trained neural networks, we entierly processed 10-minute labelled segments randomly sampled from each of the recordings. To ensure the reproductibility of our experiments, each of the three steps were performed using a dedicated processing pipeline designed with Snakemake 7. The three ROC curves are displayed on figure  5. For the final framework, the optimal decision threshold was chosen to maximize the difference 𝑇 𝑃𝑅 -𝐹𝑃𝑅. Our LSTM-based recurrent network architecture overperformed the convolution-based ones, with an area under the curve of 0.
903. The confusion matrices corresponding to the respective optimal decision thresholds of each NN architecture are presented in table  1. The amounts of false-positive pulses and false-negative pulses correspond to respectively 1. 8% and 9. 7% of the total testing data set when using the LSTM-based architecture for classification. In contrast, this percentages amount to respectively 2. 3% and 42. 9% when using the convolutive network.
To do so, we calculated a confusion matrix for classes "+": " P2/P1 ratio > 1" and "-": "P2/P1 ratio < 1" and the associated accuracy, defined as the proportion of correct predictions over the whole testing dataset. As for the pulse selection task, the recurrent architecture overformed the convolutional one. Without the curvature-based candidate peaks selection step, the LSTM-RE architecture performed the classification task with an accuracy 3% higher than our 1d-Unet. Moreover, it achieved the most accurate estimation of the P2/P1 ratio, with a mean average error of 0. 03. Achieving the candidate peaks selection step with the means of the curvature function tends to improve the algorithm's ability to discriminate pulses with a P2/P1 ratio > 1, at the cost of a slightly less accurate ratio estimation.
Final automated framework On the basis of previous experiments, we finally chose a LSTM-based recurrent both for pulses selection and for subpeaks designation. For the latter step, candidate subpeaks selection was performed using the pulse curvature. For each of the ten patients, the complete workflow was used to process a randomly chosen labelled 10-minute section. An example of such an output is presented figure  6. The performances were assessed for each individual 10-minute segment. We used the same respective metrics as above for pulses selection and subpeaks designation. In addition, we calculated the percentage of pulses that have been assigned a ratio value, and the percentage of non-missing values in the final post-processed ratio signal. Table  3  contains value calculated over the total 110-min dataset, but 10-min segment individualized metrics are available table A??.
False positive rate and true positive rate are both about 7 points higher than their respective equivalents calculated when selecting the NN architecture. However, subpeaks designation performances are consistent with previous experiments. It is noticeable that the only 2nd segment sample contains 91% of the negatively labeled pulses. In this segment, pulse selection algorithm performed with a 13. 5% false positive rate (table ??). False-positive pulses and false-negative pulses amount to respectively 1. 14% and 7. 49% of the total testing dataset. This proportions are consistent with those previously calculated on the 4344-pulse testing dataset.
In that sense, the training dataset was labelled in a quite restrictive way, to limit as much as possible the amount of pulses without a calculable P2/P1 ratio provided to the peak designation step. However, this decision has necessairly consequences on the amount of time during which a P2/P1 ratio can be displayed. In any case, recurrent architectures clearly overperformed the convolutional-based ones for pulse selection, even it is probably possible to reduce the observed gap by fine-tuning the proposed convolutional architecture. As the full succession of subpeaks is necessary to understand the pulse waveform, recurrent networks seem to be more appropriate than CNNs to perform such a classification task. In that sense, these results may contrast with similar studies performed on ECG signal, on which events such as QRS complexes have more recognizable shapes and thus make CNN more relevant for classication or detection tasks.
Concerning the consequences of missclassified pulses, it is noticeable that false-negative pulses only cause spurious missing values at the end of the data processing workflow. In contrast, false-positive pulses are provided to a peak designation algorithm that systematically outputs the two positions of estimated P1 and P2. Therefore, the latter can do much more damage to the output P2/P1 ratio signal. While we simply chose an optimal threshold that minimizes the difference 𝑇 𝑃𝑅 -𝐹𝑃𝑅, it could be relevant to optimize the decision threshold to make to algorithm more restrictive. Peak detection was performed by computing a density fonction by the means of neural networks, as it is often the case for image segmentation tasks. We chose to stick to the underlying philosphy of MOCAIP-based automated framework, which include a candidates selection step before subpeaks designation.
In the case of our testing dataset, a preselection of candidate peaks with a search for local maxima of the curvature function improved the algorithm's ability to discriminate pulses with a P2/P1 ratio superior to 1. The observed improvements in accuracy amounted to respectively 1% for the recurrent network and to 3% for our U-Net. The biggest limitation of our study is that only 10 patients recordings contributed to the pulses database. Because of this small number, we chose to include samples from each of the ten patients both in the training and in a testing datasets, in order to train our neural networks with as much diversity as possible. By doing this, we made the asumption that a single patient CP signal variability over 8 days (that is to say, the average monitoring duration) was enough to neglect the effects of a commune underlying distribution.
However, generalization abilities of our automated framework still have to be improved by expanding our datasets with further inclusions. This is all the more important that we obtained quite different false positive rates during the model selection (8. 52%) and during the final automated framework evaluation (14. 6%). While designing the data processing pipeline, we considered better taking into account the neighborhood of each single pulse. For instance, the pulse selection process could have integrated all the pulses occured over the last minute before the one to be classified, thus helping the interpretation of pulse waveform. However, it would have require a much more computationintensive training step, since the recurrent networks would have had to capture more long-term depencies. In addition, the database would have had to be composed of contiguous labelled samples, which would have had drawbacks on the diversity covered this way.
We faced the exact same issue when sampling the final testing dataset, which was particularly disbalanced with 90% of its false-negative pulses occuring in the same segment. The latter observation leads us to discuss the main drawbacks of monitoring the P2/P1 ratio. As mentioned earlier, this information is not always available, and depends on biological mechanisms still not fully understood [?]. A more complete picture of cerebral compliance could be obtained by combining P2/P1 ratio with other indicators such as mean ICP, pulse amplitude [?] or pulse shape index [?]. More generally, cerebral compliance has to be considered as part of a bundle of information available on patients. Characterizing it is especially helpful when ICP is close to the hypertension threshold, as a simple mean calculation is not informative enough on the current state of the cerebrospinal system.
Thus: 1) Training of the proposed algrithms does not require attack traffic data, which saves considerably on the time needed to collect it to create it artificially, and also on the algorithms' learning times. 2) It follows that a give AADRNN after training with normal data can accurately detect various types of attacks. 3) The proposed AD can be trained in parallel to its realtime operation (when it provides detection results) using the ongoing legitimate network traffic.
A. Botnet Attack Detection with Online Learning The original design of the AD module is targeting the Botnet attack detection. Thus, this module with AADRNN and the proposed traffic metrics was evaluated, as it is, for the detection of the Mirai Botnet attack  [42] ,  [44]  on a publicly available Kitsune dataset  [45] ,  [46]. This dataset contains 764,137 transmissions including both normal and attack traffic packets. The evaluation results displayed in Figure  2  have shown that the incremental online learning property of AD is highly successful with a low false alarm rate as it adapts to the changes in normal traffic over time. Table I compares the performance of AADRNN-based AD with that of the state-of-the-art ML methods. The numerical results in this table show the high success of our AD and its superior performance against other ML methods.
B. Simultaneous Detection of Different Attack Types The AD module was further implemented to detect various types of attacks simultaneously  [47]. To this end, two main revisions have been performed on the design of AD. First, the Metric Extraction module was exchanged with a preprocessing algorithm to apply min-max normalization on any input features provided by the developer (or in a dataset). Then, the decision-making algorithm was enhanced replacing the decision threshold with a statistical whisker which is calculated using the training data. Since extensive threshold selection often provides higher performance for the dataset than the actual test data, data-driven threshold determination, e. g. whisker-valued threshold, eliminates extensive threshold selection. During the performance evaluation of the AD module to simultaneously detect multiple types of attacks, the KDD Cup'99 data set  [48]  was used.
C. Compromised IoT Device Identification The AD methods wre also extended to identify the compromised IoT devices during DDoS attacks, and especially Botnet attacks  [49]. In this way, the AD module may enable the gateway to blacklist devices affected by malware or take preventive actions against the spread of the attack. To this end, the defined traffic metrics were extended to analyze received and transmitted traffic separately resulting in 6 different metrics. An independent detector was utilized for each existing IP address in the considered IoT network, where each detector was sequentially trained over time. Also, the output of AD was revised to obtain the infection level of the device under consideration. During the evaluation of the performance of the AADRNN based AD, we have used various types of DDoS attacks provided in three different datasets: Kitsune  [45] , MedBIoT  [50] , and Bot-IoT  [51].
Using the publicly available Kitsune, MedBIoT, BotIoT, and KDD'99 data sets, the three AADRNN based algorithms have been evaluated, showing that all three lead to a high detection accuracy for various DoS and DDoS attacks. Future research will examine methods for post-processing of the algorithms' output to attempt to further reduce false alarms by an order of magnitude, from the output data of AD algorithms. Another useful direction can be to investigate dynamic system management techniques, such as those investigated in  [52] ,  [53] , to mitigate the consequences of cyberattacks using dynamic traffic management for specific IoT systems. Whenever real-time and on-line algorithms are used, it is also important to better understand the load that they impose on the IoT devices and servers where they are installed, and also to understand how the resident operating system -which typically runs higher priority jobs -can affct the performance of the AD algorithm.
We ruled out any protein expression from the 5' vector that showed low levels of mRNA by performing dystrophin staining in the TA muscle 1 month after systemic injection of dual AAV-5' in DBA2-mdx mice, where we could only detect negligible levels of dystrophin staining (Figure  S4 ). Overall, these data show that, once injected in mice, the dual 5' and 3' vectors only lead to expression of the recombined transgene and do not express any truncated transgenes from either single vector. The hypothetical truncated protein product originating from the 5' vector would have a molecular weight around 151 kDa (red square), while the hypothetical protein originating from the 3' vector would have a molecular weight of 140 kDa (blue square). (D) RTqPCR analysis performed on RNA extracted from TA and DIA muscles using primers to specifically only the 5' or 3' transgene and the recombined transgene.
Following this, fluorescent dye stabilization was performed at 98°C for 10 minutes. VCN values were calculated using the ratio with the Titin gene amplification. For this analysis, multiplex channels were analyzed, depending on the type/concentration of probe. Primers and probes that were used are listed in Table  1. For the multiplex channel reaction dedicated to VCN analysis of the reconstituted vector, gDNA was predigested with SmaI before droplet formation and amplification, to remove the head-to tail event of concatemerization. The analysis was performed using the Quantasoft analysis pro software and efficiency of recombination was calculated as copies of DNA/µl of the recombined region divided by the copies of DNA/µl of the limiting vectors (5' or 3'). The identity and specificity of the product amplified by ddPCR was checked by sequencing of the amplicon, run on gel electrophoresis after ddPCR amplification.
The membrane was then incubated in 50% blocking buffer (Li-Cor) and hybridized with primary antibodies overnight at 4°C. The second hybridization was performed using secondary antibodies coupled to a fluorescent molecule, directed against the primary antibody. Finally, the membrane was scanned on the Odyssey reader. Protein quantification was performed on ImageJ open-source software by normalization with GAPDH or α-actinin-4.
Data Analysis Analysis will be performed at the end of the study after a blind data review and database lock. A flowchart will be drawn up in accordance with the SPIRIT statement. Concerning descriptive statistics, qualitative data will be presented as number and percentage of patients with 95% confidence interval, in each treatment group. Quantitative data will be presented by mean and standard deviation and for each arm. The absence of imbalance between the groups will be checked on the structure variables. Primary analysis will be performed on the intent-to-treat (ITT) population defined as all patients randomized. For the primary outcome, the comparison of the responder rate between the groups will use Fisher's exact test. Analyses for the secondary outcomes are as follows: -For the quantitative indicators, the means will be compared by a mixed intra-subject model for repeated measures in order to compare the mean values of the score or the indicator between the two groups, while distinguishing the specific effects of the treatment modality (shiatsu versus sham shiatsu), of the sequence of administration, the period of measurement, and the subject.
ACKNOWLEDGEMENTS We thank  Mrs. Nathalie Dorland  and  Mrs. Sophie Leclercq , professional shiatsu practitioners, for their intervention in the study. We thank the  Syndicate of Shiatsu Professionals  for their help in the development of this study. Funding. This research was funded by the  University Hospital Center of Orleans  but did not receive other funds from commercial or not-for-profit sectors. The journal's Rapid Service Fee will be funded by  University Hospital Center of Orleans.
g. , VBF with a different-flavor lepton pair in the final state), to the inclusion of new channels that have not been investigated in Run 2 before, such as VBF and VH production with a same-flavor pair of charged leptons and a hadronically decaying V, and ZH production with a three-lepton final state, and to the larger integrated luminosity analyzed. Moreover, WH production with two same sign leptons is measured for the first time in CMS. Tabulated results are provided in the HEPData record for this analysis  [15]. This paper is organized as follows. A brief overview of the CMS apparatus is given in Sect. 2. The data set and simulated samples used are described in Sect. 3. Sections 4-8 describe in detail the event selection and categorization strategy, as well as the discriminating variables used to target each final state.
In situ measurements of the momentum imbalance in dijet, photon+jet, Z+jet, and multijet events are used to account for any residual differences in jet energy scale in data and simulation  [28, 29]. The jet energy resolution amounts typically to 15% at 10 GeV, 8% at 100 GeV, and 4% at 1 TeV. Additional selection criteria are applied to each jet to remove jets potentially dominated by anomalous contributions from various subdetector components or reconstruction failures. Jets are measured in the range |η| < 4. 7. In the analysis of data recorded in 2017, to eliminate spurious jets caused by detector noise, all jets in the range 2. 5 < |η| < 3. 0 were excluded  [30]. We refer to the identification of jets likely originating from b quarks as b tagging  [31, 32]. For each jet in the event a score is calculated through a multivariate combination of different jet properties, making use of boosted decision trees (BDTs) and deep neural networks (DNNs).
In the dilepton eμ trigger, the p T thresholds are 23 and 12 GeV for the leading and trailing lepton, respectively. For the first part of data taking in 2016, a lower p T threshold of 8 GeV for the trailing muon was used. In the 2017 data set, the p T thresholds of the single electron and single muon triggers are raised respectively to 35 and 27 GeV, while they are set to 32 and 24 GeV in the 2018 data set. For both 2017 and 2018 data sets, the p T thresholds of the dilepton triggers are kept the same as the last part of the 2016 data set. The trigger selection is summarized in Table  1. Monte Carlo (MC) event generators are used in the analysis to model the signal and background processes. Three independent sets of simulated events, corresponding to the 2016, 2017, and 2018 data sets, are used for each process of interest, in order to take into account year-dependent effects in the CMS detector, data taking, and event reconstruction.
Production of a W boson in association with a γ * (Wγ * ) for masses below 100 MeV is simulated by pythia 8. 212 in the parton showering of Wγ events. Triboson processes with inclusive decays are also simulated at NLO accuracy with MadGraph5_amc@nlo v2. 4. 2. For all processes, the detector response is simulated using a detailed description of the CMS detector, based on the Geant4 package  [68]. The distribution of the number of pileup interactions in the simulation is reweighted to match the one observed in data. The average number of pileup interactions was 23  (32)  in 2016 (2017 and 2018). The efficiency of the trigger system is evaluated in data on a per-lepton basis by selecting dilepton events compatible with originating from a Z boson. The per-lepton efficiencies are then combined probabilistically (i.
In all cases events containing additional leptons with p T > 10 GeV are rejected. A summary of the different categories is given in Table  2 , with a more detailed breakdown given in Table  12. Across all categories, in the 2016 data set, events are required to pass single-or double-lepton triggers. An addi-tional requirement is placed on the lepton p T to be above 10 GeV, and the highest p T (leading) lepton in the event is furthermore required to have p T > 25 GeV. In the 2017 and 2018 data sets the threshold for leptons is increased to 13 GeV because of a change in the trigger setup. Where yields suffice, events are further split according to the charge and p T ordering of the dilepton system, p T of the subleading lepton, and number of hadronic jets in the event, as detailed in following sections.
A dedicated multivariate discriminant based on a DNN, called DYMVA in the following, is built and trained with the TensorFlow package  [69]  to distinguish signal events from DY events. The DNN is trained separately for each jet multiplicity subcategory. The architecture of the DNN is that of a feed-forward multilayer perceptron, taking 21, 22, and 27 input variables in the 0-, 1-, and 2-jet categories, respectively. These include kinematic information from the dilepton system, p miss T , and jets where present. To better constrain the top quark and WW backgrounds, two CRs are defined in each jet multiplicity subcategory, enriched in the respective processes. The full selection is given in Table  4. The selection efficiency of the requirement on the DYMVA score in 0-jet categories is found to be approximately 50, 7, and 30% for signal, DY, and total background events, respectively.