dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, all activation functions in 1D CNN, MLP, and LSTM are sigmoids, and we implemented these models using Keras API in Python. For the implementation of DT, we used the Scikit-learn library in Python setting the maximum depth and maximum number of features equal to n. Figure  10  displays the comparison of ARNN with 1D CNN, LSTM, MLP, and DT with respect to Accuracy, TPR, TNR, F1 Score, Recall, and Precision. These results show the superior overall performance of ARNN against these wellknown ML models. It can be seen that the performance of the ARNN is slightly lower than DT in terms of average Accuracy, TNR and Precision but significantly higher than the other models in terms of TPR, F1 Score and Recall. Thus although DT is slightly better than ARNN at detecting negative samples (i.
During our experimental evaluation: • The ARNN is first trained offline with ground truth data and tested with disjoint ground truth data exhibiting a high level of precision. • Then, the offline trained ARNN is tested with a simplified average input metric directly extracted from measurements, and again a high level of precision is observed. • The average metric is then used as input for offline training, while the ground truth is used in the error function, and testing is carried out with the average metric using disjoint data. • Finally, online incremental training using the testing data output for learning is also tried, without use of the ground truth. All these experiments use the Kitsune dataset, and confirm the high level of accuracy of the ARNN's predictions. Experiments are also conducted to compare the ARNN for the identification of compromised IoT nodes using real MIRAI Botnet attack data from the Kitsune dataset, with the recent state-of-the-art CDIS-DRNN technique  [43]  and the well-known 1D CNN, LSTM, MLP and DT models are also carried out, indicating that the ARNN: • Provides significant improvement compared to CDIS-DRNN, achieving 92% median accuracy and minimum 60% accuracy per node, • Outperforms the "best-of-class" CDIS-DRNN by a wide margin with respect to both TNR and TPR, and VOLUME ?, 2023 • Identifies compromised nodes in just under 3.
Non-midline incisions were more frequent in elective surgery (27. 2% [416/1635] vs 21. 7% [352/1527] in emergency surgery) and were more often performed for benign surgical indications. These key patient characteristics were monitored centrally and reported at the TMG meetings, with swift action from the trials unit whenever biases were suspected. As an example, one site was enquired about inconsistencies in the aggregate register, uploaded data and theatre logbooks, admitting that some patients were missed. Lessons learned were disseminated across all hubs and spokes to avoid similar situations (see Additional file 1: Appendix F for more details on communication with the central management team).
Discussion This study evaluated eight strategies to monitor and minimise bias and imbalances within an international cluster randomised controlled trial in surgery. Hospitallevel strategies contributed to balance in unit of exposure level strategies, which in turn contributed to balance in patient-level strategies. Although all the strategies were delivered with success, chance imbalances in monitoring measures such as elective versus emergency operations, midline versus non-midline incisions and surgery for benign versus malignant indications were observed. Monitoring data were regularly reviewed by the TMG, with intervention at a site level where required. The diverse delivery network demonstrates the generalisability of future analyses of the primary outcome measure. Our data reflect that cRCTs are prone to chance imbalances at multiple stages and a transparent reporting of mitigation strategies is crucial for an adequate interpretation of the trial results.
Data abstraction De-identified data was collected retrospectively at the individual institutional level into a standardized worksheet and deidentified data were transmitted to a central database for analysis. Data abstracted included baseline demographics, clinical comorbidities and medications, presence of cardiac implantable electronic devices, electrocardiography, echocardiography, ambulatory cardiac monitoring, and CMR. We also examined findings from invasive testing and treatment when performed, including electrophysiology study, catheter ablation procedures, and cardiac surgery. Testing procedures were done according to local institutional protocols and were not standardized across the cohort. Not all subjects had all types of imaging or testing done. For analysis, inferolateral T-wave inversions (TWI) were defined as T-wave inversion in at least two of leads II, III, aVF, I and aVL.
Cardiac magnetic resonance imaging (CMR) Ninety-seven subjects (44. 7%) had contrast enhanced CMR data available (Table  2 ). There were no differences in delayed enhancement (DE) distribution between the MVP-SCA and MVP-PVC groups. Thirteen subjects (13. 4%) had DE involving either the anterolateral or posteromedial papillary muscles. More subjects had DE of either inferior (n=18, 18. 6%) or inferolateral (n=31, 32. 0%) LV myocardium. Forty-five subjects (46. 4%) had at least one area of DE. In our cohort, 99 subjects (45. 6%) had information on presence of inferior or inferolateral DE on CMR and TWI on surface ECG. A total of 40/99 (40. 4%) patients had DE in the inferior or inferolateral LV myocardium. Of these 31/40 (77. 5%) patients had inferolateral TWI and only 9/40 (22. 5%) had DE but no corresponding inferolateral TWI (p=0.
Introduction: Broad-spectrum antibiotics such as beta-lactams and vancomycin are frequently used to treat critically ill patients, however, a significant number do not achieve target exposures. Therapeutic drug monitoring (TDM) combined with Bayesian forecasting dosing software may improve target attainment in these patients. This study aims to describe the efficiency of dosing software for achieving target exposures of selected beta-lactam antibiotics and vancomycin in critically ill patients. Methods: A prospective cohort study was undertaken in an adult intensive care unit (ICU). Patients prescribed vancomycin, piperacillin-tazobactam and meropenem were included if they exhibited a subtherapeutic or supratherapeutic exposure informed by TDM. The dosing software, ID-ODS TM , was used to generate dosing recommendations which could be either accepted or rejected by the treating team.
As pathogen MICs are not routinely reported in the unit, EUCAST clinical breakpoints for isolated pathogens (https://www. eucast. org) are used in place of a directly measured MIC. Where no pathogen was isolated, the EUCAST clinical breakpoint for Pseudomonas aeruginosa was used for piperacillin-tazobactam (16 mg/L) and meropenem (2 mg/L). For vancomycin, target trough concentrations between 15À20 mg/L or up to 20À25 mg/L in the setting of severe infections or CNS infections are used. In the present study, antibiotic dosing was initially defined by the treating team (i. e. , clinician-based dosing). If TDM results did not achieve the target exposures set by the treating team, the dosing software, Individually Designed Optimum Dosing Strategies (ID-ODS TM ), was used to generate a dosing recommendation using Bayesian forecasting and the a posteriori approach (using TDM data, dosing information and covariate data).
Fig. 1  presents the study flow chart. Adult patients (!18 years) were included in the analysis if they were prescribed vancomycin or the beta-lactam antibiotic piperacillin-tazobactam or meropenem and had at least one trough TDM measurement that did not achieve target antibiotic exposures. A validated high-performance liquid chromatography assay was used for measuring the unbound concentrations of betalactams  [18]  and the total concentration of vancomycin  [19]. Data were collected until the day of discharge from the ICU or until death, whichever occurred first. Patients were excluded from the prospective cohort study if they were pregnant, receiving renal replacement therapy (RRT) or extra-corporeal membrane oxygenation, as PK models for these populations were unavailable in ID-ODS TM at the time the activity was conducted.
It is unknown if this high rate of target attainment would still be achieved in other ICUs such as ours where higher exposures are more commonly targeted (i. e. , 100%fT>1-4XMIC). Like our analysis, Heil and colleagues reported a high rate of clinical cure (83. 7%) among their study patients. Measuring similar outcomes, Chiriac et al. [24]  investigated the accuracy of dosing software predictions for piperacillin in a population of critically ill patients requiring RRT. Using the dosing software program CADDy, the investigators targeted a piperacillin concentration range between 32-64 mg/L in patients administered piperacillin as a continuous infusion. Target attainment was achieved in 40% of their patients. In addition, there was a lower proportion of patients who developed supratherapeutic exposures (>96 mg/L) using dosing software versus those using standard doses (12% vs.
Our study did not examine the impact of dosing software on these patients. Finally, as our study utilised the software ID-ODS TM , it limits our ability to extrapolate these findings to other dosing software, although it is likely that others that include appropriate PK models would perform similarly. Additionally, although some dosing software (including ID-ODS TM ) can generate empiric dosing recommendations, it was beyond the scope of this study to measure this feature. Currently, there is no published study that has examined the comparative performance of dosing recommendations generated from different dosing software programs in achieving target exposures in difficult-to-treat patients, including the critically ill.
# - Hs (z 1 )) = 0, while the fourth equation Ψ = 0 contains the limit and transversality conditions. The last equations are the so-called matching conditions. We use the HamPath software  [12, 17] , to obtain a (numerical) zero, denoted ȳ, of the shooting function with high accuracy: S c2 (ȳ) ≈ 1. 511 × 10 -11. The switching times are t 1 ≈ 88. 61s, t 2 ≈ 455. 7s and t 3 ≈ 651. 46s. The final time is t f ≈ 661. 37s and the initial adjoint vector is p0 ≈ (3. 180 × 10 -2 , 6. 594 × 10 -1 , -2. 300 × 10 -1 ). Note that the HamPath software is based on a Newton-like algorithm which is initialized thanks to the state unconstrained trajectory. Fig. 3  depicts the control and the constraint c 2 along the resulting state constrained trajectory.
The classical difficulties about homotopic methods consist in assuring that a curve in h -1 (0) exists, is sufficiently smooth and will intersect the fixed target λ 1 in finite length starting from λ = λ 0. As a first result we have the following. Suppose h is continuously differentiable and that we know y 0 such that h(y 0 , λ 0 ) = 0 and assume rank ∂h ∂y (y 0 , λ 0 ) = N. Suppose also that 0 is a regular value of h. Then, a continuously differentiable curve crossing (y 0 , λ 0 ) and transverse to λ = λ 0 exists and is either diffeomorphic to a circle or the real line. The curves in h -1 (0) are disjoints, and we call each branch of h -1 (0) a path of zeros. To compute numerically these paths of zeros, we use the homotopic method from the HamPath software, which is based on a Predictor-Corrector algorithm with a high order and variable step size Runge-Kutta scheme for the prediction and with a classical simplified Newton method for the correction.
The idea is then to deform by homotopy, making φ max or ψ max varying, the BC-extremal associated to this trajectory and then to detect by monitoring when a change in the structure occurs. The monitoring consists in checking some conditions after each step of prediction and correction. Technically, we only have to code the conditions and then the HamPath software automatically check them after each prediction-correction step and stop the homotopy process if at least one condition is violated. Here are the three different monitorings we use: M1 check if c 1 is violated at the entry point of σ s. If yes, then we add a σ c1 arc; M2 check if c 2 is violated at the exit point of σ s. If yes, then we add a σ c2 arc; M3 check if σ s has a positive length. If not, then the arc is removed. When a change is detected, we update the homotopic function accordingly to the new structure and repeat the process.
4 , one can also compute the fuel consumption, which is given on Fig. 5. Even if it is not clear from the figure, the minimal value of fuel consumption is obtained when (φ max , ψ max ) ≈ (128. 9, 0. 661), that is for a structure following the CM procedure. To obtain these optimal values we solve the problem (CM ∆m ) with the fmincon function from the Matlab software and we use the ode45 function to compute the numerical integrations. The numerical solution we obtain satisfies the first-order optimality conditions with a tolerance of 10 -7. The switching times are t 1 ≈ 32s, t 2 ≈ 450s and t 3 ≈ 676s while we have a fuel consumption ∆ m ≈ 863kg, a final time t f ≈ 677s and the CAS and Mach optimal values are (φ max , ψ max ) ≈ (128. 9, 0. 661). Hence, there is a gap of 19s on the final time and 19kg on the fuel consumption between the solution of (SA t f ) and (CM ∆m ).
That is, the decrease of the bounds φ max and ψ max of the state constraints c 1 and c 2 allows to save up 19 kg of fuel with a 19 seconds longer flight. Optimal fuel consumption. In the previous paragraph, we have compared (SA t f ) with (CM ∆m ). As expected, solving (SA t f ) gives a better final time and solving (CM ∆m ) gives a better fuel consumption. But we can do better. Let us come back to the original problem of minimizing the fuel consumption, that is to the optimal control problem (P ∆m ) defined in Section 2. 1. To obtain the best possible fuel consumption, we set φ max = V M O and ψ max = M M O. In this case, thanks to the direct collocation method from the Bocop software  [6] , we determine that the structure σ -σ s σ + is still relevant, that is the solution we obtain follows the SA procedure.
This means that the problem (SA ∆m ) gives a better solution than the problem (CM ∆m ). To get a BC-extremal solution of (P ∆m ) with φ max = V M O and ψ max = M M O, we use the solution from the Bocop software to initialize the shooting method. This technique is classical, see  [8, 24]  for details and examples. We then obtain a trajectory for which the switching times are t 1 ≈ 47s and t 2 ≈ 668s, the final time is t f ≈ 675s and the fuel consumption is ∆ m ≈ 860kg. There is a gap of 3kg on the fuel consumption with the solution from (CM ∆m ) and a difference about 2s on the final time. Hence, even if (SA ∆m ) is better than (CM ∆m ), the difference is very small. Remark 5. 4. The Bocop software transforms an infinite dimensional optimal control problem into a finite dimensional optimization problem, applying a full time discretization of the state variables, the control variables, the dynamics and the constraints.
The minimal cost index problem We are interested now in the so-called cost index criterion which is a convex combination of the time-to-climb and the fuel consumption. Instead of comparing directly (CM α ) and (SA α ) for α ∈ [0 , 1], we solve (P α ) with φ max = V M O, ψ max = M M O, and we show that the trajectories we get follow the SA procedure. Doing this, we conclude at the end of this section, that not only the SA procedure is better than the CM procedure but it is also the best solution we have. The problem (CM α ) is solved with the fmincon function from the Matlab software and we use the ode45 function to compute the numerical integrations. The optimal control problem (P α ) is solved with the Bocop software. We present in Table  2 , for different values of α, the optimized CAS/Mach couple (that is φ max and ψ max ), the fuel consumption and the time-to-climb, associated to the solutions of problems (CM α ) and (P α ).
1 , and compared our results to the state of the art  [17]. The SHREC'19 dataset consists of 5,298 proteins extracted from 211 entries from the Protein Data Bank  [18]. They are classified according to the Structural Classification of Proteinsextended (SCOPe)  [19]  in order to evaluate our system's performance in terms of conformation retrieval. The task is made difficult by the fact that protein deformability is due both to their multiple degrees of freedom, and also to interaction with the surrounding environment: two proteins with a same conformation may not have the same shape. The paper is organised as follows. Section 2 reviews background material and introduces differential forms, discrete differential calculus and the de Rham operator. The fractional Fokker-Planck equation is derived in Section 3, from the Lévy random walk.
BIFRACTIONAL PLANCK-DIRAC WAVELETS SIG-NATURE EVALUATION Our system consists of two components: Fractio, which evaluates the Planck-Dirac wavelet signatures, and Gauß, the protein-classification deep neural network; both programmes are written in the Mathematica™ language. The OFF files corresponding to the macromolecular surfaces are stored in a Bitcoin-like blockchain  [83]. Each object is encrypted with an AES256 symmetric key  [84]. The blockchain provides many advantages over a conventional file system or database: it is decentralised, secure, and tamper-proof by design, with Byzantine fault tolerance  [83]  in addition to providing a unique address or transaction ID for each protein, thus eliminating ambiguities and mismatches associated with version control and naming  [83]. Fractio processes the proteins in parallel on forty Dual Intel Xeon Gold 6140, 2.
The calculations were performed by a Nvidia Tesla V100 (32 GB) GPU. The weights and the bias were initially estimated with Xavier's method  [93] , in which the parameters are chosen such as to preserve variance when propagated through subsequent layers. The whole approach may be summarised as follows: 1) For each protein, reduce the mesh size to 1000 polygons 2) For each protein, evaluate the BPDWS signature with Eq. (  49 ) and  (53) , that is with Fractio 3) Generate the 10-folds datasets 4) For each fold a) Train, with Adam, the multiobjective Gauß network with the training BPDWS signatures evaluated with Fractio b) Prune the trained network from all its outputs except the last one c) Evaluate the precision, recall, accuracy, Van Rijsbergen's effectiveness measure and precision-recall curve with the corresponding training set 5) Evaluate the average of the metrics for all ten folds Experimental results are presented in the next section.
EXPERIMENTAL RESULTS The SHREC'19 Protein Shape Retrieval Contest  [17]  dataset was employed in our experiments. It consists of 5,298 proteins extracted from 211 entries in the Protein Data Bank  [18] , each of which has been classified according to the Structural Classification of Proteins-extended (SCOPe)  [19]  based on their conformation (the shape of their macromolecular surface). The Structural Classification of Proteins -Extended (SCOPe) classifies protein domains according to their structural and evolutionary properties  [94]. The classification is performed according to class and fold for the structural properties, and according to superfamily, family, protein and species for their evolutionary counterparts. The SHREC'19 data was obtained by selecting proteins from the SCOPe database according to the following criterions: 1) macromolecular surfaces (3-D structure) were resolved by nuclear magnetic resonance spectroscopy, 2) the conformers (deformable shapes) must have the same number of atoms, 3) each entry must have at least four ortholog proteins, and 4) the resulting dataset was randomly subsampled in order to obtain 5298 domains.
The macromolecular surfaces (solvent excluded surface) were calculated with EDTSurf  [95]. The dataset consists of 17 classes. Classification is made challenging by the fact that the macromolecular surfaces associated with proteins are so geometrically complex, as illustrated in Fig. 1. Furthermore, they are subject to deformations as a result of the interaction with both their external environment and their multiple degrees of freedom, all of which contributes to altering their conformation. The dataset consists of 17 classes, the largest of which has 1160 proteins; there is an average of 311 proteins per class  [17]. Therefore, the dataset is highly unbalanced. Our Fractio-Gauß algorithm was compared against thirteen algorithms and variants which are described, in detail, in the SHREC'19 Protein Shape Retrieval Contest  [17].
These include three variants of the convolutional neural network-linear dynamical system framework (ConvLDSNet), the 3D Zernike moments framework (3DZM), three variants of the 3D Zernike descriptors framework (3DZD), three variants of the histogram of area projection transform framework (HAPT), the vector of locally aggregated descriptors framework (VLAD), the globally aligned spatial distribution framework (GASD), and the hybrid GASD-VLAD framework, all of which are described in  [17]. Essentially, ConvLDSNet combines convolutional neural networks with linear dynamical systems: the numbering referring to the training dataset; 3DZM employs 3D Zernike moments: a prior alignment of protein pairs with fast Fourier transform is required; 3DZD relies on 3D Zernike descriptors: the numbering referring to the classification metric; HAPT determines the probability for a point to be the centre of a spherical symmetry: the numbering referring to the parametrisation of the algorithm; Ft-PSSC is based on a fast point feature histogram (FPFH) combined with either the globally aligned spatial distribution (GASD), the vector of locally aggregated descriptors (VLAD), or both.
Our framework was evaluated using 10-fold crossvalidation: the original dataset was randomly partitioned into ten equal-sized subsamples. Of the ten subsamples, a single subsample was retained as the validation data for testing the model, and the remaining nine subsamples were employed as training data. The cross-validation process was repeated ten times, with each of the ten subsamples used exactly once as the validation data. The ten results were averaged to produce a single estimate. Evaluating the Planck-Dirac wavelet signatures was completed in 71 minutes while the total training and testing time for all ten folds was 28 minutes. The performance was evaluated and compared with five metrics: Van Rijsbergen's effectiveness measure (also known as E-measure: E), the mean average precision M, and the first (T 1 ) and second (T 2 ) tier  [96].
SHAPES Although our main objective was protein classification, it is important to note that our approach is applicable to multiple domains. For this reason, we illustrate how to apply our approach to classification of deformable partial shapes. In order to make the problem challenging, the reader should notice that no prior knowledge on the full shape is assumed, meaning that training and testing is entirely performed with partial shapes. The dataset employed is the SHREC'16 partial matching of deformable shapes  [98]  which was initially created for partial matching. This dataset consists of 599 deformable partial shapes divided into 8 classes: dog and wolf (the two being relatively similar), cat, David and Michael (again, the two being very similar), horse, centaur (a hybrid creature combining Michael and a horse), and Victoria.