dataesr/scientific-paragraphs-categorization huggingface dataset:
For further investigation of the differences between the low-rank basis of porcine and human cardiac microstructure, an extraction of the model from human training data is feasible upon availability of ex-vivo human datasets. The poor approximation of the unpersonalized models, obtained from the porcine training data set (i. e. the mean PGD modes with 0 SVD modes, the porcine atlas and the RBM adapted to the porcine average data), indicates the interspecies difference. However, when using personalization with the SVD basis, extracted from the variations across the porcine training data, the PGD model approximation significantly improves with two PGD modes and 2 SVD modes already. This shows that it is possible to personalize the model to the underlying human data set using the basis functions of structural variations, extracted from porcine data.
An error plateau is reached with 2 PGD and 4 SVD modes. This is no significant difference to the healthy porcine test cases, were the error levels off between 2 and 4 PGD modes with 4 SVD modes. For the POD model, only minor improvement is found when increasing the number of modes. The modes are not ordered by information content for the human data set. The POD model captures the main characteristic of the human show case. However, more detailed characteristics, represented by higher POD modes, are not dominant in the human show case. Both models can approximate the underlying human case, promising for the feasibility to transfer the models to human data. The un-personalized mean of the second and higher PGD modes can not be transferred to human data. We performed a fit of the data-driven PGD and POD model to the 3D volumetric data to demonstrate that the derived low-rank basis functions represent aggregated myocyte orientation well.
In personalized modelling, if no high-quality, high-resolution, 3D coverage DTI data is available, the PGD or POD model offers the potential to estimate a 3D representation of myocyte aggregate orientation on the mesh nodes form sparse data. The advantage of the models is the information transfer from high resolution data, while keeping the possibility for personalization. The functional bases give a realistic, globally heterogeneous prior. To fit the PGD model to data with low spatial coverage, a PGD of the sparse data set is required. Without changing the PGD method, the Galerkin basis functions need to be adapted to ensure that data points are available within the support of each Galerkin basis function. The new supporting points have to be a subset of the supporting points of the model.
Subsequently, the weights of the PGD/SVD model can be adapted to fit the degrees of freedom to the target data in a least squares sense. Alternatively, the PGD could be adjusted, directly using the SVD basis and fitting the weights. To fit the POD model to sparse data, the data is mapped onto the closest grid points on the common grid, where the model is defined. A gappy POD  ( Willcox, 2006 )  could be used to fit the model parameters, using a linear least-squares method. Due to the choice of the 2D modes with transmural normal direction, data is available in each imaging slice. In clinical applications, slice miss-match is commonly found, due to breathing. The spatial miss-match of anatomical data and DTI data poses an additional task, when assigning the data points to the coordinates with respect to the LV geometry.
Conclusion We have presented a new data-driven, volumetric, highresolution, parametric model of LV myocyte orientation. We have demonstrated that aggregated myocyte orientation can be represented in a common low-rank basis in shape-adapted coordinates. The parametric model can be personalized to cDTI data and approximate unseen data. A personalized, heterogeneous field of aggregated myocytes results by transferring information about major structural characteristics and inter-case variability, from the exvivo training data. The human show case suggests the feasibility to transfer the model derived from porcine data to approximate human aggregated myocyte orientation.
Apparatus A 22-inch CRT monitor (HP p1230) set at a resolution of 1,152 × 864 pixels and a refresh rate of 110 Hz was used for stimulus presentation. Observers were supported with a chin and head rest placed at a distance of 57 cm from the screen. Eye movements were monitored using an Eye Link 1000 Plus eye tracker (SR Research Ltd. , Mississauga, Ontario, Canada) at a 1000 Hz sampling rate. The eye tracker was positioned in a tower mount configuration (i. e. , with the camera positioned above the working area). A combination of Python 2. 7 and the PsychoPy toolbox  (Peirce, 2007)  was used for stimulus presentation and collection of behavioral and eye-tracking data. A7-sized paper (7. 4 cm × 10. 5 cm) with a dot grid printed in the middle was used to record observers' responses (Figure  1B ).
Analysis To quantify junction errors, we analyzed the differences between the junctions in the letter-like Downloaded from jov. arvojournals. org on 03/16/2021 targets and the responses. Five junction types that were in the target set (V-, L-, T-, F-, and X-junctions) and one junction type that was introduced in the response (K-junction) were included in the main analysis (see Figure  6A  for examples of junction types and Table  1  for the distribution of the junctions across the targets). In addition to the main analysis, we also analyzed the data including only the L-, T-, and X-junctions. In this set, the V-and L-junctions were combined, K-and F-junctions were decomposed into two L-junctions. The analyses used the raw line data (i. e. , the overall number of junctions in the response) as well as the corresponding lines (see CL accuracy).
In this sense, we try to bring together what Wallach  [40]  sees as the fundamental methodological difference between interpretation-oriented social scientists and computer scientists, who are mostly concerned with obtaining models that produce great accuracy but disregard the reasons for doing so. We work with a highly curated dataset rather than concentrate on scale and accuracy. Working with a highly curated dataset, we employ machine learning as a tool to gain new insights into the attachment relationships of our news networks and how they determine reliability. This approach could be called 'distant reading of networks'. The method is inspired by the better-known 'distant reading' meth ods  [20] , used to complement the human reading of texts with machine reasoning. The method is called 'distant' because it relies on machines rather than humans to explore relationships.
The data: the Décodex network The intertwining of communal and preferential attachment, which characterises digital networks, is easily observable in the dataset of news sources that we focus on in this article. We built a network drawing on a list of online news sources catalogued by the 'Décodex' initiative. 2 In this fact-checking project, the journalist of Le Monde reviewed several hundreds of websites active during the 2017 French presidential campaign and evaluated their trustworthiness according to four categories: 'reliable', 'imprecise', 'unreliable' and 'satirical'. A few months ahead of the French elections, we extracted the URLs reviewed by 'Décodex' and used the Web Crawler Hyphe  [19]  to map the hyperlinks connecting them. All the websites from the Décodex corpus have been crawled at a depth of one-click starting from their homepage.
Features The first step in our methodology is the featurisation of the dataset, that is the selection and preparation of the network features that will be used as input for the machine learning models. For our experiments, we introduce several new measures that consider network attachments by including aspects of community formation and preferential relations. We furthermore compute several features that consider the direction of edges, which we have earlier established to be important for readability. Our four direct neighbourhood relations are the number of incoming and outgoing links from reliable and unreliable sites. Because the websites in our network can have a very different number of neighbours (varying from one to more than two hundred)depending on their editorial practices and how they have been captured by our crawl -, we computed for each website the ratio between each type of neighbour and the total number of incoming or outgoing links of the website.
09, we penalise the connections with distant neighbours. We are interested in Katz centrality as it was shown to be effective in detecting both locally and globally interconnected nodes  [14]. Reliable sites should be described by a higher Katz centrality given that they connect both locally and globally. If we rank the data according to Katz centrality, Daily Mail is at rank 28 the highest-ranked site that is considered to be unreliable by the Décodex team. The second-highest ranked is Fox News at rank 54. We will meet both frequently throughout this article as sites where the choice of our model differs from those of the human evaluators. Modularity is computed for the cut that separates a site and its neighbours from the rest of the network ('modularity'). It is the fraction of the edges that fall within the cut minus the expected fraction if edges were distributed at random.
Modelling In our experiment, we apply all the steps of traditional predictive analytics to create a stable machine-learning model of the data. To avoid overfitting the existing data (that is constructing a model that is excessively influenced by the distribution of the training dataset), we use cross-validation. Here, the data is divided into k subsets or 'folds'. Over a pre-defined number of iterations, one of the folds is held back as test data, while all other folds are used for training. The test data is then used to perform an estimation of the performance of the model. Every data item is part of the testing fold exactly once and of the training set in all other iterations. This reduces underfitting as more data is used for training but also overfitting as all data is at some point used in testing.
The error analysis shows clearly how sensitive the decision on reliability is to the direction of hyperlinks. If the neighbours' reliable hyperlinks are the single most important feature, then the machine-reading must struggle to consolidate the network connections of Fox News with the Décodex judgement. An interesting case that shows that the model can correct human judgements is Génération Identitaire, a hard-right unreliable political site. It is counted as unreliable by the ensemble model, while Le Monde incorrectly records it as reliable. This suggests that the method can discover clear errors in the human-created data. As the ranking of features has shown the reliability of a site's neighbourhood to be the most important feature, we will move on to consider two rule-based models that allow us to investigate this relationship further, as they are focussed on the most important asymmetric relations in a dataset.
The change of judgements in rules 2 and 3 tells us more about the broader issues of predicting reliability from network attachments but also about the biases and issues characteristic of a human-created dataset such as Décodex. This is expressed as much in the correct classifications as in the wrong ones. A correct classification in rule 2 is, for instance, Russia Today, which is often seen as a Russian propa ganda instrument but can also be cited by mainstream news sites. Russia Today is at the borderline of this classification with 0. 07 for neighbours' reliable edges, which reflects their status as a site whose reliability is generally considered to be doubt ful rather than clearly unreliable. Rule 2 also tries to correct the misclassification of Génération Identitaire, which OneRule assigns to the reliable sites following the error in the Décodex original classification.
There is, for instance, the question of what should be included in a corpus of news sites. Our data included citizen journalism websites and an online campaigning site. The machine reading struggled to assign these sites to either side of the classification, as none of them is, strictly speaking, a news site. This problem is especially clear when considering a site like Hoaxbuster, a community platform to limit the circulation of hoaxes, which was considered to be unreliable by the algorithm as it links to many unreliable sites. The machine reading highlights how difficult it is to judge the reliability of news. The machine reading struggled to reproduce the evaluators' judgements for sites such as Fox News or the Daily Mail. Both belong to official news ecosystems, with reliable sites such as the BBC, e.
Results Results for the percentage of successful trials are shown in Fig. 2. To achieve a fine-grained analysis of developmental changes in success, we adopted a Bayesian generalized linear mixed model (GLMM) approach using the rstanarm package  (Gabry & Goodrich, 2017)  in R (R Development Core  Team, 2011). This Bayesian GLMM was used with success (i. e. , failure [0] or success [1] on each trial) as the dependent variable and age (in years), condition (Opaque-Cue, Transparent-Cue, or Transparent-No Cue), and Age*Condition as fixed factors. Participant (i. e. , participant's identity), school (i. e. , kindergarten's or primary school's identity), and trial number (1-24) were used as random effects. Estimated parameters are provided in Table  2. Age was categorized as a continuous variable (i.
However, as indicated by the Bayes factors, there was no evidence for a difference between the Opaque-Cue a Age groups: 3-4, first year of kindergarten; 4-5, second year of kindergarten; 5-6, third year of kindergarten; 6-7, first year of primary school; 7-8, second year of primary school; 8-9, third year of primary school. We also conducted a trial-by-trial analysis to explore the learning curves in the three conditions. Results for the percentage of successful trials are shown in Fig. 3. We adopted a Bayesian GLMM approach using the rstanarm package  (Gabry & Goodrich, 2017)  in R (R Development Core Team, 2011). This Bayesian GLMM was used with success (i. e. , failure [0] or success [1] on each trial) as the dependent variable and trial number (1-24), condition (Opaque-Cue, Transparent-Cue, or Transparent-No Cue), and Trial Number*Condition as fixed factors.
There was no evidence for an interaction effect of trial number on the Opaque-Cue and Transparent-Cue conditions (À0. 01; 95% CI = À 0. 01 to À 0. 01, BF = 0. 02). However, there was extreme evidence for an interaction effect of trial number on the Opaque-Cue and Transparent-No Cue conditions (0. 01, 95% CI = 0. 01 to 0. 02, BF > 100). Taken together, these results indicate that (a) the Transparent-No Cue condition was more difficult than the Opaque-Cue and Transparent-Cue conditions and (b) the performance in the Transparent-No Cue condition increased more steeply across the trials than that in the Opaque-Cue and Transparent-Cue conditions. Finally, we conducted two additional exploratory analyses to examine the influences of gender (female or male) and maze (A or B) on success. Again, we adopted a Bayesian GLMM approach using the rstanarm package  (Gabry & Goodrich, 2017)  in R (R Development Core  Team, 2011).
, 2016)  for the entire Indigirka Lowlands region. For a smaller study area, we classified a time series (2010-2019) of three VHR (0. 5 m) GeoEye-1 and WorldView-2 satellite images into land cover classes representing distinct types of tundra vegetation (a. o. shrub-dominated vegetation). Our approach based on the use of VHR data has the advantage that small-scale disturbances on shorter timescales can be captured that may not be evident from moderate (e. g. Landsat) to coarse resolution (e. g. MODIS) data  (Grosse et al. , 2008; Muster et al. , 2013). We applied the Potts model  (Potts, 1952) , a simple and widely used MRF regularization method, supported by validation data of areas of known change or no change in land cover. This allowed us to optimize and quantify accuracy of change detection using satellite images from different sensors and phenological stages.
We then compared EVI trends within the smaller study area to those in the entire Indigirka lowlands region. Used software and packages are listed in text S2. In addition, we quantified the dynamics of various types of surface water (permanent, seasonal and ephemeral) for the Indigirka Lowlands as derived from the Global Surface Water Map (GSWM). The GSWM contains 30 m resolution data on the presence and change dynamics of surface water as derived from the Landsat record between the 1984-1999 and 2000-2019 periods  (Pekel et al. , 2016).
Satellite data and distinguished land cover classes To assess recent changes in land cover at our site in high spatial detail, we used three VHR, cloud-free satellite images from different sensors with different timing of acquisition within the growing season. We used a GeoEye-1 image from mid-August 2010, a WorldView-2 image from early July 2015 and a WorldView-2 image from early August 2019, with comparable resolution and bandwidths (see Table  S1 ). All three images were pan-sharpened to 0. 5 m resolution using the Gram-Schmidt algorithm in ArcMap 10. 6. 1  (ESRI, 2019). Images were georeferenced manually, using permanent and recognizable features in the landscape. All three images achieved subpixel (0. 5 m) georeferencing accuracy. Correction to top-of-atmosphere reflectance was performed for all three products according to  Podger et al.
S22 ). Photographs from the summer of 2011 were used for the 2010 image, while photographs from the same summer were available for 2015 and 2019. The same pixels were used in each year as validation sites. About 15% of pixels in the validation data showed a change in land cover class. Test sites for 2019 were located in the field in August 2019 using a Real-Time Kinematic Global Navigation Satellite System (Hiper HR, TopCon, Tokyo, Japan) in a base and rover set-up using GPS and GLONASS satellites. The perimeter of 171 patches representing the land cover classes in Table  1  was recorded with sub-centimeter accuracy. Test sites for 2015 and 2010 were copied from the test sites for 2019 if visual inspection of the images showed no indication of land cover change. The same pixels were used for the test set in all three years to avoid inconsistency of accuracy assessment due to the use of different sets of pixels.
Accuracy assessment Change detection accuracy was determined from change detection confusion matrices  (Macleod and Congalton, 1998)  per combination of two images using the areas of known change and no-change from the validation data. Similar to conventional confusion matrices, these tabulate the predicted class transitions against the observed class transitions for each combination of two images. To assess the degree to which chosen parameters lead to over-or underestimation of change, we calculated Matthew's Correlation Coefficient (MCC)  (Matthews, 1975)  from a confusion matrix for a binary reclassification into change/no-change areas. The MCC is a more reliable metric than overall accuracy in case of binary classification of unbalanced datasets  (Chicco and Jurman, 2020)  and is calculated from the relative number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) in the confusion matrix.
, 2011; Sundh et al. , 1995; Van Huissteden et al. , 2005). This implies an eventual increase in carbon sequestration through peat accumulation, which may partially mitigate high initial emissions in thermokarst wetlands  (Turetsky et al. , 2020). To assess resulting net changes in the greenhouse gas balance of this ecosystem, a systematic survey of greenhouse gas fluxes associated with different vegetation classes is necessary. Satellite data consistency in terms of seasonal timing and sensor properties is an important remaining challenge in remote sensing of Arctic vegetation change  (Beamish et al. , 2020). We found that MRF techniques such as the Potts model can substantially improve the quantification of vegetation change in VHR satellite data from different sensors and phenological stages.
The recent browning observed for the study site was evident in large parts of the Indigirka Lowlands, although specific areas (e. g. coastal tundra and drained thaw lakes) have continued greening. Within our relatively short study period, land cover change was expressed on small spatial scales. Expansion of small water bodies and vegetation successions were only detected using very high resolution satellite data with extensive ground truthing. Although the influence of difference in sensors and seasonal timing of the images within this short timeseries cannot be ruled out completely, land cover change detection accuracy was improved after spatiotemporal regularization European Union's Horizon 2020 project INTERACT [grant agreement number 730938]. The funding sources had no role in the conceptualization, execution or submission for publication of this study.
Organization of the paper To sum up, we have two goals in this paper: 1. Give some theoretical guarantees to the (slightly modified) model introduced in Chiquet et al. [7]. 2. Generalize the result of Yuan and Zhang  [29]  to the case where a structural penalization is added in the estimation step. In Section 2, we introduce the model, consisting in putting a generalized Gaussian prior on the direct links before the procedure of estimation of Ω yy and Ω yx , and we detail the new convex objective. Then we provide some error bounds for our estimates, useful as theoretical guarantees of performance. Section 3 is devoted to empirical considerations. We explain how we deal with the minimization of the new objective and we test the method on simulations first, and next on a real dataset (a Canadian average annual weather cycle, see e.
Simulations and real dataset The minimization problem (2. 5) is solved using a coordinate descent procedure, alternating between the computations of Ω yy = arg min S q ++ λ,µ,η (Ω yy , Ω yx ) and Ω yx = arg min R q×p λ,µ,η ( Ω yy , Ω yx ). Each step is done by an Orthant-Wise Limited-Memory Quasi-Newton (OWL-QN) algorithm (see e. g. [1] ). The first subproblem is performed through half-vectorization (vech) to ensure symmetry and we set the objective to +∞ on S q ++ to ensure positive definiteness of the solution. The coordinate descent is stopped when Ω (t) yy -Ω (t-1) yy 2 max(1, Ω (t-1) yy 2 ) and Ω (t) yx -Ω (t-1) yx 2 max(1, Ω (t-1) yx 2 ) following two consecutive iterations t -1 and t, where > 0 is a small threshold depending on the desired precision. We are now going to try our method on synthetic data first, and then on a real dataset.
However, that clearly cannot counterbalance the extended computational times, and GenGm should not be used for such situations. The calibration of the regularization parameters is made using a cross-validation on a training set of size n t = 150 and the accuracy is evaluated thanks to the mean squared prediction error (MSPE) on a validation set of size n v = 1000, MSPE = Y + X Ω t yx Ω -1 yy 2 F q n v. (3. 1) Due to the large amount of treatments, the grids for cross-validation are not very sharp here but they will be carefully refined for the real dataset of the next section. The covariance between the outputs is R = (r |i-j| ) 1 i, j q for r = 1 2 and we work with p = 100. Each scenario is repeated N = 500 times and GenGm is evaluated with numerous values of β, from 0. 25 to 2 with a step of 0.