dataesr/scientific-paragraphs-categorization huggingface dataset:
A second objective of using RF model is to be able to obtain for each bacteria a specific signature that allows discriminating each of them. To this end, we employed the measure of "variable (i. e. , probe) importance" which was calculated from hybridization profiles as follows. For each tree in the forest (i. e. , hybridization profiles of the bacterium), the out-of-bag prediction error (OOBoriginal) was recorded. Then, a random permutation of the values for the j th probe was generated and the out-of-bag prediction error (OOBpermuted) was computed again with this perturbed dataset. This permutation disturbed the model and therefore increased the prediction error. The importance score for the jth probe was computed by averaging the difference in the out-of-bag error before and after the permutation over all trees and normalized by the standard deviation of the differences.
As the decision algorithm is based on a training database of hybridization profiles of bacterial samples, the accuracy of the decision-making support shall dramatically increase with the enrichment of this training databases, which is the intrinsic property of using the Random Forests model  [32]  for this purpose. In addition, the attribution of specific probe patterns for each of the pathogens can be obtained using this machine learning method. Therefore, our DendrisChips ® technology is highly evolvable as any additional bacterial could be searched for, provided capture probes have been designed for these new species/strains and the diagnostic algorithm has been updated accordingly. In conclusion, our DendrisChips ® technology has the power to provide a diagnostic in less than 4 h after DNA extraction, thus accelerating the medical decision making with regards to respiratory tract infectious disease, and by extension, this technology can be applied to other infectious diseases as well as to other application fields in which bacterial control is required such as in nutrition and food quality.
Acknowledgments: The authors thank the following private and public clinical laboratories:  Laboratoires Bio-Pôle Autan ,  Saint Exupéry ,  Tolosane & Arènes (Toulouse) ,  Centre de Biologie Moléculaire  ( CBM, Toulouse and Muret), Labcell (Cugnaux ), Air bio (Colomiers),  Laboratoires d'analyses Biolab Avenir Clinique Pasteur (Toulouse)  and  CRBioLim (Limoges)  for providing generously biological samples and  Jean-Louis Galinier  ( Biolab Avenir Clinique Pasteur, Toulouse ) for advices on clinical diagnosis.
Study design and procedure This pilot study was conducted during a 14-month period (March 2016-June 2017) in five medical and surgical intensive care units (ICUs) experienced in the care of ARDS patients and use of extracorporeal gasexchange devices. It was approved by appropriate legal and ethics authorities (Comité de Protection des Personnes Ile-de-France 6, Paris, France; no. 15. 1026). The clinical trial protocol was registered with www. clinicaltrials. gov (ClinicalTrials. gov identifier: NCT02606240).
Data collection Ventilator settings (VT, PEEP, RR, P plat , minute ventilation, FiO 2 ), hemodynamic parameters (mean arterial pressure, heart rate, vasopressor dose) and arterial blood-gas values (pH, PaO 2 , PaCO 2, HCO 3 -, lactate), heparin dose, and aPTTr were collected at baseline, after the run-in-time, 30 min after every VT reduction, and at least twice a day during the subsequent days on ECCO 2 R. Blood-chemistry determinations were obtained daily. Respiratory-system compliance and driving pressure were calculated according to the standard formulas  [6, 20]. CO 2 clearance by ECCO 2 R (ml/min) during the first 24 h was calculated as follows  [17] : (CtCO 2PRE -CtCO 2POST ) × 22. 4 × ECCO 2 R blood flow / 1000, where CtCO 2PRE and CtCO 2POST were the pre and post oxygenator blood CO 2 content, and CtCO 2 (mmol/l) = (0.
Statistical analyses Statistical analysis was performed by one-way analysis of variance (ANOVA) for repeated measures, followed by a Bonferroni post-hoc test for comparison between different times. Results are expressed as mean ± SD and p < 0. 05 defined statistical significance. Analyses were computed with StatView v5. 0 (SAS Institute Inc. , Cary, NC, USA) and SPSS v22 (SPSS Inc. , Chicago, IL, USA) software.
g. , cannulation-related accidents, limb ischemia, hemorrhage, hemolysis, infections, pump malfunction, membrane clotting, and catheter displacement) were reported  [10, 11, [25] [26] [27] [28]. Our results demonstrated that this strategy might be safely, efficiently, and easily applied to ARDS patients in most ICUs worldwide, because it did not require specific or large venous accesses and the RTT platform we used is widely available with minimal modification of existing devices and a simple software update. ECCO 2 R with this RRT platform has indeed obtained promising results in animals  [14]. By decreasing VT to 4 ml/kg PBW and   [6]. Importantly, we did not observe worsening oxygenation that might have indicated lung derecruitment following the mean airway-pressure decrease  [28, 29] , although some patients with the most severe forms of ARDS continued to receive nitric oxide or prone positioning following ECCO2R initiation.
The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets.
It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.
A number of studies have considered multitalker 'cocktail party' scenarios, where a listener attends to one speech source and ignores others. It has been demonstrated that both attended and unattended acoustic features can be linearly mapped to the cortical response  (9, 10, 28, 29, 38) , or, conversely, from the cortical response to the speech features  (23, 20, 14, 9, 10, 19, 34). Differences in the accuracy of TRF-derived predictions between the attended and unattended speech signal can be used to predict or 'decode' to whom a listener is attending based on unaveraged M/EEG data. Single-trial measures of auditory selective attention in turn suggests BCI perspectives, for instance, for hearing instrument control. The ability of TRF models to generalize to new data is generally limited by the need to estimate a relatively large number of parameters based on noisy single-trial M/EEG responses.
Like many aspects of machine learning, this necessitates regularization techniques that constrain the TRF model coefficients to prevent overfitting. A number of methods for regularizing the TRF have been presented in various studies. Each of these methods attempt to address the challenge of having sufficient data to compute a reliable TRF function. To reduce the data requirement, regularization can be applied in the form of a smoothness and/or sparsity constraint. To date, little work has been done to compare these methods against each other. A meta-analysis would be difficult as many variables, such as subjects, stimuli and data processing are different between each study. The present paper proposes a standardized dataset, based on the attended-versus-unattended talker discrimination task, as well as preprocessing and evaluation procedures to compare these algorithms.
Temporal Response Functions The TRF methods described below map a matrix X = [x (t,f ),c ] to a matrix Y = [y t ]: Ŷ = XW, (1) where Ŷ is the TRF model prediction in the form of a time-dimension t vector, and X is the TRF model input matrix with time-dimension t and channel-dimension c. X is augmented to include time-lagged versions of the data with a limited range of time lags, for example -500 ms to +500 ms, so that the model can handle delays and convolutional mismatch between X and Y. These time lags are denoted as dimension f and are combined with the time dimension t to form a single dimension when performing matrix multiplications. For a forward TRF model, X is a representation of the stimulus (e. g. single-channel speech envelope) and Y is the EEG response. In this case, a TRF can be computed for each EEG electrode channel.
Ordinary Least Squares (OLS) The TRF filter coefficients can be estimated via ordinary least squares: W = X T X -1 X T Y, (2) where X T X is the estimated covariance matrix and X T Y is the estimated cross-covariance matrix. The ordinary least-squares solution was here estimated using the Cholesky decomposition method, via the mldivide routine in Matlab. One advantage of the OLS estimator is that it has no additional hyperparameters that must be optimized. However, in practice the OLS estimator is often outperformed by the regularized solutions described in the following subsections. This is often the case when the regressor, X, is highdimensional, has highly correlated columns and has a poorly estimated covariance matrix given limited amounts of training data.
In the outer cross-validation loop the data were split into training/validation (90%) and test (10%) sets. In the inner cross-validation loop the regularization parameter was tuned (where applicable) and the TRF trained on the training/validation set, after which the trained TRF was tested on the test set. Using this TRF model, the classification relied on correlation coefficients between the attended audio and the EEG, and between the unattended audio and the EEG. These correlation coefficients were computed over the aforementioned restricted time window. These coefficients were used to classify whether the subject was attending to one stream or the other. For a backward TRF model, classification hinged merely on which correlation coefficient was largest (stream A or stream B). Performance of this classifier was evaluated on the test set.
Experimental Procedure The 50-s long speech segments were used to generate auditory scenes comprising a male and a female simultaneously speaking in anechoic or reverberant rooms. The two concurrent speech streams were normalized to have similar root-mean square values. The speech stimuli were delivered to the subjects via ER-2 insert earphones (Etymotic Research). The speech mixtures were presented binaurally to the listeners, with the two speech streams lateralized at respectively -60 o and +60 o along the azimuth direction and a source-receiver distance of 2. 4 meters. This was achieved using nonindividualized head-related impulse responses that were simulated using the room acoustic modeling software, Odeon (version 13. 02). Each subject undertook sixty trials in which they were presented the 50s-long speech mixtures.
Before each trial, the subjects were cued to listen selectively to one speech stream and ignore the other. After each trial, the subjects were asked a comprehension question related to the content of the attended speech stream. The position of the target streams as well as the gender of the target speaker were randomized across trials. Moreover, the type of acoustic room condition (either anechoic, mildly reverberant or highly reverberant) were pseudo-randomized over trials. In the analysis, data recorded from all acoustic conditions were pooled together.
Data Collection Electroencephalography (EEG) data were recorded from 19 subjects in an electrically shielded room while they were listening to the stimuli described above. Data from one subject were excluded from the analysis due to missing data from several trials. The data were recorded using a Biosemi Active 2 system, with a sampling rate of 512 Hz. Sixty-four channel EEG data (10/20-system) were recorded from the scalp. Six additional electrodes were used for recording the EEG at the mastoids, and vertical and horizontal electrooculogram (V and H-EOG). Approximately 1 hour of EEG data was recorded per subject. This study was carried out in accordance with the recommendations of 'Fundamental and applied hearing research in people with and without hearing difficulties, Videnskabsetiske komitee'.
EEG Data 50 Hz line noise and harmonics in the EEG data were filtered out by convolution with a 512 50 sample square window (the non-integer window size was implemented by interpolation)  (5). The EEG data was then downsampled to 64 Hz using a resampling method based on the Fast Fourier Transform (FFT). A 1st order detrend was performed on the EEG data to minimize filter startup artifacts. EEG data were highpassed at 0. 1 Hz using a 4th order forward-pass Butterworth filter. The group delay was less than 2 samples above 1 Hz. The joint decorrelation framework (6) was employed to remove eye artifacts in an automated fashion. Let X = [x tj ] be a matrix that contains EEG data from each electrode, j, for each time sample t. In this implementation, a conservative eye artifact time-point detection was first performed by computing a Z-score on 1-30 Hz bandpassed VEOG and HEOG bipolar channels and marking time samples where the absolute Z-score on either channel exceeded 4.
This is similar to the eyeblink detection method implemented in the FieldTrip EEG processing toolbox  (22). This resulted in a subset of time samples, A, indexing the temporal locations of each EOG artifact. An artifact covariance matrix R A = X T A X A was then computed from the EEG (and EOG) data, X A = [x aj ], at the artifact time samples a ∈ A. The generalized eigenvalue Frontiers problem was then solved for R A v = λRv, where R = X T X is the covariance matrix for the entire EEG dataset. The resulting eigenvectors V, sorted by eigenvalue, explain the maximum difference in variance between the artifact and data covariance matrices. Components corresponding to eigenvalues > 80% of the maximum eigenvalue were regressed out of the data. In practice, this 80% threshold is a conservative one, typically resulting in the removal of one or two components.
3. This rectification and compression step was intended to partially mimic that which is seen in the human auditory system  (27). The audio envelope was then calculated by summing the rectified and compressed filterbank outputs across channels. The audio envelope data was subsequently downsampled to the same sampling frequency as the EEG (64 Hz) using an FFT-based resampling method. The EEG and envelopes were then temporally aligned using start-trigger events recorded in the EEG. The envelopes were subsequently lowpassed at 9 Hz. As part of the cross-validation procedure, audio envelopes were finally centered and standardized (Z-normalized) across the time dimension using the mean and standard deviation of the attended speech envelope in the training data.
Statistical Analysis All statistical analyses were calculated using MATLAB. Repeated-measures analysis of variance (ANOVA) tests were used to assess differences between the regression accuracies (section 2. Given the non-Gaussian distribution of regression accuracies (range -1 to 1) and classification performance metrics (range 0 to 1), Fisher Z-transforms and arcsine transforms were applied to these measures, respectively, prior to statistical tests and correlations. This is a provisional file, not the final typeset article Wong et al.
RESULTS The TRF estimation methods introduced in Section 2 were used to decode attended speech envelopes from low-frequency EEG activity. The following sections analyze results with metrics of 1) regression accuracy, 2) classification accuracy, 3) receiver operating characteristic (ROC), and 4) information transfer rate (ITR). Results are shown for each of the regularization schemes, for both forward and backward TRF models. For each regularization scheme, the regularization parameter(s) are tuned to maximize regression accuracy. These parameter values are then used for all regression and classification comparisons. Regression accuracy compares different regularization schemes in predicting test data using the optimal regularization parameter. Classification accuracy uses the regression accuracy values to classify the attended/unattended talker and compares the different regularization schemes in performing this task.
Relation to regression accuracy The discrimination between attended and unattended speech streams from EEG data is done in two stages: the computation of regression accuracies, followed by classification. We sought to investigate how the classification accuracies obtained with each TRF model relate to the test set regression accuracies. A plot of this relationship is shown in figure  6. For forward TRF models, the average correlation between regression accuracy and classification performance is 0. 69 (T 108 = 9. 83, p < 0. 01), over all regularization schemes. For backward TRF models, the correlation between the regression accuracy and classification performance is 0. 89 (T 108 = 22. 4, p < 0. 01). This suggests that classification performance varies with regression accuracy. However, as was previously described for the backward TRF models, while Tikhonov regularization achieved a significantly higher regression accuracy compared to all other methods, it did not achieve a significantly higher classification performance compared to Shrinkage, Ridge Regression or LRA.
DISCUSSION In this study, we systematically investigated the effects of TRF estimation methods on the ability to decode and classify attended speech envelopes from single-trial EEG responses to speech mixtures. The performance of stimulus/EEG decoders based on forward TRF models (mapping from attended speech envelopes to multi-channel EEG responses) and backward TRF models (mapping from EEG response back to speech envelopes) were compared. It was found that the backward TRF models outperformed the forward TRF models in terms of classification accuracies. We hypothesize that TRF models do a better job of predicting audio (the backward model) than EEG data (the forward model) because the EEG data contains a lot of information from other brain functions. It is simpler to filter out these signals, as is done in the backward model, than it is to predict them (as the forward model would need to do to achieve higher correlation).
These analyses all relied on the explicit assumption that the human cortical activity selectively tracks attended and unattended speech envelopes. To fit the models, we made a number of choices based on common practices in literature, and with the goal of being able to compare TRF methods. For example, a 500 ms TRF kernel used as was done by others  (14). While shorter kernels have been explored as well (  23 ), a longer one tests the ability of the TRF method to handle a larger dimensionality and allows for a more flexible stimulus-response modeling capturing both early and late attentional modulations of the neural response. Additionally, we chose to focus on 1-9 Hz EEG activity as the attentional modulation of EEG data has been found prominent in this range. It is likely that other neural frequency bands robustly track attended speech (e.
The relative contribution of the individual channels to the classification accuracies were instead found via an SVM trained on correlation coefficients computed per channel, over short time segments. It can therefore be beneficial to apply dimensionality reduction techniques (e. g. independent component analysis (2) or joint decorrelation (  6 )) to represent the EEG data as a linear combination of fewer latent components prior to fitting the forward models. Alternatively, canonical component analysis can be used to jointly derive spatio-temporal filters for both audio and EEG such that the correlation between the filtered data is maximized (7).
In fact, fixing the regularization parameter to a high value did not strongly affect the decoding accuracies compared to doing a hyperparameter search (this was tested with ridge regression with a fixed large λ value). Elastic net regularization permits the adjustment of the balance between L1 and L2 regularization via the α parameter. For the backward TRF model, it was shown that a smaller α value improved the correlation between the reconstructed and attended audio stream by only a narrow margin. The α value had no significant impact on classification accuracy for either forward or backward TRF models. As such, the higher classification performance of Ridge Regression (α = 0), compared to Lasso (α = 1) may be a result of differences between solvers (MATLAB's mldivide versus GLMNET  (30) ).
Summary There are many methods that can be used to compute TRFs. The present study uses a baseline dataset and procedures for the evaluation of these TRF methods. In consideration of the multiple applications in which TRF functions are used, primarily dealing with reconstruction accuracies or classification performance, this paper considered multiple metrics of TRF performance. By characterizing the regularization and performance of the TRF methods, and the relationship between performance metrics, a more complete understanding of the validity of the assumptions underlying each TRF method is provided, as well as the impact of the assumptions on the end result. While these experiments were done with EEG data, we expect that the results apply equally to magnetoencephalography (MEG) data. The key findings from this study were 1) the importance of regularization for the backward TRF model, 2) the superior performance of Tikhonov regularization in achieving higher regression accuracy although this does not necessarily entail superior classification performance, and 3) optimal ITR can be achieved in the 3-5 s range and by adjusting the classifier discrimination function threshold.
Evaluated nuclear data are essential for nuclear reactor studies. In order to significantly improve the precision of nuclear data, more and more fundamental fission models are used in the evaluation processing. Therefore, tests of fission models become a central issue. In this framework, FIFRELIN (FIssion Fragments Evaporation Leading to an Investigation of Nuclear data) is a Monte Carlo code developed in order to modelize fission fragments de-excitation through the emission of neutrons, γ and conversion e -. To be performed a FIFRELIN calculation relies on several models such as gamma strength function and nuclear level density and of more empirical hypothesis such as total excitation energy repartition or angular momentum given by the fission reaction. Moreover, pre-emission mass yield and kinetic energy distribution per mass are necessary to process the simulation.
Above this energy, the nuclear level scheme is described as a continuum. This sketch is shown on Fig. 1  (right). The last part of the code is to determine the de-excitation path from the initial state previously sampled. A state can decrease by emitting neutron, γ or e -. The associated probabilities are calculated through: • transmission coefficients which were determined by an external code (TALYS/ECIS  [7] ) and depend on optical model parametrizations (case of neutron) • models of γ strength function (here EGLO  [8] ) (case of γ) • experimental data (case of γ and e -) Note that FIFRELIN uses the notion of Nuclear Realization developed in  [9]  and extended to neutron/γ coupled emission  [3].