dataesr/scientific-paragraphs-categorization huggingface dataset:
Following the same procedure, features are extracted at 3 different image scales. For all sub-windows on the same image line, only the maximal value of the local occurrence of each pattern among these sub-windows is retained. In that way, the resulting feature vector achieves a large invariance to view point changes and, at the same time, captures local region characteristics of a person. We refer to  (Liao et al. , 2015)  for more details. In our approach, as illustrated at the bottom of Fig. 2 , we project these extracted LOMO features of size 26 960 on a reduced linear subspace of dimension 500, in order to facilitate the later fusion and to remove most of the redundant information that is contained in these features. The projection matrix is learnt using PCA on the LOMO feature vectors computed on the training dataset.
That is, for most attributes, the positive label (presence of an attribute) appears generally less frequently than the negative one (absence of an attribute). To handle this issue, we added a weight w to the loss function: w = -log 2 (p l ), where p l is the positive proportion of attribute l in the dataset. As we will show in our experiments, for smaller training dataset (like VIPeR), it is beneficial to pretrain the CNN with a (possibly larger) pedestrian reidentification dataset in a triplet architecture on the re-identification task, and then to fine-tune the pretrained convolution layers on the actual attributes. Figure  3  illustrates this transfer learning approach. Person re-identification consists in matching images of the same individuals across multiple camera views. In order to achieve this, we learn a distance function that has large values for images from different people and small values for images from the same person.
Datasets We evaluated our approach on three public benchmarks: PETA, APiS and VIPeR (see Fig. 1 ). The PETA dataset  (Deng et al. , 2014 ) is a large pedestrian attribute dataset which contains 19 000 images from several heterogeneous datasets. 61 binary attributes and 4 multi-class attributes are annotated. In our attribute recognition evaluation, we follow the experimental protocol of  (Deng et al. , 2014; Li et al. , 2015) : dividing the dataset randomly in three parts: 9 500 for training, 1 900 for validation and 7 600 for testing. Since different approaches  (Deng et al. , 2014; Li et al. , 2015; Zhu et al. , 2017)  have been evaluated on different subsets of attributes, in our experiment we use the union of all these subsets, i. e. 53 attributes. The APiS dataset  (Zhu et al. , 2013 ) contains 3 661 images collected from surveillance and natural scenarios.
11 binary attributes are annotated such as male/female, shirt, backpack, long/short hair. We followed the experimental setting of  (Zhu et al. , 2013). A 5-fold cross-validation is performed, and the final result is the average of the five tests. The VIPeR dataset  (Gray et al. , 2007)  contains 632 pedestrians in an outdoor environment, each having 2 images from 2 different view points. 21 attributes are annotated by  (Layne et al. , 2014). Each dataset is divided into two equal-size non-overlapping parts for training and testing (images from the same person are not separated). We repeat the process 10 times and report the average result. During training, we perform data augmentation by randomly flipping and shifting the images slightly.
Parameter setting All weights of the neural network are initialised from a Gaussian distribution with 0 mean and 0. 01 standard deviation, and the biases are initialised to 0. The learning rate is set to 0. 01. We used dropout  (Srivastava et al. , 2014)  for the fully-connected layers with a rate of 0. 6. For tests on PETA, the fc1 layer, fc2 layer and PCA projected LOMO features are set to 1000 dimensions. The batch size is 100. For tests on APiS and VIPeR, fc1 fc2 layer sizes and PCA-projected LOMO feature size are 500 dimensions, and the batch size is 50. The neural network is learned "from scratch" for tests on PETA and APiS. Since on VIPeR we have only 632 training images. The network is pre-trained with triplet loss on the CUHK03 dataset  (Li et al. , Accuracy  Recall@FPR=0. 1 AUC ours fusion  (Zhu et al.
Evaluation The test protocol on PETA  (Deng et al. , 2014)  proposes to use the attribute classification accuracy. The APiS dataset's protocol  (Zhu et al. , 2013)  uses the average recall at a False Positive Rate (FPR) of 0. 1 and the Area Under Curve (AUC) of the average Receiver Operating Characteristics (ROC) curves as performance measures. As mentioned in  (Zhu et al. , 2017) , accuracy is not sufficient to evaluate the classification performance on unbalanced attributes. In our experiment, we thus use all these three measures to evaluate our approach.
Results We first evaluated the effectiveness of the 1D horizontal convolution layers, the body part division and the fusion of LOMO and CNN features using the PETA dataset. To show the effect of each contribution on the overall performance, we first implemented a baseline as a CNN with 3 consecutive convolution and maxpooling layers (C1-P3) and a multilayer perceptron using LOMO features of different PCA output dimensions as input. Then we implemented different variants of the proposed method: baseline with 2 layers of 3×3 convolution or 2 layers of 3×1 convolution, CNNs with and without body part division, and CNNs with and without LOMO feature fusion. Table  1  summarises the results. We can conclude that the spatail invariance of the LOMO feature and the rich representation of the deep CNN features are compelentary and the fusion increases the overall recall and accuracy.
1D convo-lutions and dividing into body part also slightly improves the results. By performing all these, we obtain the highest overall accuracy, recall and AUC. The comparison with the state of the art on PETA is shown in Table  3. In the literature, there are two evaluation settings for the PETA dataset with 35 and 45 attributes respectively. Table  3  shows the results on the 27 attributes that they have in common in order to compare all methods. We also display the average results for 35 and 45 attributes. Our method outperforms the state-of-the-art approach mlcnn by a margin of 3. 4%, 14. 3%, 6% points for the average accuracy recall and AUC on the 27 attributes and a margin of 3. 5%, 15%, 6. 1% points on the 45 attributes. We also outperform the DeepMar method by 9% points on accuracy.
Moreover, our approach achieves a better score on almost all individual attributes. The results on the APiS dataset are shown in Table 4. Our method outperforms the Adaboost approach with fusion features and interaction models by a margin of 6% and 2. 3% points respectively for the recall at FPR=0. 1 and AUC. Only for the AUC, Deep-Mar achieves a slightly better results (0. 5% points) which could be explained by its pre-training on the large ImageNet dataset. Finally, the results on the VIPeR dataset are shown in Table  2. Our approach achieves a 9. 8% point improvement in accuracy and 4. 1% points on recall at FPR=0. 2 compared to the CNN-based state-of-the-art approach mlcnn-p. For most of the attributes, our method obtains a better score. In summary, our approach outperforms the state of the art (including CNN-based methods) on two datasets and is on par with the best method on the third one.
CONCLUSION In this paper, a pedestrian attribute classification approach based on deep learning has been proposed. Our approach applies 1D convolutions on part-based feature map and fuses low-level LOMO features and high-level learnt CNN features to construct an effective classifier that is robust to large view point and pose variations. We proved that the learned CNN features and the hand craft LOMO features are complementary and the fusion improves the attribute recognition results. We also showed that pre-training the CNN model on person re-identification can assist attribute learning for small datasets. Finally, in our experiments on three public benchmarks, the proposed approach showed superior performance compared to the state of the art.
We listed the nights on which simultaneous photometric transits were collected in Table  2. The observation for each transit started at least one hour before the start of the transit and lasted until one hour after the transit ended. The TRAPPSIT and SPECULOOS observations were acquired in V band and Sloan g', respectively. The exposure times range from 10 to 35 seconds. Data reduction consisted of standard calibration steps (bias, dark, and flat-field corrections) and subsequent aperture photometry in IRAF/DAOPHOT  Stetson (1987). Comparison stars and aperture size were selected manually to ensure the best photometric quality in terms of the flux standard deviation of check stars, i. e. , non-variable stars similar in terms of magnitude and color to the target star.
RM model To estimate the spin-orbit angle λ from RM observations, we use the publicly available code ARoME 5  (Boué et al. 2012) , which provides an analytical model to compute the RM signal, and is optimized for the spectrographs that utilize the CCF-based approach to estimate RVs (such as HARPS). ARoME requires as input parameters the width of a non-rotating star which can be considered as the instrumental broadening profile (β 0 ), width of the best Gaussian fit to out-of-transit CCF (σ 0 ), stellar macroturbulence (ζ), stellar radius (R ), projected stellar rotational velocity v sin i, stellar quadratic limb darkening coefficients (u 1 and u 2 ), planetary semimajor axis (a), orbital period of planet (P ), planet-to-star radius ratio (R p /R ), orbital inclination 5 www. astro. up. pt/resources/arome angle (i), and the spin-orbit angle λ in order to calculate the RM signal.
Brown et al. (2017)  performed a comparison study between ARoME and other RM modeling tools, and reached the conclusion that ARoME consistently underestimates v sin i when compared to other models. Although v sin i differed, the estimated λ were in strong agreement for all the models. Moreover, ARoME is the only code which is publicly available, and we are only interested in estimating λ and in its variation, thus we decided to use the ARoME tool for our analysis.
Acknowledgements. M. O. acknowledges research funding from the Deutsche Forschungsgemeinschft (DFG,  German Research Foundation ) -OS 508/1-1. We acknowledge the use of the software packages pyGTC (Bocquet & Carter 2016) and emcee (Foreman-Mackey et al. 2013). The research leading to these results has received funding from the  European Research Council (ERC)  under the FP/2007-2013  ERC  grant agreement no. 336480  and from an  Actions de Recherche Concertée (ARC)  grant, financed by the  Wallonia-Brussels Federation (PI Gillon). This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ), and by the  MERAC foundation (PI Triaud ). L. D. acknowledges support from the  Gruber Foundation  Fellowship. M. G. and E. J. are F. R. S. -FNRS Research Associate and Senior Research Associate, respectively.
04 m w. e. yr -1. An AWS (Tien Shan, Kumtor AWS) installed at an elevation of ≈ 3660 m a. s. l. and a distance of approximately 10 km to the glacier recorded continuous meteorological data for the study period (Fig. 1 ). We used daily precipitation sums and mean daily air temperature for the modelling. Since 2010, in situ SMB has been obtained annually in late summer (Fig. 2c ). Winter measurements exist for May 2014  (Kronenberg et al. , 2016). A description of the meteorologi-   1 ).
In 2015, continuous coverage was obtained from Camera 1 but only a few images could be retrieved from Camera 2. A complete set of data was collected from both cameras for the first time in 2016. A similar set-up has been installed for Glacier no. 354 in 2014, delivering continuous coverage since implementation (Fig. 2c ). The camera is located at an elevation of 4145 m a. s. l. Images from the two cameras installed at Golubin were not used here due to limited image quality (Fig. 2b ). Figure  3  illustrates the number of camera and satellite images with satisfying quality that were used to obtain maps of snow lines for the three glaciers. Image availability for TSL mapping prior to 1998 was insufficient for the most part.
Meteorological data For Abramov, the air temperature data measured at the AWS were adjusted to the elevation of the former glaciological station by applying a constant lapse rate of -6 • C km -1  (Suslov et al. , 1980). The ERA-Interim reanalysis data set was adapted by applying mean monthly additive and multiplicative biases for air temperature and precipitation, respectively. The biases were calculated from long-term in situ measurements  (Barandun et al. , 2015). From the corrected monthly means, daily series were generated by superimposing day-to-day variability observed at the meteorological station from 1969 to 1994. For Abramov, we generated air temperature series from 1995 to 2011 and precipitation series from 1995 to 2016. More detailed information on data preparation and their suitability is given in  Barandun et al.
A surface mass balance model with a spatial resolution of 20 m was driven with daily mean air temperature and precipitation sums measured at nearby meteorological stations or inferred from reanalysis data (see Sect. 2. 1). We used a classical temperature-index melt model (e. g. Braithwaite, 1995; Hock, 2003). Melt M was calculated for each grid cell x, y and time step t based on a linear relation with positive daily mean air temperature T air (x, y, t) as M x, y, t = DDF ice/snow • T air(x, y, t) T air > 0 • 0 T air ≤ 0 •. (2) Daily air temperatures are extrapolated to each grid cell using a constant temperature lapse rate based on literature values (Table  3 ). Different degree-day factors, DDF ice/snow , were chosen for snow and ice surfaces. The surface type over the glacier area was given by the snow depth updated with modelled daily snowfall and melt.
Geodetic mass balance The total uncertainty of the geodetic mass balance includes a random and systematic error. We followed  Brun et al. (2017)  to compute the random error of the geodetic mass balance estimate but did not assess the systematic error. Uncertainties in elevation differences were quantified by computing the area-weighted mean of the absolute difference off-glacier in 50 m altitude bins. The resulting values for Abramov, 1. 0 m for 2003-2015 and 0. 6 m for 2011-2015, and for Glacier no. 354, 0. 7 m for 2012-2015, are in line with the uncertainty of 1. 3 m found over the Mont Blanc area through a comparison of similar satellite data to elevation differences measured in situ  (Berthier et al. , 2014). For Golubin, a value of 1. 6 m indicates slightly lower DEM quality. The uncertainty related to the density assumption for converting volume to mass change was assumed to be ±60 kg m -3 for time intervals larger than 5 years  Huss (2013).
Acknowledgements. This study is supported by the  Swiss National Science Foundation (SNSF) , grant  200021_155903. Additional support by the  German Federal Foreign Office  in the frame of the  CAWa  project (http://www. cawa-project. net) and the support of the Federal Office of Meteorology and  Climatology MeteoSwiss  through the project  Capacity Building and Twinning for Climate Observing Systems  (CATCOS) Phase 1 & 2, contract nos. 7F-08114. 1 , 7F-08114. 02. 01 , between the  Swiss Agency for Development and Cooperation (SDC ) and MeteoSwiss as well as the project  CICADA (Cryospheric Climate Services for improved Adaptation ), and contract no. 81049674  between the  Swiss Agency for Development and Cooperation  and the  University of Fribourg  is equally acknowledged. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  and the  Programme National de Télédétection Spatiale  grant  PNTS-2016-01.
0 ± 1. 2%, α ∼ 0, and β ∼ -1. In  Troxel et al. (2017) , however, it is found that the β correction has very little effect on the final measurements. We therefore do not correct for β when making the mass maps. We have also checked that setting β = -1 leads to negligible changes in the second moments of the map. This selection gives a total of ∼ 34, 800, 000 galaxies in the full Y1 catalog. The shear measurement method in METACALIBRATION is based on the NG-MIX method  (Sheldon 2014). The full implementation of META-CALIBRATION is publicly available and hosted under the NGMIX code repository 2. The IM3SHAPE algorithm is one of the algorithms also used in the DES SV analyses  (Jarvis et al. 2015). It is a maximum likelihood fitting code using the Levenberg-Marquardt minimisation that models the galaxies either as an exponential disk or a de Vaucouleurs profile -fitting is done with both models and the one with a better likelihood goes into the final catalog.
Calibration of bias in the shear estimate associated with noise  (Kacprzak et al. 2012; Refregier et al. 2012 ) is based on the image simulation package GALSIM 3  , but is significantly more complex and incorporates many effects seen in the DES Y1 data as described in  Z17 and Samuroff et al. (2017). The final signal-to-noise and size selections are 12 < S/N < 200 and 1. 13 < R gp /R p < 3, where R gp is the size of the galaxy and R p is the size of the PSF. The catalog has an estimated m ∼ 0. 0 ± 2. 5%, α ∼ 0, and β ∼ -1. Similar to METACALIBRATION, we do not correct for β as  Troxel et al. (2017)  showed that the correction has a negligible effect on the measurements. The final catalog contains ∼ 21, 900, 000 galaxies. The lower number relative to METACALIBRATION is due to the fact that IM3SHAPE operates on r-band images while METACALI-BRATION use all images from the bands r,i and z.
The IM3SHAPE code is publicly available 4. Details for both shape catalogs and the tests performed on these catalogs can be found in Z17. We mainly show results for METACALIBRATION as it has the higher S/N, but also constructed IM3SHAPE maps and performed several systematics tests with these. Also, as noted above, we only use the SPT wide-field region with Dec< -35 as it has been the region where most testing was done for both the shear and the photo-z catalogs. We generate 5 maps for each catalog with different source z mean selections: 0. 2 < z < 1. 3, 0. 2 < z < 0. 43, 0. 43 < z < 0. 63, 0. 63 < z < 0. 9, 0. 9 < z < 1. 3. The first redshift bin combines galaxies in a broad redshift range to allow for a large source number density and therefore higher signal-to-noise for the mass maps. This is the map with which most quantitative studies are done in this paper.
Simulations Two types of simulations are used in this work to investigate the performance of the convergence map reconstruction and the effects of noise and masking. First, we generate fully sampled, Gaussian maps with a given power spectrum using the synfast routine in HEALPIX  (Górski et al. 2005). We use the software package COS-MOSIS  (Zuntz et al. 2015) , which wraps around the CAMB software  (Lewis & Bridle 2002) , to generate the input power spectrum with the cosmological parameters: Ω m = 0. 295, Ω b = 0. 047, σ 8 = 0. 8, h = 0. 69, n s = 0. 97, and w = -1, although the particular details of the power spectrum are not very important for the tests we perform with these Gaussian simulations. Second, we use the "Buzzard v1. 3" mock galaxy catalogs based on N-body simulations as described in  DeRose et al.
Finally, for all measurements in this work, we estimate the error bars and the covariance matrix using a standard Jackknife approach. We divide the footprint into N JK Jackknife regions using a kmeans clustering code 6  and divide the mask into N JK approximately equal-area regions. Throughout this paper, we use N JK = 100. For angular correlation measurements, a fast tree-based code TREECORR 7  is used.
The synfast routine outputs three maps that are consistent with the input power spectrum: a spin-0 map and two maps for the two components of the spin-2 field. We can then identify the spin-0 map as the convergence map κ sm and the spin-2 maps as the shear maps γ sm. Since all the lensing maps are effectively smoothed, we use the 'sm' subscript to distinguish these maps (which do not contain information on scales beyond max ) from the true underlying field with infinite resolution. We denote κ E and κ B to be the E-and B-mode convergence generated from the smoothed shear maps γ sm. For visualisation purpose, all maps presented in this paper are first smoothed with a Gaussian filter of σ G =30 arcminutes, then F 1 or F 2 F 1 Gaussian F 1 Buzzard F 1 Buzzard with shape noise F 2 Gaussian F 2 Buzzard F 2 Buzzard with shape noise Figure 2.
This generally needs the construction of optimal matched filters in configuration and redshift space  (Maturi et al. 2005; Simon et al. 2009; Vander-Plas et al. 2011) , which is outside of the scope of this work. For voids the situation is more promising. We use RED-MAGIC galaxies with relatively good photo-z's (same as that used in Sec. 7. 1) as tracers of the foreground matter density and study their radial distribution. We project the data into 2D slices of 50 h -1 Mpc along the line-of-sight. We then measure the density contrast of the REDMAGIC galaxies in these 2D slices where the large voids in the maps (significant negative convergence values) are measured, compared to the mean REDMAGIC density at that redshift. The density contrast measurements at different redshifts are then used to reconstruct the radial density profile of voids.
As an example, we look at the largest void detected in the furthest 0. 9 < z < 1. 3 bin at (RA, Dec)=(62 • , -43 • ), and count galaxies within 2. 0 degrees of the void centre, which approximately corresponds to the full angular size of the void in the map. We show the resulting line-of-sight density profile measurements of the RED-MAGIC galaxies in Fig. 13. We find two extended underdensities that are consistent with supervoids of radii R v =100 h -1 Mpc and R v =220 h -1 Mpc assuming simple Gaussian void profiles  (Finelli et al. 2016; Kovács & García-Bellido 2016; Sánchez et al. 2017 ). These supervoids are quite shallow even in their centres but their size is comparable to the largest known supervoids. Most probably, these supervoids have substructure at smaller scales but that information is not accessible even using high quality photo-z data like REDMAGIC.
CONCLUSION Weak lensing allows us to probe the total mass distribution in the Universe. One of the most intuitive ways to visualise and comprehend this information is through weak lensing convergence maps, or mass maps. These maps contain the Gaussian and non-Gaussian information for the matter field, which could then either be extracted via various statistical tools, or analyzed locally for regions of special interest. In this paper, we construct weak lensing mass maps for the first year of Dark Energy Survey data (DES Y1) using two independent shear catalogs, METACALIBRATION and IM3SHAPE, in the redshift range 0. 2 < z < 1. 3 and in the region overlapping with the South Pole Telescope footprint. This yields maps covering ∼ 1, 500 deg 2 , corresponding to a total volume of ≈10 Gpc 3. With the unprecedented large sky coverage, a spherical reconstruction approach was used based on decomposing the shear field into spin-2 spherical harmonics, followed by an E/B mode separation.
2013 ) and DES SV  (Vikram et al. 2015; Chang et al. 2015). Even though the Y1 depth is shallower (and therefore noisier) than the previous maps, these very large maps provide a new perspective on weak lensing map making and the various topics one can explore with them. Moving onto the larger dataset from DES and other surveys, we expect many of the explorations in this paper to be carried out and advanced to serve as complementary probes of cosmology alongside more traditional two-point statistics. • +10 • +20 • +30 • +40 • +50 • +60 • +70 • +340 • +350 • -50 • -40 • -30 • True η (1 arcmin) -4 -2 0 2 4 ψ × 10 5 0 • +10 • +20 • +30 • +40 • +50 • +60 • +70 • +340 • +350 • -50 • -40 • -30 • Reconstruct without shape noise η (1 arcmin) -2 -1 0 1 2 ψ × 10 5 0 • +10 • +20 • +30 • +40 • +50 • +60 • +70 • +340 • +350 • -50 • -40 • -30 • Reconstruct with shape noise η (1 arcmin) -2 -1 0 1 2 ψ × 10 5.