dataesr/scientific-paragraphs-categorization huggingface dataset:
Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real-world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution.
We proposed the Mandarine Academy Professional timetabling crossover operator MAPT co and MAPT mo for mutation as two custom genetic operators to be compared against the partially mapped crossover PMX and swap mutation Swap. These two classic operators are widely used in scheduling/permutation problems. Elite configurations of each algorithm were found using the i-race package. Experiments using Mandarine Academy's historical data are discussed where we highlight the differences between algorithms and select the best-performing ones. This work is an extension of a previously published work  (Hafsa et al. , 2021) , the main differences and additional contributions are as follows: • complete mathematical modelization of the problem, • implementation and comparison of additional algorithms (SPEA 2, IBEA, and MOEA/D) to the already studying ones (NSGA II and NSGA III), • proposal and study of additional objectives and new stopping criteria that correspond to realworld scenarios, • realization of a detailed performance analysis of each approach across various datasets, and • proposal of a new timetabling dataset  (Hafsa, 2022)  for the research community with the best solutions.
DiLeap Logistic optimizes this task by employing a greedy algorithm that takes a list of events to be planned, along with their start/end dates and locations, and automatically generates a timetable. Automating the planning process makes it easier to rent a room, notify teachers and employees about upcoming sessions, and manage the company's resources. Because the planner can confirm the software proposals, courses will be scheduled only after the decision-maker approves them. Alerts about conflicts or noncompliance with constraints are also displayed to give the planner a sense of what happened during the planning process. Despite its advantages, the currently used automatic timetabling tool in DiLeap Logistic has several limitations: (1) it does not follow the owner's defined restrictions (it can mobilize resources that are unavailable or in the incorrect location) and (2) it does not handle multiobjectives (only a single objective which is maximizing the number of planned training sessions).
Crossover PMX, CX, and OX operators are frequently used to solve scheduling problems  (Chinnasri et al. , 2012). We propose a new crossover operator called MAPT co , which was inspired by  Bratković et al. (2009)  and two-point crossover. The pseudocode is displayed in Algorithm 2. MAPT co works as follows: we define N as the number of events found in solution. We then randomly select E 1 and E 2 events from parents one and two and interchange their positions. We define p c as the crossover probability in our experiments. Later in our experiments, the proposed crossover operator will be compared to a classical operator (PMX) to determine which is more practical for this specific problem. A possible conflict with constraints is possible for the aforementioned crossover operators. For example, Parent 1 and Parent 2 do not share the same number of planned sessions per event.
Dataset Mandarine Academy provided us with some historical planning data from Dileap Logistic so that we could test our strategy. A total of 152 different courses were used to generate instances. A summary of our test files can be found in Table  1. Three problem sizes are available: small, medium, and large. As test size increases, more sessions and trainings must be scheduled within a constrained planning window. This was done to see how the algorithms would react if there were more events to satisfy. There are 20 different test instances for each size, and it is important to remember that the duration is expressed in days. As previously mentioned, since this problem differs from academic timetabling, evaluating our approach using academic datasets (like ITC 2007 or ITC 2019) will be inapplicable.
This is because our objectives include business requirements and are not just academic, and the main focus of this study is organizational rather than academic. We published the data we used in our research in Harvard Dataverse, titled "Mandarine Academy Professional Timetabling Dataset" (MAPT D)  (Hafsa, 2022). MAPT D is a real-world dataset suggested for solving professional timetabling problems , a relatively underexploited scheduling category. However, we believe it can still be used as a useful benchmark dataset to help researchers compare various approaches to traditional timetabling problems  (education, health, etc. )   • Mutation probability (M p ): Probability that a child solution will be mutated. A list of possible values is chosen for each model parameter, this is shown in Table  2.
Performance comparison (NSGA 2, NSGA 3, SPEA 2, MOEAD/D, IBEA) For this task, we will be using jMetalPy  (Benitez-Hidalgo et al. , 2019) , a Python framework for single/multiobjective optimization that offers both parallel computing capabilities and easy-to-use  visualization tools. The framework can be used to create custom evolutionary algorithms/operators and define new problems.
Practical implementation For reproducibility purposes, we share a repository available at https://github. com/yunshihuang/PMCnet with our implementation in PyTorch of the proposed algorithm. The advantage of a framework such as PyTorch is that it contains many built-in functions to deal with nonlinear feedforward neuronal structures such as as  (4). These functions make an efficient use of the available GPU resources. Autodifferentiation tools are also used to compute gradient of our target function, without the need for any explicit tedious calculations. In our code, we define the parameters θ (i. e. , weights/biases of the network) as Pytorch tensors through the option requires_grad=True. This allows us to compute the loss and its gradient in parallel (using GPU) for a given list of weights/biases values, using backward PyTorch function.
minimize the averaged MSE for inspecting the ROC curves and their CI envelopes in Fig. 1. The envelopes are wider (i. e. , exhibit more variability) for smaller train sets, showing the interest and relevance of the provided posterior estimation. We now illustrate the performance of PMCnet when compared to its low complexity variant PMCnet-light, on the same control dataset. We now set N train = 1600. The results on the test set for PMCnet and PMCnet-light are summarized in Table  3. The (probabilistic) ROC curves are provided in Fig. 2 , and the metrics evolution (on test set) along training are displayed in Fig. 3. Both methods reach very similar metrics. Interestingly, both AUC and accuracy metrics converge much faster using PMCnet-light. Moreover, the latter method displays much less variability, as it can be seen in the std values and the ROC envelopes.
Comparison with benchmarks on shallow networks We now compare our method with the benchmarks introduced in Sec. given by N train + 2N test , are summarized in Table  4  for each dataset. We make use of tanh as the activation function of the hidden layer. We set (M, K) = (50, 100) for our PMCnet and finetune T for each dataset. We provide the number of samples per class for the train/validation/test splits of each classification datasets, in Tables  5, 6  and 7. We can observe that the dataset Glass has a major class imbalance, while the other datasets are relatively balanced. Next, we focus on the multi-class classification problems related to datasets Wine and Glass. The numerical results are summarized in Table  9. We can see again that our methods gives the best accuracy for both datasets.
Even more, for the dataset Wine, it can perfectly predict the class of wine on the considered test set. As for the dataset Glass, the performance of every method is not as good as those for the dataset Wine, probably due to the high class imbalance in this dataset. Our method still outperforms others in this dataset with acceptable variability. We also draw the heat map of the confusion matrix associated to these results in Fig. 6. Clearly, the PMCnet method leads to confusion matrices with the most zeros on the off-diagonal axis. For dataset Wine, we plot the mean of AUC and accuracy of test set along iterations during the training phase of PMCnet in Fig. 7. We can see that, as the training goes on, both AUC and accuracy tend to stabilize, which shows the validity of our method. As for dataset Glass, we plot the mean of AUC and accuracy of test set along iterations in Fig.
8. Again, we can see the proposed method converges gradually. First we provide the results obtained for different methods on dataset MNIST-C in Table  11 , we choose T = 20 in this case. Hereagain, the classifier derived by our method is the best among all the competitors in terms of both AUC and accuracy. Note that, on this dataset, almost all the probabilistic methods make predictions with small variability. Even so, our proposed method still performs best with a mean AUC close to 1 and very high accuracy, nearly one percent higher than benchmarks. Considering the sample size of this large dataset, this is already a great improvement. Our methods also reaches the best averaged F1 score. The heat map for confusion matrix of each method is provided in Fig. 9 , we can see that for each class, PMCnet-light is able to predict most digits correctly.
We finally discuss the performance of the compared methods in the dataset Protein. The numerical results are summarized in Table  12. For this dataset, we choose T = 5. The estimated output from our method has the smallest averaged MSE with rather small variability. We also display the box and violin plot of the square errors for each test examples, in Fig. 10. Among all methods, PMCnet-light not only has the smallest MSE in average, but also shows minimal spreading of outliers, which shows again the good performance of our method.
Conclusion This paper proposes a novel method for the Bayesian inference in neural networks that relies on adaptive importance sampling. The method is able to characterize the posterior distribution of the estimated outputs and approximate the moments of such distribution. We also propose a light version of the method for handling large datasets and deep networks. This version avoids an excess of the required memory during the training process without impacting the performance of the algorithm. The numerical experiments illustrate the good performance of this new method on severam datasets of classification and regression, using either shallow or deep networks. Our method is also competitive in terms of computational time on both training and test stages.
Performance assessment on a control scenario We first evaluate our approach PMCnet and its variant PMCnet-light on a control dataset mimicking a binary classification problem. No validation set is used in these simple examples, and we always set (M, K, T ) = (50, 100, 20). The considered shallow FCNN is described in Table  1. The ground truth labels for the control dataset are generated by setting a given (shallow) FCNN architecture for which the groundtruth vector θ is known. Specifically, we design a synthetic FCNN with L = 2 layers, with tanh as the activation function for the hidden layer, and random weight and biases entries independently drawn from N (0, 2 2 ) for every layer. We then feed the network with random N train (resp. N test ) inputs entries independently drawn from N (0, 1).
Similar conclusions are reached by Let us start with the results obtained for the binary classification problem with dataset Ionosphere. We summarize the metrics obtained on test sets in Table  8. Our finetuning led to T = 50. We can observe that our proposed method reaches best precision, specificity, accuracy and F1 among all the methods, with a small variability. ADAM-MAP reaches good performance too on this experiment. Among Bayesianbased competitors, MCDropout, and SAE reach the best results, however with lower accuracy than our method. We also display ROC curves (and CIs, when available) in Fig. 4. This illustrates again that our proposed algorithm provides a good predictive performance with small variability. In Fig. 5 , we pick some examples from the test sets of the dataset and display the histograms (with 10 bins) of {y (r,n) } 1≤r≤R obtained by Algorithm 2.
4. 1. 4. 14 Training specificationsIn all the experiments, we split the dataset into three different parts: (1)  training set used for running the inference algorithms, (2) validation set used for choosing the optimal regularization weight related to the parameter prior and the optimal iterations T , and (3) test set used for evaluating quantitatively the performance of the trained models. Unless otherwise specified, the proportion of the train/validation/test is set as 6:2:2, with N train (resp. N test ) denoting the number of examples in the training (resp. validation or test) sets. For all the methods, the train/validation/test phases are implemented in Pytorch (version 1. 7. 0) under Python (version 3. 6. 10) environment, and run on an Nvidia DGX workstation using one Tesla V100 SXM2 GPU (1290 MHz frequency, 32GB RAM).
The code of our method is made available at https://github. com/yunshihuang/PMCnet for reproducibility purposes. 4. 1. 5. PMCnet tuningRegarding the settings of PMCnet (or PMCnet-light), for t = 1,. , T , the parameters of the robust covariance adaptation are set to β t ≡ 0. 5, η t ≡ 1 t. Both γ.
4. 1. 2. We first consider a shallow FCNN with only one hidden layer of few units, i. e. , L = 2 with small S 1 , and three small size classification datasets of LIBSVM library available at https://www. csie. ntu. edu. tw/~cjlin/libsvm/. On all examples, the dimension of the unknown parameters d θ is low (few hundreds). The architectures of the chosen shallow FCNNs, as well as the full dataset size (train + validation + test).
Settings of the DNN architectures retained for classification and regression datasets. 0. 88 0. 8 0. 86 0. 7 0. 84 0. 6 0. 82 0. 5 AUC 0. 8 Accuracy 0. 4 0. 78 0. 3 0. 76 0. 2 0. 74 0. 1 0. 72 0 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 iteration iteration Figure 8: Averaged AUC and accuracy curves, on test set, as a function of iterations during training using PMCnet, on dataset Glass. 4. 2. 3. Deep networks We now move to comparisons of the methods for learning the parameters of DNNs, from larger scale datasets. Namely, we focus on MNIST-C [44] dataset for multi-class classification task and Protein (available at https://archive. ics. uci. edu/ ml/datasets/Physicochemical+Properties+of+Protein+Tertiary+ Structure) for regression task.
By combining images from diverse satellites and acquisition modes, we achieve high temporal resolution. Nevertheless, due to variations in acquisition geometries, direct image comparisons become impractical. To address this, we develop PickCraterSAR, an open-access Python tool that employs basic trigonometry assumptions to measure crater radius and depth from SAR amplitude images in radar geometry. We apply our methodology to study the crater collapse associated with the May 2021 and January 2002 eruptions of Nyiragongo volcano. Following the 2021 collapse, we estimate the maximum depth of the crater to be 850 m below the rim, with a total volume of 84 ± 10 Mm 3. Notably, the post-2021 eruption crater was 270 m deeper but only 15%-20% more voluminous compared to the post-2002 eruption crater.
Additionally, we demonstrate that the 2021 crater collapse occurred progressively while a dike intrusion migrated southward as a consequence of the drainage of the lava lake system. Overall, our study showcases the utility of multi-sensor SAR imagery and introduces PickCraterSAR as a valuable tool for monitoring and analyzing crater morphological changes, providing insights into the dynamics of volcanic activity. Plain Language Summary Changes in crater morphology provide important hints to assess the activity of a volcano. In addition to optical and thermal imagery, radar images are useful to monitor the crater as they are not limited by daylight and cloud cover conditions. Unlike in optics, where pixels are located in the picture depending on their viewing angle, pixels are located on radar images depending on their distance to the satellite.
In the presence of topography, this causes geometric distortions (shortening and layover), which complicates the interpretation of the images. PickCraterSAR, an interactive Python tool, was designed to assist the interpretation of those images and, based on simple trigonometry assumptions, to extract indicators of the crater radius and depth. Dense time series are obtained by mixing images acquired by various sensors in different viewing geometries. We apply this methodology to measure changes in Nyiragongo crater associated to the 2021 eruption. We show that the crater collapse is significantly deeper but only 15%-20% more voluminous than the 2002 collapse. Moreover, we can quantify the progression of the crater collapse that occurred after the 2021 eruption, while a magma intrusion was migrating underground southward for about a week.
Data The 2021 collapse was imaged by various SAR sensors onboard 7 different satellite constellations: ALOS-2, COSMO-SkyMed (CSK), Capella, ICEYE, Radarsat Constellation Mission (RCM), Sentinel-1 (S1) and TerraSAR-X (TSX), providing us with 44 images acquired between 2021-05-13 and 2021-07-06 along 17 different geometries (Table  1 ). In this study, we use this unique, dense time series of observations to quantitatively reconstruct the crater collapse (Figure  2 ). Merging these data into a uniform time series is challenging, because it requires combining a broad variety of image characteristics. Images are acquired in X-, C-, and L-bands, either in Spotlight, Stripmap or TopSAR acquisition mode, thus with resolution ranging from 20 cm to 15 m. We also have to deal with various incidence angles ranging from 21.
Picking Tool PickCraterSAR can read amplitude images in ENVI l3Harris(c) format (i. e. , a binary matrix ant its associated header file). The user need to create an input text file containing the list of all SAR images in the time series with their acquisition time and viewing characteristics (incidence angle, azimuth and slant range pixel spacing). We used the MasTer Toolbox  (Derauw et al. , 2020; d'Oreye et al. , 2021)  to convert raw SAR data (in SLC format) to amplitude images in slant range geometry (in ENVI format) and extract the required metadata of each image. The main module of PickCraterSAR consecutively (Figure  S2a  in Supporting Information S1) displays one SAR image of the list for the user to manually pick four ellipses corresponding to the volcano circular features (respectively, the summit crater rim, P2 contour, collapse crater rim and bottom).
Coordinates of the picked ellipses are saved and added to the input file. A 3D model of the volcano (Figure  S2b  in Supporting Information S1) taking into account the picked elliptical features is computed according to the theoretical framework presented in Sections 3. 1 and 3. 2. Additionally, PickCraterSAR displays an elevation profile of the estimated crater in the along range direction (Figure  S2d  in Supporting Information S1). The last panel displays the radius and elevation times series (Figure  S4  in Supporting Information S1). Note that we chose to pick only the diameter 2a i of each ellipse in the azimuth direction as it is the least affected by distortions. From these two points, the software computes the position of the ellipse center and the radius R i of the corresponding circle in perspective view according to Equation 2.
On the Crater Collapse Mechanism Figure  6a  also displays an ash index computed with SEVIRI data  (Clarisse et al. , 2013). According to this data, three peaks in ash emissions are clearly visible. The first one started around 07:30 on 23 May and the second around 06:15 on 25 May. The third significant peak around 04:30 on 29 May was smaller than the previous two. Ash emission peaks clearly precede by a few hours a significant increase in the crater radius and a decrease in the crater depth that could be related to large parts of P2 collapsing into the crater. Interestingly, during both events, SAR images were acquired after the ash peak, but before the collapse, clearly evidencing that the observed kilometer-high ash columns preceded the collapse. In other words, an internal explosion ejected material from the crater, producing the ash emissions.
Data Availability Statement All data used in this study are available here https://doi. org/10. 5281/zenodo. 7755707  (Smittarello et al. , 2023). PickCraterSAR is available on Github: https://github. com/mjaspard/pick_app. git and zenodo: https://doi. org/10. 5281/zenodo. 8177191  (Jaspard et al. , 2023).
FAIRshare takes the user's software repository as input and guides them through step-by-step implementation of the FAIR-BioRS guidelines. FAIRshare provides an intuitive graphical user interface at each step where the user can easily provide required inputs for items that cannot be automatically assessed (e. g. , selecting a desired license for the software), and includes automation in the backend to take over complex and/or time-consuming tasks Fig. 1  Evolution of the number of new biomedical-related software repositories created on GitHub in a given year (rounded to the hundredth when >1,000). The search consisted of looking for new repositories created in a given year with "biomedical" included in their name, tags, README, or description. To exclude repositories with data only (e. g. , CSV files or markdown text), the search was limited to repositories with a major coding language being one of the popular software programming languages.
Review of current practices and resulting recommendations. Reviewed studies. Our methodology for conducting the review is outlined in the Methods section. A list of the reviewed resources is available in the "resourcesList" tab of the "data. xlsx" file included in the dataset associated with this manuscript (c. f. Data Availability section). During the review, we sought actionable recommendations that addressed the questions listed in Supplementary Table  1. A total of 39 resources were deemed relevant to these questions and included in the analysis presented next. The information collected from each resource can be found in the "resourcesReview" tab of the "data. xlsx" file included in the dataset associated with this manuscript (c. f. Data Availability section). We summarize the key findings below for each of the categories and provide our resulting recommendations.
Category 1: Develop software following standards and best practices. None of the reviewed resources provided actionable items for following standard development practices, except one that mentions the PEP 8 Style Guide for Python Code (http://peps. python. org/pep-0008). This is understandable since such standards vary depending on many factors such as the domain of research and coding language. Similarly, there are no clear standards provided for the format of the data that software read, write, and exchange. Few of the reviewed resources  10, 11, 19  refers to the FAIRsharing Registry (https://fairsharing. org) for a curated list of community standards  20. Suggestions for best development practices are provided in 21 of the reviewed resources. Some of the reviewed resources are fully dedicated to best practices, including general best practices for scientific software FAIR-BioRS guidelines Fig.