dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Preprocessing: Cleaning and Making Data Available The preprocessing step consists in preparing the dataset to be mined (see Figure  4 ). This implies the following: (2) data cleaning, which consists in removing noise, corrupted data, and inaccurate records  (3, 153, 154)  data editing to control data quality by reviewing and adjusting it  (155)  and to anonymize data when needed with respect to data privacy standards  (4, 156, 157)  data curation to manage data maintainability over time for reuse and preservation  (158) ; and 4) data wrangling or the process of mapping data from one type to another to fit the selected mining technique (e. g. , from natural language to numerical vectors)  (159). It is an important step in the KDD process  (160)  since the quality of the analysis of a data mining algorithm relies on the data available for the analysis.
This analysis strongly indicates the need for mental health applications to be more rigorous  (172) , i. e. , by following a strict method. We have thus applied our end-to-end methodology to build a mobile application called i-decide (www. i-decide. fr) that facilitates decision processes under uncertainty. The application aims at complementing existing neuropsychological testing that take places punctually in a controlled setting by collecting longitudinal data on a daily basis. The data collected concern decision processes and all cognitive and emotional functions that impact decision-making (Boulos et al. , in revision). All data are used to feed an algorithm that learns optimal choices (that reduce long hesitations and associated anxiety as well as the percentage of regret postdecision) under uncertain conditions.
Promoter cloning For identification of the potential promoter regions we combined manual analysis and a trial version of Gene2Promoter software (Genomatix). We then designed primers using the Primer-Blast tool from NCBI (Table  1 ). Candidate primers were blasted against the whole zebrafish genome using the BLAT tool from UCSC Genome Browser. Restriction sites were added at their 5′end to enable directional cloning (underlined in Table  1 ). We used zebrafish high-molecular weight genomic DNA, extracted as previously described  (Sambrook et al. , 1990)  from 48 hpf TAB embryos. For fabp4a, the BAC DKEY-241P5 (Source BioScience) was used as a template. Each region was first cloned into pCRII plasmid using TOPO-TA Cloning Kit (Thermo Fisher Scientific). After sequencing, they were subcloned into p5′Entry-MCS plasmid from Tol2 Kit  (Kwan et al.
Image analysis Brightness-contrast adjustments were done using Fiji software  (Schindelin et al. , 2012). Fluorescence profile of MWISH images were generated using the Intensity Toolset from Imperial College of London FILM facility (https://www. imperial. ac. uk/medicine/facility-for-imaging-by-lightmicroscopy/software/fiji/). For Nile Red hyperspectral data analysis we used the spectra phasor approach using Globals for Images SimFCS 4 software (G-SOFT Inc. , Champaign, IL, USA). This method transforms the spectral data in each pixel to the real and imaginary component of the Fourier transform, as described earlier by  Malacrida et al. (2016) I(λ) is the intensity at each step, n the harmonic number and λ i is the initial wavelength. Each pixel in the image will be located at a single (G, S) position at the spectral phasor plot, yielding a cluster of points due to all pixels in an image.
Statistical analysis The statistical analysis was performed using PAST software  (Hammer et al. , 2001)  or Real Statistics Resource Pack software  [Release 7. 6, Copyright (2013 -2021 ), Charles Zaiontz, www. real-statistics. com, accessed on March 2021]. For group comparisons we analyzed normality using Shapiro-Wilk test and homogeneity of variances using Levene test. Non-normal and homoscedastic distributions were compared with non-parametric tests (Kruskal-Wallis or Mann-Whitney with Bonferroni correction) as indicated in each case. Non-normal and heteroscedastic samples were rank transformed  (Conover and Iman, 1981)  and compared using Welch test and Games-Howell post-hoc test. For the comparison of coefficient of variation we used the Fligner-Killeen test.
In 2016, the SysMod Community of Special Interest (COSI) of the International Society for Computational Biology (ISCB) was established to foster communication among systems biologists, computational biologists and bioinformaticians. Figure  1  depicts SysMod's activities, topic areas and integration into an ecosystem within the ISCB by thematic COSIs focusing on complementary yet adjacent subjects. The thematically closely related COSIs include Network Biology (NetBio), Biological Data Visualisation (BioVis), and, since 2018, Machine Learning in Computational Systems Biology (MLCSB). SysMod envisions to provide an interdisciplinary forum for discussing latest developments towards predictive, knowledge-based computer models of biological systems and processes (such as dynamic or logical models of metabolism, signalling and regulatory processes) as well as methods that advance our understanding in biological science, precision medicine and synthetic biology.
Third annual meeting: Chicago, 2018 The third annual SysMod meeting took place on July 7, 2018, during the ISMB in Chicago, Illinois, and featured three sessions: (i) 'Detailed models of individual cellular processes,' (ii) 'Comprehensive models of cells and tissues,' and (iii) 'Models of human physiology and disease'. Keynote presentations were delivered by (i) Andre Levchenko, Yale University, 'Variability and phenotype selection in invasive cancer spread,' (ii) Peter Sorger, Harvard Medical School, 'Trials, biomarkers, and data science,' and (iii) John Tyson, Virginia Tech, 'The cell division cycle: a closed loop of switches embedded in switches'  (Novak et al. , 2018). The meeting also featured ten contributed talks, four lightning talks and 33 posters. A recurrent theme was the need for multi-scale models to integrate emergent molecular behaviours and patterns into processes and data at multiple scales of biological organisation.
Fourth annual meeting: Basel, 2019 The fourth annual SysMod meeting took place on July 22, 2019, during the ISMB in Basel, Switzerland, and comprised three sessions spanning human cells and disease modelling, systems biology of microorganisms, and current trends in the field. Each of the three sessions featured a keynote speaker: (i) Trey Ideker, University of California, San Diego, 'Interpreting the cancer genome through physical and functional models of the cancer cell,' (ii) Edda Klipp, Humboldt-Universita ¨t zu Berlin, Germany, 'Systematic integration of models and data for yeast growth, division and stress response'  (Mu ¨nzner et al. , 2019)  and (iii) Jo ¨rg Stelling, ETH Zu ¨rich, Switzerland 'Systems analysis of cell-to-cell variability'  (Azizoglu and Stelling, 2019). The first session, 'Systems biology of human cells and diseases,' covered the following themes: modelling approaches for personalised medicine, disease diagnosis and treatment, and approaches to investigate heterogeneity in healthy and diseased tissues, such as tumours or in immune responses.
Related events and partnerships In addition to the annual meeting, SysMod partners with other modelling communities to provide opportunities to engage in scientifically relevant events throughout the year. For example, during the [BC] 2 conference in Basel in 2019, SysMod, in partnership with the Consortium for Logical Models and Tools (CoLoMoTo, colomoto. org; (Naldi et al. , 2015) , organised a workshop focused on developing guidelines and best practices for annotation and curation of logical models  (Niarakis et al. , 2021). During the ECCB in 2020, two additional SysMod-related events took place: (i) the hands-on tutorial on modelling software tools ('Computational modelling of cellular processes: regulatory versus metabolic systems') and (ii) the workshop on 'Advances in computational modelling of cellular processes and high-performance computing' with the two SysMod coordinators, Marı ´a Rodrı ´guez Martı ´nez and Jonathan R.
Models are developed as computational tools that incorporate a wide range of quantitative data to predict and understand how biological systems function. Driven by this core value, SysMod regularly fosters discussion among experts from various fields who contribute to transforming biology from a qualitative and descriptive scientific discipline to a quantitative science based on engineering and computational analysis. As such, SysMod has contributed to the development of systems biology as a young field. Regarding to global challenges such as the COVID-19 pandemic or climate change, SysMod, with its virtual and personal conferences and forums, paves the way for the establishments of new forms of friendly and cooperative interactions, which will determine the future of our global networking.
Funding M. Ba. was supported by the  Systems Biology Grant  of the  University of Surrey. A. D. was supported by infrastructural funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections. J. R. K. was supported by  National Institutes of Health (NIH)  [5R35GM119771]. T. H. was supported by  NIH  [5R35GM119770]. M. R. M. was supported by  Horizon 2020  ( H2020 ) [826121, 765158 and 813545].
As Internet of Things (IoT) networks keep growing with regards to the number of devices they contain, they become more attractive targets for attackers. Protecting these networks and the IoT devices they encompass is a major security challenge, and remote attestation enables checking of the integrity of devices (and thus networks). There are three categories of existing remote attestation protocols: software, hardware and hybrid attestation protocols. However, they all tackle specific issues only, such as small networks, IoT swarms, static networks, device-to-device attestation and network attestation. To provide as generic a solution as possible, which enhances security, we propose CRAFT, the first agnostic continuous remote attestation framework for IoT. CRAFT can be used in any real-world IoT network topology and can use any preexisting remote attestation protocol while remaining open to upgrades and extensions.
A. SCENARIOS DESCRIPTION, METRICS AND METHODOLOGY a: METRICS To compare the performances of CRAFT to SEDA  [6]  and US-AID  [7] , two metrics were taken into account. First, the total amount of data exchanged by the protocols was observed, as it can impact normal operations of the network and global energy consumption. Second, the number of HMAC operations were also compared, as these also impact execution time and energy consumption. Finally, we compared how these metrics evolved in different mobility scenarios. b: SCENARIOS DESCRIPTION Protocols were tested on two scenarios and with different numbers of devices to compare their performances in different conditions. The global parameters of the simulation are listed in Table  3. The first scenario consisted of randomly placed, static devices in a square layout.
B. IMPLEMENTATION DETAILS All implemented devices in CRAFT simulations were K -devices, which represented a worst-case scenario for CRAFT performance as K -devices exchange more data than L-devices. We also re-implemented two existing protocols, SEDA  [6]  and US-AID  [7] , in order to compare them with CRAFT with as much accuracy as possible. As CRAFT is a framework with no attestation capability of its own, we added SEDA attestation to CRAFT (resulting in CRAFT +SEDA) when comparing it to SEDA, and US-AID  (resulting in CRAFT +US-AID) when comparing it to US-AID. Thus, CRAFT +SEDA has the same attestation mechanism as SEDA but provides heartbeats, which either add security to the network or reduce the data overhead caused by attestation. For CRAFT +US-AID, it has the same attestation mechanism as US-AID but brings more mobility and uses the CRAFT heartbeat, comparable to US-AID's Proof Of Non-Absence but with a smaller data overhead as details in Section V-C.
Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection. Most of these techniques rely on tuning parameters that are often determined by cross-validation (CV) based methods, which raises important stability issues. To overcome this, we have developed a new dynamic bootstrapbased method for significant predictor selection, suitable for both PLS regression and its incorporation into generalized linear models (GPLS). It relies on the establishment of bootstrap confidence intervals, that allows testing of the significance of predictors at preset type I risk α, and avoids the use of CV. We have also developed adapted versions of sparse PLS (SPLS) and sparse GPLS regression (SGPLS), using a recently introduced non-parametric bootstrap-based technique for the determination of the numbers of components.
It allows us to deal with highdimensional datasets efficiently, and notably solves the collinearity problem  (Wold et al. , 1984). With great technological advances in recent decades, PLS regression has been gaining attention, and has been successfully applied to many domains, notably genomics. Indeed, the development of both microarray and allelotyping techniques result in high-dimensional datasets from which information has to be efficiently extracted. To this end, PLS regression has become a benchmark as an efficient statistical method for prediction, regression and dimension reduction  (Boulesteix and Strimmer, 2007). Practically speaking, the observed response related to such studies does commonly not follow a continuous distribution. Frequent goals with gene expression datasets involve classification problems, such as cancer stage prediction, disease relapse, and tumor classification.
This type of method was first introduced by  Marx (1996). Different modifications and improvements, using notably ridge regression  (Le Cessie and Van Houwelingen, 1992)  or Firth's procedure  (Firth, 1993)  to avoid non-convergence and infinite parameter-value estimations, have been developed, notably by  Nguyen and Rocke (2004) ,  Ding and Gentleman (2005) ,  Fort and Lambert-Lacroix (2005)  and  Bastien et al. (2005). In this work, we focus on the second type of adapted PLS regression, referred to from now on as GPLS. As previously mentioned, a feature of datasets of interest is their high-dimensional setting, i. e. , n ≪ p. Chun and Keleş (2010)  have shown that the asymptotic consistency of PLS estimators does not hold in this situation, so filtering or predictor selection become necessary in order to obtain consistent parameter estimation.
However, all methods described above proceed to classification using the entire set of predictors. For datasets that frequently contain thousands of predictors, such as microarray ones, a variable filtering pre-processing thus needs to be applied. A commonly used pre-processing method when performing classification uses the BSS / WSS-statistic: BSS j / WSS j = Q q=1 i:y i ∈Gq (μ jq -μj ) 2 Q q=1 i:y i ∈Gq (x ij -μjq ) 2 , ( 6 ) with μj the sample mean of x j , and μjq the sample mean of x j in class G q for q ∈ {1,. , Q}. Then, predictors associated with the highest values of this are retained, but no specific rule exists to choose the number of predictors to retain. A Bayesian-based technique, available in the R-package limma, has become a common way to deal with this, computing a Bayesianbased p-value for each predictor, therefore allowing users to choose the number of relevant predictors based on a threshold p-value  (Smyth, 2004).
, 2007). Thus, since this determination is specific to the dataset in question, it must be performed for each bootstrap sample, in order to obtain reliable bootstrap-based CI. We have established some theoretical results which confirm this (SI 1). Determining tuning parameters by using q-fold cross-validation (q-CV) based criteria may induce important issues concerning the stability of extracted results  (Hastie et al. (2009, p. 249) ;  Boulesteix (2014) ;  Magnanensi et al. (2015) ). Thus, using such criteria for successive choosing of the number of components should be avoided. As mentioned, amongst others, by  Efron and Tibshirani (1993, p. 255)  and  Kohavi (1995) , bootstrap-based criteria are known to be more stable than CV-based ones. In this context,  Magnanensi et al. (2015)  developed a robust bootstrap-based criterion for the determination of the number of PLS components, characterized by a high level of stability and suitable for both the PLS and GPLS regression frameworks.
A new dynamic bootstrap-based technique As mentioned in Section 1, the selected number of extracted components is crucial for reliable estimation of β PLS j , 1 j p  (Wiklund et al. , 2007). We have shown (SI 1) that selecting an optimal number of components on the original dataset and using it to perform PLS regression on the constructed bootstrap samples, as done by  Lazraq et al. (2003) , is not correct for the obtention of reliable CI. In order to take into account these theoretical results, we have developed a new dynamic bootstrap-based approach for variable selection relevant for both the PLS and GPLS frameworks. The approach consists of estimating the optimal number of components for each bootstrap sample created during the algorithm. To obtain consistent results, a robust and resample-stable stopping criterion in component construction has to be used.
While the original implementation compares K max × s models through CV MSE, with K max the maximal number of components (set by the user), this new bootstrap-based version only focuses on s models, since only one number of components is determined for each preset value of η. An illustration of the stability improvement obtained by using this new implementation, based on the simulated dataset introduced in Section 3. 2. 1 with sd (ǫ) = 5, is shown in Fig. 1.
Simulations for global comparison 3. 2. 1. Dataset simulations In this study, we used a real microarray gene expression dataset, which was created using fresh-frozen primary tumors samples, from a multi-center cohort, with stage I to IV colon cancer. 566 samples fulfilled RNA quality requirement, and constituted our database. These samples were split into a 443 sample discovery set and a 123 sample test set, well balanced for the main anatomo-clinical characteristics. This database has already been studied by  Marisa et al. (2013)  and more details on it are available in their article. In order to reduce computational time, a preliminary selection of 100 predictors was performed. Based on the original localization of the tumors as response vector, and the full 566 samples, the 100 most differentially expressed probe sets were extracted.
Benchmarked methods Using these simulated datasets, eight methods were analyzed and compared. 1. Q 2. The original bootstrap-based method, introduced by  Lazraq et al. (2003) , combined with the 10-fold CV-based Q 2 criterion  (Tenenhaus, 1998, p. 83)  for pre-selecting the number of components. 2. BIC. The bootstrap-based method, introduced by  Lazraq et al. (2003) , combined with the corrected BIC using estimated degrees of freedom (DoF)  (Krämer and Sugiyama, 2011)  for pre-selecting the number of components. 3. BootYT. The bootstrap-based method, introduced by  Lazraq et al. (2003) , combined with the bootstrap-based criterion  (Magnanensi et al. , 2015)  for previously selecting the number of components. 4. BICdyn. Our new dynamic bootstrap-based method combined with the corrected BIC criterion for successive selections of number of components.
Moreover, since Γ 1 = Γ 2 for all studied datasets, this directly implies that it retains a unique optimal number of components for each sparse support. It thus permits a choice of optimal model in a more reliable way than using the CV-based technique. Lastly, lasso regression has good stability performances, leading it, BootYTdyn and SPLS BootYT to be recommended when stability is important. The last descriptive statistic concerns the number of significant predictors retained (Table  6 ). The Q 2 -based approach is the least sparse, selecting the highest number of covariates. Both BICdyn BootYTdyn improve respectively BIC and BootYT, showing better accuracy related to their selected support. Indeed, the four expected covariates are always included in these selected supports. Once more, BootYT can be recommended for datasets with nonnegligible random noise variability compared with corrected BIC, which has to be applied for small values of random variance.
Indeed, as the random noise standard deviation increases, these methods globally build sparse models with increasing complexity. Thus, they model increasing parts of the inserted random noise, implying poor predictive abilities of their selected models compared to those obtained by applying BootYT. This is confirmed through higher values of PMSE and CV MSE, especially for datasets with non-negligible random noise variability. These results confirm the conclusions from the previous section in that using the Q 2 criterion for selecting the number of PLS components should be avoided, and that BootYT outperforms corrected BIC, except for responses with negligible random noise levels. Therefore, only BIC and BootYT are retained for further comparison. The results shown in Fig. 4  highlight that BootYTdyn is the only method that models with decreasing DoF, ensuring a complexity reduction suitable to the avoidance of prediction issues.
Indeed, BootYTdyn selects models with the lowest PMSE and 10-CV MSE. In light of these results, only BootYTyn is retained for further comparison. Comparing the two SPLS implementations with respect to their predictive abilities lead us to recommend SPLS BootYT, since models selected by this bootstrap-adapted SPLS technique feature comparable if not lower PMSE and 10-CV MSE (Fig. 5 ). Let us clarify that, in order to ensure relevant comparisons, we used ordinary PLS regressions with both the support and the number of components selected by the SPLS methods, and not SPLS methods with selected tuning parameters, for computing the 10-CV MSE. For datasets characterized by a low level of random noise variability in y, SPLS BootYT builds models inducing the smallest CV MSE and PMSE. By focusing on non-negligible random variability, BootYTdyn gives the smallest predictive errors, thus showing the high level of robustness of this approach against random noise.
Real dataset application In this section, we deal with the predictors matrix introduced in Section 3. 2. 1 and the original binary response vector. Five approaches for variable selection, adapted for the GPLS framework, are considered for comparison. 1. BootYT. The bootstrap-based method, introduced by  Lazraq et al. (2003) , combined with the bootstrap-based criterion  (Magnanensi et al. , 2015)  for pre-selecting the number of components. 2. BootYTdyn. The new dynamic bootstrap-based method combined with the bootstrapbased criterion for successive determinations of the number of components. 3. SGPLS CV. The original SGPLS method using 10-fold CV for tuning parameter selection  (Chung and Keles, 2010). 4. SGPLS BootYT. The new adapted SGPLS version using the bootstrap-based criterion for component selection.