dataesr/scientific-paragraphs-categorization huggingface dataset:
Since any vibrational mode contains contributions from multiple internal degrees of freedom, the degree of freedom that dominates the vibration was first taken into account, and then the less significant terms were considered, with degrees of freedom that contribute less than 15% ignored. When the PED results were ambiguous, the force constant was determined by three-point potential energy scans. [62] With the optimized equilibrium terms and force constants, the empirical model reproduced the QM results quite well (Table  186  in Supporting Information SDD 2. 9). Generally the deviations between QM and MM were less than 0. 03 A ˚and 38 for bonds and valence angles, respectively, while the vibrational frequencies were within 5% of the target QM values. A general consideration is the limited number of atom types in the force field such that the same bonded parameter occurs in multiple molecules, thereby limiting the ability to optimally reproduce the target data for all the molecules.
In the present study parameters previously available in CGenFF were not subject to additional optimization. All other parameters assigned by the CGenFF program have an associated penalty score, which is low when there is a closely analogous parameter based on similar atom types present in CGenFF. Such parameters with low penalties (<5 for bonds and <10 for angles) were transferred directly from CGenFF to the modified nucleotides. Thus, there were only a limited number of bonded parameters for optimization for each model compound. For this reason there are a few angles with differences >38 in the 4MC case (Table  185  in Supporting Information SDD 2. 9). Table  3  summarizes the overall statistics of MM geometries relative to the QM values using the optimized parameters. Dihedral optimization.
Conclusions The present study involved a comprehensive development of empirical force field parameters for the 112 known modified ribonucleotides. The parametrization was performed to be consistent with the philosophy behind the development of the CHARMM additive force field, such that the quality of the parameters will be compatible with the other components in the CHARMM force field. The parameters were optimized targeting QM data and further refined against experimental data when possible. Notable was the optimization of the partial atomic charges based on the reproduction of watermodel compound minimum interaction energies. Starting from the partial atomic charges obtained from the CGenFF program, additional optimization was performed using a least squares fitting approach that always reduced the RMS difference for each model compound.
Their heuristics were tested using some real-life problems obtained from a transport company in Eastern Canada. Avella et al. [1]  studied a petrol replenishment network involving one depot, a heterogeneous tank truck fleet and no time windows. They proposed a heuristic and an exact algorithm based on a route generation scheme and a branch-and-price algorithm. To test the performance of their approach, they used real-world data consisting of 25 customers and six tank trucks of three different types. Ng et al. [23]  studied two small petrol distribution networks in Hong Kong: the Hong Kong Island network and the network for the Kowloon Peninsula and the New Territories. They proposed a model for simultaneously assigning trips to trucks and stations. For this case, station' inventories were managed by the vendor who decided when to replenish each station and what quantities to deliver.
Results for the real-life instances To further prove the feasibility and effectiveness of the presented algorithm under real situations, we collected and investigated the real data of the NAFTAL petroleum company. This company is responsible for delivering various kinds of fuel (gasoline, premium gasoline, aviation gasoline, kerosene and diesel fuel) from 70 depots, serving more than 3500 petrol stations and using around 1500 tank trucks. In this experiment, we used instances that we obtained from the regional depot of petroleum products, Caroubier depot, in the city of Algiers in Algeria. For this depot, the company provided us with daily data for a period of 15 days. The data consist of the cities where the customers are located and the associated distance matrix, the order quantities with their time windows and tank truck related information, such as the number of tanks and their capacities.
Acknowledgments We gratefully acknowledge the anonymous referees for carefully reading the paper and for their helpful comments and suggestions. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://crea tivecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Published as :  Morgenstern, A. , Caët, S. & Limousin, F. Pointing and self designation in deaf and hearing children. Mouton de Gruyter. Open Linguistics,  This paper explores the issue of the development of pointing focusing on self-points within a deaf signing child's emerging system of self-reference, compared to a hearing child's use of pointing and self-words. As only one dataset of a French deaf signing child has ever been collected, this is a case study. We present arguments for a continuity hypothesis between first pointing gestures and signs. These arguments can only be confirmed when more data will have been collected in LSF (Langue des Signes Française -French sign language). However our analyses offer perspectives in line with significant studies  (Pizzuto 2007 ), aimed at clarifying the connections between the vocal and the gestural modality in early development, and the more general cognitive origins and developmental roots of language (Volterra,  Erting, 1990).
Research issues Since the studies on the (dis)continuity between pointing and signs have focused on points towards persons and pronominal reversals  (Petitto 1986 , Petitto 1987) , we wanted to investigate the development of pointing towards persons in signing children, compare them to the speaking children's uses and then try to compare the whole system of personal reference in both modalities. Only one longitudinal dataset of a deaf child signing in LSF has ever been collected  (Limousin 2010). It is very valuable data but we can only conduct a case study of deaf children's LSF development. In this exploratory paper we address two main issues:.
Transcription and coding The two children were recorded in different research contexts and different tools are necessary according to the nature of the data (sign language and/or spoken language). All of Madeleine's videos were transcribed in the CHAT format with the CLAN program  (MacWhinney 2000 , Morgenstern, Parisse, 2012). This program, used to link the Published as : Morgenstern, A. , Caët, S. & Limousin, F. Pointing and self designation in deaf and hearing children. Mouton de Gruyter. Open Linguistics, 1: 768-787. 14 transcription and the video, allows us to keep track of the context in which words and gestures are produced. Since we consider coding and transcription as a representation of our theoretical approach  (Ochs 1979) , we pay special attention to the features we distinguish.
Background Longitudinal data are data in which each variable is measured repeatedly over time. One possibility for the analysis of such data is to cluster them. The majority of clustering methods group together individual that have close trajectories at given time points. These methods group trajectories that are locally close but not necessarily those that have similar shapes. However, in several circumstances, the progress of a phenomenon may be more important than the moment at which it occurs. One would thus like to achieve a partitioning where each group gathers individuals whose trajectories have similar shapes whatever the time lag between them.
Introduction 1. Clustering longitudinal data Longitudinal data are data in which each variable is measured repeatedly over time. One way of analyzing this type of data is to cluster them; i. e. , divide the population into homogeneous subgroups. For this, different methods were proposed among which variants of k-means  [1] [2] [3] [4] [5] [6]  and various model-based classification methods relying on mixture models  [7] [8] [9] [10] [11]. The pros and cons of these approaches are regularly discussed  [12, 13]  though there are no current recommendations on which method to prefer in a specific context. The general idea behind partitioning is to group similar individuals within the same cluster. Different approaches to the concept of "similarity" are possible. They may be based on the concept of distance, resemblance, or likelihood.
It is based on the construction of the Fréchet mean between two curves, then between n curves. This method can be time consuming in case of large datasets. We introduce thus two methods that reduce the data size while keeping the essential information contained in the initial trajectories. The use of both data reduction and kmlShape yields a partitioning method that preserves the shapes of the trajectories and may be used with high-dimensional data. The sections below are organized as follows: we present first the methods used to partition the trajectories according to their shapes. Next, the performances of the methods are evaluated with artificial and real data. Then we discuss the results, the quality of the partitioning on the artificial data, and the originality of the method with real data; i.
General considerations 2. 1. 1 Notation. Let us consider a set S of n subjects. For each subject i, an outcome variable Y is measured t times. For the sake of simplicity, we consider that all trajectories have the same number of measurements t though trajectories with different numbers of measurements do not add complexity to the algorithm. The time of the j th measurement for subject i is noted x ij. The value of the j th measurement for subject i is noted y ij. The sequence Y i: ¼ x i1 y i1 ! ; x i2 y i2 ! ; :::; x it y it ! ! is called a trajectory. 2. 1. 2 k-means using shape-respecting distances. k-means is a partitioning algorithm that belongs to Classification-Expectation-Maximization (CEM) methods  [32]. This algorithm was first used with classical data  [33, 34]  but is now widely used with longitudinal data in various fields  [5, [35] [36] [37] [38] [39] [40].
As briefly reminded here, method k-means uses two tools: a distance and a mean. kmlShape requires both a distance and a mean that take the shapes into account. These tools (Fréchet distance and Fréchet means) are presented in the next section. Overall, kmlShape is a variant of k-means using: i) the Fréchet distance to calculate the distances between individuals and cluster centers; ii) Fréchet mean to construct the centers of the clusters. The stopping condition is the stability of the algorithm: when the clusters are identical at step s and step s -1, the algorithm is terminated (with a limitation of the number of iterations to avoid very long times before convergence). The pseudo code of the algorithm is given in Algorithm 1. [14]. Informally, it is often compared to a leash between two trajectories.
Data size reduction Reduction of the number of individuals. The use of the Fréchet mean approximation shifts the complexity of our first algorithm from O(t n ) to O(nt 2 ). This is an important gain, however insufficient for applying the method to large databases (thousand or tens of thousand individuals). One optimization option is to reduce the number of individuals by an identification of a small number of comparable trajectories. This suggestion of simplification is based on two facts: i) in large populations, some groups of individuals have close trajectories (because the limited number of typical trajectories); this is all the more true as the population becomes larger; ii) when two trajectories are very close, the Euclidian mean and the Fréchet mean are close (see  Fig 5). It becomes then locally satisfactory to approximate the Fréchet mean through the Euclidian mean.
Method comparisons The respective performances of kmlShape, fdakma, and k-means with small and medium datasets, case 1 and 2, are shown Table  1. We observed that kmlShape performs better than fdakma and k-means regarding the classification indices. The same was found when only one specific subgroup was analyzed (e. g. , only Case 1 with σ = 0. 05). However, the differences between kmlShape and the other methods were more marked in Case 1 than in Case 2. Also, these differences tended to decrease slightly when σ increased. Note that for these two examples, the results of kmlShape with λ = 0. 1 and kmlShape using DTW (i. e. λ = 0) were identical. Thus we give only the results of kmlShape with λ = 0. 1.
Impact of simplification in terms of time or number of individuals The respective performances of kmlShape, classical k-means, and simplified kmlShape with medium size data are shown Table  2. We observed that the performance of simplified kmlShape was close to (similar or slightly lower) the performance of kmlShape without simplification. In all cases, the performance of kmlShape was clearly better than that of a classical partitioning. The performances of classical k-means and simplified kmlShape with large datasets are shown Table  3. The simplified kmlShape outperformed clearly the classical k-means. We also observed that the performance of kmlShape with simplification was quite close to that of kmlShape without simplification.
Application to real data Cohort ICTUS. Ictus (see S1 File) is a cohort of 1380 patients with Alzheimer disease followed-up in 12 European countries  [60, 61]. These patients were included between February 2003 and July 2005 in 29 centers specialized in neurology, geriatrics, psychiatry or psycho-geriatrics with a recognized experience in the diagnosis and management of Alzheimer disease. Most of these patients were seen during memory consultations and included consecutively. These patients were examined at six-month intervals over two years. Each examination included (though not exclusively) an Instrumental Activities of Daily Living (IADL) assessment. A classical analysis of IADL trajectories using either mixture models or k-means revealed 4 groups (Fig 9. a ). The main feature of these groups is to show close consistent declines.
g. , monoclonal antibodies) which makes it necessary, from an ethical point of view, to target only the subpopulation with "rapid decline", which is not possible with the classical classification methods. Quidel database. The QUIDEL database aims to gain better knowledge on hormone profiles of women without fertility problems. This database has been described as the largest existing database on hormone profiles in normally menstruating women and includes ovary ultrasound scans on the day of ovulation  [62]. The database includes 107 women and 283 cycles with identification of the day of ovulation and daily titrations of the levels of the four main hormones of the ovulation cycle. It has been already the subject of publications  [63, 64]  The use of classical classification methods regarding the luteinizing hormone (LH) provided three typical trajectories with similar features but with slight shifts in time (Fig 10.
doi:10. 1371/journal. pone. 0150738. g009 days that follow ovulation may be important to understand some abnormalities of corpus luteum and, thus, the implantation of the embryo in the uterus. The identification of 22% of women with LH double peak profiles is important for further research in reproductive biology. UCR-CBF. The two last examples (CBF and Trace, see below) are extracted from the "UCR Time Series Classification Archive"  [67] , a collection of real and artificial datasets dedicated to studies of time series and longitudinal data. Contrarily to the approach we have adopted in our simulation study, these datasets are generated once. CBF (Cylinder-Bell-Funnel,  [68] ) is a dataset of 900 trajectories measured 128 times. These trajectories are divided into three clusters of sizes 302, 300, and 298.
The mean trajectories of each cluster are shown  Note that, in this example, the use of the DTW method gave a different (worse) result than the Fréchet mean with λ = 0. 1. The reasons for this will be discussed in the appendix A. UCR-Trace. The Trace database  [69]  (also extracted from the "UCR Time Series Classification Archive") was obtained from EDF (Electricité de France). The database includes 200 different transient classes (Fig 12. a ). We clustered the data using: • classical Euclidian k-means; • kmlShape using DWT with simplification (n S = 64, t DP = 30, randomAll, λ = 0) • kmlShape with simplification (n S = 64, t DP = 30, randomAll, λ = 0. 1) and The other methods were too time-consuming or did not converge. kmlShape λ = 0. 1 and kmlShape using DTW gave exactly the same results (we report here only kmlShape with λ = 0.
Discussion In the present article, we introduce kmlShape, a novel method of data partitioning. This method provides clusters on the basis of trajectory shape. This allows especially grouping individuals whose trajectories have similar forms but shifted positions in time. Given the high algorithmic complexity of kmlShape, we present two data-simplification methods that allow reducing the lengths of the trajectories or the number of the individuals of the population under study. In comparison with other shape-based partitioning methods, kmlShape demonstrated higher performances with the datasets tested whatever the variance, the population size, or the number of clusters. In addition, the search for loss of information due to data simplification has shown that the final partitioning is only slightly affected by this simplification (Table  2 ).
Another advantage was that each of the partition steps (reduction of the number of measurements or of the population size) is mathematically simple, graphically displayable, and easily checkable. At any moment, the user can decide to invalidate any excessive "data simplification". Finally, with real datasets, kmlShape makes it possible to detect groups of individuals of non-negligible sizes that would not be detected by other classical methods. Thus, the method paves the way to new perspectives in terms of data analysis. Within the general context of data partitioning, the problem of the optimal number of clusters is still an open issue. Numerous criteria exist, either parametric (BIC  [70] , AIC  [71] , AICc  [72] , global posterior probability  [73] ,. ) or non-parametric (Caliskin & Harabatz  [74] , Ray & Turi  [75] , Davies & Bouldin  [76] ,.
Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators-depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance-to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O 2 -A and O 2 -B bands (111.
It is thought to be the best candidate band to retrieve SIF  [25] , and the accuracy of the atmospheric correction can enable high-accuracy SIF retrieval. Methods such as FLD  [26] , 3FLD  [27] , cFLD  [27, 28]  and SFM  [23]  have been developed for use with space measurements combined with the atmospheric parameters computed by MODTRAN  [4, 12, 29, 30]. Guanter et al. estimated SIF based on oxygen absorption features using atmospheric corrections based on the MODTRAN code to avoid uncertainties caused by atmospheric absorption and scattering effects. They tested this method on Medium Resolution Imaging Spectrometer (MERIS) images and FLEX-like high-resolution data  [12, 29]. Frankenberg et al. have shown an efficient alternative fluorescence least-square retrieval method based on the O 2 -A band, with decoupling of fluorescence from scattering properties, and have applied this method to GOSAT and OCO-2 images  [31].
Generation of Simulated Data In this paper, the MODTRAN 4 and SCOPE models are used to simulate TOA radiation, including fluorescence radiation. Assuming that SIF and reflectance follow Lambert's law, the radiance at TOA, including SIF radiance, can be described as follows: L TOA " E 0 cosθ π ρ soE0 cosθ π pτ ss `τsd q R pτ doτoo q 1 ´R ρ `SIF pτ doτoo q 1 ´R ρ (1) On the right side of this equation, the first item is the atmospheric contribution to the TOA radiance signal, the second item is the surface-reflected radiance, and the last item is the fluorescence signal contribution to the TOA radiance. In the equation, ρ so is the hemispherical reflectance, E 0 is the extraterrestrial solar irradiance on a plane perpendicular to the sun's rays, θ is the solar zenith angle, R is the surface reflectance, SIF is the fluorescence radiance at the top-of-the-canopy (TOC), and ρ is the spherical reflectance of the atmosphere back to the surface, τ stands for transmittance.
The first subscript indicates incident radiation, and the second subscript indicates scattered radiation. τ ss + τ sd is the total irradiance transmittance (including direct and diffuse components), and τ do + τ oo is the spherical transmittance from the surface to the TOA. To τ ss , the incident and scattered radiation are direct solar radiation; to τ sd , the incident radiation is direct solar radiation, and scattered radiation is hemispherical diffuse radiation; to τ do , the incident radiation is diffuse sky radiation, and the scattered radiation is the direct radiation in the observer's direction; to τ oo , the incident radiation is direct radiation, and the scattered radiation is the direct radiation in the observer's direction. In Equation (1), SIF and R come from the simulation of the SCOPE model, and the other parameters are computed by MODTRAN.
The atmospheric radiative transfer code MODTRAN  [41]  is applied to extract the atmospheric spectral transfer functions for the forward modeling of TOA  [42]. To generate the database, 1080 MODTRAN cases (Table  1 ), including four sun zenith angles, four sensor heights, three elevations, five surface meteorological ranges and three vertical water contents  [11, 30, 32, 43, 44] , are simulated. Every case covers 10,000-25,000 cm ´1 at a 1-cm ´1 resolution. The atmospheric parameters, including ρ so , ρ, τ ss , τ sd and τ do , are computed from the simulated data. The SCOPE model can simulate fluorescence and reflectance on the canopy using the radiation transfer module and the leaf biochemical model. The input parameters of SCOPE are introduced in detail in the SCOPE documentation and in a paper by Ni et al.
[45]. In this study, based on the works of Damm et al. [30] , Liu et al. [44]  and Daumard et al. [43] , the chlorophyll α+b content (Cab), fluorescence quantum yield efficiency (Fqe) and leaf area index (LAI) are set varied (Table  2 ). The other parameters are set to the default values. For each atmospheric condition, 48 SCOPE cases are simulated for the generation of reflectance and fluorescence on the canopy. In the final simulation, 43,200 cases are generated. Figure  1  shows the results of the simulated data generation process. Figure  1a-c  show the canopy reflectance, fluorescence and soil spectra, respectively. The reflectance and fluorescence spectra are derived from the SCOPE model, and the soil spectrum is derived from the spectral library of ENVI. The reflectance, fluorescence and soil spectra are inputs into the radiative transfer equation to compute the TOA radiance.