dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of Random Points In RM1, we describe a method of estimating purity and completeness of the cluster catalog using the data itself, by placing fake clusters into the data and recovering the richness. While this method (described in Section 11 of RM1) is useful for estimating the selection function and projection effects, it is not appropriate for generating a cluster random catalog for cross-correlation measurements, such as the cluster-shear cross-correlation used for stacked weak-lensing mass estimates (e. g. ,  Johnston et al. 2007; Reyes et al. 2008) , as existing large-scale structure is imprinted on the random catalog. In this section, we describe a new way of generating cluster random points by making use of the z max map from Section 3. 4. A particular challenge is the fact that galaxy clusters are extended objects, and thus the detectability depends not just on the redshift, but the cluster size and the survey boundaries.
We generate a random cluster catalog that has the same richness and redshift distribution of the data catalog by randomly sampling {λ,z λ } pairs from the data catalog. To ensure that the random catalog correctly samples the survey volume, we utilize the redshift mask. Specifically, after sampling a cluster from the cluster catalog, we randomly sample a position ({α, δ}) for the random point. If the cluster redshift z λ is larger than the maximum redshift at which the cluster can be detected, we draw a new {α, δ}, repeating the procedure until the cluster is assigned a position consistent with the cluster properties. In all, we sample each cluster n samp ∼ 1000 times to ensure that any correlation measurements we make are not affected by noise in the random catalog. Having assigned a position, we use the depth map and the footprint mask to estimate the local mask fraction f mask and scale factor S, as defined in Section 3.
The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012-39559, ESP2013-48274, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. Based in part on observations taken at the Australian Astronomical Observatory under program A/2013B/012. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U. S. Department of Energy Office of Science.
In this paper we propose a novel human action recognition method, robust to viewpoint variation, which combines skeleton-and depth-based action recognition approaches. For this matter, we first build several base classifiers, to independently predict the action performed by a subject. Then, two efficient combination strategies, that take into account skeleton accuracy and human body orientation, are proposed. The first is based on fuzzy switcher where the second uses a combination between fuzzy switcher and aggregation. Moreover, we introduce a new algorithm for the estimation of human body orientation. To perform the test we have created a new Multiview 3D Action public dataset with three viewpoint angles (30°,0°,-30°). The experimental results show that an efficient combination strategy of base classifiers improves the accuracy and the computational efficiency for human action recognition.
Foremost, to deal with view-invariant issue of depth-based action recognition, we propose a novel method, robust to viewpoint variation and applicable in real-time. These methods are based on hu-man body orientation and aggregation of several base classifiers. Furthermore, to combine the skeleton-and depth-based classifiers, we study two strategies, the first being based on fuzzy switcher, and the second on combination between fuzzy switcher and aggregation. This paper is organized as follows: Section 2, briefly reviews the most relevant skeleton and depth based human action recognition approaches; section 3 introduces a novel Multiview public dataset and provides duration and accuracy evaluations for various state-of-the-art algorithms on our dataset; section 4 gives a description of our proposed method; Section 5 discusses the computational efficiency and the robustness of combination methods to viewpoint variations.
Mines Douai Multiview 3D Dataset Several 3D dataset have been created in the last years for action recognition. However, most of them are acquired from only one viewpoint angle which prevent us from measuring the effect of person's orientation. Recently, Rahmani et al. (Rahmani et al. , 2014) , created a multiview dataset but this dataset is not well structured. Thus, the creation of new publicly available multiview dataset to enhance the study of view-invariant action recognition became a necessary task. MinesDouai Multiview 3D dataset contains four types of information (Figure  1 ): RGB, depth, cleaned depth and human skeleton data, captured with only one kinect camera. For each acquisition, the subject performs the same action twice in three orientations (30°,0°,-30°) in order to produce the dissimilarity between the three viewpoint angles even for the same subject.
Skeleton-Based Action Recognition The extraction of a skeleton from depth information was a big challenge in the last decades. Recently a robust skeleton estimation algorithm proposed by Shotton et al. (Shotton et al. , 2011)  regenerates the interest on skeleton-based action recognition. To study the impact of viewpoint variation on the accuracy of action recognition, we conducted our experimentation with the most relevant state-of-the-art skeleton-based approaches. Foremost, we started by Actionlet algorithm  (Wang et al. , 2012b ) and then we tested a four alternative skeletal representations on our dataset using a combination of dynamic time warping, Fourier Temporal Pyramid representation and linear SVM. This algorithm pipeline is proposed by Vemulapalli et al. (Vemulapalli et al. , 2014).
The four alternative skeletal representations tested on our dataset are: Joint positions (JP), Pairwise relative positions of the joints (RJP), Joint angles (JA), and Individual body part locations (BPL). To make the skeleton invariant to the orientation and the location in the scene,  Vemulapalli et al. (Vemulapalli et al. , 2014)  pre-process this information with the following operations: • For scale-invariant: the authors consider personcentric coordinate as reference and they apply the normalization by taking one of the skeletons as reference; • For view-invariance: a rotation is applied to the skeleton such that the vector from right hip to left hip is parallel to the global x-axis. For fair comparison between different skeleton representations, we have used the same experimental setting with all the representations.
Discussion and evaluation of base classifiers In order to evaluate the various skeleton-and depthbased classifiers, we have divided our dataset into 2 groups. Each group contains 4 subjects with three orientations (2*3 subgroups) as shown in Figure  3. To perform the test, one subgroup is used for training and the other for testing. To put things into perspective with this little difference between HOG 2 and HON4D, it has to be noted that the first widely outperforms the second in terms of computation time. These performances are recorded using a PC with 2. 53GHz Intel Xeon CPU with 24 GB RAM (only 4GB was used). The skeleton-based algorithms show high accuracy of recognition and robustness to viewpoint variations compared with depth-based approaches. The Relative Joint Position (RJP) provides the best improvement on our dataset, achieving a maximum of 97.
To study the effects of instability of skeleton estimation on the performance of action recognition, we simulated selfocclusion and instability of skeleton by adding noise to some joints randomly (like in the realistic sce- nario). Then we conducted the test using the Relative Joint Position representation (RJP), which gave the best results with our dataset. We conducted the experimentation by increasing iteratively the amplitude of the noise up to 25 dB. The obtained results are depicted in Figure  4. We can see clearly that the accuracy of action recognition using skeleton-based approach decreased rapidly when the amount of noise is greater than 3dB. Therefore, to make the action recognition more robust, the skeleton instability should be considered by switching to another source of information such as depth which are more robust to self-occlusion.
We have fixed the same size of the DMs for all samples. For training and testing feature sets, principal component analysis (PCA) was used to reduce the dimensionality in order to optimize computational time. To test the proposed algorithm, one half of the samples of our dataset are used for training and the other half for testing. An average recognition rate of 86. 1% (248/288) was achieved. Figure  8  describes the changes in the accuracy rate of orientation estimation and the computing times, with respect to the number of frames used to compute the DMs. When 2 frames were used to characterize the human body orientation, the estimation was lower. When the number of frames exceed 5, the estimation accuracy remains around 85%.
CONCLUSION In this paper we introduced a novel approach of combining skeleton and depth information based on the amalgamation of several base classifiers. Furthermore, we proposed a new method for the estimation of human body orientation which is completely independent of human gait direction. In order to perform view dependency tests, we have created a new multiview public dataset 1. The results showed that our proposed method is robust to viewpoint variation and skeleton accuracy degradation. Furthermore, due to its computation time efficiency, it is suitable for real-1 Information to dowload the dataset can be found at: http://ia. ur. mines-douai. fr/en/datasets/ time application. Future work includes the extension of our algorithm to more than three viewpoint orientations. Since it is not a trivial task to acquire sufficient data for each viewpoint angle, an efficient solution can be used to extend any 3D action dataset to different viewpoint using point-clouds transformation.
The fire module of ORCHIDEE-SPITFIRE was turned off for each decade in turn and was also turned off before and after the decade in question in order to model the legacy carbon trajectory by fires in each past decade. We found that, unsurprisingly, fires that occurred in 2000-2009 are a carbon source (-0. 17 Pg C yr -1 ) for the carbon balance of 2000-2009, whereas fires in all decades before 2000 contribute carbon sinks with a collective contribution of 0. 23 Pg C yr -1. This leaves a net fire sink effect of 0. 06 Pg C yr -1 , or 6. 3 % of the simulated regional carbon sink (0. 95 Pg C yr -1 ). Further, fires with an age of 10-40 years (i. e. , those that occurred during 1960-1999) contribute more than half of the total sink effect of fires. The small net sink effect of fires indicates that current-day fire emissions are roughly balanced out by legacy sinks.
The tool used is the global dynamic vegetation model ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) with the prognostic fire module SPITFIRE (SPread and InTensity of FIRE). Fire occurrences are simulated in a prognostic way, with the dynamic vegetation module being activated. Our objectives are (1) to compare the simulated versus observed distribution of tree cover and tree groups, given fire disturbance; (2) to separate the contribution of legacy sink of past fires from emissions of current fires to the pan-boreal carbon balance and to further quantify the relative sink contributions by fires in different decades of the past. Being a preliminary effort, the different driving factors influencing fire contributions (such as CCN) are not individually separated; rather, their effects are included in the decadal fire contributions.
Model introduction This study uses the process-based dynamic global vegetation model (DGVM) ORCHIDEE  (Krinner et al. , 2005). The ORCHIDEE model has three sub-modules. The SECHIBA sub-module simulates the fast exchange of water and energy between the land and the atmosphere. The STOMATE submodule simulates the vegetation carbon cycle processes including photosynthesis, photosynthate allocation, litter fall, litter and soil organic matter decomposition. The third submodule simulates vegetation dynamics. The equations of vegetation dynamics are mainly taken from the LPJ (Lund-Potsdam-Jena) model  (Sitch et al. , 2003) , with modifications described by  Krinner et al. (2005). www. biogeosciences. net/13/675/2016/ Biogeosciences, 13, 675-690, 2016 For this study, the prognostic fire module SPITFIRE as originally developed by  Thonicke et al.
This corresponds to a suppression fraction of 0. 01 in sparsely inhabited regions and of 0. 99 in highly populated regions (i. e. , D p → +∞). Within SPITFIRE, fire occurrence depends on vegetation and climate conditions and has feedbacks on forest mortality through crown scorching and cambial damage, which reduces forest stem density  (Thonicke et al. , 2010). Thus, in ORCHIDEE-SPITFIRE, vegetation dynamics are affected by both climatic factors, as simulated by the dynamic vegetation module, and fire disturbances, as simulated by SPIT-FIRE. In addition to the climatic limits that give the adaptation or extinction for different tree vegetation types under specific climate and climate variability conditions  (Krinner et al. , 2005; Sitch et al. , 2003) , fires further impact the treegrassland competition and the competition within woody vegetation types.
We conducted a reference simulation (SIM fireON ) from 1850 until 2011, accounting for climate change, atmospheric CO 2 concentration change and prognostically simulated fire disturbance. We then conducted a series of other simulations (named SIM OFF ), which branch off from the SIM fireON simulation from the beginning year of each decade between 1850 and 2009. In the SIM OFF simulations, the fire module was switched off sequentially from the decade of the 1850s (1850-1859) to the 2000s  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  and switched on afterwards, with all remaining parameter settings and input data sets the same as in the reference simulation. Following Eq. (  4 ), the contribution by fires within a specific decade to the carbon balance of each year for the time after this decade would be quantified as the difference between the reference simulation and the decadal SIM OFF simulation.
In all simulations, the vegetation dynamics module of ORCHIDEE was switched on to allow the vegetation distribution to respond to climate variations and fire disturbances. The spatial domain of our simulation covers the land pixels of 44-84 • N at a 2 • resolution. The land north of 84 • was excluded as it is covered mainly by ice and snow. The model was forced by the CRUNCEP climate data at a 2 • resolution, regridded from its original resolution of 0. 5 •. The CRUNCEP consists of 6-hourly gridded climate data generated by combining CRU TS 3. 1 0. 5 • monthly climate data and NCEP 6-hourly 2. 5 • reanalysis data (thus the name CRUNCEP). Rainfall, cloudiness, relative humidity and temperature are from the CRU data set and interpolated at a 6-hourly time step following the temporal variability of NCEP.
Pressure, longwave radiation, and wind speed are from NCEP, reinterpolated on a 0. 5 • scale. The values for these variables for 1948 were also used for the period before 1948. For more details, see http://dods. extra. cea. fr/ store/p529viov/cruncep/V4_1901_2012/readme. htm. A single global annual atmospheric CO 2 concentration time series since 1850 was applied everywhere in the spatial domain of the model, which is a combination of ice core and NOAA station measurements. The fire module needs additional input data for lightning flashes and human population density. Lightning flashes were retrieved from the High Resolution Monthly Climatology of lightning flashes by the Lightning Imaging Sensor-Optical Transient Detector (LIS/OTD) (http://gcmd. nasa. gov/records/GCMD_ lohrmc. html). The LIS/OTD data set provides annual mean flash rates over the period of 1995-2000 on a 0.
5 • scale with monthly time step, which was cycled each year throughout the simulation. An annual historical population density map was retrieved from the Netherlands Environmental Assessment Agency (http://themasites. pbl. nl/tridion/en/themasites/ hyde/download/index-2. html). Both lightning and population density data sets were regridded at a 2 • resolution before being fed into the model. The reference simulation SIM fireON consists of a spin-up run from bare soil and a transient run, with the fire module being activated. For the spin-up, climate data for the period 1901-1930 were cycled and atmospheric CO 2 concentration (285 ppm) and population density were prescribed at the 1850 level. The spin-up run lasted for 400 years but contained three runs of soil-only processes each lasting 1000 years to speed up reaching equilibrium for slow and passive soil carbon pools.
3 , panel 2a and b). In contrast, MODIS data show that central and eastern Siberia is dominated by deciduous needleleaf forests (Fig. 3 , panel 3b). ORCHIDEE successfully captures this, but the spatial extent and magnitude of tree cover are overestimated (Fig. 3 , panel 3a). In addi-  tion, ORCHIDEE also erroneously allocates more deciduous needleleaf forests in Alaska and northwestern Canada than the MODIS data. We also extend the comparison of different tree group extents by including more land-cover data sets (see Figs. S2, S3 and S4). Again, when considering other land-cover maps (ESA-CCI, GLC2000 and VCF), the model error is less than when using the MODIS data set. Notably, both ESA-CCI and GLC2000 data sets indicate a larger extent of deciduous needleleaf forest in eastern Siberia compared to MODIS, resulting in much lower errors in the OR-CHIDEE simulation (nevertheless, a model overestimation of 20-50 % of ground area persists in western Siberia).
, 2010; Stocks et al. , 2003). The simulated fire frequency (0. 2-2 % yr -1 ) in Canada agrees with that reported by  Stocks et al. (2003)  using the Canadian Large Fire Database. The general spatial extent and magnitude of fires in northern Eurasia (> 54 • N) roughly agrees with GFED3. 1 data, although burned fractions in northern tundra and shrubland are overestimated. This may be because tundra is treated as generic C3 grass in the model and thus assigned a low fuel bulk density  (Thonicke et al. , 2010)  that promotes fast fire propagation. In reality tundra has a more dense growth form than temperate grasslands and therefore has a much higher bulk density  (Pfeiffer et al. , 2013). Fires are greatly underestimated by the model at the southern edge of the study area in Eurasia, with a simulated burned fraction of 0.
Multiple infections can also make the system complex to analyse (see  Bose et al. , 2016)  and may require the use of metagenomics. For samples of sufficient quality, NGS technologies provide several complementary options such as Illumina systems (San Diego, CA, USA), well suited for deep coverage sequencing and analysis of short variations, Pacific Biosciences (Menlo Park, CA, USA), Oxford Nanopore minIon (Oxford, UK) and Irys BioNano (San Diego, CA, USA) offering better continuity and larger-scale variation analysis. Software like AllPaths-LG  (Gnerre et al. , 2011) , Celera  (Myers et al. , 2000)  and HGAP  (Chin et al. , 2013)  can efficiently combine NGS data types to perform hybrid genome assembly. Gene prediction can be improved by including biological evidence such as RNA-seq (e. g.
, 2010)  are able to efficiently analyse variation from point mutations and short insertions/deletions from both pooled and unpooled samples. Other options for analysing pooled data include vipR  (Altmann et al. , 2011) , VarScan  (Koboldt et al. , 2009)  which was reported to have a particularly good variant-calling performance with deep sequencing datasets  (Stead et al. , 2013)  and Snape  (Raineri et al. , 2012) , which offers a Bayesian approach for variant frequency estimation. A novel approach is taken by the software Splinter  (Vallania et al. , 2010) , which co-sequences synthetic DNA samples with known variation in order to account for sequencing bias of the true samples, which helps increase sensitivity of rare variant detection in large pools of individuals. PoPoolation 1 and 2  (Kofler et al.
Mapping and assembly of transcripts In order to study transcriptomic differences between experimental conditions, reads first have to be mapped to a reference genome or transcriptome. Often the organism has a high-quality genome sequence, in which case raw data can be directly mapped to the genome of interest. For eukaryotic genomes with alternative splice variants, splice-aware aligners are usually most appropriate and there are many programmes to choose from e. g. TopHat2, GSNAP, MapSplice and STAR  (Wang et al. , 2010; Wu and Nacu, 2010; Dobin et al. , 2013; Kim et al. , 2013). Where the genome of interest has an existing annotation of high quality, or the user is interested only in known genes or transcripts, expression can simply be quantified for each gene/transcript in the annotation after mapping (see  Section 3.
With such a plethora of options available for alignment and assembly of reads, deciding the best option for one's dataset can be daunting. Performance benchmarks published with new software can be helpful in informing choice of analysis, however comprehensive comparative studies are particularly useful as they can provide a fuller understanding of the strengths and weaknesses of different programs under different conditions. Studies have addressed various parameters, such as accuracy of mapping, identification of exons and splice junctions and robustness to sequencing error, as well as speed and efficiency of the algorithms, using both real and simulated data sets  (Grant et al. , 2011; Lindner and Friedel, 2012; Nookaew et al. , 2012; Steijger et al. , 2013; Engströ et al. , 2013). Other studies have compared performance of de novo assemblers on EST and later, paired-end Illumina reads  (Mundry et al.
As with mapping and assembly, many tools exist for quantifying reads and determining differential expression of genes, where the normalisation of read numbers, key to comparing expression levels across samples, is an active research topic. Most differential expression software is designed to work with eukaryotic genomes, where alternative splicing is a significant consideration which can significantly affect expression quantification, e. g. Cuffdiff2  (Trapnell et al. , 2013) , EBSeq  (Leng et al. , 2013) , DEXSeq  (Anders et al. , 2012)  and EdgeR  (Robinson et al. , 2010). Comparative analyses have also been performed for many of these tools, which is important as authors can assess trade-offs and decide which factors are more crucial for their analysis  (Nookaew et al. , 2012; Rapaport et al.
Prediction is possible because miRNAs tend to silence expression of transcripts by complementary binding to the 3 untranslated region of target genes. The recognition of the target mRNA by the mature miRNA is achieved mainly by the "seed" sequence, a 6-8 base stretch at the 5 end of the miRNA in animals that shows strong sequence conservation  (Guo et al. , 2010). Because the prediction of miRNA targets based solely on this short conserved sequence would naturally generate many false positives, algorithms such as TargetScan  (Lewis et al. , 2005) , miRanda  (John et al. , 2004)  or PicTar  (Krek et al. , 2005)  are available that predict target genes of given miRNAs which also incorporate other parameters, for example, comparative genomics, secondary structure and sequence characteristics.