dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This research was funded in whole or in part by the  Austrian Science Fund (FWF)  (grant  PT1013M03318  to F. L. ). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The study was supported by the  European Union   Horizon 2020 Research and Innovation Program  under the  Marie Sklodowska-Curie action  (grant agreement  754411  to F. L. ) and in part by the  NextGenerationEU  through the grant TAlent in  ReSearch @ University of Padua -STARS@UNIPD  (to F. L. ) (project BRAINCIP [brain criticality and information processing]). L. d. A. acknowledges support from the  Italian MIUR  project  PRIN2017WZFTZP  and partial support from  NEXTGENERATIONEU (NGEU)  funded by the  Ministry of University and Research (MUR) ,  National Recovery and Resilience Plan (NRRP) , and project  MNESYS  ( PE0000006 )-a  multiscale integrated approach to the study of the nervous system in health and disease  ( DN.
The central area of the total laser-affected spot retains the initial bright contrast change throughout, whereas the contrast change reduces in the outer area, bringing it closer to its initial magnetization. Following this, at ∼20-25 ps  [35] , the outer ring of the laser-affected spot regains a brighter contrast but is separated from the central bright area by a ring of opposite, darker contrast. This separation gradually disappears at ∼100 ps, and the entire spot attains a uniform bright contrast, corresponding to the homogeneously switched state. This corroborates our discussion of Sec. II that, unlike the case of alloys, Tb/Co multilayers can be switched with single laser pulses. We observe from this dataset, however, that the switched state in Tb/Co multilayers stabilizes on a time scale of 100 ps, much slower than in GdFeCo alloys  [23] , Gd/Fe bilayers  [17] , or (Gd/Fe Co) 20 multilayers  [2] , where the switching stabilized within a few picoseconds.
ACKNOWLEDGMENTS Part of this paper is supported by the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) , the  European Union   Horizon 2020 and the innovation programme  under the  FET-Open  Grant Agreement No. 737093  ( FEM -TOTERABYTE ), and the  European Research Council ERC  Grant Agreement No. 856538  (3D MAGiC). This paper has received funding from the  European Union 's  Horizon 2020 research and innovation program  under  FET-Open  Grant Agreement No. 713481  (SPICE) and  European Union 's  Horizon  and under  Marie Skłodowska-Curie Grant  Agreement No. 861300  (COMRAD).
9  The study of the aging mechanisms of potassium-based systems, and in particular the formation of a SEI, is still at a preliminary stage and remains based on Li-ion and Na-ion literature so far. Allgayer et al. 10  performed a detailed XPS analysis on graphite electrodes cycled either in a lithium or potassium half-cell, which provides a rather complete XPS database of commercial K-based reference compounds. In order to aid the assignment of potassium species in the SEI formed in K-graphite samples, reference measurements were conducted on selected characteristic SEI compounds (e. g. , KF and KPF 6 ) or closely related compounds (e. g. , CH 3 COOK). The organic character of the SEI and its signicant thickness seem to be an obstacle to the prolonged life of K-ion systems. The higher solubilities of organic products lead to recurrent SEI dissolution/ reformation reactions, which do not allow a stable passivation layer to be obtained.
7 eV). The residual pressure inside the analysis chamber was 6. 4 × 10 -8 Pa. The photoelectron take-off angle was 45°, which provided an integrated sampling depth of approximately 5 nm. A xed analyzer pass energy of 23 eV was used for core level scans leading to an overall energy resolution of ∼0. 6 eV. Survey spectra were captured at a pass energy of 117 eV. All samples were analyzed under dual Ar + ion (5 eV) and electron (4 eV) beams. All spectra were referenced by using an internal signal, typically by adjusting the C 1s level peak at a binding energy of 284. 5 eV (adventitious carbon contribution). This calibration is consistent with the peak attributed to KF in the F 1s orbital at around 683 eV, which has already been observed in previous studies. 10  Data were analysed using Mul-tipak® soware.
Choice and reaction time data were fitted in each group using a tDDM and a standard DDM (sDDM; see SI section 5. 1 for priors). All chains converged with a Gelman-Rubin convergence diagnostic below 1. 5 (psrf = 1). Deviance information criteria were smaller for the tDDM (DIC = 22,873) than the sDDM (DIC = 23,054). Moreover, the log likelihoods of out-of-sample predictions of choices and reaction times observed for even trials by modeled choices and reaction times for odd trials were greater for the tDDM (LL = -742. 8) than sDDM (LL = -780. 8) (SI section 5. 2. ). Parameters could be recovered well using an analogous tDDM with a stepwise estimation of the drift rate, as implemented by the deoptim package in R (see SI section 5. 3, SI Table  11 ). The mean of the posterior distributions of the free parameters of the tDDM revealed that the placebo intervention influenced how much and when participants considered the healthiness and tastiness of the food during the decision process (SI Table  12  and 12a ).
In both groups the relative starting time was positive, which indicated that tastiness was considered earlier than the healthiness. Importantly, the relative time was shorter for participants given the decreased hunger suggestion than those given the increased hunger suggestion (mean(PDdecreased-PDincreased) = -0. 24, PP = 0. 86, Figure  4b , SI table  12 ). Similar results were obtained when modeling the choices and reaction times with a tDDM that used a step-wise approximation of the drift rate and was estimated using the deoptim package in R following the procedure of Maier et al. 2020  30  (SI Table  13 , section 5. 4).
Randomization The probability of being assigned to one of the two suggestion arms was fixed to p = 0. 5 and constant for the entire duration of the study. Randomization was performed before participants were enrolled using standard permutation algorithms implemented in MATLAB. The algorithm drew 2 integers of 1 and 2. If the integer was '1', the participant was assigned to the suggestion group 1 (decrease hunger suggestion). If it was '2', the participant was assigned to suggestion group 2 (increased hunger suggestion). To ensure an equal number of participants in each suggestion group the permutation was repeated 63 times for the behavioral pilots and 31 times for the fMRI participants.
Dietary decision-making task All participants performed a dietary decision-making task  19, 20, 29, 38  and a sub-group of 61 participants performed the task during fMRI. The task consisted of the participants choosing whether they wanted to eat snack foods of varying tastiness and healthiness on a trial-bytrial basis. The task counted 200 trials (behavioral pilot) or 152 trials (fMRI sub-group) for a total duration of 20 to 30 min (Figure  5 ). Each trial started with the display of a food item on a computer screen and participants indicated on a 4-point-Lickert scale, from a strong no to a strong yes, whether they wanted to eat the food item. All food stimuli were selected from a database of 600 food images validated for tastiness and healthiness ratings by 300 participants from a prior Mturk study conducted in-house.
The food images were presented on a computer screen in the form of high-resolution images (72 dpi). MATLAB and Psychophysics Toolbox extensions  45  were used for presentation of the stimulus and recording of the responses. Participants of the fMRI experiment saw the stimuli via a headcoil-based mirror and indicated their responses using a fMRI compatible response box system. The task was incentive compatible, because one food was chosen by chance for consumption at the end of the experiment  19, 20. Participants also rated each food on its tastiness and healthiness using the same 4-point Lickert scale at the end of the experiment.
MRI data acquisition T2*-weighted multi-echoplanar images (mEPI) were acquired using a Siemens 3. 0 Tesla VERIO MRI scanner with a thirty-two-channel phased array coil. Three echos were acquired for the best compromise between spatial resolution and signal quantity in the orbitofrontal cortex (OFC)  46, 47. To further reduce signal drop out in the OFC, we used an oblique acquisition orientation of 30° above the anterior-posterior commissure line  48. Each volume comprised 48 axial slices collected in an interleaved manner. To cover the entire brain, the acquisition sequence involved the following parameters: echo times of 14. 8 ms, 33. 4 ms, and 52 ms; FOV = 192 mm; voxel size = 3 x 3 mm; slice thickness = 3 mm; flip angle = 68°; and TR = 1. 25s. Whole-brain high-resolution T1-weighted structural scans (1 x 1 x 1 mm) were acquired for all 61 subjects and co-registered with their mean mEPI images and averaged together to permit anatomical localization of the functional activation at the group level.
Model specification The model was specified using the RWiener package via the run. jags function of the JAGS package in RStudio. More specifically, the tDDM was implemented by a one-dimensional Wiener process, where the state of evidence (dEt) at each timestep (dt) evolved stochastically following differential equation (  3 ): (3) dEt /dt ~ N(Et, σ 2 ) Et is the evidence accumulation defined by equations 2a and 2b above. In practice, a stochastic node (y) reflected a certain state of evidence at a specific timestep (dt) (or the predicted choice data and reaction times) and was distributed according to a univariate Wiener distribution: ycpt ~ dwieners(αt = 2, τcpt, βcpt, , δcpt , ɑcp) Choice and reaction time (RT) data were coded in a way that "no" food choices were given negative RT values and "yes" food choices positive RT values.
Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from available literature. Low growth rate 'LowRate' and high growth rate 'HighRate' were considered as variable inputs for u-value, FAR, AC efficiency, APLs efficiency, and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. Only base input data were used for cement production process parameters without applying any variable inputs. GCAM base scenarios based on input data only (without modifying variable data) for the residential building sector in Indonesia were considered the benchmark for this study.
Introduction A major portion (60%) of the global population is projected to live in urban areas by 2030  [1] , more than half (56%) of the population of Indonesia already resides in urban areas and cities  [2, 3]  and is projected to increase to almost three quarters (73%) by 2050  [4]. Reliance on urban areas and cities, the number of residential buildings and inhabitants in Indonesia (69439 thousand buildings in 2020) are increasing day by day which is contributing to human forced global heating  [5]. The building sector is responsible for a total of 40-50% of carbon emissions globally  [6]. The Global Buildings Performance Network (GBPN) is working on the building sector to tackle climate change and they report that the building sector is responsible for 35% of the world's total energy consumption where 55% of all electricity consumption is due to building operations which Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).
The total urban population was determined by using Eq. (1). 𝑈𝑟𝑏𝑎𝑛 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 = 𝑇𝑜𝑡𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 × 𝐺𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑢𝑟𝑏𝑎𝑛𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝐿𝑎𝑏𝑜𝑟 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑏𝑖𝑡𝑦 = 𝐺𝐷𝑃 𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑟 𝑓𝑜𝑟𝑐𝑒 (2) The labour productivity rate is related to GDP per capita and the total labour force in a country. The last ten years (2010-2019) labour force and GDP data  [5]  in Indonesia were considered and analysed for future prediction. The labour productivity rate was used as an input under the socioeconomic assumption in the building model. Figure  6  shows the predicted future labour productivity rate in Indonesia including labour participation rate (data presented in Table  A1  in Appendix A). The labour productivity rate was determined by using Eq. (  2 )  [31]. The labour participation rate is the ratio of the total labour force and total population which is determined by using Eq.
Building parameters Various building parameters are considered in this study to investigate carbon mitigation. The selected parameters are shell conductance or u-value, FAR, AC efficiency, GSHP, APLs efficiency, and rooftop PV. Each selected parameter is modelled at high and low rates of adoption from 2020-2050. Figure  7  shows the predicted future u-value of building envelopes (data presented in Table  A2  in Appendix A). The initial value of shell conductance was selected based on the current residential building envelope in Indonesia. The low and high rate initial data for u-values (for the year 2020) were selected 0. 275 and 0. 529 W m -2 K -1 respectively  [11]. The future projection of u-value data for low and high rates was determined based on the GCAM future projection which is under the building_det dataset  [32].
Based on current practices at a residential building in Indonesia, the low and high FAR values were selected as 1. 8  [33]  and 4  [34]  respectively. The low and high rate AC efficiencies (for the year 2020) were selected at 300% and 600% respectively (data presented in Table  A3  in Appendix A)  [35]. The future projection of AC efficiency data for low and high rates was determined based on the GCAM model future projection which is under 'Residential Cooling' at building_det dataset  [32]. Figure  8  shows the predicted AC efficiency input data for a cooling application. Besides, in the GSHP, a heat pump is able to reduce 30% energy consumption compared to a normal AC in tropical climate areas like Indonesia  [21]. The performance of the GSHP parameter was determined based on the AC efficiency parameter for cooling application only.
GCAM GCAM was used to analyse this study objective with appropriate input data. The Indonesia building energy model is nested in GCAM  [32]  where GCAM can be used for scenario analyses for carbon mitigation, building energy modelling, technology assessment studies and others. GCAM is a modelling framework, so there is more than one set of input data required to define the GCAM scenarios. Therefore, all input data set were used to develop various GCAM scenarios to determine potential carbon emissions in residential building in Indonesia. The most important thing is the evolution process of the residential building sector in Indonesia. This long term evolution process of Indonesia residential building sector is explored based on the integrated assessment framework, service based model, new technological details and GCAM.
This study focuses on mainly two areas of contribution in the residential building sector. The first contribution is to investigate the performance of carbon mitigation by each parameter such as u-value, FAR, AC efficiency, GSHP, APLs efficiency and rooftop PV. The second contribution of this study is to develop carbon emissions mitigation policies for carbon emissions and the potential implications over the long term of building regulatory policies. Figure  10  shows the research framework and study method steps including ways to use the input data into the GCAM. Based on urban population, GDP and labour productivity base data, 28 individual scenarios were explored. Based on the variable growth rate, eight integrated scenarios were also explored in this study presented in Table  2. The 'AdvTech-HighRange' was considered where each individual parameter performance is high.
Assumptions and limitations In this study, the GCAM modelling framework was used which involved several sets of assumptions to explore the future carbon emissions scenario. All the base and variable input data discussion in the building model structure section were generated based on available literature with considering some assumptions for future trends. Depending on the sets of input data, outcomes of the carbon mitigation scenario will be varied. This study explores the potential mitigation of carbon emissions for residential buildings in Indonesia. The analyse is conducted based on the GCAM model which has a set of constraints and limitations in terms of carbon mitigation in the residential building sector. In GCAM, there are mainly two variables to provide mitigation of carbon emissions data such as residential cooling and residential others.
Also, only low and high rate of FAR is applied in this study but it would be considered several FAR ranges if needed in future. Under the APLs, only APLs considered those consume electricity, apart from this, did not analyse other sources such as gas mentioned in the GCAM model. Therefore, there is a huge scope to mitigate carbon emissions under APLs parameters. GSHP was considered only for cooling by comparing the AC efficiency parameter performance however similar item such as electric water heat pump was not considered in this analysis where the scope of mitigation is huge. Under the concept of this study, only the B6 category was selected for the analysis however there are several categories related to upfront and end of life carbon in the building sector. Based on the method, only one set of base data (urban population, GDP, and labour productivity) was used for socioeconomic scenarios however more than one set of base data would be studied.
| An "imperfect knowledge" statistical model Second, we considered an "imperfect knowledge" statistical model for which we assumed that only one explanatory variable X 1 (e. g. , light) in the above simulated dataset has been measured at the relevant scale among all the environmental drivers that actually determine response variable Y (Equation 2, Appendix S1). This model represents the ecologist's imperfect understanding of attribute Y, as few sites would actually offer quantification of variation of several environmental variables at the individual scale that is relevant. The model includes a species fixed effects on the intercept and on the slope (β′ 0,j and β′ 1,j ) and a random individual effect b 0,i on the intercept with b 0,i ~ N (0, V bj ), where V bj is the intraspecific variance for species j.
We estimated the model parameters based on the simulated dataset introduced above but considering only the first explanatory variable {Y ijt , X 1,ijt }, the remaining "unknown" environmental effects being contained in the model residuals, ε ijt. (1) ln Y ijt = 0,j + 1,j ln X 1,ijt + 2,j X 2,ijt + … + N,j X N,ijt (2) ln Y ijt = � 0,j + b 0,i + � 1,j ln X 1,ijt + ijt , ijt ∼ N 0, V j F I G U R E 3 Reinterpreting observed intraspecific variability (IV): from niche widening to niche projection into a high-dimensional environment. In (a), within a given environment E0 defined along an environmental axis X1 (E0 = E(X1,0)), conspecific individuals are identical and have the same performance pA and pB, for species A (blue) and species B (orange). Species A outcompetes species B in E0. Actual measured differences among conspecific individuals, shown in (c), can be interpreted in different ways.
| A partitioning of observed variance among individual tree growth We computed annual diameter growth (G) in mm•year -1 for each tree as well as a competition index (C) as the sum of the basal area of the eight direct neighbors of each tree. The dataset included 64,125 growth estimates corresponding to 13,531 trees in total. To quantify the relative importance of the different sources of growth variability, we used a statistical hierarchical growth model (Equation  3 ) including an intercept (β 0 ), fixed effects of the log-transformed diameter (β 1 ) and competition index (β 2 ), and random effects on the intercept for the block (b 0,b , with b 0,b ~ N(0, V b )), the genotype (b 0,g , with b 0,g ~ N(0, V g )), the census date (b 0,t , with b 0,t ~ N(0, V t )), and the individual (b 0,i , with b 0,i ~ N(0, V i )).
| EMPIRI C AL IN S I G HTS: OBS ERVED INTR A S PECIFIC VARIAB ILIT Y IS HIG H AND S PATIALLY S TRUC TURED AND DOE S NOT " B LUR S PECIE S D IFFEREN CE S" IN TROPI C AL FORE S TS To explore some of our hypotheses in natural communities, we then used data from three long-term tree inventories in tropical For all three datasets, annualized growth between two censuses was computed as the difference of DBH (≥10 cm) between two consecutive censuses, divided by the time period between those two censuses. Growth estimates < -2 or > 100 mm•year -1 as well as individuals from incompletely identified species and individuals and species with a single observation were discarded prior to analysis. Mean annual growth for each individual tree was then computed as the difference of DBH between the first and the last time a tree was measured, divided by the time period between those two measurements.
| High observed intraspecific variability in tree growth in tropical forests To quantify the relative importance of intra-versus interspecific variability in each site, we used a hierarchical growth model (Equation  4 ), including an intercept β 0 , a diameter (D) fixed effect β 1 , a species random effect b 0,j (with b 0,j ~ N(0, V b )) and an individual random effect b 0,i (with b 0,i ~ N(0, V bi )) on the intercept. All data were log-transformed and scaled, and a constant of 2 mm was added to all growth values to avoid undefined logarithms. We used default Student's T priors for variance parameters, and normal distributions (with mean = 0 and variance = 1) for mean parameters. We estimated the inter-and intraspecific growth variability from the variance of the species (V bj ) and individual (V bi ) random effects, respectively.
| Spatial autocorrelation of individual growth within species at the local scale in tropical forests To test whether individual growth was spatially autocorrelated, we performed in each site, spatial analyses of the mean individual growth values. We chose a conservative approach based on mean (4) individual growth without accounting for the effect of diameter, thus without removing ontogenetic differences and considering the pattern of individual growth as it is in the field. More specifically, we performed Moran's I one-tailed tests as implemented in the ape R package  (Paradis & Schliep, 2019) , for pairs of conspecific individuals less than 100 m apart in the same plot (to avoid capturing the effect of treatment in Paracou and including the spaces between the plots). ln G ijt + 2 = 0 + b 0,j + b 0,i + 1 × ln D ijt + ijt , ijt ∼ N (0, V) TA B L For the most abundant species, we sampled 3000 individuals with a uniform probability.
The results were qualitatively unchanged (Appendix S3). With these results obtained with empirical data, we showed that (i) observed intraspecific variability can be high in tropical forests, (ii) individual growth can be spatially structured, and (iii) conspecific TA B L E 2 Features of the three tropical forest datasets used as empirical case studies. individuals can have a more similar growth than heterospecific individuals at local scales. Altogether this suggests the signature of environmental variation that was not accounted for but that typically influences individuals at fine scale in many ways.