dataesr/scientific-paragraphs-categorization huggingface dataset:
Main text In 2018 two studies demonstrated that the information contained within brightfield images of cells can be used to infer specific subcellular structures by training U-Net 1 inspired convolutional neural networks on paired datasets of brightfield images and their corresponding images with fluorescently labelled cellular structures  2, 3. The overall performance of these models can be assessed during training by calculating the mean-squared-error, or other appropriate loss functions, between the real and predicted fluorescent images on a withheld validation dataset, and after training on a separate test dataset. Whilst this gives a global measure of how well a model is performing on a labelled dataset, it does not give researchers any information on how well they can trust the individual predictions on new unlabelled data, and perhaps more interestingly -how well they can trust local regions within a predicted image.
for the task of parasite segmentation in images of liver stage malaria 6 , but has not yet been applied to brightfield to fluorescent image inference. We released our method as a python package using the scikit-image, numpy and pytorch packages. It contains two different methods of calculating the uncertainty map: (i) the first method uses weight dropout during inference, whereby dropout layers are present in all convolutional blocks of the U-Net model. Entire channels are randomly zeroed independently at each forward pass with a probability sampled from a Bernoulli distribution  7. After multiple forward passes for a single input image, the pixel-wise variance is calculated from the collected predictions and used to generate an uncertainty map. The default behaviour of pytorch is to adjust the function of certain layers (dropout, batchnorm etc.
Here, we present a method to quantify and visualise uncertainty when predicting fluorescent labels from brightfield images. As brightfield to fluorescent image inference has potential applications in high-throughput imaging studies, we hope that the community can use this method further reason about their predictions, as well as enabling researchers to subject potentially troublesome predictions to further scrutiny. Our method is released as BFNet, an open source python package which the community is encouraged to use and adapt to their needs.
21, 31 Both techniques allow the determination of the LCD and PLD within a reasonable computation time. 30, 32 However, these methods approximate the shape of the guest as a single sphere, which limits their application to rarely used monatomic gases. Unlike porous materials, molecular hosts contain cavities (or pores) inside a single molecule. Nevertheless, the LCD and PLD can still be defined for them (see Figure  3c ), and the same computational methods can be applied. The recently developed pyWINDOW package addresses the problem of determining the host cavity size (LCD) and host windows diameters (PLD) by using a sampling-based algorithm. 33 This approach benefits from the well-defined structure of hosts but is still limited to monatomic guests. Apart from the aforementioned geometric methods, there exist many simulation methods that can be used to study molecular transport in material cavities.
Typically, one of two sources of molecular structure information is used as a starting point for computations: (molecular or quantum-mechanical) modeling or crystallographic data. In this work, we used crystal structures obtained from the Cambridge Crystallographic Data Centre (CCDC) database. Structures of compounds that cannot be crystallized in conditions suitable for X-ray diffraction (e. g. , liquids) were extracted from crystal structures of clathrates (where solvent molecules are entrapped in the crystal). Most of the structures used in this work possess a single stable conformation. 4. 3. Configuration Space of a Molecule. Here we investigate the ability of a molecule to move from one area of space to another in principle. Therefore, we assume that a molecule can move arbitrarily in a continuous fashion.
In this section, we report the use of our algorithm to search for new complexes that are not described in the literature. In particular, we aimed to answer the following question: "which hosts can exhibit selectivity in the caging of several guests with similar shape?" For illustrative purposes, we selected four guests monohalobenzenes with close molecular volumes  50  and similar shapes: fluoro-(FB), chloro-(CB), bromo-(BB), and iodobenzene (IB). The hosts belong to a set of 46 shapepersistent molecules with cavities (CDB46), a modified version of the CDB41 database  27  (see the Supporting Information). Since several crystal structures are available for hosts CC1, CC3, and CC4, all of them are included. Each result is evaluated for its sensitivity to the input geometry using a threshold of 0.
Software description BoardION is organized in two components, a preprocessing script and a web application. The first component uses the information contained in the sequencing summary file generated by the guppy basecaller. Since this file can be quite large, a C++ preprocessing program treats the new lines at each execution to generate lightweight datasets with the metrics of interest. Thanks to this preprocessing, statistics can be computed and updated in the web application every minute. The second component is a web application accessible from anywhere regardless of the user's configuration. The application, developed in R, uses ggplot2  (Wickham 2009) , R shiny (Chang 2019) and R plotly  (Carson 2018)  to create dynamic JavaScript visualization. Documentation, github and dockerhub repositories, as well as an interactive demo version of BoardION are available at http://www.
These methods use dynamic equations and are based on a contact model and an optimization-based method. However, team handling involves additional issues. The distribution of forces applied to the box between the team members is an essential element to be considered. Moreover, both team members can exert additional effort on the box, for example to increase stability or due to a lack of synchrony. These efforts influence back loading  [20]  and therefore need to be taken into account. The aim of this paper was to evaluate whether it is possible to use only motion data to accurately estimate back loading during team handling. If these data are not sufficient, what additional data must be considered? For this purpose, tandem handling tasks were analyzed in the laboratory. Three different prediction methods were used to estimate back loading: one used only motion data, another used motion data and the traction/compression force applied to the box and the third used motion data and the ground reaction forces of one team member.
Biomechanical model Back loading was estimated based on a whole-body osteoarticular model, composed of 16 rigid segments (pelvis, lower trunk, upper trunk, head, upper arms, lower arms, hands, upper legs, lower legs and feet) linked by 15 joints corresponding to 35 degrees of freedom (3 for the pelvis / lower trunk joint, 3 for the lower trunk / upper trunk joint, 3 for the neck, 3 for each shoulder, 2 for each elbow, 2 for each wrist, 3 for each hip, 1 for each knee and 2 for each ankle). The geometrical parameters were subject-specific, calibrated using motion capture data and an optimization-based method  [23] [24] [25]. Body segment inertial parameters (BSIP) were extracted from anthropometric tables  [26]. For the prediction methods, discrete contact points were defined on the model, corresponding to the potential contact points with the ground or with the box.
External forces GRF were estimated by three different prediction methods (M1, M2 and M3), each compared to force platform measurement, considered as the gold standard (Fig  3 ). The three prediction methods differed in the type of experimental input data used: M1 used only motion data, M2 used motion data and the traction/compression force applied to the box and M3 used motion data and one team member's GRF. As the traction/compression force was not measured during these experiments, this force was calculated from the HF estimates. HF applied by each team member were estimated from the GRF and motion measured, by subtracting each body segment's contribution to GRF  [29]. The traction/compression force represents the minimum common HF applied by both team members in the longitudinal direction of the box (Eq 1) (Fig  4 ).
Thus, it is important information to ensure a good estimate of back loading. The next three subsections describe the gold standard and the three prediction methods. Gold standard. The gold standard method consisted in using the measurements produced by the force platforms directly. Since only the L5/S1 joint moment was computed, only the resultant GRF for each subject was considered. The two platforms under one of the two participants were used here only to measure the resultant GRF. Prediction method M1. Prediction method M1 used a motion-based prediction method developed earlier  [17, 18]. Only the kinematics of the two subjects and the box were used as input data for method M1 to predict L5/S1 joint moment. The algorithm applied a contact model with an optimization-based method. Each contact point defined on the biomechanical models was a potential linear force.
The estimated contact forces were the minimum forces in a least-squares sense while ensuring that the dynamics equations applied to both team members and the box. To improve the numerical resolution of the algorithm, the three systems (team members and box) were solved separately. The box's mass was obtained by weighing it with a force platform. The position of its center of mass was estimated by placing the box on a force platform with two different orientations. Considering the low angular accelerations of the box and its complex geometry (i. e. , the distribution of masses), the box's inertia was not taken into consideration. Prediction method M2. Method M2 used motion data (kinematics of both subjects and box) and the traction/compression force input applied to the box. As described before, traction/compression force was not measured but estimated.
Data analysis Prediction methods were evaluated by comparing the predicted and measured GRF and the L5/S1 joint moments computed with the predicted and measured GRF during the transfer phases. The asymmetrical moment was defined as the vector sum of the lateral bending and torsion moment components. For each comparison, a Pearson correlation coefficient, a root mean square error (RMSE) and a relative RMSE (rRMSE) (Eq 2)  [35]  were computed. For the L5/S1 joint moments, the mean error was also computed. A positive value corresponded to an underestimate and a negative value corresponded to an overestimate. rRMSE ¼ RMSE 1 2 X 2 i¼1 max t2½0;T� ðu i ðtÞÞ À min t2½0;T� ðu i ðtÞÞ � � ð2Þ u 1 and u 2 correspond to the predicted and measured data, respectively, and instant t varies from 0 to T (here t varies between the lift and deposit instants).
Limits and perspectives This work has several limitations. First, the tasks performed did not include foot displacements. This was constrained by the need to use force platforms. Other complementary studies involving foot displacements will have to be done, either by using several large platforms or by adding instrumented handles on the box. Nevertheless, this type of task has been used in the literature to validate prediction methods for handling tasks  [18, 19]. Secondly, the traction/ compression force was estimated from GRF and motion. These data were then considered as inputs for prediction method M2. It was reported that the estimation of hand forces using this type of method induces an error of about 20 N  [29]. The influence of this error on the prediction method could be the object of a complementary study.
Conclusion This study showed that the use of motion data alone does not allow for accurate estimation of back loading during team handling tasks. The traction/compression force applied by each handler does not affect motion but does influence back loading. Adding this force as an input allows for more accurate estimation. This force can be measured using strain gauges placed in the boxes or obtained indirectly using one force platform under one handler. Future work will also investigate the possibity of estimating this force to simplify the instrumentation. The prediction methods proposed here seem to be an interesting approach to analyze back loading during team handling in various work situations, with different team member anthropometric measurements and different box characteristics. Combining wearable motion capture systems with GRF prediction methods would allow one to perform this type of analysis in the field.
Contributions This paper introduces "a blockchain-based scheme for data usage auditing while preserving personal data confidentiality and ensuring continuous data availability. The proposed scheme relies on hierarchical ID-based cryptographic techniques, where a central master authority delegates the process of public/private keys' generation to the different participating entities, based on authentic public elements"  (Laurent et al. , 2018). ID-based Encryption (IBE) and Signature (IBS) schemes enable the data subject to encrypt sensitive data and sign transactions, relying on a unique-yet un-identifiable-identifier, respectively. The proposed solution is multi-fold. First, relying on a blockchain infrastructure, it provides a trusted and transparent environment that permits service providers to collect tamperproof evidence of received users' consent before gathering, storing, and/or processing their personal data.
In fact, the main intrinsic property of the blockchain is its suitability for data auditing purposes. It has attracted interest of the research community due to its shared and fault-tolerance database  (Zyskind et al. , 2015; Linn and Koo, 2016; Ouaddah et al. , 2016; Fu et al. , 2017; Liang et al. , 2017; Shetty et al. , 2017; Kaaniche and Laurent, 2018; Ramachandran and Kantarcioglu, 2018; Zhang et al. , 2018). "  Liang et al. (2017)  proposed a blockchain-based data provenance architecture for cloud applications. Their construction records operations' history as provenance data which are hashed into Merkle tree nodes. A list of hashes of provenance data forms a Merkle tree which are attached as a blockchain transaction. As such, it is possible to immutably prove the provenance of data exchanges.
Although the proposed approach is novel, it does not cover the definition of advanced policies or contracts regulating the usage of collected data"  (Kaaniche and Laurent, 2017b). Zyskind et al. (2015)  presented an hybrid solution for personal-data management. The proposed system combines both blockchain, as an access moderator, and off-blockchain, for data storage purposes. Proposal  Zyskind et al. (2015)  considers clients as unique owners of their personal data, thus making them aware of all the associated data being collected by service providers and how they are used. That is, when a client subscribes to a service provider, one transaction is created defining the set of access policies and another contains the hashes of data stored in an off-chain database. However, the  Zyskind et al.
Indeed, their proposed solution is based on two smart contracts, namely Document Tracker contract and Vote contract. The Document Tracker smart contract is used to keep track of changes to a given document while the Vote contract implements the voting protocol. That is, "every provenance change event has to be approved through a voting process by the vote contract. The data trails are only logged if they are approved by the Vote contract. The Vote contract implements two types of voting: simple majority voting and threshold voting"  (Ramachandran and Kantarcioglu, 2018)  where all blockchain network's minors are called to participate in the voting process. SmartProvenance implements a JavaScript client, run on the browser of each of the user's machines. The client, acting as an interface between the user and back-end smart contracts, is in charge of the synchronization with the smart contract, including the storage, the retrieval, and the validity checking of the changes.
ACKNOWLEDGMENTS This paper is an extended and revised version of our former conference work (Kaaniche and Laurent, 2017b) accepted in the 16th  International Symposium on Network Computing and Applications (NCA) , and is an extended discussion of Kaaniche and Laurent (2017c) with regard to smart contract design models and public vs. private blockchains. The authors would like to thank the reviewer for their careful reading of the manuscript and their constructive remarks.
Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land-surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, and the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches, determine the survivorship of cohorts in the disturbed patch, and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging.
We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees.
, 2007; Watanabe et al. , 2011; Smith et al. , 2001; Smith et al. , 2014; Weng et al. , 2015; Roy et al. , 2003; Hurtt et al. , 1998; Fisher et al. , 2015). These vegetation demography modules are relatively new in land models, so tremendous efforts are still under way to improve their parameterizations of resource competition for light, water, and nutrients, recruitment, mortality, and disturbance including both natural and anthropogenic components  (Fisher et al. , 2017). In this study, we aim to (1) describe the development of a selective logging module implemented into The Functionally Assembled Terrestrial Ecosystem Simulator (FATES), for simulating anthropogenic disturbances of various intensities to forest ecosystems and their shortterm and long-term effects on water, energy, and carbon cycling, and ecosystem dynamics; (2) assess the capability of FATES in simulating site-level water, energy, and carbon budgets, as well as forest structure and composition; (3) benchmark the simulated variables against available observations at the Tapajós National Forest in the Amazon, thus identifying potential directions for model improvement; and (4) assess the recovery trajectory of tropical forest following disturbance under various logging scenarios.
In section 2, we provide a brief summary on FATES, introduce the new selective logging module, and describe numerical experiments performed at two sites with data from field survey and flux towers. In section 3, FATES-simulated water, energy, and carbon fluxes and stocks in intact and disturbed forests are compared to available observations, and the effects of logging practice and intensity on forest recovery trajectory in terms of carbon budget, size structure and composition in plant functional types are assessed. Conclusions and future work are discussed in section 4.
The selective logging module The new selective logging module in FATES mimics the ecological, biophysical, and biogeochemical processes following a logging event. The module (1) specifies the timing and areal extent of a logging event; (2) calculates the fractions of trees that are damaged by direct felling, collateral damage, and infrastructure damage, and adds these size-specific plant mortality types to FATES; (3) splits the logged patch into disturbed and intact new patches; (4) applies the calculated survivorship to cohorts in the disturbed patch; and (5) transports harvested logs off-site by adding the remaining necromass from damaged trees into coarse woody debris and litter pools. The logging module structure and parameterization is based on detailed field and remote sensing studies  (Putz et al.
We refer to these typical operations as conventional logging (CL). In contrast, reduced impact logging (RIL) is a practice with extensive pre-harvest planning, where trees are inventoried and mapped out for the most efficient and cost-effective harvest and seed trees are deliberately left on site to facilitate faster recovery. Through planning, the construction of skid trails and roads, soil compaction and disturbance can be minimized. Vines connecting trees are cut and tree-fall directions are controlled to reduce damages to surrounding trees. Reduced impact logging results in consistently less disturbance to forests than conventional logging  (Pereira Jr et al. 2002; Putz et al. 2008). The FATES logging module was designed to represent a range of logging practices in field operations at a landscape level.
In the undisturbed patch, the survivorship of understory trees is calculated using an understory death fraction consistent with whose default value corresponds to that used for natural disturbance (i. e. , 0. 5598). To differentiate logging from natural disturbance, a slightly elevated, logging-specific understory death fraction is applied in the disturbed patch instead at the time of the logging event. Based on data from field surveys over logged forest plots in southern Amazon  (Feldpausch et al. , 2005) , understory death fraction corresponding to logging is now set to be 0. 65 as the default, but can be modified via the FATES parameter file (Figure  2e ). Therefore, the logging operations will change the forest from the undisturbed state shown in Figure  2a  to a disturbed state in Figure  2f  in the logging module.
Following the existing CWD structure in FATES  (Fisher et al. , 2015) , CWD in the logging module is first separated into two categories: above-ground CWD and below-ground CWD. 𝐶𝐶𝐶𝐶𝐷𝐷 trunk_agb = 𝐷𝐷 indirect × 𝐵𝐵 stem_agb × 𝑓𝑓 trunk × 𝐴𝐴 (4) 𝐶𝐶𝐶𝐶𝐷𝐷 branch_agb = 𝐷𝐷 total × 𝐵𝐵 stem_agb × 𝑓𝑓 branch × 𝐴𝐴 (5) where  stem_agb is the amount of above ground stem biomass in the cohort,  trunk and  branch represent the fraction of trunks and large branches/small branches/twig. Similarly, the belowground CWD from trunks (CWD trunk_bg , [kg C]) and branches/twig (CWD branch_bg , [kg C]) are calculated as follows: 𝐶𝐶𝐶𝐶𝐷𝐷 trunk_bg = 𝐷𝐷 total × 𝐵𝐵 root_bg × 𝑓𝑓 trunk × 𝐴𝐴 (6) 𝐶𝐶𝐶𝐶𝐷𝐷 branch_bg = 𝐷𝐷 total × 𝐵𝐵 root_bg × 𝑓𝑓 branch × 𝐴𝐴 (7) where  croot [kg C] is the amount of coarse root biomass in the cohort.