dataesr/scientific-paragraphs-categorization huggingface dataset:
The Inverse Triplet loss does the opposite by inversing the Positive and Negative samples in the equation, which encourages batch-free representations. The samples from different batches get closer, while samples from the same batch are pushed further apart. The later objective is used to prevent all samples from collapsing. If the samples from the same batch are not pushed apart, the loss would be optimal if all samples were transformed into the exact same value, which is not the desired outcome. The distance minimized in this case is the Euclidean distance, but any distance could be used. Repetitive holdout is a method to evaluate the performance of a model on a dataset. It is similar to cross-validation, however each split is random, there is no limit to the number of times it can be done on dataset and the test set is resampled for every holdout iteration.
Resampling the test set is particularly important, because some batches can be much easier to classify than others, which can make the test set classi cation much better or much worse than the validation set. Using repetitive holdout makes classi cation on the valid and test sets comparable. When splitting the dataset, the samples from a given batch must all be contained in the same split. We do this in order to inform on the generalization abilities of a model to make predictions in a new batch.
BERNN Hyperparameter Optimization We used the function optimize from the package ax-platform (https://pypi. org/project/ax-platform/) to perform a Bayesian optimization of the hyperparameters for each of the BERNN models (all implemented in PyTorch). We used 20 combinations of hyperparameters to optimize each model. The hyperparameters optimized are the following: learning rate, weight decay, dropout, number of warmup epochs, layer1, layer2, label smoothing parameter, triplet loss margin (when it applies), beta (controls the strength of KL divergence, when it applies), gamma (controls the strength of the adversarial or triplet loss, when it applies). The batch size is set to 32 and the number of epochs after warmup is set to 1000, but the training is stopped if no improvements are made in the last 100 epochs.
Classi cation with non-BERNN representations For the classi cation of raw data, any batch corrections that do not involve a DNN, we used either a Random Forest Classi er (RFC) or a Linear Support Vector Machine (LinearSVM) from the python package scikit-learn. For both, we used models from the python package scikit-learn. To deal with classes imbalance in some datasets, the parameter class_weights was set to "balanced" for both models. Automatically adjust weights inversely proportional to class frequencies. The hyperparameters optimized for the RFC were min_samples_split, min_samples_leaf, n_estimators, criterion, and oob_score. For the LinearSVM, we optimized tol, max_iter, penalty and C. The hyperparameters for the RFC and LinearSVM models were optimized using the package scikit-optimize (https://scikit-optimize.
Model interpretability For model explanation purposes and to identify the most important features for the classi cation, we used SHAP 26 to produce an example analysis. The advantage of that approach is that it makes it possible to explain the decision made on individual samples and could be used for precision medicine. It would allow identi cation of complex patterns that apply only to a subset of the samples that could not be identi ed by differential analysis. A) VAE-DANN is a variational autoencoder with a domain classi er trained with a gradient reversal layer (GRL). B) AE-invTriplet is an Autoencoder that uses the inverse triplet loss to make the new representation batch-free. The encoders for the positive, negative and anchor samples all share the same weights. In both models, step 1 is the warmup and uses the whole dataset (including the train, validation and test sets).
Acknowledgments AD research laboratory is supported by  Research and Innovation Chair L'Oréal in Digital Biology. The Orbitrap Fusion mass spectrometer utilized was supported in part by  NIH   SIG  grants  S10OD018034  and  Yale School of Medicine. Funding for this project came from  NIH  awards  AG062421  to SEA,  AG062306  to SEA, BCC & ACN, and  AG066508  to ACN. BCC is supported by an  Alzheimer's Research UK   Senior Research Fellowship , and by the  Bright Focus Foundation. Con icts of interest  S. Arnold  has received honoraria and/or travel expenses for lectures from  Abbvie ,  Eisai , and  Biogen  and has served on scienti c advisory boards of Corte, has received consulting fees from  Athira , Cassava,  Cognito Therapeutics ,  EIP Pharma  and  Orthogonal Neuroscience , and has received research grant support from  NIH ,  Alzheimer's Association, Alzheimer's Drug Discovery Foundation ,  Abbvie ,  Amylyx ,  EIP Pharma ,  Merck ,  Janssen/Johnson & Johnson ,  Novartis , and vTv.
The search for the primordial B-modes of the cosmic microwave background (CMB) relies on the separation from the brighter foreground dust signal. In this context, the characterization of the spectral energy distribution (SED) of thermal dust in polarization has become a critical subject of study. We present a power-spectra analysis of Planck data, which improves upon previous studies by using the newly released SRoll2 maps that include corrections on residual data systematics and by extending the analysis to regions near the Galactic plane. Our analysis focuses on the lowest multipoles between = 4 and 32, as well as three sky areas with sky fractions of f sky = 80%, 90%, and 97%. The mean dust SED for polarization and the 353 GHz Q and U maps are used to compute residual maps at 100, 143, and 217 GHz, highlighting variations of the dust polarization SED on the sky and along the line of sight.
XXII 2015; Planck Collaboration XI 2020). In addition, far-IR polarization measurements obtained by the BLASTPol balloonborne experiment (e. g. ,  Ashton et al. 2018) , have shown that the dust polarization fraction is roughly constant between 250 µm and 3 mm (100 GHz). These remarkable results suggest that the emission from a single grain type dominates the long-wavelength emission in both polarization and total intensity  (Guillet et al. 2018; Hensley & Draine 2022). Maps of MBB parameters (dust spectral index and temperature) have been obtained by fitting the total intensity Planck data (e. g. , Planck Collaboration X 2016; Planck Collaboration Int. XLVIII 2016). The Planck 2018 data release (hereafter, PR3, Planck Collaboration III 2020) does not provide comparable constraints on MBB parameters in terms of the polarization due to an insufficient signal-to-noise ratio (S/N) and instrumental systematics  (Osumi et al.
2021 ). These limiting factors are emphasized by the lack of polarization maps at 545 and 857 GHz which would be useful in constraining the dust SED. The frequency dependence of dust polarization data also involves polarization angles. When the dust SED and magnetic field orientation vary within the beam, the frequency scaling of the Stokes Q and U parameters may differ  (Ichiki et al. 2019; Vacher et al. 2023). Integration along the line of sight may therefore induce variations in the polarization angle with frequency  (Tassis & Pavlidou 2015; Planck Collaboration Int. L 2017). Pelgrims et al. (2021)  provided first observational evidence of this effect analyzing Planck data toward the lines of sight with multiple velocity components in H I emission. Additional emission components (e. g. , magnetic dipole and CO emission  Puglisi et al.
The paper is organized as follows. Section 2 presents the Planck data we use in this work. Section 3 introduces the reference models we used for data simulations and analysis. In Sect. 4, we determine the dust mean SED for polarization from 100 to 353 GHz. We quantify spatial variations of the dust polarization SED in Sect. 5 and the contribution of polarization angles in Sect. 6. The frequency dependence is analyzed in Sect. 7. Our results are summarized in Sect. 8.
Planck polarization data and masks The Planck satellite observes the sky in total intensity (also referred as temperature) in the range of frequency of the electromagnetic spectrum from 30 to 857 GHz, and in polarization from 30 to 353 GHz. Data were obtained from two instruments on board the satellite: the Low Frequency Instrument (LFI,  Mennella et al. 2011 ) and the High Frequency Instrument (HFI, Planck HFI Core Team 2011), with the Planck HFI measuring the linear polarization at 100, 143, 217, and 353 GHz  (Rosset et al. 2010 ). The Planck scanning strategy sampled almost all the sky pixels every six months, with alternating scan directions in successive six-month periods. The Planck mission includes five surveys, each covering a large fraction of the sky (hereafter f sky ). Maps are produced for the full-mission data set together with the survey, year, and half-mission maps, as reported in Planck Collaboration VIII (2016).
SRoll2 maps In this work, we use sky maps and end-to-end data simulations produced by the SRoll2 software. 1 This latter has been developed to improve subtraction of systematic effects. The dominant systematic effect for the polarized signal at 353 GHz in the PR3 maps is related to the poor measurement of the time transfer function of the detectors, while at lower frequencies, it is dominated by the non-linearity of the analog-to-digital converters. Both systematics have been greatly improved in a consistent way with all other known effects for the SRoll2 data-set  (Delouis et al. 2019). Hereafter, we deem Q P (ν) and U P (ν) as the Planck polarization maps at the frequency, ν, including the CMB, dust, and synchrotron as well as noise and systematics. We used the Planck HFI maps at 100, 143, 217, and 353 GHz.
We limit our data analysis to polarization at low multipoles (4 < < max = 32) and work with HEALPix pixelization  (Górski et al. 2005)  at N side = 32 (i. e. , map pixel size of 1. 8 • ). In order to obtain these maps, we follow Planck Collaboration Int. XLVI (2016) and Planck Collaboration V (2020), degrading the fullresolution maps first to N side = 1024, to ease the computation, and alongside N side = 32, we apply the following cosine filter in harmonic space  (Benabed et al. 2009 ): f ( ) =              1, N side ; 1 2 1 + sin π 2 N side , N side < < 3N side ; 0, 3N side. (1) All the maps used in this study and presented in the paper have been degraded to N side = 32 by following this approach.
Dust mean SED in polarization In this section, we derive the mean SED of dust polarization. We use Planck polarization maps at 100, 143, 217, and 353 GHz. The SED values are normalized to the reference frequency, ν 0 = 353 GHz. 2 We used the data file COM_PowerSpect_CMB-base-plikHM-TTTEEE-lowl-lowE-lensing-minimum-theory_R3. 01. txt available on the Planck Legacy archive. We follow earlier studies (Planck Collaboration Int. XXX 2016; Planck Collaboration XI 2020) using cross power spectra to determine the dust mean polarized SED for Planck data γ P (ν), the reference models γ d (ν), and the simulations γ sim (ν) normalized to ν 0 as: γ XX P (ν) = ρ ν D XX (ν × ν 0 ) -D XX ,CMB D XX (ν 0 × ν 0 ) -D XX ,CMB min , max , γ XX d (ν) = D XX ,d (ν × ν 0 ) D XX ,d (ν 0 × ν 0 ) min , max (7) γ XX sim (ν) = ρ ν D XX ,sim (ν × ν 0 ) -D XX ,CMB D XX ,sim (ν 0 × ν 0 ) -D XX ,CMB min , max , where    8 ).
For GNILC, the match is not as good -we observe in particular a significant difference for f sky = 80% at 100 and 143 GHz. For f sky = 97%, we observe a difference between the EE and BB values at 100 GHz. Apart for this latter difference, we observe that γ d (ν) values are consistent within 5% with the γ P (ν) values. All the differences between γ P and γ d may be due to averaging effects along the line of sight. This analysis shows that assuming a reference model based on total intensity data could not completely reproduce the SED observed in polarization data, thus biasing any CMB polarization E-modes and B-modes signals if used as reference for component separation methods. In order to ensure an unbiased detection of the CMB polarization at the precision required from future CMB experiments, we need to address these very small spatial SED variations in the dust polarization emission.
6. For comparison, we also compute difference maps for the reference models (see Eq. (  10 )) and simulations maps replacing γ P (ν) by γ d (ν) in Eq. (  9 ): R Q d (ν) = Q d (ν) -γ d (ν) • Q d (ν 0 ), R U d (ν) = U d (ν) -γ d (ν) • U d (ν 0 ), (10) R Q sim (ν) = Q sim (ν) -γ d (ν) • Q sim (ν 0 ), R U sim (ν) = U sim (ν) -γ d (ν) • U sim (ν 0 ). We note that for the purposes of illustration, all the residual maps shown in Figs. 6-8 consider a γ P (ν) and γ d (ν) in Eq. (  9 ) computed for f sky = 90%. One set of simulation maps [R Q sim (ν), R U sim (ν)] for the Commander model is displayed in Fig. 7. The comparison between Figs. 6 and 7 is hampered by Planck data noise but we may notice some common features and some differences. In Fig. 6 , we also note differences between frequencies among the R Q and R U residual maps, in contrast to what is observed for R Q sim and R U sim in Fig.
Power-spectra analysis To characterize the SED variations, we computed power spectra of residual maps and cross-power spectra with the reference models. We computed two cross-power spectra between (i) independent residual maps [R HM1,HM2 CMB on our analysis. D XX ,res (ν) = [R HM1 Q (ν), R HM1 U (ν)] × [R HM2 Q (ν), R HM2 U (ν)], D XX ,d,res (ν) = [R HM1 Q (ν), R HM1 U (ν)] × [R HM2 Q d (ν), R HM2 U d (ν)], (11) D XX ,sim,res (ν) = [R HM1 Q sim (ν), R HM1 U sim (ν)] × [R HM2 Q sim (ν), R HM2 U sim (ν)], where XX ∈ [EE, BB]. Figure  9  shows both sets of cross power spectra for EE (red) and BB (blue) at 100, 143, and 217 GHz for f sky = 90%. The data points over the -range 4-32 are compared with the 1σ and 2σ dispersion (dark and light gray shades) obtained with the 200 simulations, including noise and CMB anisotropies (see Sect.
Absolute values of the residuals may be obtained by scaling the figure data points by P 2 ν 0 = [2453. 6, 5950. 5, 13814. 3] K CMB for f sky = 80%, 90%, and 97% respectively. We discuss the frequency dependence later, here we focus on the comparison with the models. Figure  11   are larger by a factor of about ten larger than A res,d (ν). For these, uncertainties associated with the chance correlation between the CMB and the dust residuals may thus be somewhat underestimated. For the 97% mask, this difference is smaller. At 143 and 217 GHz, the amplitudes even coincide for the GNILC model. These results show that the reference model only accounts for a minor fraction of the total polarization SED variations in the 80 and 90% masks but a much more significant one for the brighter dust emission in the Galactic plane.
Conclusions Power spectra of the Planck data were used to characterize spatial variations of the polarized dust SED. We improved the sensitivity of previous studies by using the newly released SRoll2 maps and extending the analysis to regions near the Galactic plane. Our analysis focuses on the lowest multipoles between = 4 and 32, and three sky areas with f sky = 80%, 90%, and 97%. Maps of MBB parameters from the Commander and GNILC component separation methods applied to the Planck total intensity data are used as reference models. The main results of our analysis are as follows. -We confirm earlier studies finding that the mean SED for dust polarization from 100 to 353 GHz is very close to that A163, page 11 of 12 for the total intensity, as well as to a MBB spectrum with a spectral index of β d = 1.
53 for an assumed dust temperature of T d = 19. 6 K. -The mean SED and the 353 GHz Q and U maps are used to compute residual maps at 100, 143, and 217 GHz, which quantify spatial variations of the dust polarization SED. Residuals are detected at the three frequencies for the three sky areas. The EE and BB spectra of the residual maps are of comparable amplitude. They do not reproduce the E -B asymmetry that has been observed for the total dust power. -The residual maps are correlated with the reference Commander and GNILC models, but this correlation accounts for only a fraction of the residuals amplitude. Furthermore, we find that this fraction decreases toward high Galactic latitudes (i. e. , for a decreasing f sky ). This result shows that models based on total intensity data are underestimating the complexity of dust polarized CMB foreground.
However, this conclusion is challenged by the BB results, particularly for f sky = 80%, and by cross-spectra that show that the residuals maps at the three frequencies are not fully correlated. Further work (e. g. ,  Vacher et al. 2022a ) is needed to model theoretically the impact of polarization angle variations on EE and BB power spectra of residual maps, which we expect to depend on the correlation between the dust emission properties and the structure of the magnetized interstellar medium. Our analysis of Planck data brings out significant differences between the dust polarization EE and BB SEDs, and with respect to total intensity, setting new requirements for simulations of the dust polarized foreground and component separation methods. A significant refinement to dust modeling is necessary to ensure an unbiased detection of CMB primordial B-modes at the precision required by future CMB experiments.
Acknowledgements. A. R. acknowledges financial support from the  French space agency  ( Centre National d'Etudes Spatiales, CNES ) and the  Italian Ministry of University  and Research -Project Proposal  CIR01_00010. F. B. acknowledges support from the  Agence Nationale de la Recherche  (project  BxB :  ANR-17-CE31-0022 ) and  CNES. The authors would like to thank the anonymous referee for a thorough reading of the article and for all suggestions and comments that significantly improved the understanding of the text.
00. 010. 0000 (Mediso Medical Imaging Systems, Hungary). Immediately after CT acquisition, a 30 min dynamic PET scan and 30 s after starting the acquisition, 32. 9 ± 1. 4 MBq of 2'-deoxy-2'-[ 18 F]fluoro-D-glucose (FDG; Advanced Applied Applications, France) in 0. 4 mL saline were injected in the lateral tail vein. At the end of the first dynamic PET scan, a static 30 min PET scan was acquired with ECG and respiratory gating. PET data was collected in list mode and binned using a 5 ns time window, with a 400-600 keV energy window and a 1:5 coincidence mode. Data was reconstructed using the Tera-Tomo reconstruction software (3D-OSEM based manufactured customized algorithm) with expectation maximization iterations, scatter, and attenuation correction. The 30 min dynamic PET exam was reconstructed in 23 frames as follows: 30 s, 6 × 5 s; 4 × 10 s; 6 × 30 s; 3 × 120 s; 4 × 300 s.
Total scan time was 1min32s per slice. The cine intra-gate sequence was triggered using averaged heart and respiration rates in a retrospective reconstruction. Acquisitions were performed using Paravision software 6. 0. 1 (Bruker, Ettlingen, Germany) and T1 cine images were analyzed manually using Circle civ42 software (version 5. 13. 5, Circle Cardiovascular Imaging Inc. , Canada) to estimate global heart chamber volumes.
Cardiac strain and strain rate analysis Cardiac strain defines the level of the wall deformation of a cardiac cavity along the cardiac cycle. It is a non-volumetric parameter, complementary to the conventional functional parameters, allowing to study different spatial components of the myocardial contraction function. Strain can be measured by the speckle tracking method using ultrasound  70  , or by MRI that offers the advantage of an observer-independent complete coverage of the heart  74. In our study, we opted for strain measurements based on T1 cine sequences using a semi-automatic homemade software, CardioTrack (Sorbonne University, Paris, France)  75, 76  , based on feature tracking  77  and written in Matlab® (The MathWorks, Natick, MA, USA). The strain measurements of the left and right ventricles and of the atria were performed using the 4 chamber views for reliability and repeatability reasons.
Staining quantification and analysis A homemade machine learning based software, Fiber-ML, written in Matlab® (The MathWorks, Natick, MA, USA)  77  , was used to quantify IHC and IHF staining in the entirety of each section. The capillary density in the LV was calculated as the number of Cd31 stained capillaries divided by the number of cardiomyocytes in a given field of the IHF sections using an ImageJ® macro.
085% formic acid in mQH 2 O. The mass spectrometer acquired data throughout the elution process and operated in DDA PASEF mode with a 1. 89 second/cycle, with Timed Ion Mobility Spectrometry (TIMS) mode enabled and a data-dependent scheme with full MS scans in PASEF mode. This enabled a recurrent loop analysis of a maximum of the 120 most intense nLC-eluting peptides which were CID-fragmented between each full scan every 1. 89 s. Ion accumulation and ramp time in the dual TIMS analyzer were set to 166 ms each and the ion mobility range was set from 1/K0 = 0. 6 Vs cm -2 to 1. 6 Vs cm -2. Precursor ions for MS/MS analysis were isolated in positive polarity in the 100-1. 700 m/z range by synchronizing quadrupole switching events with the precursor elution profile from the TIMS device. The cycle duty time was set to 100%, accommodating as many MSMS in the PASEF frame as possible.
Singly charged precursor ions were excluded from the TIMS stage by tuning the TIMS using the Otof control software (Bruker Daltonik GmbH). Precursors for MS/MS were picked from an intensity threshold of 1000 arbitrary units (a. u. ) and resequenced until reaching a 'target value' of 20,000 a. u, considering a dynamic exclusion gap of 0. 40 min. The mass spectrometry data were analyzed using MaxQuant version 1. 6. 17 (Max Planck institute of Biochemistry, Germany). The database used was a concatenation of Rattus norvegicus sequences from the Swissprot database (release 2020-10) and a list of contaminant sequences from MaxQuant. The enzyme specificity was trypsin. The precursor and fragment mass tolerance were set to 20ppm. Carbamido-methylation of cysteins was set as permanent modification and acetylation of protein N-terminus and oxidation of methionines were set as variable modifications.
Second peptide search was allowed, and minimal length of peptides was set at 7 amino acids. False discovery rate (FDR) was kept below 1% on both peptides and proteins. Label-free protein quantification (LFQ) was done using both unique and razor peptides. At least 2 such peptides were required for LFQ. The "match between runs" (MBR) option was allowed with a match time 0. 7 min window and an alignment time window of 20 min. For differential analysis, LFQ results from MaxQuant were imported into the Perseus software (version 1. 6. 14). After excluding reverse and contaminant proteins from analysis, 4257 proteins were identified, among which 3203 proteins had at least one LFQ value across all samples. Before performing statistics, the data was transformed to a logarithmic scale (log2) and further filtered: only 2618 proteins with at least 3 out of 5 valid LFQ values in at least one experimental group were used for comparison between groups.
Of these, 971 proteins were identified with a value in each sample (Supplementary Fig. 8a ). Five samples/group post-ISO were performed. In the five 7d post-ISO LV apical group, two samples were outliers. However, in order to maintain homogeneous sample comparison between groups, we did not eliminate these 2 outlier samples since (i) they showed no series effect and (ii) their inclusion did not flaw the statistical confidence of this group's comparisons (Supplementary Fig. 8b ). Significant differential proteins were identified with a two-tailed Student's t-test. Only proteins with p-value < 0. 05 and |fold change|>1. 3 were kept for over-representation analysis using Ingenuity Pathway Analysis (QIAGEN Inc, version 60467501). Proteins with missing values in all samples of one group (0/5) and at least 3 values in another group (3/5) were considered as appearance / disappearance and included in the dataset of 2618 proteins (supplementary fig.