dataesr/scientific-paragraphs-categorization huggingface dataset:
Each pulse is defined by four points (low value, high value, rise time, and fall time). A Matlab script has been developed for writting automatically the spice netlist. Figure  11  shows the equivalent circuit of the motor, which is supposed to operate in linear conditions; R L R , , , P S S and C S represent the HF behavior of the phase windings; they are associated with the phase resistance and inductance R and L. The parameters are determined with the experimental data. The phase resistance R is measured in DC ( = R 0. 35 Ω). The other parameters are estimated from the impedance spectrum of the phase windings presented in Figure  12. The impedance spectrum is measured with a HP4294 impedance analyzer connected to another machine, which has the same reference than the motor of the testing bench.
Life events trigger change in travel behavior Several studies have found evidence for the influence of life events on travel behavior change  (Clark et al. 2016; Beige and Axhausen 2017; Dargay and Hanly 2004; Oakil et al. 2016; Scheiner and Holz-Rau 2015; Verhoeven et al. 2005). For the UK,  Clark et al. (2016)  using a longitudinal dataset found that a higher share of commuters who experienced changes in the workplace and residential location switched mode compared to commuters who did not experience life events. These results still hold when the availability of public transport and commute distance are taken into account and point to a separate effect of life events on commute behavior. Besides changes in residential location and employment, the acquisition of a driver's license and children moving out of the household have been shown to affect car ownership and individual travel behavior  (Beige and Axhausen 2017; Dargay and Hanly 2004; Oakil et al.
We define modality types based on multimodal travel behavior as highlighted in Fig. 1. The dataset captures modality types at two points in time for each person. The model has two parts: one part determines the influence of covariates on modality type membership, the other the transition probability of switching to another modality type between time points. Socio-demographic factors and travel-related attitudes affect the modality type at the first point in time while life events and the previous modality type influence the probability of transitioning to another modality type. We use covariates in the membership model and life events in the transition model that depend on life stage groups to investigate the interaction between life stage groups and multimodal travel patterns as well as transition probabilities.
Sample description UC Davis has conducted an annual campus travel survey (CTS) since the 2006-07 school year (e. g. Wei 2018). Each fall, a UC Davis graduate student administers the CTS to a stratified random sample of students, staff, and faculty. The survey typically achieves a response rate of 10 to 21 percent of the invited sample. The survey asks respondents about commute travel, travel attitudes, and socio-demographic characteristics. In this study, we analyze longitudinal panel data from the most recent eight CTSs (2010-2017) (see Thigpen 2019 for more information about this panel). In the overall panel sample (3471 persons), 75% of the persons answered the survey twice and 25% three times or more. We chose to construct a two-wave dataset with two points of observation in time per person.
Persons in other life stages such as retired persons or parents with children at different ages would have been interesting to explore but we are not able to distinguish them in the present dataset. But the life stages defined in this study allow us to investigate whether young persons differ significantly from parents and older adults in their responses to life events. This analysis may contribute to recent discussions about decline or stagnation in car ownership and use. In addition, we compare two millennial groups at different life stages, the results of which may add to discussions about generational differences or delay in life stage.
If we split our sample into millennials with children and without children, female and male millennials without children are equally represented in the active travelers group while only a quarter of millennial active travelers with children are women. However, female non-millennials have the same share in the active traveler group regardless of their status of parenthood. This observation suggests that millennials may fall back into gender disparities of travel behavior of former generations when they become parents. These results support the argument of other studies that behavioral differences of millennials may be temporal and partly due to delayed life stage  (Delbosc and Currie 2013). Since the numbers of observations in our dataset are relatively small in each category these results should be treated with caution and tested with a larger sample.
Features are computed for both input images and then compared to estimate the degree of similarity. This can be achieved by evaluating either a distance metric in feature space or the final classification loss. We build a Siamese CNN to match images of the same objects across multiple street-view images. Google street-view and Mapillary provide access to a huge amount of street-level images that can be used to construct very large datasets for deep learning approaches. Here, we use the former to build a multi-view dataset of street-trees and use a dataset provided by the latter for traffic signs. Both are then employed as testbeds to learn instance matching with soft geometric constraints based on a Siamese CNN model. Our main contribution is a modified Siamese CNN architecture that jointly learns geometric constellations from multi-view acquisitions jointly with the appearance information in the images.
In  [22] , a Siamese CNN and temporally constrained metrics are jointly learned to create a tracklet affinity model. Another approach  [23]  uses a combination of CNNs and recurrent neural networks (RNN) in order to match pairs of detections. In our setup and dataset, objects are not tracked from consecutive frames unlike the works mentioned, causing a background change which makes the problem more difficult. Object geo-localization from ground imagery such as Google street-view has been a major research interest for several years, for example, for street-tree detection  [24, 25]. Other works aim at geo-localizing poles in Google street-view images  [26]  and use state-of-the-art object detectors along with a modified brute-force-based line-of-bearing to estimate the locations of the poles.
As for traffic signs, the image crops vary depending on the acquisition due to Mapillary's dataset being crowdsourced. The crops can be acquired from a camera mounted on a vehicle or by pedestrians therefore providing an inconsistent setup. We thus propose to implicitly learn the distribution of geometric parameters that describe multi-view photogrammetry together with the image appearance of the objects. Our assumption is that this approach will enable cross-talking between image evidence and geometry. For example, if the same object appears with the same size in two images (but very different perspective), the triangle that connects both camera positions and the object must be roughly isosceles. That is, the object is located in between both camera standpoints. Conversely, the object in question that is viewed from the same perspective (very similar image appearance) but appears rather small will point at a pointy triangle with one very long leg (longer than the baseline) and another shorter leg.
Loss Functions We tried three different variants of loss functions for our multi-view instance matching approach and explain their details in the following. Contrastive: Our first approach is a Siamese CNN composed of two identical subnetworks that are trained with a contrastive loss function  [45]. A contrastive loss (Equation (  1 )) takes a pair of features created from the two branches of the network as input, unlike other loss functions that evaluate the network across the training dataset. The loss function's purpose is to bring matching or positive embeddings closer and push non-matching embeddings away in feature space. Therefore, the loss function encourages the network to output features that are close in feature space if samples are similar, or different features if they are not similar.
Datasets We evaluated our method on two different datasets. Both datasets differ in terms of objects, image geometry, and acquisition strategy. The Pasadena dataset consists of panorama images from Google street-view, whereas the Mapillary dataset contains mostly images acquired with various dash cams in moving vehicles. Objects of interest are trees in Pasadena and traffic signs in Mapillary. Baselines between consecutive panoramas of Pasadena are usually larger (≈50 m, Figure  5 ) than those between consecutive frames of Mapillary (usually a few meters depending on the speed of the vehicle, Figure  6 ). While panoramas of Pasadena show a 360 • view around the mapping vehicle, Mapillary images are acquired with a forward looking camera in a moving vehicle, resulting in a much narrower field-of-view.
In addition, this leads to different mappings of the same object in consecutive images, as shown in Figure  7. While objects in Mapillary images mainly experience a scale change while the vehicle is driven towards them, objects in panoramas also undergo a significant perspective change. In the following, we describe both datasets in more detail. We tested our approach on a new dataset of Pasadena, California, USA, which extends the existing urban trees dataset of our previous work  [13, 25]. It is generated from an existing KML-file that contains rich information (geographic position, species, and trunk diameter) of 80,000 trees in the city of Pasadena. For every tree, we downloaded the closest four panoramic images of size 1664 × 832 pixels from Google street-view, as shown in Figure  5. A subset of 4400 trees with four views each was chosen, leading to 17,600 images in total plus meta-data.
Note that the Pasadena inventory contains only street-trees, which makes up roughly 20% of all city trees. We drew bounding boxes around all street-trees per panorama image, which resulted in 47,000 bounding boxes in total. A crucial part of the labeling task was to label corresponding images of the same tree in the four closest views, as shown in Figure  1. As presented also in Figure  5 , the perspective changes are drastic in some cases, making it even difficult for the human eye to tell if they are of the same location. In addition, distortions often occur when the trees overcast the 360 camera. Our final dataset is composed of panoramic images containing labeled trees (and matches between four tree images per tree), the panorama meta-data (geographic location and heading of the camera), and the geo-position per tree.
Mapillary We ran the baseline methods and our methods on a new dataset provided by Mapillary (www. mapillary. com) in order to verify our results. This dataset is not to be confused with the Mapillary Vistas dataset  [49]  which is provided for a semantic segmentation challenge. The dataset contains 31,342 instances of traffic signs that are identified within 74,320 images in an area of approximately 2 km 2. On average, two traffic signs appear in each image. The dataset format is in GeoJSON, where each "feature" or identity has the following properties: (i) the geo-coordinate of the object that is attained by using 3D structure from motion techniques, which is thus affected by the GPS, and the density of the images; (ii) the objects distance in meters from the camera position; (iii) image keys to identify which the object appears in and which is used to retrieve the image using their API; (iv) geo-coordinates of the image location; (v) the object's altitude and angle; and (vi) the annotation of the sign in polygon form.
The Mapillary dataset is quite different from our Pasadena dataset in many ways. The images are crowd-sourced with forward looking dash cameras on moving vehicles, smartphones, or even panoramic rigs on hobbyists cars. Therefore, the image sizes and quality are very inconsistent, as well as the time the images were captured. Because most of the images were captured from car dash cams, the viewpoint changes are only a few meters (Figure  6 ) due to the images being consecutive frames in comparison to a GSV panorama (Figure  5 ). As shown in Figure  6 , because the camera is mostly forward looking, the objects are viewed almost from the same viewpoint with scale changes, and the objects are of a very small size in comparison to trees for instance. In addition, it is important to note that unlike trees the traffic signs are much smaller, and the best angle to capture them is from the front and not sideways due to how thin they are, as shown in Figure  6.
Does Geometry Help? We evaluated whether geometric evidence helps by comparing against a baseline without geometric features for the Pasadena and Mapillary datasets (Table  1 ). All three model architectures, Contrastive, Metric, and TripleNet, were evaluated per dataset with (w/ Geometry) and without geometric features (w/o Geometry). The only difference from w/ Geometry to w/o Geometry is that we concatenated the geometric features to our image-based features right before the decision networks, i. e. , after the feature subnetwork. Note that, for this experiment, we added geometric features at a later stage than for our full model (Ours) in order to allow a fair comparison. Adding geometric features consistently improves accuracy across datasets and architectures (Table  1 ). The Metric model architecture achieves the best results for Pasadena, whereas Contrastive works best for Mapillary.
Results Superior performance of simple concatenation of geometric features to visual features (w/ Geometry) in comparison to using only visual features (w/o Geometry) leaves room for more discriminative, joint feature embedding. Ours adds geometric features as a second input in addition to image patches resulting in jointly convolving across geometric and visual cues with the feature subnetworks (ResNet34) at an earlier stage. In fact, using both sources of evidence simultaneously as input results allows the network to reason about their joint distribution. For reasons of consistency, we report the results of w/o Geometry and w/ Geometry for Pasadena and Mapillary using the different losses, with the same datasets. Since the TripleNet model architecture clearly performed worse for the baseline experiments, we keep only Contrastive and Metric for evaluating Ours (two bottom rows of Table  1 ).
Conclusions We present a Siamese CNN architecture that jointly learns distributions of appearance-based warping functions and geometric scene cues for urban object (i. e. , trees and traffic signs) instance matching in the wild. Instead of sequentially imposing hard thresholds based on multi-view photogrammetric rules, joint learning of appearance and geometry enables cross-talking of evidence inside a single network. While our network design is only a slightly adapted version of existing Siamese CNN architectures, adding geometry to image evidence consistently improves object instance matching results for both Pasadena and Mapillary datasets. Our hope is that this idea of "learning soft photogrammetric constraints" and combining them with object appearance will unleash a whole new line of research that models warped image content and relative sensor orientation jointly.
Introduction When data are not sufficient to build a model, one may use prior information to stabilize the estimation of some parameters of the model. In population pharmacokinetics (popPK), there are two alternatives to stabilize poorly estimated parameters with prior information: either to fix them to their previous estimated values or to ''inform'' them thanks to their previous estimated values. ''Informing'' poorly estimated parameters instead of fixing them reduces the bias in cases where the parameters are slightly different in the previous population and in the population from which the sparse data were collected. To ''inform'' poorly estimated parameters, the PRIOR subroutine in NONMEM can be used, regardless of the estimation method. Indeed, priors can be included either while using a full Bayesian method (Markov Chain Monte Carlo (MCMC) Bayesian analysis) or a Maximum Likelihood Estimation such as First Order estimation (FO), First Order Conditional Estimation (FOCE), Second Order Conditional Estimation (Laplace) or Expectation Maximization methods (EM methods: Importance Sampling algorithm (IMP) and Stochastic Approximation Expectation Maximization (SAEM))  [1].
Adding a prior to a Maximum Likelihood Estimation would technically convert these into a mode a posteriori (MAP) estimation of the population parameters, even though this term does not show up on the NONMEM report. Priors are at the heart of Bayesian statistics, whereas they are optional for frequentists  [2]. Full Bayesian analysis with ''Bayesian'' priors places a prior penalty on its conditional likelihood; the same prior penalty is used on maximum likelihood with ''frequentist'' priors. The OFV is the sum of the OFV on the sparse data (O S ) and the penalty function (O P ), which reflects the deviation of the iterated parameters from their previous estimate value  [3]. Therefore, it is the sum of O S and O P that is minimized. The main advantage of the ''frequentist'' priors approach, compared to the ''Bayesian'' priors approach, is the tremendous decrease in computational time  [4].
The pros and cons of the penalty function over simultaneous fitting of all data are similar to those of the Bayesian approach. The main advantages lay in the rapidity and stability of the runs, which is especially important from the industry perspective. One of the disadvantages is the absence of an established robust method for covariate testing when using a penalty function. However, testing for covariates is possible with the penalty function unlike in the Bayesian approach. While the PRIOR subroutine in NONMEM seems to be a suitable way to analyze sparse or small datasets, literature about the PRIOR subroutine is rare. This review aims at providing a guidance on how to implement and apply the PRIOR subroutine in NONMEM.
In some studies, the use of the prior approach was preferred over pooling sparse with previous data because it allowed the analysis to be completed in one single NON-MEM run  [15]. Compared to the model built on pooled data, the model built with priors may be more stable and provide a better fit of the new data  [27] , or reduce the residual unexplained variability  [26]. To analyze sparse pediatric data when adult data are available, two articles concluded that it was better to build first the adult model (with allometric scaling) and then use it as prior for the pediatric model than to build a model on pooled data  [12, 13]. In the first one, the use of the prior approach prevented the large number of subjects and samples in the rich prior study from driving the estimates of the small new dataset of the population of interest  [12].
First, a Bayesian estimation of the individual PK parameters for each model was run on the new data (MAXEVAL = 0 in the estimation step allows the estimation of individual g conditional on the initial estimates). Then, the models were ranked by OFV or Akaike Information Criterion (AIC, which applies a penalty to models with more parameters). For each model, the distribution densities of individual g i were compared to the theoretical g-distribution N(0,x 2 ). It was hypothesized that the model adequately describes the new dataset if the distributions visually overlap and the g-shrinkage is low, as illustrated in Fig. 1. The predictive performance of each model may be evaluated by multiple simulations which are then compared to the new data: -Visual Predictive Checks (VPCs) can be plotted with the new data (external VPC).
-Normalized prediction distribution errors (NPDEs) can be used to compare the simulated concentrations with the observations in the new dataset  [37]. The models can then be compared and ranked according to p-values of tests determining whether the NPDEs follow a normal distribution (Wilcoxon sign rank t-test, Fisher test for variance, Shapiro-Wilks test). The best predictive model is the one resulting in the lowest number of Fig. 1  Plot of individual g clearances (black line) on top of theoretical g-distribution N(0, x 2 ) (dotted line). The model resulting in the top plot is to be preferred over the one resulting in the bottom plot. ETACL: g clearances. Adapted from  [43]  tests in which the NPDEs deviate from a normal distribution. In the systematic comparison of literature models by Knosgaard et al.
Knosgaard et al. analyzed both parent drug and metabolite: they combined the parent drug model and the metabolite model that performed the best in the systematic model comparison stage for each molecule  [9]. Brill et al. built a model to quantify the interaction effect of antiretrovirals on tuberculosis treatment in patients with both HIV and tuberculosis  [7]. The PK parameters of the antituberculosis drug were based on data from two phase IIb studies in subjects without antiretrovirals. The drug interaction effect parameters were based on data from two drug-drug interaction studies in subjects without tuberculosis.
The sparse data contained neutrophil and PK observations but no information on CD11b binding (neither free nor total CD11b measurements), while the model could not be simplified for mechanistic reasons. As the previous data were available, the strength of the previous estimates of CD11b binding parameters was assessable: a model with prior (previous model as prior) was built on the previous data without the observations that allowed the estimation of the CD11b binding data. Robustness was evaluated by assessing the degree of similarity between the estimates of this model and those of the reference model. In summary, one would ideally select the model which responds best to the objective (e. g. characterization of ka). If some models are equivalent as regard to the problematic, it is possible either to use the model which best describes the new data using the Bayesian estimation quality criteria or to build a new model with a meta-analysis.
In our review, only two out of 32 articles analyzing sparse data used TNPRI  [15, 29] : in both articles, the previous analysis had been done by the same team. Eighteen articles  [4-14, 23, 25, 27, 30, 32-34]  used NWPRI; in the remaining 12 articles the method was not specified  [16-22, 24, 26, 28, 31, 35] , among which six used prior only on THETA  [19, 20, 22, 24, 28, 35] , the distribution attributed to the OMEGA consequently having no impact. In practice, the implementation of NWPRI in NON-MEM is much simpler than the implementation of TNPRI. In the present version NONMEM 7. 4, TNPRI needs an output file from the reference model (msf file) that is not available when using priors from literature.
Prior values of the parameters The prior values to THETA, OMEGA 2 and SIGMA 2 should be written and fixed in the control stream in $THETAP, $OMEGAP and $SIGMAP records, respectively. In case of covariances between n random components, OMEGA 2 and SIGMA 2 matrices should be informed in $OMEGAP BLOCK (n) and $SIGMAP BLOCK (n) records. Although inter-occasion variability is different from inter-individual variability, it is also a random effect coded using OMEGA's. Thus, prior inter-occasion variability is coded in the same way as prior inter-individual variability. Usually, the implementation of priors on SIGMA 2 can be avoided because data contain strong information for estimating the residual error. In most articles, the residual error was estimated independently from the original model. Only two articles used informative priors on SIGMA 2  [7, 29] , without providing rationale for doing so.