dataesr/scientific-paragraphs-categorization huggingface dataset:
Incorporating local tracer measurements TTDs are commonly estimated from observed tracer data in precipitation and streamflow. Stable isotopes and hydrochemistry are also often observed locally (i. e. , in soil, groundwater, and xylem water) at different spatio-temporal scales (e. g. ,  Goldsmith et al. , 2019; Hissler et al. , 2020; Mennekes et al. , 2021; Quade et al. , 2019; Sprenger et al. , 2018). While these data are increasingly available and used to test spatially distributed flow tracking models (e. g. ,  Smith, Tetzlaff, Kleine, et al. , 2020) , they are also very localized and there are no clear methodologies for using them to estimate transit times in lumped approaches. We, therefore, pose the question of whether these measurements can potentially be interpreted as a subsample of a catchment water storage, and be characterized by a TTD or a SAS function.
Rigorously testing our methods There is an urgent need for all transit time estimation methods, including SAS approaches, to be rigorously tested against synthetic benchmark data sets (e. g. ,  Kirchner, 2016b; Kirchner, 2019)  with realistic degrees of complexity and nonstationarity, including realistic distributions of measurement errors (with realistic degrees of serial correlation). It is not necessary for such benchmark models to be realistic analogs of any particular real-world field site. It is essential, however, that they do not embody similar assumptions as the methods that they are used to test  (Kirchner, 2019). Otherwise, spurious success is virtually guaranteed, because the benchmark model and the methods under test will be performing essentially the same calculations (one forward, and the other backward).
Acknowledgments P. B. thanks  ENAC school at EPFL  for financial support. M. S. was supported by the  US Department of Energy Office of Science  under contract  DE-AC02-05CH11231  as part of  Lawrence Berkeley National Laboratory Watershed Function Science Focus Area. All authors thank the Associate Editor  Riccardo Rigon ,  Stefanie Lutz ,  Marialaura Bancheri  and one anonymous referee for their comments.
Experimental Design The experimental design is a factorial design with independent measures. There are 3 independent groups defined by the intersection of the 2 independent variables (whose description can be found in Table  1 : self-efficacy instruction (with or without) and the instance of unexpected events via stimulation through the HoloLens-2 headset (virtual stimulation vs. no stimulation). "SEB" = Self-Efficacy Belief group; "NSEB" = No Self-Efficacy Belief group; "CG" = Control Group. Participants were randomly assigned to three different groups, using randomization software. These groups were the control group or CG, and the two experimental groups. The first experimental group (n = 6), called "No Self-efficacy Belief" (NSEB), did not benefit from the instructions favoring self-efficacy; they were equipped with the HoloLens-2 helmet.
In our study the HoloLens-2 was only used to play pre-recorded audios and videos to the participants. Thanks to this technique, one can first observe which functions of the headset are useful in laparoscopic surgery learning situations, and thus be able, in a future study, to implement it with the necessary software. Since augmented reality is a combination of physical reality and virtual reality, it tends to improve physical training in laparoscopic simulation through the superposition of information or by an objective evaluation at the end of the performance. In order to prepare learners to deal with confusion, it seems relevant to give personalized advice that will indirectly encourage the participant or even increase the participants' interest in the task. Thus, these interventions can have an impact on how learners experience confusion  [12, 43, 44].
Background The traditional batch learning setting for machine learning is designed for finite datasets drawn from stationary distributions. Methods developed for learning from such datasets do not readily lend themselves to modern data processing applications dealing with streams of data where instances arrive continuously, generated by processes that may themselves be ever-changing. It is necessary to design new algorithms for learning from such settings, and a good place to start is from algorithms designed for batch settings. Decision Trees have been ubiquitous in batch learning settings, both as individual learners and in multiple ensembled forms such as Random Forests.
BOAT represents a typical attempt at learning from a large database that does not use a predictive sequential setting, by sampling fixed size chunks that are used to bootstrap multiple trees. A "coarse" tree is then extracted, based on the overlapping parts of the bootstrapped trees in terms of split decisions; this tree is further refined to produce a final tree by passing the whole dataset over it. The system is "incremental" in the sense that it can process additional datasets; and it is responsive to drift in that the system detects when a new dataset requires a change in split criterion at a node through a global assessment of split criterion, and causes a rebuild of the subtree rooted at that node. While key ideas that shape later trees are developed in this work, the sizes of the initial bootstrap samples are arbitrarily chosen, and concurrently the notion of anytime prediction is not entertained-there is no automated way of determining how many examples suffice to build a first reliable tree.
Further, the focus is on minimizing utilization of main memory; it is assumed that the database D is available for a corrective step in the algorithm. On the other hand, Hoeffding Tree is truly one-pass, in that it is assumed that an example is seen only once, then discarded. Meanwhile, the RainForest framework  [20]  introduces the idea of storing attributevalue-class counts at nodes, which we see in Hoeffding Tree as node statistics. Node statistics are indispensable in HoeffdingTree; they solve the one-pass problem and are critical in the application of the Hoeffding Test for split evaluation. Hoeffding Tree may be considered to be aiming to achieve the same evaluation objective as CART and C4. 5-maximal class purity-with a heuristic designed to be relevant in the streaming scenario where the data generation distribution is assumed to be static.
Assuming then a stationary distribution over the instance space, the ensemble is likely to demonstrate lower dataset dependent variance than a single tree. The objective for bagging differs from boosting in its addressal of controlling variance, and the accompanying heuristic follows. Boosting was first proposed as an approach to answer affirmatively the question of whether a combination of weak predictors-predictors that find hypotheses that perform only slightly better than random guessing-can learn strongly, that is, are able to find hypotheses "that are correct with high probability on all but an arbitrarily small fraction of instances"  [45]. In the classification setting, boosting works by using some components of the ensemble to focus on weighting misclassified examples higher so they are preferentially learned with respect to their actual frequency of occurrence, and thus potentially classified better on a test set if they were not merely noisy instances.
Convergence Hoeffding Tree probabilistically bounds deviation from the ideal batch tree. It guarantees that the expected "extensional" or "intensional" disagreement from a batch tree are each independently bound by δ p , where δ is a tolerance level and p is the leaf probability-the probability that an example will fall into a leaf at a given level. "Extensional disagreement" is defined as the probability that a pair of decision trees will produce different predictions for a given example, and "intensional disagreement" is the probability that the path of an example will differ on the two trees  [17]. It is assumed that these guarantees hold in the limit when trained on an infinite dataset denoted DT *. p is assumed to be constant across all levels for simplicity. Note that the guarantees will weaken significantly as the number of leaves increase, tending to probability bound 1 as tree size increases.
Experimental Setup We work with the MOA framework  [5] , which provides implementations of common ensembling strategies for decision trees, including Hoeffding Tree. Each ensemble method comprises either a bagging or boosting component, may involve a change detector and may also weight instances. Change detectors are usually used to determine whether to remove the worst performing trees in order to replace them with a new one if a significant increase in the error is observed. We use a testbench that to our best knowledge is the largest and most comprehensive in the literature, though there is ample room for improvement. We test on UCI datasets that are mostly drawn from real data as well as on synthetic streams with concept drift. We carefully selected twenty real datasets: two electricity datasets and the airlines and AWS dataset that are widely used in the concept drift literature, and as for the rest, the largest datasets from the UCI repository that involved an obvious classification task and had no missing values in order to reduce the confounding factor of how the algorithms handle missing values.
We omitted the physics simulation datasets Hepmass, SUSY and Higgs as these are synthetic datasets with low information  [34]. The price variable in the AWS dataset was discretized into ten equal buckets and set as the classification target; similarly, the CO concentration variable in the sensor-CO dataset was discretized into 5 equal buckets and set as the classification target. The datasets we use and their key features are listed in Table  1. UCI datasets are often ordered on some basis. As such, when processed sequentially in their native order, they provide a non-stationary data stream. We use them in this way. To assess performance on real world data in the absence of concept drift, we also use shuffled versions of each dataset. This ensures a stationary distribution. Our shuffled runs on real data use 10 randomised shuffles of each stream with fixed seeds so the experiments are reproducible.
All our experimental results and scripts are available for replicability at github. com/chaitanya-m in the exp analysis, results2, moa experiment scripts (ensembles branch) repositories; code for VFDT, EFDT, and the Recurrent AbruptDrift generator is at bitbucket. org/chaitm in the driftgen (thesis branch) repository. We use prequential accuracy as the primary evaluation measure. While the instantaneous feedback provided to the learner in prequential evaluation does not reflect a common setup in applied incremental learning, none of the learners in the study relies on or exploits it. All learners examined are general incremental learners, capable at any point in time of either updating the current model by learning from an example or applying that model to classify an example. The prequential evaluation strategy is simply a convenient incremental setting for evaluating such systems.
Experimental Results In order to compare the relative prequential accuracy of EFDT as a base learner with the prequential accuracy of VFDT as a base learner, we tabulated their wins (datasets for which each achieves lower error) by ensemble and class of streams (UCI unshuffled, UCI shuffled 10-seed, Synthetic 10-seed). For each ensemble technique and class of stream we present the outcome of a one-tailed binomial test to determine the probability that the ensemble with EFDT as base learner would achieve so many wins if wins and losses were equiprobable (represented by the p-value). We use a statistical significance level of α = 0. 05. The confidence interval represents the 95% spread for probability of success for the EFDT-based ensemble in the comparison. To address potential concerns about multiple testing, we also for each set of experiments present the outcome of a Holm-Bonferroni correction.
Standard UCI streams Tables 3-13 show the effect due to EFDT on 20 UCI data streams, which are largely drawn from real data. The prequential accuracy performance matches expectations; there is minimal complex drift in these scenarios, mostly arising from concatenating files together, as many streams were made available in files that were each associated with a single class. Under these conditions, class boundaries change periodically, an instance of a trivial concept drift. Five of these tables show bagging ensembles, which favor EFDT over VFDT. Table  3  shows the performance of vanilla OzaBag, in which EFDT outperforms VFDT on 16 streams, underperforms on 2 streams, and draws level on the remaining 2 streams. The p-value noted is 0. 00066, well within a standard significance level of 0. 05.
04813, 0. 08353, 0. 11894, 0. 3238. Only the first three alternate hypotheses would be rejected using a Holm-Bonferroni multiple testing correction, resulting in findings of significant improvements in the number of data streams for which the ensemble technique is more accurate using EFDT than VFDT for OzaBag, OzaBagAdwin and Adaptive Random Forest. In summary, as expected, the greater variance induced by EFDT's less conservative splitting mechanism proves productive when drift is not extreme. All types of ensemble learner achieve lower accuracy for more datasets than not when EFDT is used as a base learner in place of VFDT. The frequency of these wins is statistically significant at the 0. 05 level for all ensemble techniques other than LeveragingBagging in the case that ADWIN is disabled (when the use of EFDT still achieves lower error for 13 datasets, but VFDT achieves lower error for 6, preventing a statistically significant win).
Our ranked p-values are 0. 00377, 0. 00377, 0. 00377, 0. 00591, 0. 04813, 0. 04813, 0. 24034, 0. 24034, 0. 31453, 0. 75966. The first four alternate hypotheses would be rejected using a Holm-Bonferroni multiple testing correction, resulting in findings of significant improvements in the number of stationary data streams for which the ensemble technique is more accurate using EFDT than VFDT for OzaBag, OzaBagAdwin, Adaptive Random Forest and OnlineSmoothBoost.
EFDT based ensembles demonstrate superior prequential accuracy performance within a significance level of 0. 05 when used as a base learner with all boosting strategies: OzaBoost (Table  30 , p-value 0. 00014), OzaBoostADWIN (Table  31 , p-value 0. 01133), ADOB, BOLE and OnlineS-moothBoost (Tables  32, 33 , and 34, all three p-values 0. 00077 on account of 20 wins and 4 losses). Plain EFDT outperforms plain VFDT on 16 synthetic streams (Table  35 , with VFDT outperforming on the remaining 8. The ranked p-values are < 0. 00001, 0. 00014, 0. 00077, 0. 00077, 0. 00077, 0. 00077, 0. 00331, 0. 01133, 0. 41941, 0. 84627. The first eight null hypotheses would be rejected using a Holm-Bonferroni multiple testing correction, resulting in findings of significant improvements in the number of synthetic nonstationary data streams for which the ensemble technique is more accurate using EFDT than VFDT for OzaBag, LevBagAdwin, Adaptive Random Forest, OzaBoost, OzaBoostADWIN, ADOB, BOLE and OnlineSmoothBoost.
Finally, in vivo functional analysis needs to be able to explore different conditions with temporal resolution sufficient to resolve neuronal activity  7. While in principle such a dataset (with replicates and different conditions) would enable a mechanistic understanding of how a specific neuronal circuit transforms information, there is no single technology that can provide all of that temporal and spatial insight. Conversely, correlative multimodal imaging (CMI) pipelines  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]  are needed to integrate information from different sources on the same specimen. These approaches simplify data acquisition and management burdens by e. g. enabling targeted high-resolution recordings. They can give orthogonal information of functional/temporal (e.
Extending its operation to the 3rd dimension through volume EM (vEM) can in principle deliver the structural insight required to extract computational mechanisms from volumes of tissue  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]. It remains, however, highly time consuming and challenging for volumes exceeding ~0. 005 mm  3  (~months to years of data acquisition and processing times). Moreover, due to the limited penetration depth of electrons in tissue, vEM involves physical sectioning steps and is thus destructive and error-prone. All this together makes contemporary vEM approaches wellsuited to obtain individual high-quality datasets for up to (~100-1000 µm)  3  volumes  6, [34] [35] [36] [37] [38]  ; but they lack the robustness and scalability to routinely combine directly with functional imaging on their own or provide statistical information on the tissue scale from biological replicates.
X-ray computed tomography (CT), i. e. 3D volumetric X-ray imaging, is a technique that can be useful in providing contextual information over large volumes: A single conventional X-ray CT scan can cover larger volumes than most vEM approaches  11. Furthermore, X-ray imaging relies on contrast mechanisms that are often analogous to those relevant in EM (elements that provide strong back-scattering or absorption signals in EM do also result in strong absorption and phase contrast in X-ray images). This in turn makes sample preparation compatible with both techniques and simplifies correlating datasets obtained by these two modalities. While the spatial resolution of conventional laboratory X-ray µCTs (LXRT) can reach micrometre length scales  39  , the broad polychromatic spectra and low spatial coherence of most commercial laboratory systems significantly limit the achievable image contrast and density resolution.
1g1 ). In order to assess the benefit of high-brilliance, partially coherent X-rays, we imaged the same samples using SXRT at the beamline I13-2 at the Diamond Light Source  58  (Fig. 1e2-h2 ; Supplementary Fig. 2 , see "Methods" for details on the SXRT methodology). Comparing the images of the same specimen obtained with both X-ray modalities revealed that SXRT provided a sufficient increase in spatial resolution to identify substructure within each glomerulus (Fig. 1f2 ). Cell bodies of projection neurons (Fig. 1g2, h2 ) and smaller ones of juxtaglomerular cells (Fig. 1f2 ) were readily resolved in SXRT. Moreover, apical dendrites of mitral cells were recognisable in the SXRT datasets (black arrows, Fig. 1g2 ). To quantify to what extent SXRT is capable of delineating dendrites in general, we used tissue samples from both hippocampus and olfactory bulb (Fig.
2 , Supplementary Fig. 3 ). After SXRT (Fig. 2a , b, Supplementary Fig. 3g ) we acquired serial block-face electron microscopy (SBEM) subvolumes at an isotropic voxel size of 50 nm (olfactory bulb) and at 80*80*50 nm 3 (hippocampus) (Fig. 2c , Supplementary Fig. 3h ) as higher resolution "ground truth" data for assessing which features can be reliably identified in SXRT. For this, both EM and SXRT datasets of each region were warped into the same space  59  , allowing us to identify the same neuronal features in each modality (Supplementary Fig. 4 ). Notably, as highly X-ray absorbing elements provided the strongest contrast in scanning EM (SEM) imaging as well  11, 60  , the patterns revealed by SXRT and SBEM were virtually identical (e. g. Fig. 2b, c ), which simplified warping procedures between these two modalities.
We tagged the locations of the somata of 50 mitral and 132 pyramidal cells, and we asked 3 tracers to independently trace the apical dendrites of all those cells in both SXRT and SBEM as far as possible (Fig. 2d , Supplementary Fig. 3a, b, ). The consensus traces performed on SBEM data were used as ground truth, and independent tracers could trace dendrites in 44 cells on average. In the OB, the majority of apical dendrites were traced correctly in SXRT (29/44, averaged across three tracers, see "Methods") and could be followed for up to 369 µm, often sufficient to traverse the whole EPL (traceable length averaged among 3 tracers = 151. 4 ± 24. 6 µm, mean ± SEM) (Fig. 2d , e, Supplementary Fig. 3a, b ). Tracing apical dendrites was efficient across experiments (Supplementary Fig. 5 ) and reproducible on SXRT datasets from the same specimen acquired at two different parallel beam microtomography beamlines (I13-2 at the Diamond Light Source and TOMCAT at the Swiss Light Source) (Supplementary Fig.
3i-l ). This suggests that dendrite-level anatomical data can be efficiently achieved with many of the SXRT beamlines currently available at synchrotrons around the world. Most mistakes in following a dendrite's true trajectory (10/13, averaged across three tracers) happened at the region linking the soma and the apical dendrite (red lines in Supplementary Fig. 3a ). In this region, the contrast between intracellular space and extracellular space is weaker than elsewhere in the dendrite, due to a higher density of endoplasmic reticula (Supplementary Fig. 6 ). Moreover, in a few cases (2/44) the tracers stopped tracing before that linkage region was solved (black lines in Supplementary Fig. 3a ). Excluding the cells lost at the linkage region, the expected traceable length became both larger and less variable (average among 3 tracers = 201.
5× interquartile range). The grey dashed line marks the lost threshold used. Source data for (e) are provided as a Source Data file. onl, olfactory nerve layer; gl, glomerular layer; epl, external plexiform layer; mcl, mitral cell layer; ipl, inner plexiform layer. Fig. 3e, f ), possibly due to the thinner diameter of those dendrites, the higher degree of similarity in the trajectories between dendrites compared to the OB, and variation in staining, amongst other factors. Altogether, these results indicated that, with appropriate contrast enhancing measures (e. g. staining), SXRT can be used to delineate subcellular features (e. g. neurites) reliably for several hundreds of micrometres. Resolution estimates obtained with Fourier Shell Correlation were consistent with these findings (LXRT: 4 µm voxels, ~15 µm resolution; SXRT: 325 nm voxels; resolution of ~2-3 µm; SBEM: 50 nm voxels, resolution ~400 nm (Supplementary Fig.
4a , b, Supplementary Fig. 9 ). We targeted the medial stratum radiatum with SBEM to analyse dendritic spines on selected dendrites (Fig. 4a-d ). We acquired a SBEM dataset of a volume of 82 × 82 × 39 µm 3 at a voxel size of 10 × 10 × 50 nm  3. We then traced in the SBEM volume the apical dendritic tree of 7 pyramidal neurons previously identified in SXRT (Fig. 4b, c ) and annotated all dendritic spines in those dendrites (adding up to a total of 3019 spines) (Fig. 4c ). Of all spines 14% (417/3019) contained a spine apparatus (Fig. 4d ), a specialised form of endoplasmic reticulum present in large spines and involved in calcium homoeostasis and synaptic plasticity  5, [66] [67] [68] [69]. As suggested previously  70  , spines were denser on apical oblique dendrites than in trunk dendrites and the same applied to spines containing spine apparatus (Supplementary Fig.
9 ). Hippocampal pyramidal cells have been proposed to differ according to soma position (deeper or more superficial in the pyramidal cell layer). To assess whether spine properties depend on soma depth, we linked the traces of apical dendrites in the high-resolution SBEM volume with those in the SXRT dataset, reaching the cell body layer (Fig. 4b ). This allowed us to give each dendritic branch and spine their specific neural "depth" identity based on the position of their soma within the thickness of the pyramidal layer (i. e. more superficial or deeper). Spine density was independent of soma position (Supplementary Fig. 9 ), but more superficial pyramidal cells indeed had a substantially larger density of spines containing spine apparatus (Fig. 4e, f ). This supports the notion that some properties of CA1 pyramidal neurons may systematically vary between neurons, depending on soma depth within the CA1 pyramidal layer  65, 71, 72.