dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets Verbal autopsy Data The goal of verbal autopsy is to get some information from family about the circumstances of a death when medical certification is incomplete or absent  [24]. In such a situation, verbal autopsy can be used as a routine death registration. A list of p possible symptoms is established and the collected data X = (X 1 ,. , X p ) consist of the absence or presence (encoded as 0 or 1) of each symptom on the deceased person. The probable cause of death is assigned by a physician and is encoded as a qualitative random variable Y. We refer to  [39]  for a review of automatic methods for assigning causes of death Y from verbal autopsy data X. In particular, classification methods based on Bayes' rule have been proposed, see  [10]  for instance. Here, we focus on data measured on the deceased persons during the period from 1985 to 2010 in the three IRD (Research Institute for Development) sites (Niakhar, Bandafassi and Mlomp) in Senegal.
Binary handwritten digit data Handwritten digit and character recognition are popular real-world tasks for testing and benchmarking classifiers, with obvious application e. g. in postal services. Here, we focus on the US Postal Service (USPS) database of handwritten digits which consists of n = 9298 segmented 16 × 16 greyscale images  [25]. The dataset is available online at http://yann. lecun. com/exdb/mnist. The random vector X is the binarized image and is represented as a p-dimensional vector with p = 256. The class to predict Y is the digit so that L = 10. A sample extracted from the dataset is depicted on Figure  1.
Experimental design The implementation of the classification method requires the selection of the hyper-parameter ω = (t, σ) where t is the threshold (see Section 2) and σ is the kernel parameter see  (6). To this end, a double cross-validation technique is used. The dataset of size n is randomly split M = 50 times into a learning set L m of size τ n and a test set T m of size (1 -τ )n where τ ∈ (0, 1) is a proportion parameter and m = 1,. , M. On each learning set L m , the optimal hyper-parameter ωm is selected by a 5-fold simple cross-validation, m = 1,. , M. The resulting optimal hyper-parameter ω is computed as the empirical mode of the set {ω 1 ,. , ωM }. Finally, the mean Correct Classification Rate (CCR) is computed on the learning sets L m , m = 1,. , M and on the test sets T m , m = 1,.
Results obtained with Sylla & Girard kernel We first investigate the use of Sylla & Girard similarity measure (5) when plugged into  (6). The CCR are computed for α ∈ {0, 0. 1,. , 1} and for several proportions τ thanks to the double cross-validation procedure described in the previous paragraph. It first appears on Figure  2  that the graphs are not symmetric with respect to α = 0. 5. This means that the coding of the observations does affect the classification. This is different from the linear case, see Lemma 2. It is also apparent that the optimal value of α does depend on the dataset. However, in both considered cases, α = 0. 1 permits to outperform the RBF kernel associated with α = 0. 5. Thus, the selection of an optimal value of α is of interest. It can be easily done by introducing α as an additional hyper-parameter in ω and thus selecting it by double cross-validation, see Paragraph 5.
Results obtained with the 76 kernels from [37] The goal of this paragraph is to compare the performance of the classification methods obtained by combining the 76 similarity and dissimilarity measures presented in  [37]  with the exponential kernel  (6). For the sake of completeness, the results obtained with Sylla & Girard kernel presented above are also included. The classification results are summarized in Table  2  when τ = 63% of the dataset is used to train the classifier. Only the results associated with the 18 best kernels (in terms of CCR computed on the test set) are reported. It appears that these kernels achieve good classification results on both datasets with CCR ∈ [78. 7%, 89. 7%]. It is also interesting to note that 8 kernels out of the 76 of  [37]  appear in the top 18 on both test datasets, namely: Euclid, Hellinger, Dice, 3w-Jaccard, Orchia1, Gower & Legendre, Roger & Tanimoto and RBF.
Comparison with other classification methods The proposed classification method is compared to the Random Forest method (RandomForest package, version 4. 6-10 from R software), the kNN method (fitcknn function from the statistics and machine learning toolbox of Matlab) and the SVM method (library libsvm, version 3. 2 from Matlab). The "one-against-all" implementation of the SVM classification method is used. SVM and Random Forest methods were used with their default parameters. In particular, in case of Random Forest method, the number of trees to grow is set to ntree=500 and the minimum size of terminal nodes is set to nodesize=1. Some additional experiments reported in Table  5  and Table  6  showed that the obtained classifications were not very sensitive to these parameters: The CCR computed on the test set remains approximately constant when nodesize ∈ {1,.
, 10} and ntree ∈ {250, 500, 750, 1000}. The number k of neighbours in kNN method is selected using the double cross-validation procedure. Sylla & Girard kernel is plugged into pgpDA, kNN and SVM methods with α ∈ {0. 1, 0. 2,. , 0. 9}. The selection of α by double cross-validation has also been implemented, the resulting value is denoted by α in the following. It appears in Table  3  and Table  4  that, on the verbal autopsy dataset, pgpDA method yields better results than SVM, kNN and Random Forest methods on the test set. Since, on the learning set, the CCR obtained with Random Forest is larger than the CCR associated with pgpDA, kNN and SVM methods for all values of α, one can suspect that Random Forest overfits this dataset. One can also observe that the CCR associated with pgpDA slightly depends on α (CCR ∈ [72.
0%, 76. 4%]) whereas CCR associated with SVM and kNN are very sensitive to α (CCR ∈ [61. 2%, 74. 6%] and CCR ∈ [53. 1%, 61. 8%] respectively). At the opposite, SVM, kNN and Random Forest yield better results than pgpDA on the handwritten digit dataset. The CCR associated with pgpDA is however satisfying, it is larger than 87. 1% whatever the value of α is. This may due to the small number of classes (L = 10 here, L = 22 in the previous situation) which makes the classification problem not so difficult. The selection by double cross-validation of the parameter α in Sylla & Girard achieves good results for all the considered classification methods. The selected value remains stable accross the experiments: α ∈ {0. 3, 0. 4} with pgpDA, α ∈ {0. 1, 0. 2} with SVM and α = 0. 3 for kNN. It is a first step towards an automatic choice of the similarity measure in the classification framework.
[37]verifies condition (3) for all N > 0. Letting N → ∞, one can conclude that 1/S 2 defines a proper kernel classification method. -Finally, in view of[38], Proposition 3. 22 (ii), (iii) and Proposition 3. 24 (ii), it follows that K defines a proper kernel classification method. Correct Classification Rate (CCR) on the verbal autopsy dataset (top) and on the handwritten digit dataset (bottom). The results are sorted by decreasing values of the CCR computed on the test set. The train set includes τ = 63% individuals from the initial dataset. The last column refers to the equation number in[37].
Correct Classification Rate (CCR) on the verbal autopsy dataset (top) and on the handwritten digit dataset (bottom). Sylla & Girard kernel is plugged into pgpDA, SVM and kNN methods for α ∈ {0. 1, 0. 2,. , 1}. The CCR associated with the parameter α selected by the double cross-validation procedure is emphasized. The train set includes τ = 63% individuals from the initial dataset.
Correct Classification Rate (CCR) obtained with Random Forest (nodesize=1 and ntree=500). The training set includes τ = 63% individuals from the initial dataset. 1 67. 0 67. 6 67. 0 67. 1 66. 8 66. 3 66. 1 65. 9 500 67. 4 67. 8 67. 3 67. 0 67. 4 66. 9 66. 7 66. 4 65. 9 65. 7 750 67. 5 67. 6 67. 4 67. 1 67. 2 66. 9 66. 7 66. 5 65. 8 65. 8 1000 67. 9 67. 7 67. 3 67. 3 67. 2 67. 0 66. 7 66. 6 66. 2 65. 5.
This model presents the advantage in this context to be parsimonious and to allow the visualization of the clustered systems. Numerical experiments confirm the good behavior of FunFEM, particularly compared to state-of-the-art methods. The application of FunFEM to BSS data from JCDecaux and the Transport for London Initiative allows us to identify 10 general patterns, including pathological ones, and to propose practical improvement strategies based on the system comparison. The visualization of the clustered data within the discriminative subspace turns out to be particularly informative regarding the system efficiency. The proposed methodology is implemented in a package for the R software, named funFEM, which is available on the CRAN. The package also provides a subset of the data analyzed in this work.
The data correspond to station occupancy statistics in the form of free slots, available bikes over several time frames and other station activity statistics derived from station occupancy data collected every 5 minutes. The clustering is performed using a Gaussian mixture model based on features such as the average number of available bikes at different periods of the day. It should be noted that such techniques do not really take advantage of the temporal dynamic of data. In  Froehlich, Neumann and Oliver (2009) , two types of clustering are compared, both of which are performed by hierarchical aggregation. The first one uses activity statistics derived from the evolution of station occupancy, whereas the second directly uses the number of available bicycles throughout the day. Other studies, such as  Lathia, Saniul and Capra (2012) , use similar clustering techniques and data.
To this end, we introduce a novel model-based clustering method devoted to time series (and, more generally, functional data) that is able to take into account the nature of the BSS data. The proposed methodology, called FunFEM, is based on the discriminative functional mixture (DFM) model, which models the data into a single discriminative functional subspace. This subspace subsequently allows an insightful visualization of the clustered data and eases the comparison of systems regarding the identified patterns. A family of 12 models is also proposed by relaxing or constraining the main DFM model, allowing it to handle a wide range of situations. The FunFEM algorithm is proposed for the inference of the DFM models, and model selection can be performed either by BIC or the "slope heuristic.
" In addition, the selection of the most discriminative basis functions can be made afterward by introducing sparsity through a ℓ 1 -type penalization. The comparison of 8 European BSS using FunFEM allows us to identify pathological and healthy patterns in the system dynamic and to propose practical improvement strategies based on the most efficient systems. The paper is organized as follows. Section 2 presents the BSS data used to analyze and compare several European bike sharing systems. Section 3 introduces the DFM model, its model family and the FunFEM algorithm. The model choice and selection of the discriminative functions are also discussed in Section 3. Numerical experiments on simulated and benchmark data sets are then presented in Section 4 to validate the proposed approach. Section 5 presents the analyses and comparisons of 8 bike sharing systems using the FunFEM algorithm.
Based on the comparison results, recommendations to BSS providers and city planners are made. Finally, Section 6 provides concluding remarks. 2. The BSS data. In this work we want to analyze station occupancy data collected over the course of one month on eight bike sharing systems in Europe. The data were collected over 5 weeks, between February, 24 and March, 30, 2014. Table  1  lists the BSSs included in this study and some summary statistics on the systems. Figure  1  visualizes the locations of the studied systems. The cities were chosen to cover different cases in terms of the geographic positions of the city (south/north of Europe) and to cover a range of system sizes, from small-scale systems, such as Nantes, to much larger systems, such as Paris. The station status information, in terms of available bikes and docks, were downloaded every hour during the study period for the seven systems from the open-data APIs provided by the JCDecaux company 1  and by the Transport for London initiative.
11) where θ (q) k = (π (q) k , µ (q) k , Σ (q) k , β (q) ) is the set of parameters for the kth component updated in the M step. 3. 6. Model selection. We now discuss both the choice of the most appropriate model within the family and the problem of selecting the number K of groups and the intrinsic dimension d. On the one hand, it is first of interest to select the model of the DFM family that is the most appropriate to model the data at hand. On the other hand, the problem of selecting K and d can be, in fact, recast as a model selection problem. The idea here is to consider, for instance, a DFM model with K = 2 and the same DFM model with K = 3 as two different models among which one wants to choose. Thus, because a model is defined by its parametrization, its number of components K and its intrinsic dimensionality d, model selection criteria allow us to select the best combination of those three features required for modeling the data.
Here, we propose selecting the discriminative basis functions by constraining the optimization problem (3. 7) of the F step such that the loading matrix U is sparse (i. e. , such that U contains as many zeros as possible). To this end, we follow the approach proposed by  Bouveyron and Brunet (2014) , who rewrite the constrained Fisher criterion as a ℓ 1 -penalized regression problem. We therefore use their algorithm [Algorithm 2 of  Bouveyron and Brunet (2014) ] to maximize the optimization problem (3. 7) under ℓ 1 -penalization. 4. Numerical experimentations. This section presents numerical experiments to validate on simulated and benchmark data the approach presented above, before applying it on the BSS data. Here, BIC and the slope heuristic are challenged on a set of simulated curves. A sample of n = 100 curves is simulated according to the following model, inspired by  Ferraty and Vieu (2003) ,  Preda (2007) : Cluster 1: X(t) = U + (1 -U )h 1 (t) + ε(t), t ∈ [1, 21], Cluster 2: X(t) = U + (1 -U )h 2 (t) + ε(t), t ∈ [1, 21], Cluster 3: X(t) = U + (0.
4. 3. Comparison with state-of-the-art methods. This last numerical study aims at comparing the FunFEM algorithm with state-of-the-art methods on four real data sets that are commonly used in the functional clustering literature. The data sets considered here are as follows: the Kneading, ECG, Face, and Wafer data sets. Appendix A provides a detailed description of those data sets. FunFEM is here compared with the six state-of-the-art methods: kmeansd 0 and kmeans-d 1  [Ieva et al. (2013) ], funclust  [Jacques and Preda (2013) ], funHDDC  [Bouveyron and Jacques (2011) ], fclust  [James and Sugar (2003) ]  and curvclust  [Giacofci et al. (2013) ]. The two kmeans-based methods use, respectively, the L 2 -metric between curves (kmeans-d 0 ) and between their derivatives (kmeans-d 1 ). The four other methods assume a probabilistic modeling.
Funclust assumes a Gaussian distribution for the functional principal components scores, whereas funHDDC, fclust and curvclust directly model the basis expansion coefficients. Table  5  presents the clustering accuracies (according to the known labels) on the four data sets for FunFEM and the six clustering methods. FunFEM turns out to be very competitive with its challengers on those data sets. FunFEM outperforms the other methods on all data sets except the second one where it is the second best method. On the kneading, ECG and wafer sets, the improvement over state-of-the-art methods is significant. It is also worth noticing that the model selected by BIC (the model associated with the higher BIC value) often provides some of the best possible results. 5. Analysis of bike sharing systems.
This section now presents the results of the application of FunFEM to one month of stock data from eight bike sharing systems (Managed by JCDecaux Ciclocity and Serco) in Europe. As explained in the Introduction, clustering is a principal way to summarize the behavior of BSS stations, and this approach has already been used in the literature. This study proposes going further here. The FunFEM algorithm presents a few advantages compared to existing works for dealing with the BSS data considered here and for comparing the eight studied systems. First, conversely to previous works, FunFEM explicitly addresses the functional nature of BSS stock data and, as we saw earlier, it outperforms multivariate and functional clustering techniques in most situations. Fun-FEM is therefore expected to perform well on the BSS data and to provide meaningful clusters from the operational point of view.
Conclusion. This work was motivated by interest in analyzing and comparing several European BSSs to identify common operating patterns and to propose practical solutions to avoid potential issues. To this end, the discriminative functional mixture (DFM) model was proposed to model the functional data generated by the systems. In this framework, the data are modeled into a discriminative functional subspace. The FunFEM algorithm has been proposed for the inference of the DFM model. The selection of the most discriminative basis functions can also be made afterward by introducing sparsity through a ℓ 1 -type penalization. Numerical experiments have demonstrated the efficiency of the proposed clustering technique for both simulated and benchmark data. FunFEM appears to be a good challenger to the best state-of-the-art methods.
The ECG, Face and Wafer data sets are benchmarks taken from the UCR Time Series Classification and Clustering website. 4 The ECG data set consists of 200 electrocardiograms from 2 groups of patients sampled at 96 time instants and has already been studied in  Olszewski (2001). The Face data set  [Xi et al. (2006) ] consists of 112 curves sampled from 4 groups at 350 instants of time. The Wafer data set  [Olszewski (2001) ] consists of 7174 curves sampled from 2 groups at 152 instants of time. For these three data sets, the same basis of functions as for the kneading data set has been arbitrarily chosen (20 cubic B-splines).
Tools based on "intentions" The first category combines the tools based on "intentions". The intention is to express, in its conceptual form, a constraint that is imposed or not, but influencing the geometry of a project  (Faucher and Nivet, 2000). These numerical simulation tools use inverse simulation to identify solutions able to satisfy the designer's desired effect (or intention). The intention type tools include the SVR  (Houpert, 2003) , SOLIMAC  (Siret, 1997)  and DE VISU  (Nivet, 1999)  software prototypes along with the inverse simulation model of daylighting  (Tourre, 2007). None of them have been applied beyond the experimental prototype, since they were developed in the context of doctoral research. Tools based on "intentions" such as the inverse simulation model of daylighting  (Tourre, 2007)  are very specialized.
Tools based on "references" The tools based on "references" help the designer to reflect, characterize and compose effects (luminous, tectonic, constructive, etc. ) by manipulating photographic reference images. These methods are adapted to the designer's needs at the initial draft stage when the project is not yet well defined as they allow the designer to explore several potential solutions. The MaTerre'iO  (Fernandez, 2010) , DILEM'MAtériaux  (Tornay, 2011)  and KALEIDOSCOPE  (Scaletsky, 2003)  software prototypes along with the "Daylight Design Variations Book" (TNO-TUE Centre for Building  Research, 2000)  website are part of this category (Table  1 ). The latter is also an exception in that it provides a breakthrough and is of particular interest to one of the issues raised in our research.
Intentions To identify solutions able to satisfy the designer's desired effect (or intention). SVR software (prototype)  (Houpert, 2003). SOLIMAC software (prototype)  (Siret, 1997). DE VISU software (prototype)  (Nivet, 1999). Inverse simulation model of daylighting (prototype)  (Tourre, 2007). References To reflect, characterize and compose effects by manipulating photographic reference images. MaTerre'iO software (prototype)  (Fernandez, 2010). DILEM'Matériaux software (prototype)  (Tornay, 2011). KALEIDOSCOPE software (prototype)  (Scaletsky, 2003). Daylight Design Variations Book website (TNO- TUE Centre for Building  Research, 2000). Knowledge To provide knowledge and references on specific issues in order to select a solution. Climate change adaptation by design: a guide for sustainable communities  (Shaw et al.
"ENVI-Met 3"  (Bruse, 2010) , "Integrated Environmental Solutions (IES) © ", and "Autodesk Ecotect Analysis © " are software packages found in this category. The "Autodesk Ecotect Analysis © " software serves as a good example of the strength and weaknesses of a numerical simulation tool. First, the software is compatible with many existing CAD tools. The designer can therefore create a single 3D model for the overall design of the project and carry out various simulations and analyzes. There exist a number of them, including: complete building energy audits, thermal performance, water consumption and cost evaluation, solar radiation, daylighting, as well as shadows and reflections. The reporting of results is also very diverse. Indices, summaries, graphs and diagrams are suitable for conducting quantitative analyses.
Priority was given to the simplest ones, as they can quickly be used by students. Tests were conducted on the three simplified computation tools "LUMcalcul"  (Demers and Potvin, 2004) , "PET"  (Potvin et al. , 2004)  and on some indicators of "LCZ"  (Stewart, 2011). The same goes for analog simulation tools such as artificial sky (GRAP, 2010a) and the heliodon (GRAP, 2010b). Among numerical simulation software, which require considerable training time, only Autodesk Ecotect Analysis © was tested: Université Laval already held operating licenses and the facilitators at the design studio as well as at the seminar had sufficient training and experience using this tool to assist students in applying it. The 7 DSTs were handled by the 14 students in the design process of their residential project at various times, depending on the teams.
Results and discussion Results are presented in a way that combines tools with similar features (category, project status) or dealing with similar issues (UHI mitigation or passive architectural strategies. First, results are discussed on the use of the manual "Sun Wind and Light"  (Brown and DeKay, 2000)  as a hybrid DST in relation to the simplified computation tools "PET"  (Potvin et al. , 2004)  and "LUMcalcul"  (Demers and Potvin, 2004). These three tools are tailored to the initial design phases and address one or more passive architectural strategies. Second, the results of the integration of indicators in the workshop project are discussed separately as they concern essentially the urban scale, which is not the case with previous tools. Third, the appraisal of the use of the numerical simulation software Ecotect Autodesk Analysis © is examined by comparing it to the analogical simulation tools "mirror box" and the heliodon.