dataesr/scientific-paragraphs-categorization huggingface dataset:
Two strategies are considered: either MS/MS spectra from known compounds are algorithmically predicted and tentatively matched to experimental spectra, or the experimental spectra are computationally investigated to reconstruct the molecular structure  [45]. The first approach relies on 'known unknowns,' which lacks completeness compared to MS/MS spectral databases. On the other hand, the latter approach relies on deep-learning methods such as support vector machines, enabling the modelling between fragmentation spectra and molecular fingerprints after large-scale model training. Databases can subsequently be searched after structure prediction to find already existing compounds. If the prediction is missing from databases, valuable information on substructures and chemical classes is still obtained.
This approach was chosen as the full metabolome of G. fossarum is not documented yet, and thus no information is available regarding the biomolecular content. For compound identification, we used MS/MS database from CSI:FingerID available in SIRIUS  [26]. This in-silico approach relies on the combination of the precursor exact mass, the fragmentation spectrum and eventually the RT to predict molecular formulas using the exact mass and the isotopic pattern, and structure propositions using MS/MS spectraNext, SIRIUS was employed on the 10 most discriminant features from HILIC(+) and the 16 from RPLC(+) using CID and EAD spectra to provide structural insights. In HILIC(+), we confirmed the identification of N-acetylglucosamine (considering all optical isomers) for the [M + Na] + adduct at m/z 244.
Conclusion In conclusion, SWATH-DIA has demonstrated significant benefits for characterizing the metabolome and lipidome of the crustacean amphipod Gammarus fossarum during the reproductive stage, in comparison of IDA/DDA acquisition mode. The Zeno pulsing effect, facilitated by the Zeno trap, enhances detection sensitivity, leading to a more comprehensive molecular fingerprint crucial for biologically relevant features. These benefits are evidenced by a minimum 5-fold increase in fragment intensity for over half of the fragments in all datasets (52%). Moreover, 78% of features exhibit at least twice as many fragments per fragmentation spectrum with Zeno pulsing. These results advocate for employing Zeno pulsing alongside the SWATH-DIA approach, which synergistically combines extensive coverage and enhanced detection sensitivity.
Additionally, these advancements are underscored by a substantial 57% increase in the number of confident putative identifications based on MS/MS databases across all datasets, emphasizing the methodology's significant contribution to improving compound identification accuracy and reliability. Regarding compound annotation, the use of EAD by the targeted approach ZENO-MRM HR has significantly contributed to elucidating specific lipid species in contrast of CID MS/MS spectra. For instance, PC 36:3, previously identifiable only through CID in positive ion mode, was successfully elucidated as PC 18:1(9)/18:2(9,12) using EAD in one single acquisition (one polarity). However, EAD-based identifications rely on the MS/MS reference database in MS-DIAL 5. 1, and this database-driven approach has limitations as it restricts identifications to "known unknowns" in the database, introducing bias into the identification process.
To address this limitation, a database-free approach employing SIRIUS has been explored. Through a Multiple Kernel Learning approach for structure prediction, previously unknown features in both HILIC(+) and RPLC(+) datasets have provided valuable insights into potential structures and substructures. Despite these advancements, caution is warranted when interpreting results from machine learning tools since annotation has to be confirmed by synthetic standards. 1. Discriminant features in HILIC and RPLC. A MS-DIAL identity number is given for each feature. RT as well as the precursor mass-to-charge ratio are given and form the unique serial number RT_m/z. The p-value related to the Wilcoxon Mann Whitney non-parametric test is displayed. FIGURE LEGENDS.
Acknowledgments The authors thank the  French Ministry of Higher Education, Research, and Innovation (Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation)  for the PhD fellowship of  Thomas Alexandre Brunet. Valentina Calabrese  was supported by a post -doctoral fellowship  of the  SENS  research funding of the  Université Claude Bernard Lyon  1. This work was supported by the  French National Agency Research  (Young investigator Grant  ANR-18-CE34-0008 ) and performed within the framework of the  EUR H2O'Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL) , within the program " Investissements d'Avenir ". The authors also benefitted from the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering stimulating  scientific discussions and collaborations for more integrative approaches.
N -acetylglucosamine was matched through CID MS/MS database search to its [M + Na] + adduct through four characteristic fragments: three neutral losses m/z 226. 0604, 124. 0365, 143. 0301 and one radical fragmentation m/z 112. 0500 illustrated in Figure 7's top blue spectrum, alongside their respective fragmentation patterns. Despite achieving a decent matching score (879 MS-DIAL v. 4 score), some ambiguities remained regarding the functional group on the hexose carbon n°2, purportedly identified as an acetamide. Since no CID fragmentation occurred on the carbon n°2 group, the structural confirmation of this group remained elusive, relying solely on the congruence of the global precursor ion mass and the match of the hexose fragments. This raises the possibility of the compound being an isobaric form of N-acetylglucosamine, distinguished exclusively by the carbon n°2 group.
2014, Blance and Spannowsky 2021). A ubiquitous library for quantum computing is Qiskit  (Abraham 2019). Qiskit is authored by IBM and distributed under the Apache 2. 0 licence which provides both quantum algorithms and backends. A backend can be either a local machine or a remote machine, which can be an emulated one or a real quantum computer. Qiskit abstraction over the type of the selected machine makes designing and using quantum algorithms seamless. Qiskit implements quantum versions of two support vector-like classifiers, named quantumenhanced support vector classifier (QSVC) and variational quantum classifier  (VQC, Havlíček et al. (2019) ). In practice, experiments on artificial datasets suggest that quantumenhanced support vector machines (SVMs) offer a provable speed-up compared to classical algorithms  (Liu et al.
2019 ) -see section Direct classification of covariance matrices under Additional Information. Table  1. Instantiation of VQC and QSVC classifiers in pyRiemann-qiskit. To calculate the class prototype, we need to select a distance and provide an optimiser. We model the distance as a convex problem, based on frobenius distance (python method fro_mean_convex). The model objective is to find the matrix which is at minimum distance to all covariance matrices. This matrix is then designated as the barycenter or class prototype of the covariance matrices. The optimiser is the component which is responsible for finding the coefficients of this barycenter, based on the model objective and the covariance matrices used as inputs. For the optimiser, pyRiemann-qiskit relies on Qiskit implementation of QAOA (Quantum Approximate Optimisation Algorithm).
Classification of vectorised covariances matrices The number of qubits is limited (especially on real hardware) and this puts a limit on the length of the feature vectors that can be used with quantum programming. In pyRiemannQiskit, we use the method of tangent space vectorisation. It consists of creating covariance matrices from the signal then projecting them into the tangent space of the Riemannian manifold. The result is a feature vector with reduced dimensionality. The dimensionality can be further reduced using PCA, for example, in order to match the number of available qubits. The code snippet below (Table  3 ) demonstrates how to perform a dimension reduction of the signal epoch using Xdawn  (Rivet and Souloumiac 2013) , apply the tangent space method, then further reduce the size of the feature space to match the capability of the quantum backend in charge of running our quantum classifier (here QuanticSVM).
Usage licence Usage licence: Other IP rights notes: BSD https://doi. org/10. 5281/zenodo. 7565763 Wiki: https://github. com/pyRiemann/pyRiemann-qiskit/wiki 3-Clause "New" or "Revised" License Download page: https://pyriemann-qiskit. readthedocs. io/en/latest/installing. html Bug database: https://github. com/pyRiemann/pyRiemann-qiskit/issues Repository Type: Git Browse URI: https://github. com/pyRiemann/pyRiemann-qiskit/ Homepage:.
Governing and supplementary equations To simulate the combined effects of ARO and warming on the different groups of aquatic primary producers, we developed a process-based mathematical model, which we implemented in MATLAB (R2020a). The model comprises a series of ordinary differential equations (ODEs) similar to those proposed in previous works  (Jäger et al. , 2010; Vasconcelos et al. , 2016). These equations describe the following state variables: the live carbon (C) biomass of phytoplankton (as volume concentration, eq. 1, specific to each phytoplankton group), periphyton, hereafter understood as the growth on the vertical inner surface of the microcosm vases (as areal density, eq. 2), epiphyton, i. e. , periphyton growing on macrophyte surfaces (as areal density, eq. 3) and macrophytes (eq.
Statistical analyses Non-parametric statistical tests were used to test for differences between treatments related to the ARO application or the temperature. These analyses were conducted using the software R (R Core Team, 2020; v4. 0. 0). For each of the three primary producer groups differences in biomass were tested via the Kruskal-Wallis test followed by Dunn's post hoc test without further correction due to the low number of treatments. Further, biomass of each primary producer group was extrapolated to the whole biomass per microcosm, including periphyton growing on the inner walls of the microcosm. Based on this total microcosm primary producer biomass, the effect sizes (as Hedges' g) were calculated using the esc package  (Lüdecke, 2019; v0. 5. 1). Using the mean biomass of each primary producer group as well as the total biomass of all primary producer groups together, pie charts were created for simplified presentation of the share of each primary producer group within each treatment.
Acknowledgments Special thanks to  Valeria Lucero Reimann Salazar ,  Simon Asmus  and other members of IGB who helped during the microcosm experiment, and to  Herwig Stibor ,  Joseṕhine Leflaive ,  Joey Allen  and other members of  CLIMSHIFT  for providing valuable comments as part of their role in the project and for their help in the preparation of the microcosm experiment and the development of the process-based model.
B. MODELLING QTP is modelled using Modelica  [75]  with Dymola software from Dassault Systèmes to support the simulation and generate data using fine-scale modelling rather than physical equations. Each component is modelled, including: water properties, pumps inertia, and pipe roughness, using the Modelica standard library  [76]  and building library  [77]. The components are graphically assembled to build the QTP simulation model. An overflow outlet is added to the four   and the same number of unknown variables. These equations could not be used for the MPC controller due to some ifthen-else conditions. A hybrid MPC controller is mandatory and leads to complex analyses, design and optimisation techniques  [78].
C. DATA GENERATION The nature of the input signals is essential for dynamical system identification. The relation between the input signals and the outputs variations are used to describe the dynamical system. Pseudo-Random Binary Sequences (PRBS) as input signals are able to excite the system with a widespread frequency spectrum in order to acquire a unique set of parameters  [17]. In this work, we performed the data acquisition of the QTP digitally using Dymola with the fine-scale dynamical system from Modelica program. The sampling period is equal to 5s and, Differential Algebraic System Solver (DASSL) is used during simulations to allow solving the Modelica model comprising mixed differential and algebraic equations  [79]. The PRBS signals applied on pumps q a and q b are visible in Fig.
8. a and outputs signals in   from all subspace. To assess neural network architectures in this work, data separation is performed according to the tank levels' value ranges. First, the data are processed to keep data within the waterlevel constraints (Fig. 9. a and Fig. 9. b); second, the data are divided into sequences of 32 samples length; third, the sequences are separated into two groups, where the first group data are picked when the sequenced water levels of tanks 1 and 2 (h 1 , h 2 ) remained between 0. 4 m to 0. 8 m (Fig. 9. c and Fig. 9. d). The remaining data thus constituted the second group and to form the test data (e. g. , water level lower than 0. 4 m or greater than 0. 8 m) (Fig. 9. e and Fig. 9. f). Finally, the first group is divided into two, with 5% for the validation data and the remaining for the training data.
E. COMPUTING MACHINE IMPLEMENTATION 1) Neural network training Neural networks are implemented on computing machines using the open-source Julia programming language  [81]  and Flux package  [82]. This package allows easily implementing neurons, shortcut connections for ResNet, parallel connections for PolyNet, deep networks with multiple layers and all necessary training and fidelity measure computing. The hyperparameters are optimised using the BlackBoxOptim package which implements metaheuristics algorithms  [83]. We chose the separable NES derived from Natural Evolution Strategies  [84].
2) Process control Controller simulation is performed using Simulink. The Modelica model is first exported to Functional Mock-up Unit (FMU) for simulation. The controller interacts with the FMU according to Fig. 12  and is implemented through JuMP  [85]  using a multiple-shooting numerical method  [72]. The neural network is defined with JuMP from the Flux object function as a JuMP user-defined function, which allows automatic differentiation to compute the derivative  [87]. It is not necessary to rewrite the neural network within the optimisation modelling since Flux and JuMP are both in Julia's ecosystem. In addition, the chosen optimisation solver is Ipopt  [86]. The set points used during simulations are shown in Tab. 5. The set-points are selected to evaluate control performance with different tank water levels.
In addition, the set-points 1 and 3 are located inside the train data area and the set-points 2 and 4 are located outside the train data area. This choice will make possible to appreciate the extrapolation capability Several simulations are performed to analyse control performance regarding the implemented neural network. For each neural network architecture (FNN, ResNet, PolyNet), the 25 networks with the lowest MSE with test data are simulated, and for each simulation run, a comparative performance index is calculated to assess networks' influence on control. This index is defined as: J := 1 N s 12000 k=2700 e T x [k]Qe x [k] + e T u [k]Re u [k] (23.
A. SYSTEM IDENTIFICATION The number of neural networks trained with the metaheuristic algorithm is   FNN, 964 for ResNet and 966 for PolyNet), which are depicted hereafter. Fig. 13  depicts the boxplot function loss of the neural networks after training and hyperparameters optimisation. The MSE with training data is visible in Fig. 13. a, MSE with validation data in Fig. 13. b and MSE with test data in Fig. 13. c. Fig. 13. a and Tab. 7 show that the 1 st quartile losses are reduced for neural networks related to numerical integration compared to the FNN. In addition, the lowest MSE is reached by ResNet for the median and 3 rd quartile, and the same is observable in Fig. 13. b and Tab. 8 for the MSE with the validation data. Note that all networks have a relatively equivalent performance in MSE with the  training data and validation data, but the result is clearer when considering the generalisation properties, according to the results obtained with the test data in Fig.
13. c and Tab. 9. In this case, both neural networks related to the numerical integration method achieve a reduction of the median, 1 st and 3 rd quartile MSE compared to the FNN, and ResNet achieve the lowest MSE compared to the FNN and PolyNet. The hyperparameters' influence over MSE with test data is shown in Fig. 14. The figure presents MSE with test data over hyperparameters optimisation with hidden layers, neurons, activation functions, optimisers and epochs for the FNN (first line), ResNet (second line) and PolyNet (third line). First, boxplots of MSE over hidden layers for FNN, ResNet and PolyNet are depicted in Fig. 14. a, Fig. 14. f and Fig. 14. k. The results show that increasing the number of hidden layers increases the median MSE test loss for the FNN, with 1. 34 × 10 -5 with one hidden layer, 3.
32 × 10 -5 with two hidden layers and 3. 86 × 10 -5 with three hidden layers. Increasing the number of hidden layers reduces the median MSE with test data for ResNet and PolyNet, 2. 59 × 10 -6 and 7. 09 × 10 -5 with one hidden layer, 1. 83 × 10 -6 and 1. 03 × 10 -5 with two hidden layers, 1. 51 × 10 -6 and 4. 16 × 10 -6 with three hidden layers. Second, we can analyse the impact of the number of neurons per hidden layer (see Fig. 14. b, Fig. 14. g, Fig. 14. l). The results show that the algorithm investigates 10 to 18 neurons for FNN, 6 to 15 neurons for ResNet and 4 to 15 neurons for PolyNet, while the neurons' values range from 4 to 20 neurons. Third, we can audit the MSE with test data over the activation functions (Fig. 14. c, Fig. 14. h, Fig. 14. m). The swish activation function allows a reduction for FNN networks (Fig.
14. c); for instance, the median MSE is equal to 5. 04×10 -4 for sigmoid, 9. 42×10 -4 for softsign, 1. 32×10 -5 for swish and 1. 77×10 -4 for tanh. The ResNet median MSE with test data is equal to 1. 34×10 -6 for sigmoid, 1. 73×10 -6  for softsign, 1. 60 × 10 -6 for swish and 2. 86 × 10 -6 for tanh. The PolyNet median MSE with test data is equal to 2. 54×10 -6 for sigmoid, 1. 19×10 -5 for softsign, 3. 67×10 -6 for swish and 1. 80 × 10 -5 for tanh. The results show that the sigmoid allows a reduction of the median MSE for ResNet and PolyNet. Fourth, we can explore MSE with test data over optimiser, adam, nadam, oadam and radam (Fig. 14. d, Fig. 14. i and Fig. 14. n). Note that nadam  [65] , oadam  [66]  and radam  [64]  are derived from adam  [63]. The FNN median MSE with test data is equal to 2.
In Fig. 14. j with ResNet, the algorithm chose most epochs from 30 to 36, and the median MSE remains equivalent. Fig. 14. o shows that increasing the epoch allows a reduction of the median MSE with data until 30 epochs with PolyNet. We can regard the ResNet and PolyNet number of layers, (see Fig. 15. a and Fig. 15. b). The algorithm investigated 1 to 4 and a jump to 8 layers for ResNet but from 1 to 9 for PolyNet. Fig. 15. a shows that the median MSE plateaus from 1 to 4 layers with 1. 62 × 10 -6 for one layer, 1. 85 × 10 -6 for two layers, 1. 54 × 10 -6 for three layers and 1. 70 × 10 -6 for four layers.
The second consisted in proposing a methodology to ensure a fair comparison of ResNet and PolyNet with the classical FNN. Common evaluation criteria have been defined. A systematic investigation of hyperparameters via metaheuristic optimization has been proposed. Finally, the data have been segmented in an original way to highlight the generalization capabilities. The results of this work confirm, first of all, the particular interest of the two architectures ResNet and PolyNet studied. ResNet and PolyNet achieve equivalent performance on learned data. It is even observed, which was unexpected, that the generalization capacities of the ResNet surpass those of the PolyNet, in spite of the more complex architecture of the latter. Beyond that, we analyzed the quality of MPC waterlevel control obtained via MPC, considering in turn FNN, ResNet and PolyNet based prediction models.
A survey was also sent after the JOBIM 2021 conference. The purpose of the post-conference survey is to collect data related to the attendees' question-asking behavior during the conference as well as their previous experiences with and general opinions on academic conferences in STEM. Therefore, we divided the post-conference survey into three sections: contact information, question-asking during the JOBIM 2021 conference, and general experiences at conferences (see Fig. 3 ). On the 695 participants at JOBIM 2021, 525 consented to the use of their data for this study and 152 answered the post-survey.
Observation of the virtual conference During the JOBIM 2021 conference, an observational study was conducted to collect data that is relevant to attendees' question-asking behaviors. Given the fact that the conference took place virtually on the videoconferencing platform Zoom, the research team gathered observational data through : 1) an observation forms filled out by a team of observers and 2) attendance reports generated from Zoom. The observation team consisted of 8 observers (6 women, 2 men), including researchers from the research team as well as conference attendees who volunteered to support the observational study. Prior to the conference, an online version of the observation form was developed and tested to assist the recording of observational data. During the conference, observers followed a set of guidelines (see Supplementary Materials) to observe the planned sessions for observation and to fill out the observation form accordingly.
The observation form was a key tool to collect data on question-asking behavior, for example, the number of questions asked during each session, type of the questions, and gender of the question askers 9. To minimize the human errors that could potentially occur during observation, each session was observed by at least 2 observers. By the end of each session, observers submitted the observation forms online. Zoom attendance and Q&A report where exported for each session. The gender of question askers was identified using the following procedure: 1) Use self-identifying gender from the registration survey when available, 2) Identified from firstname, 3) in last resort, we queried the name of the asker through Google search engine and identified gender from a portrait. Q&A report were manually curated to remove comments containing no question ("thanks", "clap-clap",.
Qualitative data analysis To analyse data collected from in-depth interviews, we adopted qualitative coding as the key method to transform data into findings with the support of one of the popular CAQDAS (Computer-assisted Qualitative Data analysis Software), MAXQDA. The process of analysing qualitative data in this study combined inductive and deductive approaches. On the one hand, an initial codebook (see Supplementary Materials) was developed prior to coding, based on research questions and interview guidelines. Based on the codebook, researchers were able to apply predefined codes to the interview data. On the other hand, line-by-line coding was performed on all interview transcripts. Associating with the constructivist grounded theory, this approach made sure that close attention was paid to all collected data and that researchers had the opportunity to extract as much information as possible from the data.