dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, the projection can end up in a node or on an edge. In further we define a projection function {p, } = Proj(x, G), returning a couple containing the index of the edge which is the closest one to x and the position of the projec-tion from the beginning of the edge E p ( ) as a fraction of the edge length ∈  [ , ]. Therefore, if = then x is projected into E p ( ) and if = then the projection is in E p ( ). If ∈ ( , ) then the projection is on a linear segment, connecting E p ( ) and E p ( ). A detailed description of ElPiGraph and related elastic principal graph approaches is available elsewhere [ ]. The ElPi-Graph package implemented in Python is available from https:// github. com/sysbio-curie/ElPiGraph. P. Implementations of ElPi-Graph in R, Matlab, Java and Scala are also available from https: //sysbio-curie.
github. io/elpigraph/index. html. When analyzing the clinical datasets, the principal tree inference with ElPi-Graph was performed using the following parameters: R = ∞, α =. , µ =. , λ =. After the initial principal tree was constructed, it was pruned and the terminal segments were extended. The pruning consists in eliminating the final terminal segments of the tree containing only one single edge. Extending the terminal segments consists in extrapolating the segment in order to have most of the data points projected on the edges of the terminal segment and not at its terminal node. Both functions are standard principal tree post-processing choices, implemented in ElPiGraph package.
Partitioning (clustering) the data according to the principal graph segments Embedding a graph to the data space allows us to partition (cluster) the dataset in several natural ways: for example by assigning each datapoint to the closest node or the closest edge. However, these ways do not fully suit our purposes, since they do not reflect the intuition of "trajectory". So it is natural first to decompose the graph itself into linear fragments without branching (we will call them non-branching graph segments or simply segments) and afterwards to cluster the dataset according to the closest segment. This is the idea of the data partitioning used in the paper, and it is described with more details below. By the branching node in a graph we denote any node with connectivity degree larger than (deg(V i ) > ), and by the leaf or terminal node of the graph we denote a node with degree less than (deg(V i ) < ).
In case of a new branching node (not visited before) one goes into deeper level of depth-first recursive process. After all edges of a graph are partitioned into segments, one can partition (or, cluster) the dataset accordingly to the closest segment which can be done in two ways. Firstly, choose nearest edge to a given datapoint and associate the point to the segment to which that edge belongs to. However, a simpler approach is much more computationally efficient: calculate distances from datapoints to nodes and choose the segment which contains the nearest node. In case this node belongs to several segments (therefore, it is a branching node), we choose the segment which contains the second nearest node among all nodes which belong to the corresponding segments. If the number of nodes in the graph is large enough then both approaches will produce (almost) identical results (Figure  , B ).
Dimensionality reduction and data visualization using principal graphs To visualize the principal graph, each datapoint is first associated with the closest ElPiGraph edge in full dimensional space, and the distance to the projection onto the edge is recorded. We then embed the graph structure in D by computing a forcedirected layout with the Kamada-Kawai algorithm [ ]. Each datapoint is placed orthogonally on a random side of its associated edge, at the distance proportional to the distance to the projection in the initial space. The proportionality constant is called scattering parameter, which is adjusted by a user, or can be optimized in order to preserve, in the best way, the structure of the distances betweeen the datapoints in the initial data space. Edge widths can also be used to visualize the values of a variable or any function of the variables defined in the nodes of the graph.
Associating class labels and data variables and principal tree segments Segment labeling of the data points induced by the structure of the principal graph represent a categorical label which can be associated to the dataset variables of various types. In order to test if there is an association of the tree segments to a categorical variable (including binary as a particular case), we used the standard independence χ test. In order to test statistical association between tree segments and numerical variable (including ordinal as particular case), we used the standard ANOVA test representing the independent tree segment variable through the standard one hot encoding into a set of binary variables. If the test was significant then we evaluated the significance of each of the segments by looking at the value and the p-values of the generalized linear model coefficients for each segment.
Figure. Principal tree recapitulating the multidimensional structure of the myocardial infarction complications dataset. Distribution of classes along the tree is visualized in the large panel. Various modes of data visualization are shown in the small panels. 'Tree segments (branches)' shows partitioning (clustering) of data points accordingly to the linear fragments connecting branching points and/or leaf nodes (called 'non-branching segments' in this work). 'AGE' is an example of a continuous variable visualization using color gradient of data points. 'Classes associated with segments' shows data points of only those classes which have statistical associations with one or more segments. 'Artrial fibrillation' and 'Lethal cases' show visualization of a binary variable, with edge width reflecting the trend (in case of 'Lethal cases' it can be interpreted as lethality risk estimate).
Figure. Clinical trajectory analysis of the myocardial infarction complications dataset. A) Visualization of R values for the regression between clinical variables and the pseudo-time along clinical trajectories. B) Examples of regression analysis for a binary (logistic regression), continuous and ordinal (Gaussian kernel regression) clinical variables. C) Pseudotime plots for clinical variables selected by regression analysis. For binary variables, the probability inferred by logistic regression is shown. For ordinal and continuous variables, non-linear regression line is shown. Complication variables associated to the clinical trajectories are shown with thick lines (for example, LET_IS_ represents the survival probability. ) Vertical dashed lines indicate the positions of tree branching points along pseudo-time.
Figure. Analysis of clinical trajectories in large-scale diabetes dataset. A) visualization of various clinical variables on top of the metro map layout of the principal tree. Partitioning the data accordingly to the principal tree segments is also shown in the top left corner. B) Visualization of four categorical variables related to the administered drug treatments and their dose changes. Here, the data points are shown in semi-transparent background, while on top of each graph node the relative proportions of the associated (the closest) data points are shown as a pie-chart. The size of the pie-charts are proportional to the number of points associated to each node. C) Pseudotemporal dynamics of clinical variables correlated to readmission-and long stay-associated clinical trajectories (shown as solid and dashed lines on the left).
Trajectory analysis from the snapshot data is a widely used approach in modern molecular single cell studies, where the genomewide measurements of individual cell states are inevitably destructive. Collecting the information about a large number of cell states allows reconstructing the underlying hidden cellular dynamics without following each individual cell in physical time [ ]. Dynamic phenotyping of cell states is a rapidly emerging concept in this scientific field [ ]. Elastic principal graphs (ElPiGraph) is an established general machine learning method which is widely used for the purpose of reconstructing cellular trajectories from the single cell data, in the form of principal trees or other more or less complex graph topologies [ ]. Here we suggest to apply ElPiGraph to quantification of clinical trajectories in large clinical datasets, which requires adapting ElPiGraph to the datasets characterized by mixed data types and presence of non-randomly distributed missing values.
Figure. Figure. Principal tree recapitulating the multidimensional structure of the myocardial infarction complications dataset. Distribution of classes along the tree is visualized in the large panel. Various modes of data visualization are shown in the small panels. 'Tree segments (branches)' shows partitioning (clustering) of data points accordingly to the linear fragments connecting branching points and/or leaf nodes (called 'non-branching segments' in this work). 'AGE' is an example of a continuous variable visualization using color gradient of data points. 'Classes associated with segments' shows data points of only those classes which have statistical associations with one or more segments. 'Artrial fibrillation' and 'Lethal cases' show visualization of a binary variable, with edge width reflecting the trend (in case of 'Lethal cases' it can be interpreted as lethality risk estimate).
Figure. Figure. Clinical trajectory analysis of the myocardial infarction complications dataset. A) Visualization of R values for the regression between clinical variables and the pseudo-time along clinical trajectories. B) Examples of regression analysis for a binary (logistic regression), continuous and ordinal (Gaussian kernel regression) clinical variables. C) Pseudotime plots for clinical variables selected by regression analysis. For binary variables, the probability inferred by logistic regression is shown. For ordinal and continuous variables, non-linear regression line is shown. Complication variables associated to the clinical trajectories are shown with thick lines (for example, LET_IS_ represents the survival probability. ) Vertical dashed lines indicate the positions of tree branching points along pseudo-time.
Figure. Figure. Analysis of clinical trajectories in large-scale diabetes dataset. A) visualization of various clinical variables on top of the metro map layout of the principal tree. Partitioning the data accordingly to the principal tree segments is also shown in the top left corner. B) Visualization of four categorical variables related to the administered drug treatments and their dose changes. Here, the data points are shown in semi-transparent background, while on top of each graph node the relative proportions of the associated (the closest) data points are shown as a pie-chart. The size of the pie-charts are proportional to the number of points associated to each node. C) Pseudotemporal dynamics of clinical variables correlated to readmission-and long stay-associated clinical trajectories (shown as solid and dashed lines on the left).
The dataset can be downloaded from UCI repository at https://archive. ics. uci. edu/ml/datasets/diabetes+ 130-us+hospitals+for+years+1999-2008 or from Kaggle at https://www. kaggle. com/brandao/diabetes. The dataset represents care at US hospitals and integrated delivery networks. It years ( -) of clinical includes over features representing patient and hospital outcomes. The data contains more than , hospitalization cases with patients suffering from diabetis characterized by attributes.
Access Control Access Control (AC) aims at restricting access to resources. Traditionally, Access Control can be divided in two categories: Discretionary Access Control (DAC)  (Lampson, 1974; Saltzer and Schroeder, 1975; Harrison et al. , 1976)  and Mandatory Access Control (MAC)  (Bell and LaPadula, 1973; Biba, 1977). In DAC models, users can set, modify or share the access control of their resources. Most modern operating systems such as Windows, GNU/Linux and Mac OS are based on DAC models. On the contrary, MAC refers to a family of models where owners do not have to choose the rights over their resources. In this type of access control, the system assigns security labels or classifications to resources (for instance "classified," "secret," or "top secret") and allows access to subjects or applications depending on their level of clearance.
g  From the academic point of view, researchers have focused on several problems, including emails leakage protection  (Zilberman et al. , 2011) , network and Web based protection  (Caputo et al. , 2009)  and misuse detection in database  (Harel et al. , 2010; Harel et al. , 2012). Moreover, solutions have been proposed to improve detection methods by using machine learning  (Gafny et al. , 2010; Mathew et al. , 2010; Li et al. , 2015). Closer to industrial preoccupations  (Alawneh and Abbadi, 2008) , have proposed a framework to protect the data shared between collaborative organizations. Finally, some works have been proposed to tackle sensitive data  (Chae et al. , 2015)  or confidentiality  (di Vimercati, 2011).
, 2016; Ruoti et al. , 2016)  and authentication  (Stobert and Biddle, 2014)  while other works have proposed mechanisms such as recommendation systems  (Liu et al. , 2016)  and indicators  (Felt et al. , 2016)  to help the end-users. Finally, other works have been proposed in the DLP field to take into account usability. For instance  (Ko et al. , 2014) , have proposed a user-centric mantrap-inspired DLP solution, implemented in Debian Linux to inform end-users about potential data leak and allow them to fully decide sending or not the data. In addition, DLP vendors such as Clearswift n and Teramind o propose adaptive dashboards and reporting in order to make their products more usable and user-friendly. However, to the best of our knowledge, no survey has been proposed to specifically target the end-users perception toward data leakage within companies.
ONLINE SURVEY In this Section, we present the online survey that we have designed to collect data from a broad audience of employees having different profiles (in terms of position and computer skill) and working in different companies (in terms of size and activity sector). The ultimate goal was to gather information on employees and their awareness, perception and knowledge regarding security policies and data leakage, within their working environment, to investigate three research questions: • What are the mechanisms they would prefer to avoid unintentional data leakage? Therefore, we have defined the most pertinent questions to collect useful answers for these research questions and have feedbacks of the participants on: • Context (size of the company and activity sector), position and skill; • Awareness, knowledge and perception toward security policies and data leakage; • Data leak prevention mechanisms.
FEEDBACKS TOWARD DATA LEAK PREVENTION MECHANISMS In this section, we present the results of the questions 14 and 15. Preferred Anti-Data Leak Mechanisms The goal of the question 14 (see below) was to collect information about the preferences of the participants regarding anti-data leak mechanisms in terms of non-intrusiveness, ease-of use and efficiency. Question 14: According to you, what are the most efficient mechanisms to avoid data leakage? • Notify users that an action is going to cause a data leakage and let her/him choose (e. g. , popup messages). • Notify users that an action is going to cause a data leakage and prevent this action. • Send an email to the administrator/manager to notify her/ him and automatically prevent the action. • Send an email to the administrator/manager in order for her/him to choose if the action can be performed or not.
2014). Here, we developed a mechanistic-statistical model accounting for ecological diffusion, logistic growth and imperfect detection varying in space and time. The goals of our study were to i) provide a template to simulate scenarios and assess the ability of our method to reliably forecast the fate of populations in time and space and ii) provide an easy and convenient way to implement the approach in software heavily used by statisticians and ecologists such as JAGS and OpenBUGS. To assess the performance of our approach, we performed a simulation study to assess bias and precision of parameter estimates and evaluate forecasting performance in contrasted scenarios of varying species-level detectability and number of monitoring sites. Finally, we fitted our model to opportunistic data on wolves in South-Eastern France between over nine years  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015).
, L and O x L = Cs. Exceptions are the cells bordering non-habitat cells as the latter are excluded from the dynamics due to the reflecting boundary conditions. The system 6 was solved using the lsoda method  (Petzold 1983 ) through the R-package deSolve  (Soetaert et al. 2010)  and equation 4 was then approximated as follow: >(?, A) = ~N(O, A)PO Q , ≈ Ä Ä ÅtÇ ( ∩ Ñ i(v,x) yVtu v,x , Ay, Ö x45 Ü v45 ( 8 ) where ÅtÇ ( ∩ Ñ i(v,x) y is the surface of the intersection between the cell u(}, D) and the study area Ç ( in which counts occur.
We then defined a high detectability (HD) scenario in which the individual-level detectability was set at 0. 8, and a low detectability (LD) scenario in which this probability was set at 0. 2. For each scenario (LR-LD, LR-HD, HR-LD, HR-HD), we simulated 100 datasets and we fitted the model to each dataset. We calculated the relative bias and mean squared error (MSE) for the carrying capacity K, the intercept of the growth rate R, the linear and quadratic effect of forest density on the growth rate, the diffusion coefficient and the linear and quadratic effect of human density on the diffusion coefficient. Note that in the simulation study we assumed that K, R and q were constant over space and time. To explore the ability of our model to forecast the abundance of individuals per site in the four scenarios, we fitted our model to the first ten years and forecasted the distribution over second ten years.
We also used environmental and anthropogenic covariates to model spatial variation in parameters Ri and Di. Using the CORINE Land Cover ® database (U. E -SOeS, Corine Land Cover 2006), we calculated forest cover as the average percentage of mixed, coniferous or deciduous forest cover for each site. Because forests may structure the ungulate distribution (i. e. prey species), we expected that forest cover would have a positive effect on the logistic growth rate Ri  (Louvrier et al. 2018). Human density was found in previous studies to influence habitat choice and dispersal of wolves in Italy  (Corsi et al. 1999; Marucco & Mcintire 2010). We therefore considered human density as a candidate covariate possibly explaining spatial variation in the diffusion parameter Di. Human population density was averaged in each 10x10 km from a 1x1 km raster from the Earth Observing System Data and Information System (EOSDIS).
Bayesian inference To complete the Bayesian specification of our model, we specified Gaussian priors with mean 0 and variance 1 for all estimated parameters, except for parameter \ for which we used a logistic function limited between 0 and 0. 2. Parameters from the observation process and those from the state process were updated in two different blocs. We implemented our simulations in OpenBUGS  (Lunn et al. 2010 ) and the wolf analyses in JAGS using the JAGS package mecastat  (Rey et al. 2018 ). We used Markov chain Monte Carlo (MCMC) simulations for parameter estimation and forecasting  (Hobbs & Hooten 2015). We ran three MCMC chains with 40,000 iterations preceded by 10,000 iterations as a burn-in. We used posterior medians and 95% credible intervals to summarize parameter posterior distributions.
Forecasting To forecast the abundance of individuals per site, along with the associated prediction uncertainty, we needed to assess the probability distribution of the true state in the future when data will be collected, conditional on the collected data in the past  (Williams et al. 2018 ). The Bayesian formulation of our model allowed assessing the forecast densities by simulating yearly data from t = 2, …, T + 1 and sampling >(i, T+1) on each iteration of the MCMC chains. The posterior distribution was then assessed from the forecast distribution by sampling into the forecast NT+1. In the simulation study, we compared this posterior distribution with the simulated data for the year 20. For the wolf case study, we assessed the probability that the site i was occupied, which boiled down to calculating P(z i = 1) where z i is the latent status of the site (occupied or not) as the number of MCMC iterations producing a strictly positive abundance, i.
Comparison with dynamic site-occupancy models In  Louvrier et al. (2018) , a dynamic site-occupancy model was fitted to the same dataset, at a national level and between 1994 and 2016. We found in this previous study that when forest cover was high, the probability for an unoccupied site to be colonized the year after increased as well. This can be related to the logistic growth rate parameter, because once a site is colonized, the population will start growing. We found the same effects of sampling effort and road density on the species-level detectability, which can be explained by the fact that maximum abundance per site is low enough to guarantee a linear correspondence between species-detectability and individual-level detectability. In comparison with the map of occupancy estimated with a dynamic site occupancy model (top right panel of Figure  7  in  Louvrier et al.
Figure2: Performance of the model in the high resolution / high detectability scenario (left panels) and in the low resolution / high detectability scenario (right panels). For each of the 100 simulated datasets (on the Y-axis), we displayed the median (circle) and the 95% credible interval (horizontal solid line) of the parameter. The actual value of the parameter is given by the vertical dashed red line. The estimated bias (noted as "B") and MSE are provided in the legend of the X-axis.
Acknowledgements We gratefully acknowledge the help of people who have collaborated with the wolf monitoring network supervised by the  French game and wildlife agency (ONCFS). We thank  Lionel Roques  and  Olivier Bonnefon  for helping us building the model. We also warmly thank  Marc Kéry  for his constructive comments on the manuscript. JL is thankful to the GDR 3645  Ecologie Statistique. We thank the  Montpellier University  and the  ONCFS  for a PhD grant to the first author. This work was partly funded by a grant from  CNRS  and " Mission pour l'interdisciplinarité" through its "Osez l'interdisciplinarité call. ".
We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over 3. 6×10 5 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created DES SV map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. Our DeepMass † method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering with the optimal known power spectrum still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence.