dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were processed using Fit2D software and pair distribution functions were produced using the PDFgetX3 software. Infrared Spectroscopy: In situ infrared analysis were performed using a Fourier transform infrared spectrometer (Bruker Optik GmbH) equipped with a MCT-detector (32 scans, resolution 4 cm -1 ). The sample was mounted and heated in the same way as for the in situ wideangle X-ray scattering. Ex situ infrared spectra were collected using a Thermo Scientific Nicolet is5 FTIR spectrometer (ATR-Diamond mode) (32 scans, resolution 4 cm -1 ). Raman Spectroscopy: Raman spectra were collected using a confocal Raman microscope (α300; WITec) equipped with a CCD camera (DV401-BV; Andor), a Nikon objective (10×) and a 532 nm laser (40 accumulations, integration time 1 s). A high-resolution grating of 1800 g mm -1 was used and the spectral resolution was ≈1 cm -1.
Acknowledgements The research was supported by a  German Research Foundation  grant within the framework of the  Deutsch-Israelische Projektkooperation DIP. The research leading to this result has been supported by the  project CALIPSOplus  under Grant Agreement 730872 from the  EU   Framework Programme for Research and Innovation HORIZON 2020. Z. Z. was supported by  China Scholarship Council (CSC) , the  Fundamental Research Funds for the Central Universities  (WUT:  2019IVA067 ), and the  National Natural Science Foundation of China  ( 21905217 ). The authors thank  Chenghao Li ,  Stefan Siegel ,  Jeannette Steffen  ( MPI of colloids and Interfaces, Potsdam ), and  Philip A Chater  ( Diamond Light Source Ltd. ) for technical assistant. The authors thank  Wouter J. E. M. Habraken  for useful discussions.
Indeed, NS11394 induced a significant decrease in average EPSC frequency in WT male (À61% + À8. 7%), WT females (À47% + À12. 57%), and bhlha9 KO females (70% + À12. 30%), but not in bhlha9 KO males (Figure  3G ). NS11394 had no effect on average EPSC amplitude in all four groups (Figure  3H ). Taken together, these data demonstrate that loss of BHLHA9 causes a male-specific impairment of the ionotropic GABAergic signaling. To assess the mechanisms underlying the impaired response of bhlha9 KO males to the positive modulation of NS11394, we performed multiple immunoelectron microscopy (immuno-EM) experiments to quantify the GABA content in vesicle-containing dendrites (V1) originating from GABAergic interneurons and contacting C-LTMR central terminals (GIa glomeruli) in WT and bhlha9 KO mice of both genders.
Two-temperature choice tests Two mice were placed simultaneously in each lane of the two temperature choice apparatus (Bioseb). Mice were tracked for 10 min using the Bioseb software. During the first day, both plates were kept at 20 C during 10 min. Days after this acclimatizing period, 2 plates were individually warmed or cooled to different temperature (42 C to 16 C) and kept at the appropriate temperature for 10 min test. A 1h time lapse was respected between 2 different tests.
5; KCl 2. 4; CaCl 2 2. 4; NaHCO 3 19. 5; MgSO 4 1. 3;KH 2 PO 4 1. 2; HEPES 1. 25; glucose 10; pH 7. 4) equilibrated with 95%O 2 -5%CO 2 for at least one h before starting patch clamp recordings. Spinal slices were placed in a recording chamber bathed with warmed (31 C) ACSF Electrophysiological measurements were performed under the control of an Olympus BX51 microscope using a multiclamp 2B (Molecular devices). Patch pipettes (7-11 U) were filled with potassium gluconate-based pipette solution (in mM: Potassium D-Gluconate 120; KCl 20; CaCl 2 0. 1; MgCl 2 1. 3; EGTA 1; HEPES 10; GTP 0. 1; cAMP 0. 2; Leupeptin 0. 1; Na 2 ATP 3; D-Manitol 77; pH 7. 3). All drugs were purchased from Sigma. Synaptic analysis was performed using Spike 2 software and at least 3 min of recordings were used to quantify EPSC parameters.
Following purification with Agencourt AMPure XP beads (Beckman-Coulter, Villepinte, France), the size and concentration of libraries were assessed by capillary electrophoresis. Following sequencing (HiSeq 4000, Illumina, San Diego, CA) using a 100-nt paired-end read protocol (65-82 x106 reads per sample), sequences were mapped to the mm10 UCSC mouse genome assembly resulting in 91%-93% correctly mapped reads, which were used for differential gene expression analyses. Raw counts from mapped reads mapping to mm10 exons were obtained using featureCounts from the Subread package  (Liao et al. , 2013)  (Version 1. 6. 1). Differentially expressed genes were called using DESeq2  (Love et al. , 2014)  (version 1. 14. 1) using a false discovery rate (p-adjusted value in DESeq2) threshold of 0. 05 and at least a two-fold change in expression.
EM quantification and statistical analysis To assess whether loss-of-function of bhlha9 affects C-LTMR central terminals organized in non-peptidergic type Ia glomeruli (GIa) and synaptic contacts, a quantitative analysis was performed in lamina II as follows: (i) counting of GIa, immunolabeled or not for GINIP and/or VGLUT3 in WT versus KO male and female mice; (ii) counting of GABA-immunolabeled vesicle-containing dendrites (V1) engaged in the GIa glomerular configuration in WT versus KO male and female mice. A total of 10 randomly selected sections/animal for WT (males and females) and bhlha9 (females and males) mice were examined and quantification was directly performed on ultrathin sections within 90x90 m squares of 200 mesh EM grids by an operator blinded to the experimental groups. Quantitative and statistical analyses were performed by using ImageJ software (NIH, Bethesda, USA) and Graph Pad Prism 6 (GraphPad software, San Diego, CA).
ACKNOWLEDGMENTS We are grateful to the  Moqrich lab members  for fruitful scientific discussions. This work has been funded by the  European Research Council (ERC)  starting grant paineurons  260435  and the  Agence Nationale de la Recherche (ANR) Myochronic  to A. M. M. B. 's PhD thesis was funded by the  Ministere de l'education nationale et de la recherche and la Fondation pour la recherche medicale (FRM). N. B. -A was supported by the  Israel Science Foundation  ( 1051/15 ).
Space-filling techniques are mainly excluded, and deterministic regression-based simulations are assessed for both global metamodeling and optimization. Characterizing features of existing methods are exposed, such that they can be categorized. Furthermore, a comparative review is performed on a broad spectrum, including various reference functions and adaptive techniques based on different characteristics. For sound analysis and understanding, results can be replicated using a MATLAB  [78]  implementation of all investigated techniques as well as investigated benchmark tests, which is supplied on Github. The review is organised as follows. In Sect. 2 an overview of ordinary kriging surrogate model is given. Then, in Sect. 3, goals and general features of adaptive schemes for ordinary kriging are introduced.
Steps of Adaptive Sampling Schemes The general algorithm workflow of an adaptive sampling technique for global metamodeling is depicted in Fig. 4. Consider an initial design of experiments = {x 1 , … , x m } associated with a data set as defined in Eq. (  1 ). The creation of the surrogate model begins by fitting M from this data. Then, supplementary sample points are included to the dataset D through successive iterations until reaching a convergence or stopping criterion.
A strategy is called collapsing when two or more points differ by only one or very few parametric dimensions. Therefore, outputs may be identical for these points, in case their corresponding inputs differ only with respect to dimensions with very low influence on the response (see e. g. [40] ). For kriging, collapsing points lead to numerical issues through ill-conditioned matrices. As LHD enforces concomitant difference between sample points in terms of all dimensions, see Eq. (  11 ), non-collapsing property is intrinsically ensured through the process. Thus, even if one or more parametric dimensions have insignificant influence on the output, the LHD data set would still be usable for building kriging metamodels.
Size of the Initial Design of Experiments Decisions about the number of samples included in the initial design appear relatively arbitrary in the literature. However, globally, it can be sketched as a compromise between two perspectives. Small initial designs lead to starting metamodels associated with large lack of knowledge, which could mislead the first steps of the adaptive procedure  [34, 50]. On the contrary, large initial designs may cause high computational cost due to numerous evaluations of the black-box, which might be avoided using a smaller initial dataset and supplementary points designed by adaptive sampling  [18]. Thus, the size of initial dataset is generally chosen by the user in dependence on the application. Main features for decision are the dimension of the parametric space, spacefilling quality of the initial sampling algorithm, computational cost due to the evaluation of the black-box function and a priori knowledge about the complexity of the mapping M over the parametric domain.
Smart Strategies to Combine Exploration and Exploitation Exploration and exploitation offer to all appearances opposing strategies to build adaptive dataset. However, instead of considering them as contradictory paths requiring a definitive choice between them and restricting the design ability to one scenario, it seems more appealing to append both of them in hybrid adaptive sampling approaches to benefit simultaneously from both features. Thus, advanced adaptive procedures are built by combining exploration and exploitation to yield the global refinement criterion as follows  [72]  with w local and w global the weights for the local exploitation and global exploration, respectively, such that the summation of both weights equals to 1. The combined strategy is specifically defined through both functions local(x) and global(x).
k-Fold Cross-Validation Considering the k-fold cross-validation as employed in Fushiki  [32] , the dataset D is divided in k mutually exclu- sive and collectively exhaustive subsets denoted D i , i. e. Then, k -1 subsets are chosen as training subset to establish a metamodel, whereas the remaining subset is employed for validation and estimation of a performance score. The process is repeated k times such that all the subsets are successively used for validation, and the cross-validation error is evaluated as the mean of the k results. However, this general tool is not commonly used for adaptive techniques, whereas the specific form called leave-one-out cross-validation has been frequently employed for exploitation purpose.
Space-Filling Cross-Validation Tradeoff (SFCVT) The Space-Filling Cross-Validation Tradeoff (SFCVT) approach combines a leave-one-out cross-validation for exploration and a distance criterion to ensure an exploration character  [4]. The authors define a normalized LOOCV error as (26) x m+1 MIPT = arg max x ⋆ ∈C � min x i ∈X ‖x ⋆ -x i ‖ �. In order to interpolate the error over the parametric space, a kriging metamodel for the error ênorm LOOCV is built based on the dataset D = {(x i , e norm LOOCV (x i ))}. Then the supplementary sampling point is defined as the solution of the following constrained optimization problem where the space-filling metric is estimated as detailed in Box 2. Thus, the distance condition ensures that the new point is created further than a certain euclidean distance to pre-existing points.
Smart Sampling Algorithm (SSA) Using the Smart Sampling Algorithm (SSA) proposed by Garud et al. [33] , a new sample point is defined as the solution of a set of optimization problems based on a combination of cross-validation exploitation and distance-based exploration. As proposed by Zhang et al. [117] , exploration is performed by maximizing the crowding distance metric CDM given by Eq. (  21 ). Indeed, a point x corre- sponding with a large value of CDM(x) would be localized relatively far away from the m samples already incorporated in the dataset. In order to incorporate an exploration component the authors compute CDM(x j ), ∀j ∈ [1, m]. (32) x m+1 CVVor = arg max x ⋆ ∈ C sens ‖x ⋆ -x sens ‖. Afterwards the resulting values are sorted in descending order using ordering index p = 1, … m.
Plots illustrating set of experiments provided by adaptive processes correspond to one realization randomly chosen among the ten performed realizations. The later a sample point has been added to the dataset the brighter the color of the dot representing it is. Furthermore sample position highlighted in bright red indicate that the point is closer than 0. 0005 in the normalized parametric space to an existing sample point, which could possibly result in numerical issues due to clustering behavior. Alternative algorithms are investigated on a large variety of test cases.
Analysis of the Optimization Problems All investigated adaptive sampling techniques rely on solving optimization problems in order to design a new sample point. The complexity of the cost function drives the choice of the solver and directly affects time and computational effort required to find the optimum. Thus, features of alternative objective functions are exposed here to lead to sound use of adaptive sampling methods. Consider the one-dimensional problem setting as depicted in Fig. 10. The blue dotted line indicates the target function. The black dots symbolize the positions of the initial samples. It can be seen they are not evenly distributed due to the small size of initial data set. Furthermore, peculiar dataset including two leftmost samples lying quite close to each other has been chosen to analyze how cost functions deal with this feature.
The metamodel built from this initial dataset is represented by the red line. From that set, alternative optimisation problems to be solved for designing the 11-th observation point are studied through the shape of their corresponding cost function over the whole parametric domain. To simplify the visualization, cost functions have been transformed and normalized into a score denoted RC which lies between 0 and -1, thus all corresponding optimization problems would be a minimization to look for the sample position corresponding to the global minimum, which equals -1 if not submitted to any constraint.
Analysis of Different Sets with 25 Samples For every sampling technique, the positions of the samples when reaching a size of 25 samples in the dataset and the corresponding metamodel are displayed on a normalized space in Fig. 13. Two main failures of the investigated techniques can be highlighted. First, some methods feature an exploitation not sufficiently significant to describe properly the localized non-linearity, such as ACE (Fig. 13b ), EIGF (Fig. 13f ), LOLA (Fig. 13h ), MIPT (Fig. 13j ), SFVCT (Fig. 13l ) and TEAD (Fig. 13n ). The problem of ACE seems to be the definition of the distance constraint because the global minimum is not well captured even though the samples predominantly lie around the hump. MIPT being only based on exploration, most of samples are located within the linear domain.
Nevertheless this behavior highly depends on the initial dataset. For instance, if the initial dataset included a point located in the hump trough, its output would have been smaller than all other sample outputs leading to a majority of EI sample points generated in the hump. AME (Fig. 13c ), MEPE (Fig. 13i ) and SSA (Fig. 13m ) capture the behavior of the target function rather well by balancing exploration and exploitation, even if they still include a lot of ineffective points in the linear part of the domain. CVVor (Fig. 13d ), Lipschitz (Fig. 13g ) and MASA (Fig. 13k ) sample the space ideally. A majority of points can be found at the hump whereas the linear domain is seldom sampled.
When reaching a dataset of 19 samples, a discrepancy of performances can be observed among alternative methods, where Lipschitz is clearly the best performing technique followed by MASA and MEPE. In order to investigate the reliability of the adaptive schemes in terms of ease of optimization and reproducibility of results, the evolution of the variance of the NRMSE over the 10 realizations is depicted along the sampling process in Fig. 14c  and d  AME show the biggest variations with CVVor peaking at around 0. 2%. However, for almost all methods the optimization scheme is able to yield negligible variations. This quantity will not be represented for further one-dimensional cases, as no difference appears between one-dimensional performed tests. The evolution of the mean normalized error at the global minimum over the sampling process is shown in Fig.
15a  and b. Unsurprisingly more significant difference among alternative approaches can be observed for this local error measure. CVVor, Lipschitz and MASA remain high performers, whereas MEPE is not as good for local objective. The relative improvement in terms of NMAE min from the initial set to datasets including 10 and 20 samples is depicted in Fig. 16b. The variation is here even more apparent. Only 4 methods are able to improve performances by adding the first 3 samples. Considering 19 samples, four methods still performs worst than initial dataset whereas MASA, CVVor and Lipschitz show the best performance.
Analysis of Different Sets with 40 Samples Alternative positions of samples when a dataset of 40 observations is reached, are displayed in Fig. 17b-o , as well as the corresponding surrogate models. Three sampling problems can arise. Some techniques, such as ACE (Fig. 17b ) and WAE (Fig. 17o ), do not possess enough exploration character to generate any sample outside the initially identified hump. A second problematic case is when the exploration component is pronounced enough to sample other parts of the parametric domain but not sufficient to find the second hump. The sampling behavior of LOLA (Fig. 17h ) can be categorized in this group. The last issue is when the overall domain is sampled enough to reveal the second hump but then the exploitation behavior of the technique is not proficient enough to describe accurately both humps.
Gramacy and Lee Function M Gr,1D The next one-dimensional problem is the Gramacy & Lee function defined for x ∈ [-1. 5, 1. 0] as From the initial dataset and corresponding metamodel represented in Fig. 19a  it can be observed that the response function feature large gradient variation, and peculiarly the largest gradients do not lie in the area of the global minimum. Alternative sample points obtained when the set of experiments reaches 30 samples are exposed in Fig. 19. With regard to global metamodeling, it can be seen that the exploration component of some adaptive technique is too pronounced for that benchmark test, which has a complex behavior. This is particularly the case for EI (Fig. 19e ) and WAE (Fig. 19o ), and to a lesser extent also for ACE (Fig. 19b ), AME (Fig. 19c ), CVVor (Fig.
19d ), Lipschitz Fig. 19  Sample positions after reaching 30 samples in the dataset and corresponding metamodels for the M Gr,1D problem (red point indicates a sample very close to a neighbor point) (Fig. 19g ) and LOLA (Fig. 19h ). However schemes based only on exploration such as MIPT (Fig. 19j ) is not sufficient to capture the numerous fluctuations of the true function. Therefore for functions with an irregular pattern, multiple local maxima and minima and large local gradients, adaptive techniques with high degree of exploration and a small but significant exploitation component should be preferred, such as EIGF (Fig. 19f ), MEPE (Fig. 19i ), MASA (Fig. 19k ), SFCVT (Fig. 19l ), SSA (Fig. 19m ) and TEAD (Fig. 19n ). This can also be observed from the global error measure NRMSE which is plotted over the sampling process in Fig.
Michalewicz Function M Mi,2D The first two-dimensional problem is the Michalewicz function given by where the parametric space is (x 1 , x 2 ) ∈ [0. 0, ] 2. The target surface of the function over the normalized space is displaced in Fig. 25a. The function shows an irregular response behavior. The initial sample positions are depicted as black dots in Fig. 25b  over the normalized absolute error of the initial metamodel. It can be seen that the initial surrogate model yields a large error in the upper right-hand corner of the parametric space as well as in the area around the steep valley of the response surface at y ≈ 0. 9. Proficient adaptive sampling techniques should sample in these areas. The position of the global minimum of the function is symbolized by the grey dot. From an initial dataset comprising 20 points, one realization of dataset with 45 samples for each adaptive technique is plotted over the initial error map in Fig.
Initial Dataset The initial sample positions as well as the normalized maximum absolute error of the initial metamodel are depicted in Fig. 28b. The position of the global minimum is indicated by a grey dot. It can be noticed that the initial error is larger and its map more complex than for previous case M Mi,2D due to the complexity of the true function. To obtain an accurate metamodel, adaptive sampling process should sample in each subdomain associated with large error. This task appears cumbersome as seven disconnected subdomains are to be targeted.
Analysis of Different Sets with 75 Samples Realizations of sample positions when reaching 75 samples in the dataset are plotted over the error map of the respective metamodel in Fig. 29. For sake of comparison, the color scale is kept fixed for all schemes as the color scale of the initial error map (Fig. 28b ). WAE (Fig. 29n ) and to a lesser degree SSA (Fig. 29l ) show clustering behavior, due to the weak exploration component of these methods. As noted in previous examples, SSA samples are mainly located near to the boundaries of the parametric space, therefore SSA is able to properly estimate the function in the problematic area close to (0, 0). However the final error (Fig. 29l ) in the central region of the parametric domain is still comparably high. ACE (Fig. 29a ) and CVVor (Fig. 29c ) fail to sample in the domain near to (0, 0).
Regarding the Goal of the Study Two major goals of study are contemplated, global metamodeling and optimization. For global metamodeling the studies have shown that adaptive sampling techniques with higher exploration component yield more proficient metamodels, so MASA, MEPE and MIPT are recommended. WAE and EI lead to unpredictable and unfavorable performances for this application. For optimization, MEPE and EI outperform other strategies. However EI is strongly dependent on the size of the initial sample. Performances are good for a large enough dataset, based on evenly spread samples. MIPT is not recommended for optimization, because it does not include any exploitation feature. WAE is also not dependable in this regard and a bad choice overall. The rest of the methods perform more or less similarly with regards to optimization.
Regarding Properties of the Initial Design of Experiments Crucial properties of the design of experiments are the size of the initial dataset, the parametric dimension of the problem and the risk of running into clustering issues. Small initial design of experiments are a challenge for adaptive schemes. Higher emphasise on exploration provides better robustness regarding small initial design of experiments. So, MASA, MEPE, MIPT and SFCVT are favored in that scenario. On the other hand EI and ACE fail to cope well with small initial designs of experiments. Adaptive techniques with low complexity are favorable to tackle high-dimensional parametric space, a crucial limiting factor being computational time and resources to design the new sample. Indeed, high-dimensional parametric dimensions usually require a large dataset-size and therefore more adaptive sampling steps.