dataesr/scientific-paragraphs-categorization huggingface dataset:
The batches are taken randomly from the training dataset, Adam  [25]  is a gradient-based optimizer of stochastic loss functions (i. e. , in case where n in Equation (  5 ) is smaller than the size of the training dataset). The optimization is performed over several epochs, one epoch being achieved when the entire dataset has been presented to the neural network for optimization. The two most sensitive parameters of the optimizers are the batch size n and the learning rate which is an internal parameter of the Adam optimizer. To avoid overfitting, dropout layers are activated with a dropout rate of p D > 0 and the L2-regularization parameter γ, see Equation (  5 ), is strictly positive. Table  1  gives all the parameters used in our setup. A sensitivity study of the training parameters is presented in Section 5.
1. The CNN for the 2018-dataset (CNN-2018) was trained in 28 epochs on 180 × 10 3 samples and validated on 45 × 10 3 samples selected from 44 Sentinel-1 SAR images. After epoch 20 the validation accuracy was not increasing. CNN-2018 was produced with the TensorFlow v1. 13 and Keras 2. 2. 4 libraries. The CNN code, weights and example of usage are available on GitHub repository  [26]. The CNN for 2020-dataset (CNN-2020) was trained using TensorFlow v2. 1 and Keras v2. 2. 4-tf libraries in 30 epochs on 840 × 10 3 samples and validated on 360 × 10 3 samples selected from 255 images. Samples were split between "ice free", "young ice", "first-year ice" and "old ice" classes in the proportion 2:1:1:2. The validation accuracy stopped significantly increasing after epoch 20. Training was performed on computers with configuration specified in Table  2.
CNN-2020 Accuracy of CNN-2020 trained on 255 images is a bit higher-0. 916 when computed over all samples in the validation dataset. The highest accuracy corresponds to ice free and old ice-0. 98, the lowest to young ice-0. 76. If only samples with high enough probability are considered then accuracy increases even further. Figure  8  shows three confusion matrices computed for all samples (A), for samples with minimum probability of 0. 5 (B) and 0. 6 (C). Setting the probability threshold to 0. 5 decreases the total number of valid samples only by 1. 4 % and 0. 6 by 4. 6 %. Further increase of the probability threshold decreases the number of valid samples significantly and is not advised. Change in the probability threshold mostly affects accuracy of young ice and first-year ice predictions-accuracy grows from 0.
Although it is impossible to validate all pixels inside fast ice areas, the presence of open water near coasts is obviously wrong and can be explained by very smooth and dark-looking fast ice. In contrast to the 2018-dataset, the model trained for the 2020-dataset is not tuned using the validation dataset. The validation dataset is used only to perform the early-stopping procedure (training stops after 15 epochs without improvement) aimed at reducing the computation time of the training. Experiments on the 2018-dataset showed that the early-stopping does not affect the final score (i. e. , there is no over-fitting): the validation dataset is thus still valid to compute relevant classification scores.
Sensitivity to Preprocessing Parameters In this section the effects of two critical parameters on the preprocessing summarized in Section 2. 3 is addressed. The first parameter is the size of the sub-images considered in the input of the neural network. A big size gives all the necessary information to classify but can add noise if a sub-image contains a mixture of classes. A small size of the sub-image gives more spatially accurate information but can fail to provide enough spatial context to the ice-chart pixel. The second parameter is the distance used to withdraw from the training set pixels that are too close to the border to another class. If this distance is too small, sub-images in the dataset can be misclassified or have mixed ice type within the sub-image. If this distance is too large, too many samples from the training set could be withdrawn.
25, 50 As mentioned in Section 2, one ice chart pixel has to match with one SAR sub-image. In consequence the pixel's resolution of ice chart image is 1 km for 25 × 25 SAR sub-images and is 2 km for 50 × 50 SAR sub-images. Thus a fair comparison of the class distance parameter has to be done in kilometers. Table  7  shows accuracy for different values of distance to the class border as well as SAR sub-image size: for the same kilometer distance, 50 × 50 images give the best results and for both image size, accuracy is better when border distance increases. Note the improvement of accuracy with the distance to the class border is due to two factors: (i) the training set includes less mixing pixels and so the training is not impaired by noisy labels; (ii) the validation dataset includes also less noisy labels, and so the performance is evaluated more robustly.
Impact of Thermal Noise and other Properties of Input Data Removal of thermal noise from SAR data  [15] [16] [17]  is a costly task and the sensitivity of our method to the noise in the SAR images was addressed. A CNN with the same architecture as on Figure  5  was trained on raw SAR images (with no denoising performed). The final accuracy is 0. 89, which is close to the accuracy obtained with denoised image 0. 92. The denoising allowed improvements mostly for the ice-free class, see for example, Figure  11. It shows that, even if denoising can improve the final accuracy, the selected neural network has the potential to process noisy satellite images which offers promising perspectives for the segmentation of SAR images. As can be seen from comparing the overall accuracy of CNN-2018 and CNN-2020 the impact of satellite data precision and texture noise removal algorithm is not significant.
Inconsistencies between ice charts were also observed in previous studies (see, for example, Figure  9  in  [15] ). The largest and most obvious of these inconsistencies can be corrected manually prior to CNN training but a lot will still remain in the training dataset. The CNN approach to sea ice type classification is split in two phases: (i) semi-automated training phase and (ii) fully automated application phase. The first phase consists of two major steps-data preparation and CNN training. Data preparation is a tedious and time consuming process that must include human judgment both for labeling data and for quality control. In our study we have used the NIC ice charts and overcame the first bottleneck-at least on our side no extra effort is needed for the labeling procedure. Quality control of the training data is also automated as much as possible using the "Distance to border" and "CNN segment size" filters (see Section 2.
It was also shown that a CNN can be applied to SAR data without thermal noise removal on the preprocessing step. Understandably, the classification accuracy decreases to 89% but remains reasonable. The main advantages of the new algorithm are the ability to classify several ice types, higher classification accuracy for each ice type and higher speed of processing than in the previous studies. The relative simplicity of the algorithm (both texture analysis and classification are performed by CNN) is also a benefit. In addition to providing ice type labels, the algorithm also derives the probability of belonging to a class. Uncertainty of the method can be derived from these probabilities and used in the assimilation of ice type in numerical models. Given the high accuracy and processing speed, the CNN-based algorithm will be proposed to the Copernicus Marine Environment Monitoring Service (CMEMS) for operational sea ice type retrieval for generating ice charts in the Arctic Ocean.
Phase contrast and fluorescence images of live cells were recorded every 3 min via automated time-lapse microscopy using an inverted microscope (AxioObserver or Nikon Eclipse Ti-E) with epifluorescence capabilities and a temperature-controlled stage (custombuilt, IGBMC). For candidate maintenance/inheritance analyses, inductions were fixed to 3 hrs for all strains, long enough to sufficiently induce Gal1 in all strains and observe differences to the WT, yet short enough for Gal1-GFP to degrade during the repressions, which were fixed to 4 hrs. Cell-tracking data processing and analysis. We used custom-made PhyloCell and Autotrack softwares (available on Github https://github. com/gcharvin) written in MATLAB (MathWorks) for image segmentation, cell-tracking, fluorescence measurements, and lineage analysis  (Goulev et al.
, 2017). Gal1 intensity. Gal1-GFP fluorescence was measured and normalized to cell area. Mother and daughter definitions. Nuclear division markers (NLS-tagged mCherry) were used to automatically define relationships between mother cells and their specific daughters and record their birthtime in PhyloCell. Mothers are defined as cells born before i1 and therefore present throughout both inductions. Calculations with daughter cells were restricted to cells born during r2 (born after i1 and before i2), though mothers could give rise to daughters at other times (not included in our analyses). Density estimates for delays. Density estimates used a Gaussian kernel with a bandwidth given by Scott's rule as 1/n 5 where n is the number of data points.
Screen data processing and analysis. Data measurements. For each strain the mean, median, and standard deviation of Gal1 intensity was monitored over time. All values were collected using the standard background subtraction method and are in arbitrary units. In addition, for each timepoint the percentage of cells that are expressing Gal1 was calculated using an intensity threshold of 150 a. u. When necessary, linear regression was used to adjust for any gradient in nutrients due to diffusion rates through each chemostat. Quality control. The whole microfluidics device was imaged during overnight growth in raf at 4x magnification. Microchambers that were not completely filled with cells by the start of the memory experiment were excluded from analysis. For quality control within the microfluidics device, any row of cells with markedly different Gal1 expression or growth rate was eliminated from analysis.
Intensity and induction timing analysis of screen data. For Gal1 expression level, we estimated the intensity of GFP fluorescence using a weighted linear fit. The weight w TP was necessary to avoid fitting out-of-focus images and relied on the number of properly defined cells NbC TP from the segmentation at the respective timepoint TP w TP = NbC 2 TP 50 2 + NbC 2 TP The fit used the data acquired between 30 min before and after the estimated timepoint to include 3 frames for each microchamber. To estimate Gal1 induction timing, we fit a smoothed cubic spline to timepoints after the start of the respective inductions. We used an intensity threshold to identify induced cells in i1 and defined a minimum percentage of induced cells i 0 , using the minimum percentage of expressing cells 1 hr prior to i1 (~0%), or the minimum percentage of expressing cells +/-20 min from the start of i2.
We used the mean of repeats from the 3 experiments to compute a local regression curve for each of the 4 data points (intensity and delay during i1 and i2) in each experiment. These curves were then used to standardize all individual microchambers towards an average experiment. Outlier detection (strains of interest). Our complex dataset justified testing a number of methods for outlier identification. We identified outliers in the 2D distributions of our unmerged and condensed datasets according to intensity and delay values by using a cutoffbased approach to detect strains that are repeatedly different from the norm. First, outlier cutoffs were made for i1 by combining a percentage-based cutoff with an interquartile range approach. For this, the percentage was set at the 2% and 98% quartile and r for the interquartile range was set as 1, ~2% for a normal distribution.
ACKNOWLEDGEMENTS We thank  Youlian Goulev  for support on the cell-tracking microfluidics setup and  Michael Strasser  for guidance on the modeling. We thank  Matthias Meurer/Michael Knop  and  Ben Timney/Michael Rout  for plasmids and  Charlie Boone  for yeast strains. Work in the R. S. laboratory was supported by the  DFG  through  SFB 1064  and  SFB 1309 , the  EpiTrio consortium as well as AmPro program  ( ZT0026 ) and the  Helmholtz Gesellschaft. A European Research Council (ERC)  starting grant, no. 260797 , supported work in the A. K laboratory. P. B. is a Marie Curie IIF fellow and an  EMBO  LTF fellow. J. B. and S. J. M. were supported by a SystemsX. chgrant  DynamiX-RTD  ( 2008/005 ) and EPFL. G. C. was supported by a grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Apparatus The experiment utilized a standard PC. Stimulus and response timing were controlled with E-Prime 2 software (Psychology Software Tools, Pittsburgh, PA). The experiment files, along with the raw data, participant averaged data, and R scripts are available on the Open Science Framework (https://osf. io/7fwae/). Participants responded to purple, orange, and grey stimuli with the "J," "K," and "L" keys, respectively, on an AZERTY keyboard. Although we did not enforce specific fingers for the three keys, all participants in this and related studies defaulted to the standard keyboard resting position (i. e. , right index on the J-key, right middle finger for the K-key, and right ring finger for the L-key).
Results Analyses focused on mean correct response times during the main phases of the experiment (i. e. , practice phase excluded). Trials for which participants did not respond before the 1500 ms deadline were excluded, but no other response time trims were performed (as has been our standard practice with this paradigm). Error data are not reported here given that they were far too noisy to produce anything meaningful and that the general length of the reported analyses was already long, but there were no speed-accuracy trade-offs and the error data are available for download (along with the response time data and R scripts for both dependent measures) on the Open Science Framework (https://osf. io/7fwae/). In all analyses, subblock was treated as a linear factor, which is more sensible than treating the sub-block as an unordered factor.
The only exception was the addition of some Bayes tests, which we added in response to editor feedback. All Bayes tests were conducted with the BayesFactor package in r with the default Cauchy prior and 100,000 recomputes to increase precision. The contingency effect as a function of the mega-blocks is presented in Figure  3  and the subblock means and standard errors are presented in Table  A1  for both response times and errors (see also the R scripts).
Funding Information This research was supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  (contract  ANR15-IDEX-0003 ) to  James R. Schmidt , Grant  BOF09/01M00209  of  Ghent University  to  Jan De Houwer , and Research Program G073317N of the  Research Foundation -Flanders (FWO) , and the  Research Fund of KU Leuven  ( C14/17/047 ) to Agnes Moors. Psychology,  60 ,  1367-1377. DOI: https://doi. org/10. 1080/17470210600955645Geukes, S. , Gaskell, M. G. , & Zwitserlood, P. (2015). Stroop effects from newly learned color words: Effects of memory consolidation and episodic context. Frontiers in Psychology, 6, Article 278. DOI: https:// doi. org/10. 3389/fpsyg. 2015. 00278Giesen, C. , Schmidt, J. R. ,  & Rothermund , K. (2020). The law of recency: An episodic stimulus-response retrieval account of habit acquisition.
QUANTIFICATION AND STATISTICAL ANALYSIS Volocity (PerkinElmer) was used for quantification of the paraspeckle size and sum intensity of smFISH signals with intensity and size threshold. Fiji software (NIH) was used for quantification of the Sx values (distance between the peak and peak) of the paraspeckles observed by SRM using a plot profile  (West et al. , 2016). The sizes of paraspeckles observed by EM were determined with AnalySIS (Olympus Soft Imaging Solutions). Prism 7 software (GraphPad) was used for graphing and statistical analysis.
Acknowledgments The authors thank  C. Fujikawa  and  A. Kubota  for technical support and the members of the  Hirose laboratory  for valuable discussions. This research was supported by  KAKENHI grants  from the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan  [to  T. Yamazaki  ( 17K15058 ,  19K06479 ,  19H05250 ),  T. Yamamoto  ( 19H05259 ,  18K03558 ), and  TH  ( 26113002 ,  17H03630 ,  17K19335 ,  19K22374 ,  20H00448 ,  20H05377 )],  JST ,  PRESTO  Grant Number  JPMJPR18KA  (to T. Yamamoto), the  Mochida Memorial Foundation for Medical and Pharmaceutical Research  (to T. Yamazaki), the  Naito Foundation  (to T. Yamazaki), the  Takeda Science Foundation  (to T. Yamazaki), and  Tokyo Biochemical Research Foundation  (to TH).
A simple way to address potential issues with authorship is to have a linked database or table where contributors can add and edit their contributions over time as the project evolves. Their entries in the database or table can be associated with, for example, their ORCID, for verification, and aligned with the CRediT scheme. As early as possible after establishing criteria for authorship and rules for identity verification, the core team should define the structure of the paper. This step will help simplify assigning participants to different sections and limit the number of people working on the same parts of the text. Part of this can involve assembling small working groups responsible for smaller modules of the project. This common understanding of the responsibility of each contributor can be effective in maintaining 'high-performing collaborative research teams'  (Cheruvelil et al.
Reflect on your choice of platforms Collaborative authoring A number of freely available authoring platforms and services facilitate real-time open and online collaboration on research articles and have a wide variety of functions and features (Table  2 ). The range of tools and services available to researchers changes constantly, and we anticipate that more software and platforms will emerge in the future as the value and nature of collaboration evolves. It is necessary to be aware of and distinguish between commercial versus free and open-source software (FOSS) services and services that target a mainstream audience versus those that were developed for researchers, with researchspecific features for data privacy and security, intellectual property protection measures, and licensing. Overleaf and Authorea are popular authoring tools specifically for the creation of scholarly works and subsequent online submission to a range of scholarly publishing outlets.
One additional benefit of using Overleaf or Authorea is the availability of templates for submission and reformatting to multiple academic journals. Preparing manuscripts for submission and reformatting the manuscript to meet the criteria of multiple journals are two tasks that can be performed in both platforms conveniently. Overleaf features in-built connectors to Zotero and other reference management systems, 2 while Authorea makes use of the cross-platform BibTeX format to integrate literature from the reference management tool of your choice. 3456  Due to its instant availability and the comfort of linking your Google account with other related services, and despite the public criticism and concerns surrounding issues of data usage and text mining for commercial purposes, Google Docs has become an increasingly popular authoring tool for researchers.
An interesting FOSS alternative to proprietary and commercial platforms like Authorea and Overleaf is CryptPad. With a promise to encrypt transmitted data, this office suite puts user privacy first and thus ensures that no third parties will be able to illicitly access what you and your group are doing. It features a whole collection of tools, including collaborative spreadsheet and rich text document editors similar to Google Docs; a Kanban task management board; and modules for small surveys and code sharing as well as online presentations and a whiteboard, which might come in handy for those wanting to use online technology to support their seminars; and more. However, CryptPad has a more minimalistic set of in-text editing features which can hinder some forms of collaborative authoring.
This can make working with more complex text formatting, image descriptions, or tables more challenging, and it can be hard to collaboratively work on projects that contain graphics and tables. GitHub and GitLab can also be used for collaborative authoring. As a noteworthy example of how textual collaboration in scholarly communication using GitHub works, see the The Journal of Open Source Software (JOSS), which primarily runs on the platform. Similarly building on a GitHub-based workflow, Manubot is a toolchain approach that allows for writing, tracking, and converting Markdown-based text into a variety of output formats via a collection of pre-configured tools such as Pandoc  (Himmelstein et al. 2019 ). We have not yet had the chance to try Manubot in more detail, but it appears to be an option for those technically savvy enough to play around with customizing and tweaking their Git workflow.
Our experience with GitHub Markdown, however, has been generally positive. For example, the Foundations for Open Scholarship Strategy Development document  (Tennant et al. 2019a ) was primarily authored using collaborative tools originally designed for software development and written in Markdown, and it was easy to publish this both as a dynamic webpage and as a preprint (see Recommendation 10). Last but not least, and somewhat closing the gap between collaborative text-editing and code and issue management via GitLab/GitHub, HackMD deserves a mention. For those who know how to write in Markdown,  7  HackMD offers a split-screen online editor in which Markdown input is directly rendered to standard text. Users can directly feed HackMD with Markdown files/templates from GitHub/Gitlab snippets (aka 'gists') or stored in Dropbox/Google Drive, and export options include HTML output (styled and raw), open document (LibreOffice) files (in beta), and exportation of projects to GitHub.
HackMD also offers Markdown templates for open online presentation slides 8  as well as a GitBook-style book template. 9 Pro tip: Using Zotero integration with groups When working on a text as a group, sometimes each contributor has their own set of references that they will want to add to the manuscript that is being written. For this, Zotero offers the 'Group' feature, which participating Zotero users can easily use to add references to their 'Group Library. ' This group collection of references, compiled collaboratively as a basis for referencing, can be shared with all contributors as well as readers, making this open data easily accessible for everybody. An example of a group Zotero library is one maintained by the Center for Open Science (https://perma. cc/64GV-2HAT) or the collaborative MOOP library at https://bit.
For example, within the Open Science MOOC (Massive Open Online Course), 18  some members are exploring a variety of options to link the requirements of translation management with the underlying open approach to content development already established via GitHub  (Tennant et al. 2019b ). Among the existing plethora of online translation management solutions out there, 19  they have found the localization management platforms Crowdin and Weblate to be particularly promising because these platforms offer free plans for dedicated FOSS projects. 20 Keep in mind, though, that both services are owned by for-profit companies, and the free open-source tiers might be discontinued in the future, so an option to export any project's data (translation files) should always inform the choice of translation platform.