dataesr/scientific-paragraphs-categorization huggingface dataset:
Software and data availability. The BioEmergences workflow was published as a standalone software  6. The MecaGen modelling platform was published as an open source software  18. The scripts for the statistical analysis and for the construction of the prototype from empirical data are provided here as a MATLAB library (Supplementary Software 1). The compressed folder contains a preprocessed version of the five sea urchin datasets. The raw data is made publicly available on the BioEmergences website: http://bioemergences. eu/bioemergences/pubs/seaurchin.
Simultaneous localization and mapping (SLAM) is widely used in many robotic applications and autonomous navigation. This paper presents a study of FastSLAM2. 0 computational complexity based on a monocular vision system. The algorithm is intended to operate with many particles in a large-scale environment. FastSLAM2. 0 was partitioned into functional blocks allowing a hardware software matching on a CPU-GPGPU-based SoC architecture. Performances in terms of processing time and localization accuracy were evaluated using a real indoor dataset. Results demonstrate that an optimized and efficient CPU-GPGPU partitioning allows performing accurate localization results and high-speed execution of a monocular FastSLAM2. 0-based embedded system operating under real-time constraints.
Authors in  [1]  presented a hardware software co-design approach of the importance weight calculation and particle update on a NIOS II processor. Moyers et al. [2]  presented a fixed-point version of Fast-SLAM 2. 0 algorithm and describes its implementation on a configurable and extensible very long instruction word (VLIW) processor. Chau et al. [3]  presented a heterogeneous implementation of adaptive particle filters based on an field-programmable gate array (FPGA) and a CPU for mobile robot localization and people tracking applications. There is no full implementation of FastSLAM2. 0 on an embedded CPU-GPGPU published to this date. In the state of the art, we found that only the particle filter, as an algorithm of its own used for filtering, has recently been studied. Tosun et al. [4]  presents a parallelization of particle filter-based localization and mapping on a multi-core architecture.
Real dataset-based evaluation SLAM algorithms are interesting applications when it comes to explore in a real indoor/outdoor environment. Evaluation of SLAM algorithms requires a set of different sensor data which are necessary for benchmarking. Sensor data can be obtained either by using a real instrumented robot or an available dataset. In our evaluation, we used a real indoor dataset  [24]. This dataset provides a set of different sensor data. We have used data of encoders and a monocular camera.
Algorithm dependencies and threshold definition FB1 depends on the number of integrated odometric data at each iteration. FB2 depends on the number of landmarks in the particle map, the number of extracted features and the size of both images and corner descriptor. FB3 depends on the number of matched landmarks. FB4 depends on the number of matched landmarks to be In our experiments, we set a threshold value for each parameter in order to bound the processing time of each FB. We set the thresholds as follows: • Number of odometric data is unbounded. It is related to encoder frequency used in experiment. • Image size is fixed by dataset used in experiment: 320 × 240 pixels. • Size of descriptor: 16 × 16 pixels. • Maximum number of landmarks for each particle is set to 500. Note that this value is decreased for even larger number of particles to avoid exhausting memory.
Hardware software matching Signal processing community has always been interested in implementing algorithms they developed. Evolution of the heterogeneous architectures and tools has allowed designing complex systems that we have not even dare to consider few years ago. We have gradually moved from a separate study of algorithms and architectures, to a more formalized approach taking into account simultaneously algorithm and system architecture for an efficient matching. Algorithm architecture matching consists of a simultaneous study of the algorithm and the architecture in order to perform an optimized implementation of the algorithm taking into account different constraints (real time and embeddability). Throughout this work, we aim to implement the Fast-SLAM2. 0 algorithm on a heterogeneous embedded architecture.
Embedded GPGPU programming Embedded GPU resources can be accessed by a programmer using various programming languages. CUDA presents the user with a C language for direct application development on NVIDIA GPUs which restrict code portability specifically to NVIDIA hardwares. Tegra K1 is selected to evaluate our implementation. OpenCL on such architecture is not available. Moreover, it is important to note here that at the time of writing this paper, OpenCL drivers for other boards with embedded GPUs are not available in the public realm. Embedded boards that have the software support to use OpenCL for GPGPU programming contain GPUs with only low numbers of cores which are not really dedicated to highperformance computing. In  [27] , authors used OpenCL on a Vivante GC2000 GPU with 4 SIMD cores on the i.
MX6 Sabre Lite development board. In  [28] , Maghazeh et al. used OpenCL on a Mali-T628 MP6 with 8 cores on the ODROID board and on a MALI-T604-MP4 GPU with 4 cores on the ARNDAL board. Therefore, using OpenCL in our work is not a good choice. Such boards with powerless embedded GPGPU may not be a good choice for a computationally intensive algorithm. As and when such boards, with a powerful embedded GPU and their respective software drivers supporting OpenCL, become available, it will be worthwhile to consider OpenCL as a programming language. This work adopts OpenGL for developments. The majority of current embedded GPUs support OpenGL which is independent to a specific architecture. Adopting OpenGL for general purpose computing requires a prior experience in graphic programming. Although, it provides code portability between different embedded GPUs which makes the resulting implementation independent and allows conducting a comparative study between different embedded GPU architectures.
Recent works adopted OpenGL for GPGPU programing. Hendeby et al. [6]  used OpenGL for general purpose GPU particle filter. Weinlich et al. [29]  and Oliveira et al. [30]  presented a comparison between different GPGPU programming languages: OpenCL, OpenGL, and NVIDIA CUDA. They proved that by adopting the same optimization strategy among these languages, the gap is slightly different among them in terms of acceleration. To use OpenGL for GPGPU, a typical GPGPU application program is based on three phases: (i) upload a suitable shader; (ii) allocate appropriate processing units for vertex and fragment shader; (iii) draw a suitable quad to trigger computation and download results.
To exemplify GLSL source code, Algorithm 8 describes fragment shader code needed for inverse depth initialization. sampler2DRect is a specific pointer to the active texture unit. Hence, partPose points out input particle poses. The first line of the algorithm makes a texture look-up and retrieves the particle pose stored in a four-dimensional vector pos_particle. The next two lines compute consecutively the camera pose and the landmark position in the environment. The first view camera pose (x i , y i ), the azimuth θ i , and the elevation φ i are stored in a specific variable referred to as gl_FragColor.
Experimental results In this section, we analyze the processing times of the proposed heterogeneous implementation discussed in Section 4. The experimental tests as well as the evaluation of processing times were based on our methodology discussed in Section 3. First, all processing times reported in our work as well as the localization results are obtained using a real dataset (Section 3. 1). Second, we separately evaluate each functional block to synthesize the full FastSLAM2. 0 implementation results (Section 3. 2). Third, since each FB has parameter dependencies, evaluation was conducted based on a set of defined thresholds to bound the computation time (Section 3. 3). Finally, a FB may occur once or more time in a single iteration depending on its parameters. We report the mean of processing time t FB x and t FB x respectively, computed relatively for the MOTS and for a single occurrence (Section 3.
Processing time evaluation The one-core, quad-core, and CPU-GPGPU implementations were run and time evaluated for 500 iterations using Rawseeds dataset discussed in Section 5. 1. Computing performances on one-core and quad-core CPU are used as a baseline to analyze the GPGPU acceleration results. As discussed in Section 5. 2, a large number of particles is needed to achieve an accurate localization results. Therefore, we choose to conduct experiments with the number of particles that gives more accurate results. Figure  14  presents the workload of each FB when running the one-core implementation with 4096 particles. FB1 is a time-consuming functional block. FB1 occurs several times in one iteration as many encoder data are acquired to reconstruct robot trajectory using motion model. Also, computing the initial covariance matrix P m is time consuming.
A solution is to bound the processing time by defining a threshold value for each parameter. To explore the parallel computing power of the embedded GPGPU, we report the overall processing time of the monocular FastSLAM2. 0 for the three implementations. Different numbers of particles are used to better study the complexity. When using the same dataset and the number of particles, the speedup achieved by the GPU implementation compared to one-core and quadcore implementations, respectively, is shown in Fig. 16  (in log scale coordinate). For few particles (2 4 ), CPU implementation performs better than quad-core and GPU implementations. The quad-core implementation seems not to perform well for few numbers of particles (2 4 and 2 8 ). This is due to the fact that many data are shared between threads and memory access is sequential among different threads, specially synchronization barriers among the currently running threads.
Conclusions This article proposed an efficient matching of monocular FastSLAM2. 0 algorithm on a heterogeneous embedded architecture. The first complete parallel FastSLAM2. 0 implementation in literature on a embedded GPGPU is described. Using a real dataset, the parallel CPU-GPU implementation is shown to outperform a quad-core CPU implementation for many particles while maintaining the same localization accuracy. The absolute performance of FastSLAM2. 0 on an embedded GPGPU relies on the number of embedded cores (i. e. , processing elements). As the number of processing elements steadily increases and can be expected to match the number of particles needed for an accurate monocular FastSLAM2. 0 system intended to operate in large-scale environment, GPGPU is an interesting alternative architecture for monocular FastSLAM2.
The availability of climatic data, especially on a daily time step, has become very rare in West Africa over the last few years due to the high costs of climate data monitoring. This scarcity of climatic data is a huge obstacle to conduct hydrological studies over some watersheds. In this context, our study aimed to evaluate the capacity of Tropical Rainfall Measuring Mission (TRMM) satellite data to simulate the observed runoffs over the Bafing (the main important tributary of the Senegal River) before their potential integration in hydrological studies. The conceptual hydrological model GR4J (modèle du Génie Rural (Agricultural Engineering Model) à 4 paramètres Journalier (4 parameters Daily)) has been used, calibrated and validated over the 1961-1997 period with rainfall and Potential Evapotranspiration (PET) as inputs.
The MIKE SHE model was calibrated over the 1991-1996 period and then validated over the 1987-1990 period with observed data following by the simulation of runoffs over the 2003-2005 period using observed rainfall data and measured rainfall via satellite. The simulation based on TRMM data were inconclusive for the Bafing Makana watershed. The objective of the present study is to evaluate the capacity of TRMM rainfall data series to simulate runoffs of the Bafing basin using the GR4J conceptual model with much longer series of data. The Bafing is an important tributary of the Senegal River since it accounts for about 40%-60% of the river's discharge in Bakel  [44]. GR4J is a data and parameter parsimonious model, and its capacity to simulate runoffs in the African context has been demonstrated by several studies  [45, 46].
Observed data (rainfall and temperatures) were provided by the National Meteorology Agencies in Guinea and Mali. Twelve rainfall stations and five temperature measurement stations have been used. Tables  1  and 2  provide an inventory of the available data. The quality and duration of these data are variable, especially according to the country. Hydrological data of the Bafing Makana station originated from the database of the Organization for the Development of the Senegal River (OMVS). They deal with the 1961-2004 period with a near-zero missing data rate (0. 003%). The reference period that has been chosen for this study is 1961-2004. More recent data are not taken into account  Observed data (rainfall and temperatures) were provided by the National Meteorology Agencies in Guinea and Mali.
Twelve rainfall stations and five temperature measurement stations have been used. Tables  1  and 2  provide an inventory of the available data. The quality and duration of these data are variable, especially according to the country. Hydrological data of the Bafing Makana station originated from the database of the Organization for the Development of the Senegal River (OMVS). They deal with the 1961-2004 period with a near-zero missing data rate (0. 003%). The reference period that has been chosen for this study is 1961-2004. More recent data are not taken into account because they are not available or not accessible from the Guinean and Mali Meteorological Agencies and OMVS. However, we used 43 years of data that concern wet and dry periods that West African countries experienced over the last periods.
The 1961-2004 period has specificity to the present concomitant data (rainfalls, temperature and runoffs) with relatively few gaps. Figure  1  shows the spatial distribution of stations. km ˆ25 km) spatial resolution has been used. These data are a combination of TRMM synthetic images and other satellite-provided images  [57]. Thus, the Aqua microwave sensors, National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program (DMSP) satellites, as well as visible/infrared data from geostationary satellites have been taken into account when estimating rainfall algorithms  [58]. These estimates were then adjusted by including monthly ground climate measures generated by Global Precipitation Climatology Project (GPCP) and Climate Assessment and Monitoring System (CAMS) networks to provide TRMM 3B43 monthly data.
The adjustment coefficients calculated for these data were finally applied to three-hourly data in order to determine the final TRMM 3B42 data, Version Number 7. This TRMM 3B42 v7 dataset was used due to its availability and successful results achieved over the world, particularly in the West Africa area. Gosset et al. [39]  highlighted that the performance of TRMM data compared to gauges over Niamey is high when numerous statistical criteria are considered (overall bias; correlation of the time series and spatial patterns; frequency distribution of the rain rates). However, Moazami et al. [59]  indicated that Version 6 of TRMM 3B42 underestimates (by around 25%) mean annual precipitation over the entire country of Iran, with a value of 0. 17 mm/day.
Comparison of Observed and Calculated Flows Rates with Observed and TRMM Rainfalls The Hydraccess free software  [77]  widely used in West Africa (and notably in hydrological services for database management) was used to calculate observed characteristic discharges and simulated characteristic discharges with observed and TRMM rainfalls by using daily data over 1998- ř N i"1 pQ obs,iQobs q 2 ´8, 1 1 VE ř N y"1 V sim,y ´řN y"1 V obs,y ř M y"1 V obs,y ´8, `8 0 VE m 1 N N ř y"1 |Vsim,y´Vobs,y| V obs,y ´8, `8 0 Q obs,i is the observed flow at time step i; Q calc,i is the simulated flow at time step i; Q obs is the average flow observed; N is the total number of time steps of the simulation period; NSE is the Nash Sutcliffe coefficient; F agg is the multi-criteria aggregating function; VE is the cumulative volume error; VE m is the yearly mean volume error; V obs,i is the observed volume at time step i; V sim,i is the simulated volume at time step i.
Comparison of Observed and Calculated Flows Rates with Observed and TRMM Rainfalls The Hydraccess free software  [77]  widely used in West Africa (and notably in hydrological services for database management) was used to calculate observed characteristic discharges and simulated characteristic discharges with observed and TRMM rainfalls by using daily data over 1998-2004. Annual flows have been ranked in decreasing order and frequencies calculated. Values exceeding 1 day, 10 days, 20 days, 1 month, 2 months, etc. , have been identified (Table  4 ).
Finally, the parameters that best reflect the rainfall-flow relationship have been used to simulate flows over the 1998-2004 period with ground and TRMM data. The results show consistency between satellite estimates and ground-based observations. Similar conclusions have been drawn by Gosset et al. in their study on the AMMA-CATCH sites, by Casse et al. in their study on the Niger River basin and by Collischonn et al. [62]  on the Amazon basin. However, the satellite tends to overestimate rainfall between 1 mm and 15 mm. This result is consistent with recent findings of Diallo  [79]. The cross-validation of the GR4J model with observed data shows satisfactory results, which are consistent with the studies of Amoussou et al. on the Mono basin (Benin) as well as with those conducted by Le Lay  [46]  on the Donga basin (Benin), with Nash criteria superior to 0.
[28]  showed that the quality of satellite rainfall is not homogeneous, since it varies according to climatic and topographic conditions. Besides, it would be useful to carry out some tests on the capacity of other satellites data to simulate runoff in order to make comparison between them. For the purpose of this study, the GR4J model has been calibrated and then validated with observed data, assuming they constitute the reference data. However, some authors  [30, 90]  have shown that ground data are less accurate than those collected via satellites. In this regard, there are several studies suggesting that the cross-validation of the hydrological model should be carried out based on satellite data  [28, [91] [92] [93]. It would be interesting to do so. However, one of the major obstacles to use the TRMM data is its short time series.
This research starts by analysing the current state of artistic heritage in Italy and studying some examples in Europe: we try to investigate the scope of non-formal learning in artistic context, mediated by advanced technology. The framework within which we have placed our investigation is that of lifelong learning and lifedeep learning. The research considers a number of excellent Italian and European museums in context of lifelong learning activities and offers a software prototype that has been built specifically for studying the artistic experience, namely M_ART about the Basilica of St. Ambrogio in Milan. The quantitative/qualitative methods adopted analysed about 400 subjects. The goal pursued, in logic of hybridization of knowledge in scientific and humanistic vocation, was to consider the application of advanced technologies in non-formal settings, with particular reference to the artistic sphere.
The prototype was initially tested on a sample group, during an on-site visit. A questionnaire was then submitted in order to gather information that would help improve and refine the content. The sample group comprised 50 subjects of whom 46 were women and 4 were men, aged between 22 and 34 years. Educational backgrounds included psychological-pedagogical (27%), linguistic (23%) and technical (50%). 47% of the group had acquired history of art notions from school and 53% through passion and curiosity. The majority of interviewees were more inclined to use traditional audio-guides, rather than mobile or multi-media devices, although 50% of subjects declared to own a tablet. Analysis of data collected shows that the "sample group" encountered no particular problems using the M-ART guide created specifically to promote continuous learning.
The results were then extracted and interpreted using SPSS (Statistical Package of Social Science). The sample group that the questionnaire was submitted to ranged from 14 to 64 years of age with 38% aged 22-23, and an overall 65% of the group aged between 19-23. The rest of the group was distributed equally between the ages of 24 and 64. The sample group cannot be considered equally homogenous in terms of gender: males comprise 26% and females 74%: 92% did not specify the subject of their bachelor degree, whereas 18% of the group stated to be studying for a degree in humanities (law, learning and developmental sciences, education sciences, psychology), and 10% stated to have already graduated. Furthermore the sample group indicated the following types of employment: services 26%; commerce 9%; education 10%; various types of temp work 4.
This paper presents an extensive intercomparison and validation for the ozone (O 3 ) product measured by the two Infrared Atmospheric Sounding Interferometers (IASIs) launched on board the MetOp-A and MetOp-B satellites in 2006 and in 2012 respectively. IASI O 3 total columns and vertical profiles obtained from Fast Optimal Retrievals on Layers for IASI (FORLI) v20140922 software (running up until recently) are validated against independent observations during the period 2008-2014 on a global scale. On average for the period 2013-2014, IASI-A and IASI-B total ozone columns (TOCs) retrieved using FORLI are consistent, with IASI-B providing slightly lower values with a global difference of only 0. 2 ± 0. 8 %. The comparison between IASI-A and IASI-B O 3 vertical profiles shows differences within ± 2 % over the entire altitude range.
A. Boynard et al. :  Seven years of IASI ozone retrievals from FORLI ical tropopause region; this can be explained by the low O 3 amounts leading to large relative errors. In this study, we also evaluate an updated version of FORLI-O3 retrieval software (v20151001), using look-up tables recalculated to cover a larger spectral range using the latest HITRAN spectroscopic database  (HITRAN 2012)  and implementing numerical corrections. The assessment of the new O 3 product with the same set of observations as that used for the validation exercise shows a correction of ∼ 4 % for the TOC positive bias when compared to the UV ground-based and satellite observations, bringing the overall global comparison to ∼ 1-2 % on average. This improvement is mainly associated with a decrease in the retrieved O 3 concentration in the middle stratosphere (above 30 hPa/25 km) as shown by the comparison with ozonesonde data.