dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , logistics studies' dominant language). Step 2. In this step, we select databases: First of all, Google Scholar and WoS, one of the famous and important databases, were used to search for a set of interest publications. To avoid missing any publication from peer-reviewed academic journals, the previous set was completed with publications from other databases of international editors, namely ScienceDirect, IEEEXplore, Taylor & Francis, Wiley Online Library, and Step 3. In this step, we define keywords and construct database queries: this task started with searching publications having the primary terms (or topic): Logistics, modeling, control, Petri nets, and (max, +) algebra. Afterwards, a refinement searching in which correlated keywords identified from the first review were added, namely models/ methods, supply chain management, optimization.
The case study As mentioned before, the proposed approach may be applied to a massive network with various sites (e. g. , suppliers, warehouses, customers). However, to simplify the approach, we present a simple case study to show the proposed models' effectiveness. More precisely, we present a simple supply chain (Fig. 4 ). The data of the considered network is presented in whats follows: number of sites is eight, the number of vehicles is four, and the capacity of these vehicles is 20 pallets. The waiting and traveling times (with hour as time unit) of vehicles are presented in Fig. 4. After a formal validation of the proposed TCPN models using CPN Tools, we implement the (max, +) algebra models in the Scilab environment. Indeed, the modularity of the models suggests using efficient software such as Scilab, which allows to model systems with a large number of variables.
A UGA-42 Firefly point scanning laser device (RAPP Optoelectronic) with a 365-nm laser was coupled to an inverted epifluorescence Zeiss Axio Observer Z1 microscope. The laser focus was calibrated using a chromium calibration slide (RAPP Optoelectronic) prior to each experiment. At 50% duty cycle (the power used for the cell experiments and the minimal output we could measure using the laser meter), the power of the laser was 660 nW (measured using a laser meter from LABMASTER®) with a laser spot size of 4. 4 μm 2 with the usage of an OD3 filter. This results in an irradiance of 15 mW/cm 2 (OD2: 150 mW/cm 2 ). The substrate underneath the cell was illuminated using a custom circular ROI with a diameter of 8. 86 μm (area = 62 μm 2 ) by using the SysCon software. The ROI was scanned with 5 scans per illumination (Duration 234.
5 V. Images were obtained with 2 × 2 binning and 2x analog gain every 30 s for 15 min. Images were captured using the Zen Blue software. Focal adhesion image processing and analysis. All image processing was done using the Fiji distribution of ImageJ 29 with the Morpholib plugin library  30. For the reference images (Fig. 2a  and b ), paxillin-RFP images were first filtered with a Gaussian Blur (sigma = 1), then the Grays LUT was inverted and contrast was enhanced. To analyze illuminated versus non-illuminated areas, first ROIs containing several FAs for these areas were cropped, and then each ROI was separately processed and analyzed. For focal adhesion segmentation, a custom ImageJ macro was developed. In short, images were first filtered using a Gaussian blur (sigma = 1. 5), then a 3D White top Hat filter (element = cube, xyz-radii were all set at 5) from the Morpholib plugin was applied, after which background was subtracted using a rolling ball of 20.
Then a mask was generated using the Otsu autothreshold. Small noise particles were then excluded by Area opening (pixel size = 20). The total size of all FAs was then measured using the resulting mask. The relative increase in total FA area was determined as total FA size at t/total FA size at t 0. Optimization of irradiation conditions in T cells. For T-cell activation, we employed a Zeiss Cell Observer HS system with a ×20 alpha objective and an AxioCam MRm Rev. 3. The DAPI channel (100% power, 10 mW/cm 2 ) was used for UV irradiation. A sequence of ten UV pulses with a duration of 1, 0. 5, or 2 s were applied within 1 min to activate rotation of the motor. The Ca 2+ fluorescent signal was followed for 15-20 min. Calcium imaging and data analysis. Jurkat T cells and primary T cells were loaded with Fluo-4/AM (1 μM) in serum-free RPMI-1640 media at room temperature for 30 min.
Subsequently, a constant flow of 2-15 μL/min was applied for 8 min. After 2 min of constant flow, the UV LED with a maximum intensity at a wavelength of 365 nm and a power of ~0. 75 W/cm 2 was switched on for 4 min. Force calibration was performed in DNA overstretching experiments with linear flow ramps from 0 to 1000 μL/min controlled via the syringe pump. The actual flow velocity was monitored via the height of the fluid in the reservoir (Fig. 3a ). TPM data analysis. The digital videos were analyzed using the open-source software ImageJ 1. 50i (Wayne Rasband, National Institute of Health, USA) and a Particle Tracker plugin for ImageJ written by Sbalzarini and Koumoutsakos. The Particle Tracker provides x and y positions of each individual bead for every frame. The ImageJ software includes all trajectories into a table that was further analyzed in MATLAB regarding their Brownian motion and bead displacement under fluid flow.
Before data analysis, all trajectories were drift-corrected using a master curve generated by averaging the trajectories of three to five immobile beads  31. The heterogeneity of the molecular motor attachment results in a statistical distribution of biotin-binding sites leading to variety of tethered particle motion. The Brownian motion of all beads in one frame was recorded for 60 s prior to every measurement. Only trajectories with a RMS position fluctuation above 100 nm were included in further analysis to omit unspecific beads with non-specific attachment. This lower limit reflects an effective tether length L = (3*〈RMS〉²)/2b 32 of above 375 nm assuming a Kuhn-length of b ¼40 nm. Surface preparation for AFM. The Au(111) was purchased from Arrandee Metal GmbH + Co. KG in Germany and used as substrate for AFM experiments.
Acknowledgements A. d. C. , B. Q. and M. H. acknowledge support from  Deutsche Forschungsgemeinschaft  ( SFB 1027 ). M. H. acknowledges support from the  Alexander von Humboldt Foundation and Deutsche Forschungsgemeinschaft  ( SPP 1782 ). A. d. C. acknowledges support from  European Union  ( FET Mechanocontrol ). N. G. acknowledges support from the  Laboratory of Excellence for Complex Chemical Systems (LabEx CSC). A. J. G. acknowledges support from the  US National Institute of Health  ( R01 EB024322 ). We thank  Dr. Emmanuel Terriac  and  Dr. Aleeza Farrukh  for their assistance with imaging.
A series of machine learning algorithms has been implemented and bundled together with several "utility" functions in a single package for the Julia programming language. Currently, algorithms are available in the areas of classification (perceptron, kernel perceptron, pegasos), neural networks (feed-forward), clustering (kmeans, kmenoids, gmm, missing values imputation) and decision trees/random forests. Development of these algorithms started following the theoretical notes of the MOOC class "Machine Learning with Python: from Linear Models to Deep Learning" from MITx/edX. This paper presents the motivations and the general approach of the package and gives an overview of its organisation. We refer the reader to the package documentation for instructions on how to use the various algorithms provided or to the MOOC notes available on GitHub (Lobianco, 2020) for their mathematical backgrounds.
Motivations and objectives BetaML provides one of the simplest way to run ML algorithms in Julia. While many packages already implement specific ML algorithms in Julia, these are fragmented across different packages and often value performances more than usability. For example the popular Deep Learning library Flux (Mike  Innes, 2018) , while extremely performant and flexible, adopts some designing choices that for a beginner could appear odd, like avoiding the neural network object from the training process, or requiring all parameters to be explicitly defined. In BetaML we made the choice to allow the user to experiment with the hyper-parameters of the algorithms, learning them one step at the time. Hence for most functions we provide reasonable default parameters that can be overridden when needed.
For example, modelling, training and collecting predictions from a feed-forward artificial neural network with one hidden layer can be as simple as: using BetaML. Nn mynn = buildNetwork  ([DenseLayer(nIn,nHidden) , DenseLayer(nHidden,nOut)], squaredCost) train!(mynn,xtrain,ytrain) ytrain_est = predict(mynn,xtrain) ytest_est = predict(mynn,xtest) While much better results can be obtained (in general) by scaling the variables and/or tuning their activation functions, the training parameters or the optimisation algorithm, this code snippet already runs the model using common practices like random mini-batches. Still BetaML offers a fair level of flexibility. As we didn't aim for heavy optimisation, we were able to keep the API (Application Programming Interface) both beginner-friendly and flexible.
If a great level of flexibility can already be achieved by just employing the full set of model parameters, the greatest flexibility is obtained by customising BetaML and writing, for example, its own neural network layer type (by subclassing AbstractLayer), its own sampler (by subclassing AbstractDataSampler) or its own mixture component (by subclassing AbstractMixture). While the library is designed for Julia users, the documentation provides examples for using the package from R or Python (thanks to JuliaCall  (Li, 2019)  and PyJulia  (Arakaki et al. , 2020).
respectively). A few packages try to provide a common Julia framework of the various ML algorithms available in Julia, like ScikitLearn. jl (St-Jean & Okon, 2020), AutoMLPipeline. jl (Palmes, 2020)  or MLJ. jl  (Blaom et al. , 2019). They build up on existing Julia (and/or Python) ML specialised packages. While avoiding the problem of "reinventing the wheel," the wrapping level may unintentionally introduces complications for the end-user, like the need to load the models and learn framework-specific concepts as model or machine in MLJ or @pipeline and fit_transform! in AutoMLPipeline. We chose instead to bundle the main ML algorithms directly within the package. This offers a complementary approach that we feel it is more beginner-friendly. We believe that the BetaML flexibility and simplicity, together with the efficiency and usability of a Just in Time compiled language like Julia and the convenience of having several ML algorithms and data-science utilities all in the same package, will support the needs of that community of students and researchers that, contrary to industrial practitioners or computer science specialists, don't necessarily need to work with very large datasets that don't fit in memory or algorithms that require distributed computation.
Package organisation The BetaML toolkit is currently composed of 5 modules: Utils provides common data-science utility functions to be used in the other modules, Perceptron supplies linear and non-linear classifiers based on the classical Perceptron algorithm, Nn allows implementing and training artificial neural networks, Clustering includes several clustering algorithms and missing value attribution / collaborative filtering algorithms based on clustering and finally Trees implements decision trees classifiers/regressors together with their most common ensemble method, random forests. All sub-module functionalities are re-exported at the root level, so the user doesn't need to deal with the sub-modules, but just load the main BetaML module.
Often ML algorithms work better if the data is normalised or dimensions are reduced to those explaining the greatest extent of data variability. This is the purpose of the functions scale and pca respectively. scale scales the data to µ = 0 and σ = 1, optionally skipping dimensions that don't need to be normalised (like categorical ones). The related function getScaleFactors saves the scaling factors so that inverse scaling (typically for the predictions of the ML algorithm) can be applied. pca performs Principal Component Analysis, where the user can specify either the number of dimensions to retain or the maximum approximation error that she/he is willing to accept, either ex-ante or ex-post, after having analysed the distribution of the explained variance by number of dimensions. Other "general support" functions provided are oneHotEncoder, batch, partition and crossValidation.
Concerning the last category, several functions are provided to assess the goodness of fit of a single datapoint or of the whole dataset, whether the output of the ML algorithm is in R n or categorical. Notably, accuracy provides categorical accuracy given a probabilistic prediction (as PMF) of a datapoint and ConfusionMatrix allows a detailed analysis of categorical predictions. Finally, the Bayesian Information Criterion bic and Akaike Information Criterion aic functions can be used for regularisation.
The Nn module Artificial neural networks can be implemented using the functions provided by the Nn module. Currently only feed-forward networks for regression or classification tasks are fully provided, but more complex layers (convolutional, pooling, recursive, …) can be eventually defined and implemented directly by the user. The instantiation of the layers required by the network can be done indeed either using one of the layer provided (DenseLayer, DenseNoBiasLayer or VectorFunctionLayer, the latter one being a parameterless layer whose activation function, like softMax or pool1d, is applied to the ensemble of the neurons rather than individually on each of them) or by creating a user-defined layer by subclassing the AbstractLayer type and implementing the functions forward, backward, getParams, getGradient, setParams and size.
The Trees module Like for the other modules the two algorithms provided by the Trees module (decision trees and random forests) have an API that tries to maximise the flexibility and user-friendliness: users can train a tree (forest) by just using buildTree(xtrain,ytrain) (buildForest(xt rain,ytrain)) and then obtain the predictions with predict([trained tree or forest object],ytrain). The nature of the task (classification or regression) is automatically determined by the numerical nature of the training labels but it can be overridden by the user, together with many other parameters. Support for missing data and the direct usage of mixed categorical and numerical dimensions in the data (without the need to encode the categories) make the algorithms of the Trees module very convenient to use.
Quantification of Receptor Density The cells were detached using a 10 mM EDTA solution (Merck, Darmstadt, Germany) at 4 • C and were stained with a Phycoerythrin (PE)-labeled anti-EpCAM antibody (Abcam, Cambridge, MA, USA) or PE-labeled anti-HER2 antibody (Abcam, Cambridge, MA, USA). A PE-labeled IgG1 isotype control antibody (Life Technologies Carlsbad, CA, USA) was used as a negative control. The fluorescence intensity for each antibody was measured on a MACSQuant Analyzer 10 flow cytometer (Miltenyi, Bergisch Gladbach, Germany), and a calibration curve was generated using the BD QuantiBrite™ PE fluorescence quantitation kit (BD Biosciences, Franklin Lakes, NJ, USA), according to the manufacturer's instructions. The data were analyzed with FlowJo 10. 0. 8rl (BD Life Sciences, Ashland, OR, USA).
Determination of Binding of DARPin to Cell Surface Receptors Calu-3, BT-474, and IMR-90 cells were seeded in 24-well culture plates. The cells were grown overnight and subsequently incubated for 2 h with Alexa Fluor 680-labeled anti-EpCAM or anti-HER2 DARPins at 37 • C. The cells were then washed twice with PBS and detached with PBS containing 5 mM EDTA for Calu-3 and BT-474. The IMR-90 cells required detachment with trypsin. The cells were centrifuged at 300× g for 3 min and resuspended in PBS. The cells were measured using the MACSQuant Analyzer 10 flow cytometer (Miltenyi, Bergisch Gladbach, Germany), and the data were analyzed with FlowJo 10. 0. 8rl (BD Life Sciences, Ashland, OR, USA). [33] , with the exception that a 2YT medium (16 g•L peptone, 10 g•L yeast extract, 5 g•L NaCl) containing 50 μg mL -1 ampicillin (Sigma-Aldrich, St.
Quantification of DARPin Penetration Depth in Tumor Spheroids A python-based macro for Fiji  [34]  was created and used to quantify the penetration depth of the fluorescently labeled DARPins into the BT-474 spheroids (Supplementary Methods). The slice representing the widest cross-section, and hence the maximum distance from spheroid rim to its center, was used for the quantification of penetration. First, the region of interest (ROI) was manually selected around the spheroid. After determining the ROI center, 360 radial lines covering 360 • were drawn from the center to the border of the ROI. Next, a Gaussian blur was applied before determining the signal intensity along the lines. The maximum intensity values determined for 360 lines were averaged. The half-maximal intensity was calculated, which was set as the rim of the spheroid, and was used to determine the distance of penetration.
After 48 h, protein diffusion through the Matrigel matrix and the specific targeting of cancer cells was investigated by adding Alexa Fluor 680-labeled DARPins in one of the side channels and filling up the two reservoirs with the exact same volume of medium immediately before imaging. Using the LASX software (Leica Microsystems, Wetzlar, Germany) with a Leica TCS SP8 confocal microscope (Leica Microsystems, Wetzlar, Germany) equipped with an HCX PL APO 10x/0. 40 dry objective (Leica Microsystems, Wetzlar, Germany), one position per chip was selected and time lapses were recorded by taking an image of each marked position every 1 min for 1 h. For the image analyses and subsequent comparisons with the simulated data, Fiji was used. The Matrigel region of the middle channel was manually selected and plot profiles of fluorescence intensity across this channel were generated for the images at 0, 15, 30, 45, and 60 min.
Mathematical Model of DARPins Diffusion In Vitro A binding-diffusion mathematical model was implemented by introducing the custom governing equations (Equations (  1 ) to (  3 )) in the finite element software COMSOL Multiphysics v5. 5 (Stockholm, Sweden) using the equation-based interface. A schematic of the input variables related to the therapy and the tissue, as well as the modelled outcomes, is given in Figure  S4. The underlying coupled equations were initially reported by Thurber et al. [14]  and Torres et al. [28] , and describe the kinetics of unbound DARPin (1), receptor (2), and DARPin-receptor complex (3), respectively.
226 (8. 230)  [69, 70]  using the underlying event tune CUETP8M2T4  [63]  for SM tt events for the 2016 (2017, 2018) period, the CUETP8M1  [71]  tune for all other background and signal events in the 2016 period, and the CP5 tune  [64]  for all background and signal events of the 2017 and 2018 periods. The nominal top quark mass is 172. 5 GeV in all the samples. The GEANT4 package  [72]  is used to simulate the CMS detector for samples of the SM processes, the tt + DM signal processes, and SUSY signal samples where m t 1 m χ 0 1 is close to the top quark mass. The CMS fast simulation program  [73, 74]  is used to simulate the detector response for the remaining signal samples. The effect of additional interactions in the same event (referred to as pileup) is accounted for by simulating additional minimum bias interactions for each hard scattering event.
The observed distribution of the number of pileup interactions, which has an average of 23 and 32 collisions per bunch crossing for the 2016 period, and for the 2017 and 2018 periods, respectively, is reproduced by the simulation. Simulated background events are normalized according to the integrated luminosity and the theoretical cross section of each process. The latter are computed at next-to-next-to-leading order (NNLO) in QCD for DY  [75] , approximately NNLO for tW  [76] , and NLO for WW, WZ, ZZ  [77] , ttW and ttZ  [78]. For the normalization of the simulated tt sample, the full NNLO plus next-to-next-to-leading logarithmic (NNLL) accurate calculation  [79] , performed with the TOP++ 2. 0 program  [80] , is used. The PDF uncertainties are added in quadrature to the uncertainty associated with the strong coupling constant (α S ) to obtain a tt production cross section of 832 +20 -29 (scale) ± 35 (PDF+α S ) pb assuming m t = 172.
4. The jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be, on average, within 5 to 10% of the generated momentum over the whole p T spectrum and detector acceptance. Additional pp interactions within the same or nearby bunch crossings can contribute with additional tracks and calorimetric energy depositions to the jet momentum. To mitigate this effect, charged particles identified as originating from pileup vertices are discarded, and an offset correction is applied to correct for the contribution from neutral particles  [94]. Jet energy corrections are derived from simulation to bring the energy of a jet measured from the detector response to that of a particle-level jet on average. In situ measurements of the momentum balance in dijet, photon+jets, Z+jets, and multijet events are used to account for any residual differences in jet energy scale between data and simulation  [95].
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP, CIN-.
Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4. 0 mm on average with either architecture and the heavy preprocessing scheme.