dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Gender and ethnicity 1 clues are two of the most fundamental and important demographic attributes of human beings, which remain unchanged all through lifetime. While people can easily recognize the gender and ethnicity of each other through their facial appearances, it is still a non-trivial problem for computers. Automatic face-based gender and ethnicity classification has promising applications in Human Computer Interaction (HCI), Business Intelligence (BI), surveillance, video and image retrieval, database indexing, and can provide useful information for face recognition.
Facial images were first normalized to the size of 30 × 30, and compressed to the dimension of 40, and then classified by the back-propagation SexNet. Their experiments on a set of 90 images (45 males and 45 females) displayed an average error rate of 8. 1% compared to the one of 11. 6% from the Psychophysical studies of five humans. Moghaddam and Yang  [2]  introduced a gender classification method, in which facial images were firstly down sampled into the size of 21 × 12, and then classified by Support Vector Machine (SVM). An error rate of 3. 38% was achieved on a database containing 1755 samples. Lu and Jain  [3]  proposed an ethnicity classification method, in which facial images were analyzed at multiple scales, an LDA classifier was constructed for each scale, and the final matching score was obtained by fusing all similarity measurements under product rule.
A classification accuracy of 96. 3% was achieved on a union database containing 2630 samples of 263 subjects. Texture feature based approaches describe local texture changes of certain areas to discriminate male from female or distinguish different ethnicities. Due to its low computational complexity, Shakhnarovich et al. [4]  investigated Haar-like features in combination with Adaboost for gender and race classification. Later, Hosoi et al. [5, 6]  extracted Gabor features and adopted SVM for the same issue. Approximately an accuracy of 91. 6% was obtained for gender classification on a database with 1240 facial images, while the one of 94% was achieved for ethnicity classification using 1991 face photos. Lian et al. [7]  improved SVM by the proposed Min-Max Modular SVM (M 3 -SVM) to classify Gabor features, and M 3 -SVM performed better (around 6% higher) than SVM in gender classification on 12,912 probes with the variations in facial expression, pose, occlusion, etc.
In 2007, Lu and Lin  [8]  compared boosted Haarlike and Gabor features with SVM to recognize sex on 518 frontal facial images of size 24 × 24 from the FERET database, and pointed out that Gabor features were more effective than Haar features for the given task. In the same year, Yang and Ai  [9]  proposed an LBP (local binary patterns) based method for demographic attribute classification, and they showed that with the help of AdaBoost, LBP features achieved promising results in both tasks of gender and ethnicity classification on the subsets chosen from FERET and PIE, prior to the ones reached by Haar features. Guo and Mu  [10]  used Gabor features and reported benchmark performance for classification of five ethnicities on largescale dataset MORPH-II containing more than 55,000 facial images.
O'Tool et al. [13]  applied Principal Component Analysis (PCA) to 3D coordinates of 130 faces to extract features, and reported a peak correct rate of 96. 9% in a 17-dimensional subspace, better than the best score of 93. 8% achieved based on their corresponding gray-level images in a 20dimensional subspace. They further pointed out that by combining both modalities at the feature level, final performance was improved to 97. 7% with a minimum subspace of 32 coefficients (half 3D based and half 2D based). Han et al. [14]  manually selected some regions on 3D face meshes, and calculated the ratio of surface area and volume of these regions in comparison to the whole face as features for gender classification. An error rate of 17. 44% was obtained by an SVM classifier on the GavabDB database.
Wu et al. [15]  proposed a supervised method namely weighted Principal Geodesic Analysis (PGA) to extract gender discriminating features from 2. 5D facial needle-maps, and an accuracy of 97% was obtained on the Max-Planck face database which comprises 200 facial sans. Hu et al. [16]  separated 3D faces into five regions, and introduced SVM to gender classification. The final result was obtained by combining the similarity scores of these five regions. An accuracy of 94. 3% was achieved on a mixed database with 945 face models. Toderici et al. [17]  adopted a high-level demographic feature estimated from the 3D meshes of the human face, and an accuracy of 99. 6% was achieved for a two-class (Asian vs. White) classification on FRGC v2. 0.
Decision level fusion is made for final decision. Competitive performance is achieved on the FRGC v2. 0 and BU-3DFE databases, highlighting the effectiveness of the proposed approach. The remainder of the paper is organized as follows. Section 2 introduces LCP features in detail and highlights its improvements to LBP based ones. Section 3 presents the feature selection process as well as the decision-level fusion. Experiments and results are displayed and discussed in Section 4. Section 5 concludes the paper.
Experiments on FRGC v2. 0 FRGC v2. 0  [43]  is one of the most comprehensive datasets publicly available for 3D face analysis. It contains 4007 textured 3D face models of 466 subjects, and each face model is made up of a 3D point-cloud and its 2D texture counterpart. Among the subjects, 22% are Asian, 68% are white, and 10% are others. While 57% are male and 43% are female, with the age distribution: 65% 18-22 years old, 18% 23-27 and 17% 28 years or over. The database was collected during the 2003-2004 academic year, and contains time and illumination variations. Expressions such as "Neutral", "Happiness", "Surprise", "Disgust", "Sadness",and "Other" are included in the database as well. Gender classification is a typical binary classification problem, but the one for ethnicity classification is generally not the case.
However, since the distribution of 3D face samples in current public databases (including FRGC v2. 0) with ethnicity labels available is generally unbalanced, we also treat the task of ethnicity classification as a binary classification problem, in the same way as the previous studies do  [19, 17]. Asian and white subjects are thus chosen from the entire database for both gender and ethnicity classification, and in totality there are 3676 face samples belonging to 319 white and 99 Asian people. Even though all face samples in the FRGC v2. 0 dataset are nearly frontal, Iteratively Closet Point (ICP)  [44]  is adopted to align the face model to the reference that is pre-defined, in order to control the error caused by slight pose changes. We then extract a pair of registered facial range and texture image from the aligned face model and all of the facial texture and range images are normalized so that the outer corners of two The Adaboost algorithm for LCP based feature selection (Ref  [40] ).
Performance of L1 and L2 distance in clustering based quantization In order to evaluate the performance of L1 and L2 distance in clustering based quantization adopted in LCP, we randomly select 20 textured 3D face models from the FRGC database, and extract the features of local circular patterns LCP 2,8 from both the texture and shape modality as plotted in Fig. 3 (a) and (d). The clustering results using L2 distance and L1 distance with the same number of clusters (59, identical to the number of bins in the LBP descriptor with 8 neighboring pixels) are shown in Fig. 3. InFig. 3, the x-axis denotes the index of each element in an LCP code starting from the left-top position as shown in Fig. 1(a) , and the y-axis displays the exact value of each element. In this case, the number of pixels around a central pixel is set at 8, thereby leading to 8 elements in an LCP code, and their values vary in the range of  [-255, 255].
Furthermore, we compare the computational cost for each distance metric, i. e. L1 and L2. Experiments are carried out on a PC with Intel Core i3 CPU using Matlab implementation. With 314,960 training local circular patterns, the computational time taken by K-means clustering with L2 distance repeated 10 times is 12,233 s, while that for L1 distance is only 600 s. L1 distance is thus computationally more efficient than L2 distance as well.
where B and A are the two histograms to be compared, and B i and A i are the ith bin value of B and A. 20 samples are randomly selected from the FRGC v2. 0 database as in the previous experiment, and Gaussian random noises with deviations vary from 1 to 10 and are added on range and texture images. Fig. 5  shows the average difference of histograms for LBP, LCP-L2, and LCP-L1. From the comparison, we can conclude that in both modalities, the clustering based quantization (LCP-L2, LCP-L1) outperforms binary quantization (LBP) under noise influence. Clustering with L2 distance performs better than L1 distance. Diff B; A ðÞ ¼ X i jB i -A i jð 11Þ.
As we defined in Section 2. 2, N is the number of local circular patterns used to compute cluster centers. Actually, in our experiments, N corresponds to the number of 2D or 3D facial images randomly selected, since we have to ensure that these patterns are in an even distribution for face analysis. Therefore once a facial image is chosen, these local cir-cular patterns of all pixels are used in the K-means clustering. We further vary the number of facial images for training and observe its impact on classification performance on the FRGC v2. 0 dataset. In gender classification, when this number reaches about 50 and 60 for 2D and 3D modality respectively, their accuracies remain stable; while in ethnicity classification, this number is around 30 and 40. Meanwhile, we cannot set this number too large in order to avoid overfitting.
As a result, in the following experiments, we set it at 60 so that it fits the two modalities in both tasks. A 10-fold cross validation is adopted to evaluate the performance of the proposed approach, in which the database is randomly partitioned into 10 folds. Experiments are carried out 10 times, and each time 9 folds are exploited as the training set, and the remaining 1 fold as the testing set. Thus, each fold is tested once. We ensure that each subject is only assigned to one fold, so that the classification is person independent. Fig. 6  shows the results of gender and ethnicity classification. As we can see, for gender classification texture features outperform shape features, while for ethnicity classification shape features perform better than texture features. In both tasks of gender and ethnicity classification, performance in either of the single modality is enhanced by combining shape and texture modalities.
Experiments on BU-3DFE BU-3DFE  [47]  is also one of the most popular databases in 3D face analysis, especially for 3D facial expression recognition. It contains 100 subjects among which 56 are female and 44 are male, ranging from 18 to 70 years old. All individuals are asked to perform six prototypic expressions. Each includes four levels of intensities, and there are hence 25 instant 3D expression models for each subject (plus one model with a neutral expression), leading to 2500 models in total. Median filter is utilized to remove spikes and cubic interpolation is adopted to fill holes. We employ ICP to align the face model to the pre-selected reference to correct possible pose variations. The registered facial range and texture image are then generated from each aligned face model and all of the facial texture and range images are normalized to the size of 140 × 140 pixels as in FRGC v2.
The experimental results of gender and ethnicity classification achieved are up to 95. 50% and 99. 60% respectively on the FRGC v2. 0 dataset, and 95. 60% and 97. 42% respectively on the BU-3DFE dataset, which clearly demonstrate the advantages of the proposed method. In future work, we will investigate possible solutions that are more effective to multi-modal facial gender and ethnicity classification, e. g.
Acknowledgments This work was supported in part by the  National Natural Science Foundation of China (NSFC)  under Grant  61202237 ; the  Specialized Research Fund for the Doctoral Program of Higher Education  (No. 20121102120016 ); the research program of  State Key Laboratory of Software Development Environment  ( SKLSDE-2013ZX-31 ); the joint project by the  LIA 2MCSI lab between the group of Ecoles Centrales and Beihang University ; and the  Fundamental Research Funds for the Central Universities.
Materials and methods From January 1, 2008 to July 1, 2012, all new patients with SCCHN identified from the Multidisciplinary Consultation Meeting registry were sent to the Lyon Sud Hospital Center (referral center). Patients with nasopharyngeal or sinusoidal-nasal carcinoma were excluded. We then observed in this group all patients who were diagnosed as metastatic and treated as such. We collected data on age, sex, performance status for general health, tobacco use, alcohol consumption, T stage, N stage, the site of metastases, diagnostic procedures, therapeutic decision and its implementation, as well as evolution of the disease in the patient. Survival was calculated using the Kaplan-Meier method.
We reanalyzed the surface displacements observed at the Lazufre volcanic complex in the Southern Andean Central Volcanic Zone using GPS measurements made between 2006 and 2008 and a large InSAR data set. We performed a detailed spatiotemporal analysis of the displacements using a principal component analysis inversion method (PCAIM). The PCAIM reveals a source with no significant changes in shape and dimension and with a remarkably linear strength increase over the whole period of observation (i. e. , 2003-2010). Then we used a three-dimensional mixed boundary element method (MBEM) to invert the first component of surface displacement as obtained from PCAIM. We explored a continuum of geometries from a shallow elliptic crack to a deep massive truncated elliptical cone that could represent a sill or a large magma chamber, respectively.
Based on seven ERS interferograms acquired between 1995 and 2000,  Pritchard and Simons [2002]  presented the first evidence of a large-wavelength surface displacement affecting the Lazufre Volcanic Complex. The displacements are assumed to have started sometime between 1996 and 1998 (the temporal resolution of the used ERS data set did not allow Pritchard and Simons to be more precise about the displacements onset). Around this time, a large-wavelength inflation covered an elliptical area with a 30 km NNE-SSW major axis, a 25 km minor axis and a maximum displacement observed at the center of the surface displacement pattern of about 1 cm yr 21. Using ASAR-ENVISAT Interferometric Synthetic Aperture Radar (InSAR) data collected over the same area between  March 2003 and April 2005 , Froger et al.
We produced a database of about 550 interferograms from ascending and descending ASAR images acquired between 2003 and 2010. These data were combined to determine the vertical and easting components of the displacement field, and the results were then compared with data from GPS observations acquired during three field surveys carried out between 2006 and 2008. To invert the large time series of InSAR images and GPS data and better analyze the time-dependent behavior of surface displacement, we use the principal component analysis inversion method (PCAIM, http://www. tectonics. caltech. edu/resources/pcaim/) [Kositsky and Avouac, 2010; Lin et al. , 2010; Perfettini et al. , 2010]. This analysis of long InSAR time series and GPS data provides results which are important for the understanding of the ongoing process.
, 2009; Henderson and Pritchard, 2013; Pearse and Lundgren, 2013]  (Figure  1 ). The LST2 station was located outside the deforming area to be used as a stable reference in further network adjustments. The LST7 station is located near the center of the area affected by displacements. The GPS observations acquired using dual-frequency receivers (sessions mostly lasting from 24 to 48 h) have been processed and adjusted using GAMIT/GLOBK 10. 35 software  [Herring et al. , 2010]. The final 1r accuracy is estimated to be 5 and 10 mm for horizontal and vertical components, respectively. The resulting GPS displacements observed between May 2006 and November 2008 are shown in Table  1. These ground measurements confirm surface uplift at a rate comparable to previously published estimates. The maximum vertical displacement was measured at LST7 station (10 cm observed over the 2.
ASAR Data A total of 64 ASAR-ENVISAT images acquired over the study area between 2003 and 2010 were used in this study (see Table  2 ). Thirty images were acquired from descending orbit (track 282, swath 2) and 34 from ascending orbits (track 318, swath 2 and track 404, swath 6). From this image data set, we produced a set of about 550 interferograms spanning between 35 and 2625 days. The interferograms were generated with the DIAPASON software [V C CNES/Altamira-information, 1996] using the two-pass method described by  Massonnet and Feigl [1998]. The orbital and topographic contributions were modeled and removed using precise orbit data from ESA DORIS and the SRTM digital elevation model oversampled to 45 m. The SRTM digital elevation model was also used to provide a geographic framework (UTM-WGS84) for the interferograms.
InSAR Time Series Analysis In order to characterize the time dependency of displacements as accurately as possible and to reduce the inherent redundancy within the interferogram data set we used a two-steps approach. The first step was to generate a time series for each track relative to the earliest SAR image using a technique similar to the one proposed by  Usai et al. [2003]. The second step, involved decomposing the displacement maps obtained from the first step into their principal components using the approach proposed by  Kositsky and Avouac [2010]. The principal component decomposition allows the displacement maps to be resolved into a superposition of several components, each of them characterized by its own temporal evolution. This type of decomposition is particularly advantageous for displacement inversions in which the displacement signal can be represented by a small number of components thus considerably reducing the amount of data to be inverted.
PCAIM Decomposition The principal component analysis-based inversion method (PCAIM) relies on principal component analysis of the surface displacement time series. In this approach, the displacement data are decomposed into a linear combination of principal components. Each component is associated with its own spatial vector,  Geochemistry, Geophysics, Geosystems 10. 1002/2014GC005370 singular value, and time function, where the singular value is a measure of the significance of the component. PCAIM makes it possible to handle large and heterogeneous data sets, unevenly distributed in time, such as those obtained by combining multitrack InSAR data and GPS measurements. Furthermore, contrary to classical principal component decomposition, it makes it possible to take the data uncertainties properly into account.
It allows the number of data to be reduced by about two orders of magnitude without losing many details of the displacement. Our resulting subsampled InSAR time series consists of 30 epochs on 14,561 observations, 16 epochs on 14,535 observations, 18 epochs on 14,116 observations for the track 282, 318, and 404, respectively, giving a total of 9. 2 3 10 5 InSAR measurements. The three InSAR time series are combined into a large displacement data matrix X of 43,212 lines (total number of subsampled measurements multiplied by the number of tracks) and 64 rows (the total number of epochs). For each time series, we interpolated the displacement values and errors at the missing epochs over the period spanned by the time series and we extrapolated displacement values, assigning an infinite error for missing epochs outside the period spanned by the time series.
The InSAR data are weighted either inversely proportional to the estimated variance or at zero for extrapolated missing values. The N 3 M data matrix X is first centered such that the time series X 5X2hXi has a mean value of zero. Next PCAIM decomposes X into the sum of its principal components: X 5U:S:V s (1) where U, S, and V have a size of N 3 N, N 3 M, and M 3 M, respectively, and s means that the matrix is transposed. U is the spatial functions matrix where the lines correspond to the subsampled data and the columns to the principal components, and it is only space dependent (N 3 N matrix). S is the matrix of the principal values, a diagonal matrix with r nonzero eigenvalues (i. e. , r is the S matrix rank) that gives the magnitude of each component. V is the time function matrix as it is solely time dependant (M 3 M matrix).
, 2007; Ruch et al. , 2008; Anderssohn et al. , 2009; Henderson and Pritchard, 2013; Pearse and Lundgren, 2013]. Time functions for second and third components show signs of transient signals which we attributed to tropospheric effects. In order to determine how the data are represented by the components, we computed the reduced Chisquare v 2 r of the residual to the data for the 1 to kth components using the following formulation  [Lin et al. , 2010] : v 2 r 5 1 N2k X X2X svd r ! 2 (2) where N refers to the number of subsampled InSAR data, k is the number of principal components selected for the signal reconstruction, X is the subsampled InSAR data matrix, X svd is the reconstructed data matrix Geochemistry, Geophysics, Geosystems 10. 1002/2014GC005370 obtained by multiplying the first spatial components of k by their singular values and by their time functions, and r is the InSAR data standard error estimated to 5 mm.
The interpolated missing epochs are not considered in the estimation of the v 2 r. If the signal is correctly reconstructed for a component then the Chi-square value is expected to be close to 1; otherwise the uncertainty of the measurements are either over or underestimated. The v 2 r of the residuals between the observed and the reconstructed time series for the first to the eighth component ranges from 0. 25 to 0. 003. These values lower than 1 indicate that the residuals between data and the reconstructed time series lie within the data uncertainty whatever the number of components used, thus the data can be sufficiently well estimated by the first component alone. The Chi-square value of 0. 25 obtained for the residual between the data and the first component might indicate that our estimation of the InSAR data uncertainty of 5 mm is overestimated but this would be misleading.
Consistency of InSAR and GPS Measurements An independent way to evaluate the relevance of the first principal component as an estimation for the InSAR data is by comparing it with the GPS measurements. In order to make the two sets of geodetic data comparable in time, we reconstructed the InSAR displacements at the epoch of the GPS measurements using the first principal component time function obtained from the PCAIM analysis. Then, to make the data sets geometrically comparable, we projected the GPS vectors into the respective LOS for each InSAR track. The InSAR time series are in good agreement with the LOS-projected GPS displacements for all tracks (see Table  3 ). The differences between the two data sets range from 0. 02 to 1. 26 cm for a root mean square of about 1 cm. These differences are assumed to result mainly from orbital uncertainties, uncorrected atmospheric effects in the SAR images and the larger uncertainties in the GPS vertical determinations which are propagated into the LOS projection.