dataesr/scientific-paragraphs-categorization huggingface dataset:
Moderating Variables: Data for the moderating variables have been gleaned from Compustat. The firm innovativeness construct has been defined and operationalized in multiple ways among earlier studies  (Kotabe et al. , 2002; Rubera & Kirca, 2012). In general, firm innovativeness is defined as a firm's receptivity and propensity to explore new ideas as well as the adoption of new processes and practices that lead to reinvention and development of new ways of doing things  (Lin, Lee, & Hung, 2006; Rubera & Kirca, 2012). One of the widely used approaches to measuring firm innovativeness is inputs such as R&D expenditure and patents  (Rubera & Kirca, 2012). Innovativeness inputs may not always turn into increased revenues because R&D expenditures do not always turn into actual products. Nonetheless, scholars argue that money spent on R&D has an impact on profitability  (McAlister, Srinivasan, & Kim, 2007; Rubera & Kirca, 2012)  because firms that invest more in R&D show greater dynamic efficiency and greater flexibility compared to firms that with fewer investments in R&D  (McAlister et al.
Furthermore, higher advertising intensity assists the focal firm in cutting through the message clutter in a given industry. That is, advertising intensity reinforces customers' awareness of the focal firm's sociallyresponsible initiatives  (Servaes & Tamayo, 2013; Rahman et al. , 2017). Advertising intensity was operationalized as the ratio of the yearly dollar amount of advertising expenditure to the yearly dollar amount of sales revenue (R. Zhang, Zhu, Yue, & Zhu, 2010; Huang & Wei, 2012; Servaes & Tamayo, 2013). 4. 1. 4. Control Variables. Data for control variables were obtained from Compustat. As firm performance is affected by other activities beyond green performance, a set of firm and industry control variables were introduced, drawn from the firm performance literature. This study included the following control variables: firm size, brand equity, leverage and industry HHI (Herfindahl Hirschman Index).
Sample size and sample period The sampling frame for this study was the green ranking of the top US firms published by Newsweek. The sample for this study covers a panel of 97 firms over the period 2010 to 2015, except for the year 2013 in which Newsweek did not publish a green ranking. Financial firms were excluded due to differences in financial reporting policy between financial and non-financial firms. 0 F 1  We also excluded firms that are not consistently present in the green ranking during the analysis period. We also excluded firms for which data for most of our key variables were not available in Compustat. 1 F 2  For instance, data on our moderating variables were not available for some firms/years. Combining both Newsweek's green score and Compustat databases left us with a maximum of 497 firm-year observations.
g. ,  Servaes & Tamayo, 2013). However, estimating panel data with OLS may result in biased estimates because of unobserved heterogeneity  (George, 2005). While the standard random effect or fixed effects model can capture these relationships, these models do not account for autocorrelation, heteroskedasticity and cross-sectional dependence in the dataset  (George, 2005). Hence, we used the feasible generalized least square (FGLS) estimation technique because it provides reliable estimates in the presence of heteroskedasticity, autocorrelation and cross-sectional dependence, which are likely to be present in a panel dataset such as ours  (George, 2005). To examine whether the results of this study are methodological artifacts, we carried out several robustness tests including the use of alternative estimation methods.
Data were collected from www. electoral-vote. com. This variable was used as a dummy variable equaling 1 for a Blue State and 0 for a Red State. Our results with the two alternative estimation techniques remain consistent with the main results, thereby providing support for the robustness of our findings. These results are available on request from the authors. with similar prior studies (e. g. ,  Servaes & Tamayo, 2013) , we replaced the missing values of advertising and R&D expenditure with zero and re-estimated the model (M1). The results reported in Panel B of Table  5  show that our main findings remain qualitatively unchanged. As an additional test for robustness regarding control variables, we followed earlier studies (e. g. ,  Servaes & Tamayo, 2013)  and incorporated three additional control variables, namely.
Ultrathin sections (50nm) were deposited onto formvarcoated copper grids (Plano) and postcontrasted using 1% uranyl acetate in water and ultrostain (Leica). TEM images were acquired on a JEM 1400plus (JEOL) using the TEMCenter and tile scans with the ShotMeister software packages (JEOL), respectively. Imaging flow cytometry and data analysis. 5x10 6 cells were stained with appropriate antibodies for 20min on ice in PBS + 2% FCS and analyzed on an ImageStream X MKII imaging flow cytometer (Merck). Mfge8-eGFP+ cells were gated using the IDEAS software. Then TIF-images of Mfge8-eGFP + cells from each sample were exported (16bit, raw) and analyzed by the CAE using a graphical user interface. The results were stored in two separated *. pop files containing the object numbers of apoptotic and EVdecorated cells.
; https://doi. org/10. 1101/2020. 04. 14. 040691 doi: bioRxiv preprint labeling kit. The cell suspension was mixed with an equal volume of the labeling solution in the dilution buffer and incubated for 5 min at RT. Labeling reaction was stopped by addition of 2 ml fetal bovine serum (FBS) followed by with washing complete DMEM (10% FBS, 1% Penicillin). To induce apoptosis, cells were treated with 1μg/ml of Staurosporine (Sigma Aldrich) in serum free DMEM for 2 hours at 37°c followed by three washes. Cells were removed by centrifugation (500g). To collect PKH26-labeled vesicles, including apoptotic bodies, supernatant was ultra-centrifuged at 100,000g for 90 min. Prior to injection into mice, vesicles were resuspended in PBS. Data sets used for deep learning. All datasets examined in this study were acquired using the ImageStream X MKII (Luminex).
For the machine learning approach only brightfield images and MFG-E8-eGFP + or PKH26 + fluorescent images were used. All images were cropped to 32x32 pixels and exported as 16-bit raw TIF images. No further pre-processing was performed on the pixel intensities (e. g. normalization or scaling). The in vitro annotated training dataset D1 consists of 27639 cells (27224 apoptotic, 415 EV + ). The apoptotic cells in this dataset were stained with MFG-E8-eGFP in vitro, while the 415 EV + cells were prepared from splenocytes after injection of PKH26-labeled vesicles. The in vivo annotated dataset D2 consists of 200 cells (100 apoptotic, 100 EV + ). The M4 in vivo dataset consists of 382 cells (199 apoptotic, 183 EV + ). The M1, M2, and M3 datasets were BM cells acquired from 3 irradiated mice and consist of 14922, 16545 and 17111 unannotated cells, respectively.
The M5 and M6 datasets were acquired from BM of two non-irradiated mice and consist of 5805 and 5046 unannotated cells, respectively. Datasets D1 and D2 were imaged with a 40x objective, while datasets M1, M2, M3, M4, M5 and M6 were imaged with a 60x objective. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 14, 2020. ; https://doi. org/10. 1101/2020. 04. 14. 040691 doi: bioRxiv preprint Data analysis strategy. A novel pipeline combining unsupervised deep learning with supervised classification is used for cell classification, and compared to deep learning and classical feature based classification.
Random Forest: The scikit-learn  (Pedregosa et al. , 2011)  Python implementation of the Random Forest  (Breiman, 2001)  algorithm was used. The number of trees (n_estimators) was set to 1000, while the number of features to assess at each split (max_features) was set to 'sqrt'. In all subsequent experiments when we refer to CAE or CP accuracy, we mean the accuracy obtained by a Random Forest trained on the pretrained CAE (CAE-RF model) features or CP features (CP-RF model), respectively.
An interpretable deep learning approach is able to discriminate EV-decorated cells from dying cells. The imaging analysis software IDEAS is very powerful in extracting and identifying features that help to discriminate different cell subsets  (George et al. , 2004). To generate a mask for separation of MFG-E8-eGFP + cells into PS + EV + live or PS + apoptotic cells, we manually selected 50 images of each type of MFG-E8-eGFP + cells and analyzed their brightfield and fluorescence characteristics (Suppl. Fig. 3A ). Based on the manually selected MFG-E8-eGFP + apoptotic and EV + cells we generated gates that included the was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 14, 2020.
2B ). We next tested three different machine learning approaches with these data: (i) a Convolutional Neural Network (CNN) for imaging flow cytometry  (Eulenberg et al. , 2017) , (ii) our proposed method CAE-RF (a classifier trained on features learned by a CAE as displayed in the scheme of Fig. 2 (C, D ) (iii) CP-RF, a classifier trained on pre-defined Cell Profiler features  (Blasi et al. , 2016). In order to estimate the effect of inter-experiment batch effects on classification performance, all methods were tested twice on the same in vivo stained dataset (manually selected apoptotic and EV + cells from irradiated mice) and their performance was assessed using the Area Under of the receiver operating characteristic curve (AUC)  (Hastie et al. , 2009). During the first trial both, the training (MFG-E8-eGFP + BM cells from irradiated mice) and test experiments were stained in vivo.
For classification MFG-E8 fluorescence is more important than brightfield. The CAE-RF and CP-RF methods were trained using 5-fold cross validation  (Hastie et al. , 2009). Both CAE-RF and CP-RF agreed that features derived from the fluorescence channel were more important than brightfield features, for the task of apoptotic cell was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 14, 2020. ; https://doi. org/10. 1101/2020. 04. 14. 040691 doi: bioRxiv preprint identification (Fig. 3A ). Quantitative assessment of CAE-RF performance was Error bars correspond to 95% Wilson confidence intervals (nM2=16545, nM3=17111, nM5=5805, nM6=5046). A subset of cells was annotated manually for each dataset and sorting was performed using the CAE-RF (C) and IDEAS gating (D).
"Unknown" cells fail to lie on the apoptotic or EV + gate using IDEAS gating. The classification accuracy reported in parentheses for each confusion matrix corresponds to the accuracy if "unknown" cells are omitted. done first by training on in vivo stained BM cells from irradiated mice. Then it was used to identify apoptotic and non-apoptotic cells in new experiments with data from irradiated (Fig. 3B ; M2, M3) and non-irradiated mice (Fig. 3B ; M5, M6). The performance of CAE-RF sorting was compared to standard gating on manually defined features (Fig. 3B ). Both methods agree that more apoptotic cells are present in irradiated than in non-irradiated mice (Fig  3B ). Subsequently, a subset of cells was manually annotated for each dataset, to quantitatively assess the classification performance of both methods CAE-RF (Fig.
22 Then necrotic cells (live/dead + ) were excluded from further analysis (Suppl. Fig. 5A ). MFG-E8-eGFP + cells were gated and their TIF images (16-bit, raw) exported using the IDEAS software. CAE-RF results with the classification apoptotic/EV + were re-imported into IDEAS and separate fcs-files containing all cells or only MFG-E8-eGFP + /apoptotic cells and MFG-E8-eGFP + /EV + cells were generated for further analysis in FlowJo. CD11b + cells were less decreased, as they have lower sensitivity to g-irradiation (Rodrigues-Moreira et al. , 2017) (Fig. 4D ). However, also in the CD11b + myeloid compartment, the frequencies of dying cells increased significantly upon irradiation (Fig. 4C, D ). Frequencies of EV + live cells also increased significantly in both, CD11b + and B220 + cells during irradiation (Fig.
The copyright holder for this preprint (which this version posted April 14, 2020. ; https://doi. org/10. 1101/2020. 04. 14. 040691 doi: bioRxiv preprint To determine if these APC-molecules co-localized with MFG-E8-eGFP + EVs or showed a different distribution on the T cell surface, we used the "bright detail similarity" (BDS) feature, a standard co-localization method provided by the IDEAS software. Here, cells with a score <0. 7 (Fig. 7B ; left panel, BDS <0. 7) showed no or weak co-localization, while cells with a score >0. 7 (Fig. 7B ; right panel, BDS >0. 7) showed substantial co-localization. Analysis of the BDS score revealed that on 73% of all EV + CD9/63 + CD8 + T cells MFG-E8-eGFP and CD9/63 co-localized on the same EVs (Fig. 7B ). We found similarly high frequencies of CD8 + T cells with CD54 (82%), MHCII (66%) and CD86 (86%) co-localizing with MFG-E8-eGFP on the same EVs (Fig.
(E) Left: All models were trained on an in vivo stained dataset of 401 cells (M4), then tested on an independent in vivo stained dataset of 200 cells (D2). Right: the same models were trained on a new dataset of 27,639 cells (D1), where the apoptotic cells were stained in vitro, introducing a batch effect. Next, they were tested on the same 200 cell dataset as before (D2). Sorting performance is displayed as area under curve (AUC). (F) Demonstration of the batch effect introduced by in vitro staining of apoptotic cells. Right column: random subset of in vivo stained apoptotic cells. Left column: random subset of in vitro stained apoptotic cells. In vitro stained cells fluorescent staining is evenly distributed inside the cell, while in vivo stained cells exhibit more complex shapes and abnormalities in the distribution of the fluorescent dye.
ACKNOWLEDGEMENTS We acknowledge the  Core Facility Flow Cytometry at the Biomedical Center, Ludwig-Maxmilians-Universität München , for providing the ImageStream X MKII imaging flow cytometer. N. K. C. , A. L. , T. K. were supported by a  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) fellowship through the  Graduate School of Quantitative Biosciences Munich (QBM)  and N. K. C. is supported additionally through the  School of Life Sciences Weihenstephan, Technical University of Munich, Germany. F. J. T. acknowledges financial support by the  Graduate School QBM , the  DFG  within the  Collaborative Research Centre (CRC )  1243  (Subproject  A17 ), by the  Helmholtz Association  ( Incubator grant   sparse2big , grant #  ZT-I-0007 ), by the  BMBF  (grant#  01IS18036A  and grant#  01IS18053A ) and by the  Chan Zuckerberg Initiative DAF (advised fund of Silicon Valley Community Foundation ,  182835 ).
L183 data The single pointing observations of L183 allowed the detection (above 3. 5σ) of more than 100 lines from 46 different molecular species, including iCOMs, carbon chains, molecular ions, and deuterium substituted species (see Table  1 ). The data processing, analysis, and line identification through the CDMS  (Müller et al. 2005)  and JPL  (Pickett et al. 1998 ) molecular spectral line databases, were performed using the GILDAS 1 and the CASSIS 2 software. For the species where more than two transitions have been observed (see Table  B. 1), the analysis was carried out through a Markov chain Monte Carlo (MCMC) method implemented within CASSIS  (Vastel et al. 2018b ). The MCMC method consists of an iterative process that goes through all the parameter space (column density, excitation temperature, source size, linewidth, and v lsr ) with a random walk and heads into the solutions, by a χ 2 minimisation.
For H 13 CN, N 2 H + , and NH 2 D, the opacity was obtained directly using the hyperfine splitting routine (HFS) implemented in the GILDAS/CLASS software. Under the assumption that all the hyperfine components have the same excitation temperature, the same width, and do not overlap, the HFS fitting routine provides the linewidth and the opacity, from which the T ex can   6 ), below). When two lines were detected for the same molecular species, the column density derived for the transition involving the lower E up was chosen as representative, and, in case of similar E up , the one with the lowest opacity. The error on the column density was obtained from σ N tot = σ W N tot /W, where W is the integrated intensity and its associated error, σ W = (cal × W) 2 + [rms × √ 2 × FWHM × ∆v] 2 , (5) is provided directly from the analysis with CASSIS.
Physical model of the L183 core The aforementioned astrochemical model is based on a spherically symmetric representation of the density and temperature distributions in the central part of L183 based on Herschel/SPIRE maps of the cloud at 250, 350, and 500 µm  (Griffin et al. 2010). The cloud has been mapped as part of the Herschel photometric survey of planck-detected Galactic cold clouds  (Juvela et al. 2010 ). We used level 2 pipeline-reduced data, downloaded from the Herschel Science Archive (HSA), created using the Standard Product Generation (SPG) software version 14. 1. 0. The SPIRE maps were used to calculate the dust colour temperature, T C , and the H 2 column density, N(H 2 ), distributions. A modified blackbody function with the dust emissivity spectra index β = 1. 5, as used previously for L1544, was fitted to each pixel after smoothing the 250 and 350 µm images to the resolution of the 500 µm image (∼40 ), and resampling all images to the same grid.
A second analysis using the radiative transfer code MOLLIE  (Keto & Rybicki 2010)  was hence performed to check the results obtained in the LTE approximation. De-excitation rates for the collisional systems para-H 2 /A-CH 3 OH and para-H 2 /E-CH 3 OH  (Rabli & Flower 2010a) , available in the LAMDA database  (Schöier et al. 2005) , were then used for the statistical equilibrium calculation: the lack of data for ortho-H 2 does not hamper our modelling, because of the very low H 2 ortho-to-para ratio (OPR) expected in PSCs (e. g. Sipilä et al. 2013 ). Also, it should be noted that the data in the LAMDA database are available for kinetic temperatures as low as 10 K. A and E symmetries of methanol were treated separately in the simulations, considering rotational levels below 38 and 45 K, respectively.
As done previously  (Bizzocchi et al. 2014 ), the initial CH 3 OH abundance, which is the input parameter to MOLLIE, is translated to a radial abundance profile: the A-CH 3 OH radial abundance corresponding to the best-fit input abundance is shown in Fig. 5  as an example. The resulting beam-averaged value of the CH 3 OH column density (including both A and E species) is (4. 9 ± 0. 7) × 10 13 cm -2 , assuming a 15% calibration error; despite the scatter of points in the rotational diagram described above, this value is in good agreement with the one found in the LTE approximation (roughly a factor 2) (see Fig. 4 ). From the spectral distribution of the emerging radiation field data cube, the modelled spectra were extracted from the central pixel, convolved with the 30m telescope main beam, and compared to the observations.
7 , this line is predicted to be considerably sub-thermally excited, even at the high gas density of the cloud centre. This is due to an artefact of the coupled state approximation used in the calculation of the collisional rates  (Rabli & Flower 2010b) ; the rate coefficients for the collisional transition connecting the state 0 0,0 to the lower 1 1,1 are set to zero in the dataset used  (Rabli & Flower 2010a ), but it is very likely that this transition has a small, but non-zero, rate coefficient. Post-alteration of this collisional rate, even by a fraction of the 2 1,2 -1 1,1 upward rate (75%), makes it possible to better model the emission of the 0 0,0 -1 1,1 rotational transition, without altering the modelling of the other transitions considered above (see Figs. A. 1 and A. 2; see also an extended discussion in  Bizzocchi et al.
Acknowledgements. The authors thank  François Dulieu  and  Jacob C. Laas  for fruitful comments on the SO 2 chemistry. Evelyne Roueff  is also gratefully acknowledged for discussions on reaction rates of some complex organic molecules in our model. We also thank  Elena Redaelli  for providing the L1544 SPIRE density profile. The work by A. I. V. is supported by  Latvian Council of Science  via the Project  lzp-2018/1-0170.
Policy Rollouts: Task-Set Execution Given a parameterized policy, π θ , we wish to determine the evolution of the robot's states and actions. The policy is therefore rolled-out, meaning that the task-set is executed on the robot, either in simulation or reality, and the state and action data are recorded, {S, A} = rollout(π θ ) , (5) where S and A are the concatenations of the states and actions over the entire rollout. This implies that the full control architecture, as described in section 2. 1, is employed until the task execution is complete, meaning that the execution must occur in a finite amount of time and should be finished in the duration dictated by the CoM policy ρ( CoM ), t CoM π. However, if the robot falls, then π CoM will not be completed in t CoM π. The policy rollouts are therefore assigned a maximum execution time, t max > t CoM π , to allow for possible delays in the task execution but to avoid recording failed rollouts indefinitely.
With the π * θ from simulation as a starting point, the PS is continued by performing rollouts on the real iCub. For these rollouts we look at two cases. In the first, the BO surrogate function training is bootstrapped with training data from the simulated rollouts and further trained on data from the real rollouts. In the second nonbootstrapped case, the surrogate function is trained only using the real rollout data. For both cases, the π * θ from the simulation rollouts is used as the initial policy for the real rollouts, warm starting the PS. To limit the number of falls, the BO search space bounds are restricted to a 10cm cube around the initial θ * mid , for the real rollouts. Ten rollouts are performed for both cases. All code and data for these experiments is open-source and can be found here: https://github.
ACKNOWLEDGMENTS This work was partially supported by the  European Commission , within the  CoDyCo  project ( FP7- ICT-2011-9 , No. 600716 ). The authors would like to thank  Jorhabib Eljaik  ( Sorbonne Université ) and  Gabriele Nava ,  Stefano Dafarra ,  Francesco Romano ,  Daniele Pucci ,  Silvio Traversaro ,  Francesco Nori  ( IIT ) for their great help and support in the realization of the experiments related to this work. This manuscript has been released as a Pre-Print at https://hal. archives-ouvertes. fr/hal-01620370/document.
J. S. H is supported by INSERM, the French National Research  Agency  (NADHeart  ANR-17-CE17-0015-02 ,  PACIFIC ANR-18-CE14-0032-01 ,  CORRECT LMNA ANR-19-CE17-0013-02 ),  BPIFrance  ( 2018-PSPC-07 ), the  ERA-Net-CVD  ( ANR-16-ECVD-0011-03 ,  Clarify  project),  Fédération francaise de cardiologie , the  Fondation pour la recherche médicale , and by a grant from the  Leducq Foundation  ( 18CVD05 ).