dataesr/scientific-paragraphs-categorization huggingface dataset:
30  , Experiment 1). For the fMRI dataset, labels were instead defined by the experimenter to be as close as possible to the verbal descriptors of the AudioSet taxonomy  34. We estimated the semantic embeddings for the verb plus noun sentences by considering three models: GloVe 18 (unsupervised learning of corpora statistics; n parameters = 300), a word2vec trained on the Google News dataset: GNewsW2V 17 (n parameters = 300) and the Google Universal Sentence Encoder: GUSE  19  (n parameters = 512). For GloVe and GNewsW2V, we estimated one semantic embedding for each sound stimulus by averaging across the semantic embeddings for the name and verb sound descriptors. With GUSE, we instead directly estimated one semantic embedding for the name plus verb sentence. Each of the GloVe, GNewsW2V and GUSE models included one single model component.
For each DNN, we selected as model components the activations in the last pooling layer of each convolutional block, as well as in each of the remaining noninput layers not part of a convolutional block (7, 14 and 9 model components for VGGish, Yamnet and Kell's network, respectively). For the behavioral experiment stimuli, layer activations in the VGGish and Yamnet model were estimated for each of two https://doi. org/10. 1038/s41593-023-01285-9 subsequent 0. 975 s windows and then averaged across windows. For both the behavioral and fMRI datasets, the Kell model included a total of 557,152 parameters (minimum = 1,024; maximum = 177,504), the VGGish model included a total of 192,640 parameters (minimum = 128; maximum = 98,304), and the Yamnet model included a total of 380,928 parameters (minimum = 6,144; maximum = 98,304).
For both datasets, significance testing relied on a permutation-based framework (Mantel's test, relying on the permutation of rows and columns of the distance matrix) with maximum-statistics correction for multiple comparisons (MC  50  , one-sided inference for all tests exception done for the contrasts which relied on two-sided inference). Note that our permutation framework does not make any assumption about the distributional properties of the data. We carried out six subsequent analyses. First, we assessed whether all the model components together, or all the acoustic-model components, or all the semantic model components or all the DNN model components predicted a significant portion of the behavioral or fMRI distances, and carried out pairwise predictive variance contrasts between the acoustics, semantics and DNN model classes (MC correction across mode classes or contrasts, and across fMRI ROIs).
Finally, we carried out an analysis generalizing model representations from fMRI to behavioral data, that is, predicting behavioral dissimilarities from fMRI mapped, by linear regression, onto the space of computational model distances. We used two different CV schemes for the behavioral and fMRI data. The behavioral data were collected on two separate sound sets (living and nonliving sound-generating object), explicitly selected to have a different semantic structure (and potentially correlated acoustical structure). The fMRI data were instead collected for four separate stimulus sets characterized by the same semantic-category structure and more homogeneous acoustical structure. Because of these design properties, we cross-validated across stimulus sets only for the fMRI analyses. For both datasets, we used a repeated split-half approach for cross-validating predictive variance estimates (R 2 CV ) across groups of participants (behavioral data: ten participants for training and ten for testing; fMRI data: three participants for training and two for testing; 100 random splits considered for the behavioral data; all the ten possible splits of three and two participants considered for the fMRI data).
Acknowledgements We thank  B. Morillon ,  P. Belin ,  Y. Cao  and  S. A. Kotz  for useful comments on previous versions of the manuscript, and  T. Piquemal  for help with the extraction of NLP embeddings. This work was supported by the  French National Research Agency  ( ANR-21-CE37-0027  to B. L. G. ;  ANR-16-CONV-0002 ILCB ;  ANR11-LABX-0036 BLRI ), the  Dutch Research Council  ( NWO 406. 20. GO. 030  to E. F. ) and the  Dutch Province of Limburg  ( SAS-2019-00061  to E. F. ).
Data analysis Data were analysed using custom Matlab and Python code, as well as publicly available Matlab and Python code for the characterization of the stimulus conditions using computational models (see Methods). Custom analysis code have been deposited at https://doi. org/10. 5061/dryad. 0p2ngf258 For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All data used in the analyses are available at the following Dryad repository: https://doi. org/10. 5061/dryad. 0p2ngf258.
a b s t r a c t Colorimetric measurements are valuable in studying paleoenvironmental changes in sediment archives such as loess-paleosol sequences. These measurements allow for the identification of climate-sensitive minerals such as hematite, goethite, and secondary carbonates, as well as the observation of stratigraphic changes influenced by paleoclimate variations. Herein, a detailed workflow protocol emphasizing mineral abundance extraction by determining true band amplitudes is presented. Moreover, we present a protocol for colorimetric measurements that eliminates container bias, allowing the analysis and re-analysis of stored sediment quickly and inexpensively. Finally, we introduce a new R-package ('LESLIE') for graphical data display and enhancement. The protocol and its validation are demonstrated on the Suhia Kladenetz loess-paleosol sequence of northern Bulgaria.
Instrumental settings for DRS-VIS measurements All colorimetric measurements of the visible light range (VIS) were performed with a Konica Minolta CM-600d spectrophotometer. Before measurements, the spectrophotometer was calibrated with Konica Minolta-provided white and black standards. The resolution of Diffuse Reflectance Spectroscopy (DRS) data is 10 nm in the visible light range from 400 nm to 700 nm. The measurements were performed with a 0. 8 cm wide open oculus, a D65 norm light source, the specular component SCE, and an observer angle of 2°(CIE 1931 standard observer angle). Each sample was measured five times to calculate the mean and standard deviation.
DRS measurements performed on bare material For measurements carried out on bare material, approximately 20 g of material was placed on white paper and compressed with a massive (2 kg) plain surface stainless steel cylinder to avoid shadows and to simulate the compaction inside the plastic boxes. The five measurements were acquired at different positions of the compressed sample material (see Fig. 1 a ). Each time, the sample material was re-mixed and compressed again with the stainless-steel cylinder to avoid user-induced cracks of the material, which would lead to artificially lower L * values due to shadows between non-compressed material. Only raw data was extracted from the Konica Minolta CM-600d device using the Konica Minolta SpectraMagix TM software. Raw data consists of reflectance values from 400 to 700 nm in 10 nm intervals and device-internally calculated L * , a * , b * values.
Afterward, this data was plotted for each wavelength in biplots (see Fig. 2 d, e,  and f  for examples at 430 nm, 530 nm, and 600 nm, respectively) to investigate their correlation and dependency. Linear models were calculated, and linear regression functions were determined and listed in Table  1. Thirty-five sediment samples of the 1136 sampling depths from the Suhia Kladenetz (SK18) loess and paleosol sequence (LPS) near Pleven in northern Bulgaria were selected. For a detailed description of the SK18 LPS, please refer to  Jordanova et al. (2022)   [12]. The samples selected for this study represent a mixture of end-member colorimetric values such as high and low a * , high and low L * , and high and low b * to build robust regression functions. The boxes are made of non-magnetic ultra-clear plastic (Caubère reference 221 (19 ×19 ×10 mm, 4 cm 3 ), material: crystal-clear polystyrene PS with a thickness of 1 mm) and were stored using layer-wide wipes to avoid scratches from non-consolidated sample material.
Color contrast enhancement Our contrast enhancement algorithm is motivated by two ideas presented in Zeeden et al. [19 , 20 ]  (color values presented rectangles in stratigraphic order) and Sprafke et al. [21]  (contrast enhancement) but seeks to simplify and strengthen the outcome. Zeeden et al. [19 , 20 ]  provided an R-Script to represent colorimetric values as rectangles in stratigraphical order. Sprafke et al. [21]  used contrast enhancement based on MS Excel TM to identify cold and warm periods, represented by rather "cold " and "warm " colors. Here, we combine both approaches to provide an enhanced stratigraphic overview of an LPS in a documented R package called 'LESLIE' (LoESs coLorimetry sIgnal Enhancement)  [22]  designed to re-calculate the combined L * , a * , b * values to RGB values and to perform a userdefined number of enhancement cycles.
The R package and it's example data allow the user to adjust the data structure for the R package using the given example (prepare your data with depths in one column and L * , a * , b * values in columns 2-4). Depth limits are user-defined or are calculated automatically if no depth interval for each sample is given. For usability reasons, we limited the user interaction to selecting a file, the number of enhancement cycles, and a few graphical adjustments parameters. The number of suitable enhancement cycles is subjective. We recommend two to three enhancement cycles, which we consider sufficient. However, users may want to test with more enhancement cycles. The re-calculated and enhanced values are returned as numerical values to be exported as comma-separated files (CSV), among other possible formats, and used in different applications.
Acknowledgments We thank  Fatima Mokadem  and the  Laboratoire de Géographie Physique: Environnements quaternaires et actuels (LGP) ,  UMR 8591 CNRS -Université Paris 1 -UPEC , Thiais, France, for granting access to the spectrophotometer Konica Minolta CM-600d and the Fourier Transform Infrared Brucker Vector 22 spectrometer, equipped with Attenuated Total Reflection sampling accessory. The authors also thank  Dr Tobias Sprafke  for sharing the calculations for colorimetric contrast enhancement performed in MS Excel TM. This research recceived no specific grant from public, commercial, or not-for-profit funding agencies.
We present a survey of ground state-rotational lines of deuterated molecules towards L1544 and HH211, observed with the single-dish 20 m radio telescope at the Onsala Space Observatory. Our observations cover CCH, HCN, HNC, and HCO + , and their deuterated isotopologues. To avoid optical depth limitations with the main isotopologues, we also observed 13 CCH, C 13 CH, H 13 CN, HN 13 C, H 13 CO + , and HC 18 O + This data set allows for a unique comparison between the deuteration in a very dynamically evolved pre-stellar core on the verge of forming a low-mass star, and a young Class 0 protostellar core, and thus, to study the influence of the evolutionary stage on the deuterium fractionation. Such a comparison is important to understand how the deuterated molecules are inherited in the earlier stages of formation of a low-mass star, and eventually in its planetary system.
Observations The data presented in this paper were obtained with the 20 m radio telescope at the Onsala Space Observatory. The observed spectra are centred on the dust emission peaks of the sources (L1544: α 2000 = 05 h 04 m 17 s. 21, δ 2000 = +25 • 10 ′ 42 ′′. 8; HH211: α 2000 = 03 h 43 m 56 s. 80, δ 2000 = +32 • 00 ′ 50 ′′. 0). The first part of the observations was done in May 2020 (project ID: O2019b-04). A follow-up project observed the second part from January to May 2021 (project ID: O2020b-05). For the observations, we used the 3 mm  (Belitsky et al. 2015)  and the 4 mm receiver  (Walker et al. 2016)  in combination with the OSA spectrometer at 19 kHz resolution, with 625 MHz bandwidth and dual polarisation. We applied the dual beam-switched mode, with the off position shifted by 11.
8 arcmin in the azimuth direction. The intensity calibration was done using standard Dicke-switched type calibration, switching between a hot load and a sky direction close to the source. The 1σ pointing accuracy is estimated to be 3 arcsec. The frequency resolution of the data corresponds to a velocity resolution of about 0. 07 km s -1 at 84 GHz. The weather conditions during the observations were variable, with the system temperature ranging between 120 and 300 K. The observed transitions are summarised in Table  A. 1. The corresponding spectra towards L1544 and HH211 are presented in Figs. A. 1 and A. 2, respectively. A selection of the observed transitions is given in Table  1  and Fig. 1. The average beam size of the observations is about 50 arcsec, which corresponds to a physical size of 8500 au for L1544 and 16 000 au for HH211.
The data processing was done using the GILDAS software  (Pety 2005 ) and the Python package PYSPECKIT  (Ginsburg & Mirocha 2011; Ginsburg et al. 2022). The antenna temperature T * A was converted to the main beam temperature T mb using the relation T mb = T * A /B eff. The corresponding values for the main beam efficiencies (B eff ) of the 20 m telescope are given in Table  1.
Species Transition Frequency (a)  B eff T mb,peak rms V LSR (b) Notes. Numbers in brackets give the uncertainty on the last digit. ( a) Extracted from Cologne Database for Molecular Spectroscopy  (Müller et al. 2001 ). ( b) The uncertainties of the fits are smaller than the velocity resolution. Therefore, the error on V LSR and ∆v are given by the observed spectral resolution, 0. 07 km s -1. ratio > 3; C 13 CH shows five of the seven components with S/N > 4. For both, we selected the transition with the highest S/N, (N=1-0, J=3/2-1/2, F 1 =2-1, F=5/2-3/2). The deuterated isotopologue, CCD is much brighter, and seven of the nine hyperfine components are detected (S /N > 8). Some of them show self-absorption, thus, we selected the line with the lowest S/N (≈8), N=1-0, J=3/2-1/2, F=5/2-3/2, to avoid optical depth issues.
Assuming a Gaussian line profile, the integrated main beam temperature is calculated by 1 2 √ π/ ln 2 • ∆V • T mb,peak. The corresponding parameters for each transition are listed in Table  1  and Table 2. We derived the partition function from the energies and degeneracies of the rotational levels (see  Mangum & Shirley 2015) , taken from the Cologne Database for Molecular Spectroscopy (CDMS  Müller et al. 2001). However, as some of our transitions are only moderately optically thin, we applied the optical depth correction factor  (Goldsmith & Langer 1999 ) to all of our lines to derive the total column density: N tot = N thin tot τ 1 -e -τ. (3) The optical depth is derived using: τ ν = -ln         1 - T mb f J(T ex ) -J(T bg )         , (4) where we assumed a filling factor f = 1.
Molecule Frequency T Notes. ( a) Extracted from the Cologne Database for Molecular Spectroscopy  (Müller et al. 2001 ). ex (K) Q(T ex ) E u (a) (K) A (a) (s -1 ) n crit (cm -3 ) g u (a) H 13 CO + 1. 2(1) 2. 2(2) HC 18 O + 0. 12(2) 0. 11(2) DCO + 3. 1(3) 5. 4(5) HN 13 C 1. 6(4) 0. 7(2) DNC 9(1) 4(1) that they both trace the same gas, with similar properties along the line-of-sight. Colzi et al. (2020)  show that in the local interstellar medium the carbon isotopic ratio is molecule-dependent and varies with time, volume density, and temperature, and can deviate significantly from 68. For example, for a fixed density and temperature, it may change by a factor of two between 10 5 and 10 6 yr of core evolution, depending on the molecule. In this work, we applied the canonical value, taking note of the uncertainties it might introduce.
Radiative transfer modelling In this section, we use non-LTE radiative transfer simulations to model the observed molecular lines. We applied the software LOC (Line Transfer with OpenCl, Juvela 2020), which is based on ray tracing. To reduce the complexity of the simulations, we made the simplifying assumption of a one dimensional, spherically symmetric grid for both of our cores. For CCH, HCN, H 13 CN HCO + nd DCO + we used the latest collision rate coefficients available on the Leiden Atomic and Molecular Database (LAMDA,  Schöier et al. 2005; van der Tak et al. 2020; Dagdigian 2018; Hernández Vera et al. 2017; Denis-Alpizar et al. 2020; Pagani et al. 2012 ). In the case of HCN, H 13 CN and DCO + we applied the hyperfine-resolved rate coefficients. For the collisional coefficients of H 13 CO + nd HC 18 O + we used the rates of HCO + from LAMDA and scaled them with the corresponding reduced mass of the isotopologues.
Physical structure of HH211 There is no model of the physical structure of HH211 available in the literature. We computed it using data from the Herschel Science Archive (HSA). Following  Harju et al. (2017) , we used the archive Herschel/SPIRE dust continuum emission maps to derive the temperature and column density maps. From those, we extracted radial temperature and volume density profiles for the outer regions of the core. For the inner regions, we used powerlaws following  Crimier et al. (2010)  and  Motte & André (2001). For the velocity, we assumed free fall with the literature mass of the protostar  (Lee et al. 2018). A detailed derivation of the physical structure of HH211 can be found in Appendix D.
Acknowledgements. We wish to thank the anonymous referee for their constructive comments. K. G. thanks  Maria José Maureira  for useful discussions. S. S. and K. G. wish to thank the  Max Planck Society  for the  Max Planck Research Group  funding. All others authors affiliated to the MPE wish to thank the  Max Planck Society  for financial support. The  Onsala Space Observatory  national research infrastructure is funded through  Swedish Research Council  grant no. 2017-00648. C. T. B. and F. L. acknowledge financial support from the  European Research Council  (Consolidator Grant COLLEXISM, Grant No. 811363 ).
Numerical results All the times given below are sequential times in seconds on a Intel(R) Core(TM) i7-8700 CPU @ 3. 20GHz machine on Linux. The input polynomials we consider in our tests are 53-bits floating point approximations of random dense hyperbolic, elliptic and flat polynomials which integers coefficient factors with a uniform law in the interval [2 -8 , 2 8 ]. For root isolation, we also consider 53-bits floating point approximations of resultant polynomials of two bivariate random dense hyperbolic polynomials. We generate sets of random complex points with rational real and imaginary parts with numerator and denominators uniformly chosen in [2 -16 , 2 16 ]. 6. 2. 1 Evaluation. We used PWEval to evaluate random dense hyperbolic, elliptic and flat polynomials of increasing degree 𝑑 at 𝑑 points.
Introduction The purpose of this study is to quantify the benefits of heat cogeneration using electric power plants. Due to the very different characteristics of the various power sources (gas, wind, hydraulic, solar, nuclear,. ) potentially present on a territory, to the fluctuating nature of the power demand and the production of these sources, the modeling of the behavior of such a mix of power sources is rather complicated and requests sophisticated tools. In a previous paper published, we presented MIXOP-TIM, a software toolbox dedicated to the overall analysis of an electric mix installed in a given territory  [1]. The toolbox includes a Monte-Carlo simulation of the mix, and a frequency-domain spectrum analysis software, to assess the load-following capabilities of the mix. In order to evaluate the economic benefits of cogeneration, the descriptive capability of the software platform has been extended to include modeling of both power and heat production for each power source.
2  The hypotheses of the study 2. 1 The whereabouts of MIXOPTIM MIXOPTIM is a simulation software able to calculate the performance of a mix of energy sources on a territory. Many other simulation tools dedicated to the same objective do exist. Most of them are time-step models where * e-mail: b. bonin900@laposte. net the behavior of the mix is followed for each time increment, according to a given scenario for the power demand  [2]. The originality of the Mixoptim approach lies in the fact that it is a Monte-Carlo simulation tool that explores the entire phase space for the variables of the problem, ie the power demand on the territory, and the availability of the power sources at time t, whereas other simulation tools based on a time-step approach explore only one path (or scenario) in this multidimensional phase space.
Economic indicators with-and without heat cogeneration The performance indicators calculated by the MIXOPTIM code are average quantities extracted from the Monte-Carlo simulation of the mix. They are: -the probability of a grid power outage (dimensionless, but can be expressed as the average duration in minutes or hours of power outage per year). This indicator characterizes the security of supply provided by the mix. -The time-averaged power imported from and exported to the neighboring territories. This indicator characterizes the degree of energetic independence provided by the mix. -The time-averaged hourly production cost of the electric power demanded on the territory. -The time-averaged hourly production cost of the heat demand on the territory. -The time-averaged hourly CO 2 production.
Refrigeration and cooling 12 Other uses of energy 97 Total 320 (pure, ie excluding the electric power used to produce heat); The average low-temperature heat demand for the 2019 French metropolitan territory has been assumed to be 60 GWth. It is also assumed that the heat demand remains the same with-and without cogeneration, and that it is fulfilled 75% by natural gas and 25% by electric heaters in the no-cogeneration option 1. Therefore, in the cogeneration option, the electric consumption of the territory decreases because the electric heating of homes shall be partially replaced by district heating. It is assumed that the cogenerated heat is produced at 100 • C, with a subsequent loss in electric conversion yield for the nuclear source (from 35% to 25%). Under these assumptions, the following results have been obtained using the MIXOPTIM software in the options with-and without cogeneration (Tab.
This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.