dataesr/scientific-paragraphs-categorization huggingface dataset:
Few traits (pa, rsa, and rv) showed CVe of higher magnitude (39 %, 39 %, and 48 %, respectively). It should be noted that these traits were computed using the WinRHIZO software program and are calculated from the "trl" and "adr" variables (Table  2 ). The degree of experimental precision for the non-destructive traits determined by image analysis was equal to or greater than that of the "frw" and "drw" destructive measurements (Table  4 ). Thus, the use of image-based methods to measure root system traits, rather than tedious time-and labor-consuming destructive methods, does not lead to lower degrees of experimental precision. Selective accuracy (SA) has also been proposed as a statistical tool for evaluating the quality of the experiment because it simultaneously considers the magnitude of the residual variation, the number of replicates and the coefficient of genotypic variation  (Resende and Duarte, 2007).
For example, the destructive trait "fresh root weight" (frw) showed a series of significant high positive correlation with the image-based variables "total explored area" (ea) and "maximum root width" (mrw) values measured using the ImageJ software program as well as with the variable "total root length" (trl) as measured by the WinRHIZO software program (0. 69, 0. 73, Min = minimum; Max = maximum; TA = total amplitude; SD = standard deviation; CV (%) = coefficient of phenotypic variation. 2 frw = fresh root weight; drw = dry root weight; trl = total root length; adr = average diameter of roots; tips = number of root tips; mrd = maximum root depth; ea = total explored area; mrw = maximum root width; pa = projected area; rsa = root surface area; rv = root volume; mrd_mrw = ratio mrd:mrw; trl_ea = ratio trl:ea (root density).
Acknowledgments SMM holds a PhD scholarship from  CAPES  ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil). This study was conducted within the framework of an  Agropolis Fondation/Empresa Brasileira de Pesquisa Agropecuária (Embrapa) / CAPES  project granted to  MG, CLM  and  JBD  and received support from the  Agropolis Fondation  under reference ID  1402-006  Non-invasive phenotyping of RSA traits Sci. Agric. v. 77, n. 5, e20180420, 2020 through the  "Investissements d'avenir" program  ( Labex Agro : ANR-10-LABX-0001-01 ). We thank the  Instituto Mato Grossense do Algodão (IMAmt, Brazil)  for partial funding of this research, and  Sebastião Araújo  for photographic work.
The lack of datasets about new vulnerable applications is also discussed by Nagappan and Shihab in  [14]. They address the need for more sophisticated lightweight tools that would prevent malicious code from getting into smartphones through mobile app stores. In  [15] , Chen et al. propose mass vetting (MassVet), a lightweight mechanism that is capable of quickly discovering unknown malice at a large scale (e. g. , Google Play Store). The authors use an app's view structure and navigation relations among views to produce a view graph whose nodes correspond to view items (active widgets) and arcs between nodes show navigation paths. These graphs are further compared with those that original apps on the market have. The fields of mobile computing (exemplified with mobile apps) and IoT (exemplified with hundreds types or models of device) present many similarities in terms of affordability, wide use, and limited control; thus, they should benefit from each other.
Gaps in vetting IoT Compared to vetting mobile apps (Section 2), there is a major gap in (VIoT). In addition to Section 1's obstacles that undermine IoT, this gap is also due to the nature of IoT. IoT mixes physical processes with cyber connectivity, making it different from other software-related disciplines  [16]. The first set of references that we reviewed are more concerned with the security and privacy of IoT applications than with developing a comprehensive guide for vetting things that would reveal their vulnerabilities. Meanwhile, the second set of references run tests to identify vulnerabilities of IoT devices that are already in operation  [16]. There is a consensus that IoT vastly impacts the way we view, use, and interact with smart devices  [17]. However, security remains a concern that could turn IoT misuse into a serious that security may represent the greatest obstacle to IoT growth  [18].
Implementation and experiments A system demonstrating the VIoT framework was developed using Python 3, Java 14, JavaScript, MQTTLens [34] and Node-RED  [35]. MQTTLens is an add-on for the Chrome browser allowing to publish messages to an MQTT broker, to subscribe to MQTT topics and to receive messages. This latter is a flow-based programming paradigm in which a variety of processes called "nodes" are connected together to form applications called "flows", instead of writing code. Node-RED runs on local workstations, the cloud, and edge devices. It has become an ideal tool for the Raspberry Pi and other low-cost hardware. Node-RED runtime is built on top of Node. js and takes its full advantage in terms of eventdriven, non-blocking I/O model. It can be used to build and deploy applications due to its browserbased editor by wiring hardware devices, APIs and online services.
Node-RED implements the testbed and includes an MQTT server so that IoT devices could be easily connected via port 1883 using Transport Layer Security (TLS). We considered sensors as things to vet. The vet engineer interacts with the sensors running on the testbed using JSON requests via the deployer module (i. e. , MQTTLensTool). The way a sensor was implemented consists of 3 connected objects: living. lights, bed. alarm, and kitchen. aircondition. They were created and deployed by the Node-RED server. On top of Node-RED, other parts (i. e. , the tester and the inspector) were implemented using Python language (Spyder Tool V3. 7  [36] ). To illustrate a vulnerability, we considered sensing duty and set its frequency to every time interval. A vulnerability would consist of communicating differently from the claimed frequency (V s 1 ) by using the "change" node.
Thus, during the experiment, the Python program related to sensing duty was launched in order to check whether the stored states were as expected. If not, it displayed a notification of a vulnerability. In the following, we considered 2 series of vulnerability-related experiments: vetting duration and vetting efficiency. These experiments run on Windows 10 Asus TUF Gaming i7-8750H @ 2. 206Hz, 12Go. Before all, the VIoT engineer sends a JSON request to a living. lights's sensor via MQTTLens Tool as per Figure  12. 1. Vetting duration: to assess the framework's time performance for detecting vulnerabilities. We computed the time of sending a request and the time of detecting a vulnerability. We considered 10 requests with an 2-second time interval between each one, where each request was tampered by changing frequency vulnerability.
[24]  discuss a security testbed for IoT devices that iTrust Lab has developed. First, they report on the experience of using an IoT search engine, SHODAN [26], to discover vulnerabilities of IoT devices. It is worth noting that this experience targeted devices that were already in operation, while we insist that vetting aims at detecting vulnerabilities prior to putting devices into operation. The proposed testbed emulates different types of testing environments that simulate the activity of multiple sensors and perform predefined and customized security tests along with advanced security testing analysis. Siboni et al. consider 4 security aspects of IoT devices: architecture, which investigates attacks on hardware and software; network connectivity, which investigates attacks on data distribution; data collection, which that investigates data collection with regard to privacy invasion and information theft; and, finally, countermeasures and mitigation, which that investigate how to reduce the security and privacy risks that IoT devices could cause.
8±0. 4 kG, and possibly up to 3. 6 kG. The moderate mass accretion rate and strong magnetic field Article number, page 1 of 8 arXiv:2004. 00848v1 [astro-ph. SR] 2 Apr 2020 makes DoAr 44 a prime target to investigate the magnetospheric accretion process in this system. The overall results of the multi-instrument campaign are reported in an accompanying paper  (Bouvier et al. , in prep. ; hereafter Paper I). Here, we present and analyze the unique datasets obtained from long baseline optical interferometry, using VLTI/GRAVITY. Section 2 describes the observations. Section 3 presents the analysis of the interferometric visibilities and phases both in the K-band continuum and across the Brγ emission-line profile. Section 4 provides an interpretation of the results in terms of a simple geometrical model for the star-disk interaction region in this system, based on the interferometric observables.
The datasets thus consist of long sequences of integration on DoAr 44, interleaved with sky exposures and interferometric calibrator observations. The calibrators were chosen to be unresolved at the longest baselines, located close to the target on the sky, and having a similar magnitude in the K band. Several calibrators were observed in order to derive the instrumental transfer function accurately. Weather conditions were excellent during the first night and we recorded 26 files of five minutes each over a period of 3. 7 hours. At the start of the second night, we recorded only one hour of exploitable data, as the domes had to be closed to high winds thereafter. The journal of observations is given in Table  1.
3 • when considering the central spectral channel only. The squared visibilities can be fit as a function of the baseline with geometrical models, while the closure phase measures the degree of asymmetry of the emitting region (e. g. ,  Monnier 2007). Figure  1  displays results from the dataset obtained on June 22, 2019. The K-band continuum-emitting region of DoAr 44 appears to be partially resolved (V 2 < 1) and centrosymmetric as suggested by a closure phase close to 0 •. The results obtained from the observations performed on June 23, 2019 are consistent with the results of the first night but do not significantly add to the signal to noise of the visibilities or phases due to their limited duration. To model the visibilities, we followed the approach  Lazareff et al. (2017) and Gravity Collaboration et al.
Acknowledgements. We would like to thank all the individuals who have contributed to build such a powerful instrument as GRAVITY. We thank the referees for their reports that improved the accuracy and clarity of the manuscript. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  742095 ;  SPIDI: Star-Planets-Inner Disk-Interactions , http://www. spidi-eu. org). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
2012; Jactel 2011)  or as intimate mixtures  (Castagneyrol et al. 2014; Damien et al. 2016 ), but with efficiency at young ages. To experiment how tree species richness drives various ecosystem processes and functions over a rotation, the ORPHEE experiment, which belongs to the worldwide Tree Diversity Network (http://www. treedivnet. ugent. be/), has been planted in 2008 in a site of the Landes de Gascogne  (Castagneyrol et al. 2013). This experiment spans species richness levels from 1 to 5 native trees species including mixed pine-birch plantations. In this study, we tested whether mixing pines and birches can promote stand productivity in the long term, i. e. , along a 50-year forest cycle, by using simulation runs with the forest gap model FORCEEPS, at two different tree densities. To calibrate the model, we used experimental data from monospecific pine and birch stands as well as literature, while validating the model on mixed stands.
Instead, we started the simulation from the data inventory (exact patch composition taking seedling mortality into account, but also variability in seedling heights) carried out at the beginning of the ORPHEE experiment. Growth (i. e. , stem diameter increment at breast height) was modelled in two steps. First, optimal growth was calculated for each tree using an empirical equation  (Moore 1989; Bugmann 1996). Second, actual tree growth was calculated by modifying the optimum rate according to a set of environmental factors limiting growth. Specifically, considering the annual growth of tree j, a maximum diameter increment I D f was first calculated, depending on the size of the tree (i. e. , its dbh D j ) and on a species-specific maximum growth rate g, as shown in Eq. (  1 ): I D f ¼ g D f 1- H j H max 2H max -b Â e cD j ð ÞÂ cD j þ2 ð Þ ð1Þ where H j is the height of the tree, H max is the maximum height reachable by the species in optimal abiotic conditions, and b and c are species-specific constants.
Although the parameters used are inherited from ForClim  (Didion et al. 2009 ) (see Supplementary material), i. e. , according to theoretical assumptions, it has been recently shown that more realistic parametrization may be extremely difficult to achieve so far  (Hülsmann et al. 2018; Vanoni et al. 2019) , especially with the kind of data available in our specific case. Site characteristics included soil nitrogen content, maximum water holding capacity, and climate conditions. It is noteworthy that in FORCEEPS, climate variables consist of time-series (monthly temperature and precipitation sums) that can be either simulated or observed, which facilitates the calibration and validation in new sites. For a more detailed description of the model, see Supplementary material, and see Table  1  for a detailed description of species parameters.
Study sites Before proceeding with the simulations for P. pinaster and B. pendula, we had to calibrate and validate FORCEEPS for these two species in the conditions of the ORPHEE experiment. To do so, we used experimental data from ORPHEE and data from long-term experimental plots from the surroundings and, whenever necessary, information from the scientific literature. The ORPHEE experiment (https://sites. google. com/view/ orpheeexperiment/home) is located in Southwest France, about 20 km south of Bordeaux. Mean annual temperature is 12. 75 °C, and mean annual rainfall is 876 mm  (1996 -2016, Cestas Pierroton meteorological station, INRA). The soil is a sandy and hydromorphic humic podzol (spodosol). In order to calibrate in particularly the allometry parameter for ages from 5 to 20 years old, we used data from two older experimental plots to obtain information about dimensions of adult trees for both species (DBH and total height): the ISLANDES network  (Bernier et al.
(2013) ). We compiled data from the ORPHEE experiment for the two target species, between 2012 and 2015, i. e. , years for which both DBH and height of trees were available (before 2012, the trees were too small to measure DBH). In each plot, only the height of core trees at the center of the plot was measured to avoid edge effects (total n = 36), which reduces the number of sampled trees (see Supplementary Table  4 ). Among these core trees, seven trees per plot were selected and measured for both DBH and total height. As shown in  Damien et al. (2016) , at the time of the survey, oaks were 5 to 8 years old and on average less than 1 m tall, i. e. , much smaller than pines and birches and could be confounded with understorey vegetation. As a consequence, the effect of oaks can be neglected in this study, so that mixtures of pines with one of the oaks or mixtures of birch with one of the oaks were considered as "low density" pine or birch monocultures, resulting in a gradient of pine/birch density, from 2500 stems/ha in real monocultures (100 trees in 400-m 2 plots) and pine-birch two species mixtures to 1250 stems/ha (50 pines or 50 birches in mixture with one oak species in 400-m 2 plots).
On these selected monoculture plots (4 plots per block and per species, thus 224 potential measured trees per species and per year), data were available for 878 and 874 tree-year couples for birch and pine trees respectively, for which DBH and height had been measured, for the calibration of the allometry parameter s (Eq. (  2 )). However, as the DBH-height allometry should also be inferred with adult trees, i. e. , at least up to 20 years old, and thus we also used data from two older monospecific experimental plots to obtain DBH and height data for adult trees: the ISLANDES network for 18-year-old birch trees (n = 82) and the GIS COOP -Castillonville trial for 18-year-old pine trees (n = 224) (Supplementary Table  4 ).
Validation of the model's outputs To validate the growth increments predicted by FORCEEPS with the selected parameters for each species, data from ORPHEE only allowed short-term comparison (up to 8 years for tree height and 3 years for DBH according to the data that were available). As our long-term simulations mostly depend on diameter increments, we thus first chose to use the DBH data to test growth patterns at the individual level. We compared the observed annual tree diameter increment in the ORPHEE experiment with the annual increments in diameter simulated by FORCEEPS for the same trees. We carried out this comparison for each species and for the 3 years for which these data were available  (i. e. , 2013, 2014, and 2015). We selected the trees growing in the most competitive environment in ORPHEE plots, i.
Calibration of allometry between diameter at breast height (DBH) and height at the individual tree level The best values for the s parameter according to the optimization algorithm were 103 and 62 for birch and pine respectively (Table  1 ). Tree heights simulated according to Eq. (  2 ) with these best values for s were accurately predicted (r 2 = 0. 85 and r 2 = 0. 78 for birch and pine respectively, Fig. 2 ). Although using data from several sources (ORPHEE, GIS COOP -Castillonville trial, ISLANDES network) may lead to a slight overestimation of the height of the smallest pines (i. e. , mostly in the first years) and to an underestimation for the largest pines, the accuracy of our predictions for diameter-height relationships was strong enough to be used in FORCEEPS simulations.
Effect of species addition on pine trees (P mix 1500 -P 750 and P mix 750 -P 375 ) Consistent with the pattern described just above, pine trees in the mixture were barely affected by their mixing with added birches at high density (P mix 1500 vs. P 750 ), while they experienced an increase in productivity (Supplementary Fig. 10 ) and biomass accumulation at the lowest density (P mix 750 vs. P 375 ). Fig. 2  Observed (open circles) and predicted (red line) tree height against tree diameter at breast height (DBH) for birch (upper panel, r 2 = 0. 85) and pine (lower panel, r 2 = 0. 78), using Eq. (  2 ) with the fitted value for the parameter s (see Table  1 ). Black circle: ORPHEE data; blue circles: ISLANDES data (only birch trees); green circles: GIS COOP -Castillonville data (only pine trees).
It will be for example of great interest to compare resistance and resilience to severe drought events in pure and mixed stands. However, this will necessitate improving the mortality part of the model, which is currently a major challenge on the agenda of forest dynamics modellers. Second, as a management module has been developed in FORCEEPS, one may test the effect of thinning mixtures in order to explore how this could strengthen overyielding. One could also try to identify at which age birch trees should be harvested in order to optimize wood production in mixtures with pines (for instance around 20 years, as suggested by results from Fig. 5 ). Third, our model provides the opportunity to test how tree diversity may affect the sensitivity of trees to herbivory in the long term. Actually, in Southwest France, pine stands are periodically subject to outbreaks of processionary moths (Thaumetopoea pityocampa  (Li et al.
Conclusion In this study, we have shown that even in the case of very productive monospecific forests, like P. pinaster in SW France, promoting mixtures especially through species addition (i. e. , complementing pines at low density with additional birches) may achieve the same yield. Although its limits forbid any conclusion regarding operational transfer in this specific case, this study illustrates how calibrating forest models with data from monospecific plots offers a powerful tool to investigate overyielding in mixture stands, even only considering the niche partitioning hypothesis. Acknowledgments The GIS COOP network benefits from the financial support of the French Ministry of Agriculture and Forest since 1994 and from the project Pinaster (2015-2019): Conseil régional de Nouvelle-Aquitaine (16004034), DRAAF Aquitaine (ADV15R072000012), FEDER (FEDER-FSE2014-2020 Axe 1).
The model, for which we proposed as Ensemble Patch Likelihood Aggregation (EPLA), combined both deep learning and traditional machine learning techniques. It was trained using the TCGA-COAD data set, and then transfer learning was implemented to fine tune the model using an Asian-CRC cohort curated locally, which enhanced the generalizability of this model. More importantly, we also demonstrated the interpretability of the model by identifying the crucial pathological signatures generated by the MIL model and linking them with MSI genomic and transcriptomic profiles.
Patient cohorts and dataset partition In this study, whole slide images (WSIs) of two large cohorts were collected, and an MS label was assigned to each WSI based on the patient's microsatellite measurement. The first cohort (TCGA-COAD), retrieved from The Cancer Genome Atlas, comprised 429 frozen tissue slides diagnosed as colon adenocarcinoma (COAD) with stage I to IV. MSI score of each sample within the cohort was measured using the MSIsensor algorithm based on tumor-normal paired genome sequencing data  [22] ; tumors with MSIsensor scores of ≥ 10 were defined as MSI, whereas those with MSIsensor scores of < 10 were defined as microsatellite stability (MSS)  [23]. In this cohort, 358 cases were labeled as MSS and 71 cases were labeled as MSI. The second cohort (Asian-CRC), collected from Tongshu Biotechnology Co.
ROI delineation, tiling, and data preprocessing All WSIs were digitalized at 20× objective lens with a predefined pixel resolution (~0. 5μm/pixel). In order to reduce the influence of unrelated areas and alleviate the workload of the classification method, regions of carcinoma (ROIs) on WSIs were manually annotated by expert pathologists, according to the following rules: (1) the tumor cells should occupy more than 80% of a ROI, i. e. , the interstitial component is less than 20%; and (2), obvious interfering factors, including creases, bleeding, necrosis and blurred areas, should be excluded. The annotation was performed using Aperio ImageScope (Aperio Technologies, Inc. ). Given the extremely large image size (typically 100,000 × 50,000 pixels) of a WSI, the WSIs were subsequently tiled into 512×512 patches.
The initial parameters of the model were trained in the training set of TCGA-COAD, and the transfer learning technique was implemented using the Asian-CRC data to generalize the model across cohorts with a high degree of heterogeneity. The transfer learning was conducted by reusing the model weights in the patch-level discriminators and then fine-tuning the weights using a small amount of labeled Asian-CRC data. In addition, we gradually added more Asian-CRC data for model fine-tuning to explore the impact on model performance. All codes were implemented in Python 3. 6. 5 and run on a workstation with Nvidia GPUs (P40). As for the minimal requirement, a desktop with CPUs and the above dependencies can run our algorithm for inference, which is widely available and easy-to-use for physicians and biologists.