dataesr/scientific-paragraphs-categorization huggingface dataset:
2. SEPAR, a two-level privacy-preserving transparent multi-platform proof-of-concept crowdworking system that enforces a precise point in the design space guided by cutting edge practical regulations currently discussed by societal organizations, legal entities and enterprises. SEPAR uses a simple language for expressing global regulations and mapping them to SQL constraints to ensure semantic clarity. It ensures privacy using lightweight and anonymous tokens, while transparency is achieved using a blockchain shared across platforms for both crash-only and Byzantine nodes. 3. A formal security analysis of SEPAR and thorough experimental evaluations. The paper is organized as follows. The overall vision of the design space for regulation systems is presented in Section 2. Section 3 presents the SEPAR model within the design space.
¬R¬I • Disclosures to the platforms and workers that have received the task t from r but that are not involved in π: δ π R¬I • Disclosures to the participants that are directly involved in π (and have thus received task t): δ π RI Definition 2. 1 Let Π be a set of crowdworking processes executed by ς an instance of SEPAR over a set of participants. We say that ς is δ Π -Private if, for all π ∈ Π, for all computationally-bounded adversaries A, the sets of disclosures (δ π ¬R¬I , δ π R¬I , δ π RI ), assuming arbitrary background knowledge χ ∈ {0, 1} * , the distribution representing the adversarial knowledge over the input dataset in the real setting is computationally indistinguishable from the distribution representing the adversarial knowledge in an ideal setting in which a trusted third party cp executes the crowdworking process π of ς:  R, T  ) where i ∈ {¬R¬I, R¬I, RI}, and REAL denotes the adversarial knowledge in the real setting and IDEAL its counterpart in the ideal setting.
Classes of Regulations In the second set of experiments, we measure the overhead of privacy-preserving techniques, e. g. , group signatures and tokens, used in SEPAR. We consider the basic scenario with no regulation (i. e. , no need to exchange and validate tokens and signatures) and compare it with three different scenarios where each task has to satisfy a single target, a two-target, and a three-target regulation. The system consists of four platforms and the workload includes 90% internal and 10% cross-platform tasks (the typical settings in partitioned databases  [34, 35] ) where two (randomly chosen) platforms are involved in submission and claim transactions of cross-platform tasks (note that all platforms are still involved in the verification transaction of each task). We also assume that completion of each task requires a single contribution (and obviously a submission and a verification transaction).
However, none of them provides tools to manage external regulations: to the best of our knowledge, SEPAR is the first to support a multi-platform crowdworking context, with external regulations, transparency, and privacy expectations at the same time. In the context of permissioned blockchains, Hyperledger Fabric  [4]  ensures data confidentiality using Private Data Collections  [1]. Private Data Collections manage confidential data that two or more entities want to keep private from others. Quorum  [14]  supports public and private transactions and ensures the confidentiality of private transactions using the Zero-knowledge proof technique. Quorum, however, orders all public and as private transactions using a single consensus protocol resulting in low throughput. SEPAR is inspired by various permissioned blockchain systems, and more specifically by SharPer  [3] , a permissioned blockchain system that uses sharding to improve scalability.
For the ultrasonic wave simulation, we used the spectral-element numerical tool SPECFEM3D  [28]. In the second section, we present the numerical models for the wave equation, and we indicate our motivations for choosing the SPECFEM3D code. In Section 2, the CFD data used to reproduce the PLAJEST experiment are detailed, and the frozenflow hypothesis is justified. The fourth section describes a huge task of implementing the modeling of the wave propagation in a heterogeneous medium resulting from the CFD modeling. It is a very new collaborative work combining CFD simulation and wave propagation simulation. In the last section, we demonstrate that the temporal temperature fluctuations could be monitored by ultrasounds. The study is focused on the measurement of the frequency of these fluctuations, as it would prove the potential of such complete modeling for giving a 3D description of a heterogeneous flow and for monitoring it in time, thereby adding a fourth dimension to the simulation.
The spectral-element method (SEM)  [42] [43] [44]  is a technique that is similar to FETD but reduces the limitation of FETD. The main difference is the type of basis function used for each method. While FETD uses Gauss points for its spatial discretization, SEM uses higher-order Gauss-Lobatto-Legendre points. SEM uses a modified formulation that leads to a perfectly diagonal mass matrix  [45] , and thus it is much more numerically efficient than FETD. We therefore selected SEM as the numerical method to model wave propagation in our target, i. e. , liquid sodium flow. We selected a numerical code SPECFEM3D, one of the most efficiently implemented SEMs for solving wave propagation in many types of material (elastic, viscoelastic, poroelastic, etc. ). SPECFEM3D has already been applied for many cases and used with several HPC environments, e.
Fluid Dynamics Modeling in PLAJEST Experiment and Frozen-Flow Hypothesis A large benchmark exercise was performed under the auspices of an international collaboration on thermal hydraulics for sodium-cooled fast-reactor development with participation from the Japan Atomic Energy Agency (JAEA), the U. S. Department of Energy (DOE) and the French Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA). It was based on experiments performed to study the effects of thermal striping, where three differentially heated jets mix inside a cavity  [25]. These experiments were carried out either in water or in liquid sodium, and velocity and temperature data were provided. The object of the benchmark was to numerically predict the results of these experiments and make comparisons with available measured data.
After selecting the domain, we built the first-/second-order hexahedral mesh for SPECFEM3D using the meshing software CUBIT developed by Sandia National Laboratories (USA) (Step 5). The actual mesh type that SPECFEM3D uses is not a first-nor second-order hexahedral mesh but a fourth-order spectral element. Thus, we then performed this conversion as the final step of mesh preparation (Step 7). In order to speed up this conversion process, we used the IOSS (IO Systems) library. This library is included in the finite element analysis supporting software called SEACAS (Sandia National Laboratories). After finishing the preparation of mesh data, the temperature field transfer was carried out, i. e. , interpolation of temperature values defined at the barycentre of each tetrahedral finite element to corner nodes of the hexahedral spectral elements (Step 8) using MEDCoupling.
We also drew on previous studies concerning ultrasonic wave propagation in liquid sodium to define all physical parameters for the wave simulation. The novelty of this study consisted in making a wave propagation simulation for several CFD time steps to validate the potential of ultrasonic measurements to follow flow fluctuations. Our analysis of flow fluctuation enabled us to prove that the frozenflow hypothesis was completely valid in this study. It should be underlined that such a hypothesis is rarely numerically studied. The study required the development of dedicated tools to implement the wave propagation modeling on high-performance computers. The SPECFEM3D code was chosen as the numerical calculation tool for its numerical efficiency because of matured development for massively parallelized computing on HPCs and the application of the spectral-element method.
Acknowledgments: This study was granted access to the  French HPC resources of TGCC/CINES  under allocations 2016107626, 2016047165 and 2016047748 made by  GENCI. We also thank  Antoine Gershenfeld  and  Pierre Emmanuel Angéli , from the laboratory of CEA/DES/ IRESNE/DM2S/STMF, for sharing their thermal-hydraulic data of PLAJEST and their advice on how to analyze these data. They provided elements for the Figure 7. This work was developed within the framework of the  MISTRAL joint research laboratory between Aix-Marseille University ,  CNRS ,  Centrale Marseille  and  CEA.
INTRODUCTION Image segmentation using learning-based approaches is an active research topic. One of the most common issues in this task is related to highly unbalanced datasets. Several strategies have been proposed in order to compensate less populated classes. They can be mainly clustered in two categories: 1) Data-level methods, increasing artificially the number of training samples via data augmentation through over-sampling and under-sampling training samples; 2) Algorithm-level methods, without modifying the training data distribution, the decision process increases the importance of smaller classes  (Johnson and Khoshgoftaar, 2019). In this paper, we focus on the second approach, by modifying the loss function to penalize model mistakes similar to focal loss  (Lin et al. , 2017). According to  (Jun, 2020) , loss functions can be mainly divided in two groups: 1) Statistical-based such as Cross-Entropy (CE) and some of its variants such as Weighted CE  (Ronneberger et al.
We have evaluated our proposal with several segmentation datasets such as MNIST, Cityscapes  (Cordts et al. , 2016) , SHREC'20 point clouds and aerial images  (Mnih, 2013). The use of power losses improves the performance in binary and multiclass segmentation (section 4). Fig. 1  illustrates a comparison between the proposed loss functions and other classical losses as cross-entropy, Jaccard and Dice score. The abscissas represent the predicted value ŷ and the ordinates the corresponding loss value. We will see that a higher value of p in our generalized Jaccard loss function improves model convergence by shifting the focus to improve harder predictions. The structure of the paper is as follows: Section 2 describes the proposed loss function; Section 3 introduces the experimental design to evaluate our proposal; in Section 4 the results of semantic segmentation comparing several loss functions with different types of images are reported.
EXPERIMENTAL DESIGN Power Jaccard loss is validated in semantic segmentation frameworks with several datasets: MNIST, Cityscapes, aerial images from Toronto University and SHREC'20 challenge. We selected Unet based architectures and performed some variations to the model (number of filters), the training stage (dataset size and batch size) and compared the incidence of our proposal. Each configuration is repeated several times to evaluate stability and repetitiveness, which is a common issue in neural networks  (Scardapane and Wang, 2017). As evaluation metrics, we used mean IoU, accuracy and recall scores.
Grayscale images MNIST dataset contains grayscale images of 28x28 pixels with digits of ten classes from zero to nine and one digit instance by image. We randomly selected 140 images per class and built a pixel-wise ground truth  (Zhou, 2018). Then, the dataset was divided in the three common subsets as follows: 1000 for training, 200 for validation and 200 for test. Two segmentation problems have been tackled: 1) Binary segmentation to distinguish between background and digit pixels; 2) Multiclass segmentation in ten classes. In both cases, we selected an Unet model with three levels of depth where the number of filters was changed between two, four and eight. Diverse batch sizes were used: 1, 10 and 50. Each configuration was evaluated with different losses: binary or categorical CE, classical Jaccard and power Jaccard with several values of p.
Aerial images We performed two binary segmentation tasks: 1) Road and no-road (1108 images for training, 14 for validation and 49 for testing); 2) Building and nobuilding (137 images for training, four for validation and ten for testing). Fig. 3  presents two images from the dataset and the corresponding ground truth. Unet initialized from ImageNet with Mo-bileNetV2  (Sandler et al. , 2018)  as feature extractor is used. Adam optimizer with a default learning rate of 10 -3 and a patience equal to five.
Urban scene images Cityscapes dataset is composed of 5K color images with divided in three subsets: training (2975), validation (500) and test (1525) and annotated with 30 classes in the context of autonomous driving. Fig. 4  shows an image from this dataset. We have selected a subset of four classes relevant for autonomous driving to perform semantic segmentation in unbalanced data: person, car, road and background. etation. Segmentation is obtained from 2D Bird eye view (BEV). Fig. 5  shows a point cloud with ground truth labels.
Point cloud projections We divided segmentation of 3D point clouds in two simpler problems: 1) Segment lower points (low slice) in building and car classes; 2) Segment higher points (high slice) in building and vegetation classes. Fig. 5b  shows BEV projections of low and high slice Fig. 5a. Ground and poles have been discarded because: 1) Ground can be extracted using an analytical approach such as the Lambda Flat Zone method proposed by  (Hernández and Marcotegui, 2009)  and then extended for  (Serna and Marcotegui, 2014)  to compute Digital Elevation Model (DEM); 2) Pole class is problematic because a single traffic sign, very different from other pole instances, contains 70 % of the pole class points in the whole dataset. We computed hand crafted features based on the BEV projections  (Serna and Marcotegui, 2014)  from 3D point clouds.
Multiclass segmentation We varied batch size between 1, 10 and 50 and repeated five times each configuration, as presented in Table  2. From Table  2 , it can be seen that power Jaccard allows to obtain higher score compared against crossentropy and classical Jaccard. Fig. 6  presents some predictions obtained with the best model achieved using power Jaccard with p = 2 and batch size of one. We observed that in the best models, the errors were in general at pixel-wise level. This type of errors can be solved by means of regularization techniques such as voting systems  (Alpaydin, 1997). periments, the use of power losses leads to better results by a small margin and highest accuracy was obtained with p = 1. 5. One may also note that BCE has the lowest validation accuracy in both datasets. The small differences between different loss functions occur because of to the use of an already trained model as feature extractor, as described in Section 3.
Urban scene images Table  4  reports the results on Cityscapes dataset. Power losses improve the performance on less populated classes such as person and car thanks to the higher penalty for worst predictions (see Fig. 2 ). The relative improvement in IoU between crossentropy and power Jaccard with p equal to 2 in background was 0. 1949%, in person was 22. 46% and in car was 10%. On the road, it worsened by 1. 39%. In the mean IoU, the relative improvement was 2. 69%. Fig. 7  shows the predictions of the image presented in Fig. 4  using the models trained with different losses. It is seen how the influence of the power term qualitatively improve the segmentation of the person class.
CONCLUSIONS In this work, we propose generalized loss functions to perform semantic segmentation by introducing power Jaccard. We evaluated it in different types of images such as gray-scale, RGB and point cloud projections in binary and multiclass segmentation tasks. Obtained results demonstrate that the use of power losses outperforms classical losses such as cross-entropy, Jaccard and Dice score. In order to evaluate the stability of the models, we repeated several times the same configuration and we stated that the use of power losses helps to increase the rate of convergence. This is useful in deep learning models where the stability of the models is critical and it is strongly associated with the randomness of the initialization parameters. We performed several experiments with different types of images, different dataset of segmentation task, demonstrating that the advantage of power losses is not an isolated case.
). Note that Power Jaccard performs better on smaller classes than the classical one. Quantitative results are given in Table4 Metric CE Focal BCE Dice p = 1 IoU 0. 341 ± 0. 000 0. 570 ± 0. 016 0. 427 ± 0. 173 Best IOU 0. 341 0. 605 0. 787 Metric Jac. p = 1 Jac. p = 1. 5 Jac. p = 2 IoU 0. 341 ± 0. 000 0. 761 ± 0. 144 0. 761 ± 0. 020 Best IOU 0. 341 0. 809 0. 788 Table 6: Performance in high slice from SHREC'20 dataset.
This tool is based on a simple principle that was implemented in many sca↵olders since SSPACE  [12] : long-range data (mate-pair reads, long reads, linked reads, proximity ligation. ) provide information on the linkage between contigs that can be used to group and orient them into sca↵olds. As GraphUnzip takes as input and produces as output assembly graphs, it only connects contigs that are actually adjacent in the genome and yields gap-less sca↵olds, i. e. supercontigs. GraphUnzip is compatible with any assembler that produces an assembly graph. We tested GraphUnzip on the genomes of the human HG00733 and the potato Solanum tuberosum. GraphUnzip is available at github. com/nadegeguiglielmoni/GraphUnzip.
Inputs GraphUnzip requires an assembly graph in GFA (Graphical Fragment Assembly) format. The Hi-C input is a sparse matrix, such as the one obtained when processing the reads with hicstu↵  [13]. hicstu↵ also provides a module to convert other file formats (e. g. cool, a common Hi-C format) to a sparse matrix. The long reads are mapped to the assembly graph using GraphAligner  [14].
If both long reads and Hi-C input data are provided, strengths based on long reads are used first because they are more reliable locally, and strengths based on Hi-C are only used if some edges are flagged as dubious. Edges identified as weak in the previous calculation are removed. Then, every contig that has more than one strong edge and no dubious edge at one end is duplicated as many times as the number of these strong edges. Such contigs are typically collapsed homozygous regions that need to be present in several copies to be included in every haplotypes. All the copies retain the edges of the original contig at its other end. This entails that the duplication of contigs creates many new (and potentially artefactual) edges. Contigs that are unambiguously linked are merged in supercontigs that will be handled as regular contigs thereafter.
Solanum tuberosum assemblies HiFi, ONT and Hi-C reads published in  [18]  were retrieved from the NCBI Sequence Read Archive with the Bioproject accession number PRJNA573826. The HiFi reads were assembled using hifiasm with the parameter -l 0, and the p utg assembly graph was used for downstream analyses. All HiFi reads and the ONT reads longer than 25 kb were mapped to the assembly using GraphAligner with the parameter -x vg. Hi-C reads were processed with hicstu↵ using the parameters --aligner bowtie2 --enzyme MboI --iterative. GraphUnzip was run with parameters --accept 0. 40 --reject 0. 10 --exhaustive --whole match --minimum match 0. 8. All non-ambiguous paths in the GFA were merged using Bandage. To check the output of GraphUnzip, we mapped the published assembly to the assembly graph using GraphAligner.
Acknowledgments This project was funded by the  Horizon 2020 research and innovation program  of the  European Union  under the  Marie Sk lodowska-Curie  grant agreement No  764840  (ITN IGNITE, www. itnignite. eu). Part of this analysis was performed on computing clusters of the  Leibniz-Rechenzentrum (LRZ)  and the  Consortium des Équipements de Calcul Intensif (C ÉCI)  funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11.
We implemented the promotion-mod module in Java using the Java API for XML Processing (JAXP). Its three sub-modules are: (1) the access control requirements parser authzParser-mod; (2) the access control requirements mapping module authzMapping-mod and; (3) the promoted service contract construction module contractCreator-mod. The sub-module authzParser-mod receives as input the localContract and it outputs the list of access control requirements named LAR contained in this file. The LAR list contains the access control requirements specified with the domain-specific authorisation attributes and possibly the attributes of the domain's LTS implemented with WS-Trust. Then, the LAR list is passed as input to the sub-module authzMapping-mod which computes another list of access control requirements (named FAR) specified with the authorisation attributes of the federation.
To promote HelloService in the federation ICV, its contract is passed to the promotion-mod module which replaces each access control requirement expressed with the dialect of IUG by the corresponding one expressed with the dialect of the federation, using the mappings defined by IUG. The attributes of the iugSTS issuing the security token in IUG are also replaced by those of the STS of the federation. The result of this promotion is published in the service registry of ICV 2. Thus, other domains can discover the HelloService and understand its access control requirements. We implemented the service promotion by the means of a portable jar file. The mapping policy database is here a collection of Java HashMap instances included in the archive file. During the execution, the WSDL file of the service to promote is given in the parameters of the application as illustrated by the top of Figure  3.
■ THEORY AND METHODS We describe the ESMACS protocol, the dimension-adaptive sampling method, as well the methods to compute the Sobol index and uncertainty amplification factor. The last three methods are more extensively described in one of our previous studies of the CovidSim epidemiological code. 25 SMACS Protocol and Ensemble Simulations. The protein target of our investigation is the bromodomaincontaining protein 4,  17  which is currently a major and rapidly evolving focus for the pharmaceutical industry. Preclinical and early stage clinical studies have shown that inhibitors targeting the protein exhibit promising efficacy in pathologies ranging from cancer to inflammation. BRD4 has recently become something of a benchmark system for free energy calculations, which we have investigated extensively using our binding affinity calculator for diverse compound data sets.
17, 28 Here we use one of the compounds studied previously,  17  and investigate the sources of uncertainty along with the quality of binding free energy predictions. The preparation and setup of the simulations are implemented using ESMACS. More details can be found in our previous publications. 17, 29 We use the same force field as described previously: the AMBER ff99SB-ILDN force field for the protein, TIP3P for water molecules, and the general AMBER force field (GAFF) for the ligand with partial charges calculated using restrained electrostatic potential (RESP) module in the AMBER package. The molecular system is solvated in orthorhombic water boxes. The minimal distance between the protein atoms and the box edges is set to be 14 Å as in our previous publications. It is treated here as one of the parameters included in the UQ study.