dataesr/scientific-paragraphs-categorization huggingface dataset:
DYMOA NRS with motorcycles and cars In 2014-2017, a new project called DYMOA (Infrastructure Diagnosis and Vehicle Dynamics for Motorcycles and Cars) was carried out to acquire Naturalistic Riding Data, in order to obtain knowledge about the vehicle dynamics of PTW and the use of road infrastructures by riders. 26 private motorcycles and 5 passenger cars were implemented with smartphones used as Event Data Recorders (EDR), fixed under the driver seat. In three regions of France (see Fig. 1 ), during 18 months, the devices continuously collected data on riding, on driving behavior and on maneuvers performed by the riders and drivers. The measurements come from the smartphone's accelerometers, gyro meters, and GPS. Trajectories and syntheses of dynamic parameters were collected in real time for all their professional and personal travels.
MOSCOVICE NRS with motorcycles In the framework of a PhD thesis by  Tamisier (2017)  that measured the effectiveness of different communication methods on actual speed reduction by riders, about 40 motorcycles were equipped with the same device as in DYMOA. The data collection was carried out in two regions of France and lasted 3 months; only work commutes were recorded. The dynamic behavior of the couple rider-PTW was collected thanks to the same algorithm as DYMOA, described in the next section. More information about this experiment is available in  Cornen et al. (2019). The study presented here exploits the data of 29 riders having travelled about 25500 km. Table  1  summarizes the main characteristics of the three experiments and the four fleets of vehicles, which were compared in this work.
The motorcyclist leans with his motorcycle, which implies at first glance that he endures little to no lateral acceleration in the motorcycle coordinate system. The lateral acceleration is more representative of the lateral behavior of the cars and the roll rate is more accurate to measure the lateral behavior of motorcycles. The yaw rate represents for both types of vehicle a clear measure of cornering and its comparison is more relevant. As expected, there is nearly no lateral acceleration for the PTWs, whereas the cars lateral accelerations exceed 2 m/s² from 5. 3% to 6. 7% of the time and 3 m/s² about 2% of the time. The cars reach lateral accelerations of 4 m/s² and more around 0. 4% of the time, which is rare but way higher than what is observed for PTWs. The roll and yaw rates were compared only for DYMOA and MOSCOVICE, since this data was not available for SVRAI.
The SVRAI data does not allow drawing such graphs, because the limits for accelerations were only +/-11 m/s² versus +/-15 m/s² and the roll and yaw rates were not counted in the syntheses. For all three types of crossing, there is a lower extent of synthesis in the vertical direction, corresponding to longitudinal acceleration, for cars compared to PTWs. It expresses lower longitudinal accelerations, i. e. lower braking maneuvers and accelerations. The most extreme levels of acceleration and braking were achieved by the DYMOA motorcyclists. As expected the extent of roll rate is higher in PTW whereas for cars the extent of lateral acceleration is higher. For the crossing with the yaw rate, extreme values are also reached by PTWs, representative of a more intense use of the vehicle dynamic capacities.
Levels of speed The comparison was done first for the DYMOA and MOSCOVICE data with similar format. Concerning the speed distributions, the interval is 5 km/h and the range 0-150 km/h, with a category "superior to 150 km/h", whereas for the SVRAI speed data the interval is 10 km/h and the range 0-130 km/h with a last category "superior to 130 km/h". Fig 4  (Left)  shows the time spent in different levels of speed for all cars and all PTWs in these experiments, as percentage of the total time. To make the comparison easier, the first interval 0-5 km/h was not represented. It would be the highest category in terms of time spent, due to the fact that the vehicles are often at rest during their journeys. The first observation is about PTWs: there is a big difference between the two experiments.
SVRAI drivers speed data contradicts DYMOA under 30 km/h and between 90 and 120 km/h. SVRAI motorists drive faster: there are less often below 90 km/h than DYMOA motorists, and even less often than motorcyclists under 80 km/h, and again between 100 and 120 km/h. Over 120 km/h, they spent even more time than DYMOA drivers who were already above motorcyclists. The SVRAI experiment lasted one year in three different regions of France, so it is comparable with DYMOA. However, the roads on which the vehicles travelled were probably different, with cars travelling more on motorway.
Conclusion The real use of vehicle dynamic capacities by riders and drivers were compared using data from three NDS or NRS experiments conducted in France. The distributions of accelerations, roll rate, yaw rate and speeds are quite different between the two types of vehicle. The levels of positive acceleration are clearly higher for motorcycles than for cars in terms of time spent in medium and high levels and in terms of extreme values reached. The motorcycles also brake more often and with a higher intensity, but the difference is less marked with car drivers. Overall, the drivers brake more intensely than they accelerate. On the contrary, motorcyclists accelerate more than they brake, due to the capacities of their vehicle. Whereas it is more difficult to compare the lateral dynamic behavior between cars and PTWs, the analysis of the lateral acceleration, of the roll rate and of the yaw rate provides some interesting results.
A landmark example is the collaborative and volunteer-based OpenStreetMap project that involves millions of registered users, including hundreds of thousands of contributors, which has proven its effectiveness in many environmental and humanitarian operations. A high diversity of experiments and innovations are under development, from web-and location-based projects that engage citizens in collecting the data of wildlife observations  [5] , to the integration of augmented reality interfaces for the promotion of biodiversity education  [6]  and the combination of citizens' reporting of public, geographical and remote sensing data to evaluate the forest tree decline  [7]. Specific attention is paid to the identification and study of the different levels of citizen engagement  [6]  and to the integration of spatial and temporal dimensions as discriminating criteria  [8].
[8]. In the present work, their experimental data were reproduced numerically using two different hydro-morphodynamic models, SedFoam  [13]  and FLOW-3D  [14]. These two hydro-morphodynamic models were chosen on the basis of several aspects. SedFoam uses a complex coupled multi-phase flow RANS model for the fluid phase as well as for the particles phase while FLOW-3D uses RANS modeling for the fluid flow and a transport scouring model based on a transport equation to calculate the transport of particles. The differences between SedFoam and FLOW-3D can allow to explain the two particular phenomena observed by Martino et al. : a change of the dune shape and a digging-refilling of the scour. SedFoam  [13]  is a Eulerian three-dimensional two-phase flow RANS model for the sediment transport based on the open-source toolbox, OpenFOAM.
FLOW-3D is a commercial computational fluid dynamics (CFD) software which uses RANS modeling that works based on the finite volume method to solve the fluid dynamics equations as well as the volume of fluid method to track the fluid -air surface interaction. This hydromorphodynamic model uses a three-dimensional sediment transport and scour model that is not considered as a coupled RANS multi-phase flow model. In this hydro-morphodynamic model, the fluid flow is calculated using the Reynolds averaged Navier-Stokes equations with turbulence closure but without taken the particle phase into consideration. The RNG K -ε model is used and calibrated by the software to assure numerical stability  [17, 18]. As for the sediment transport, a conventional equation of the sediment transport like the Nielsen equation  [19]  as well as the Meyer-Peter and Muller equation  [20]  are implemented and used to calculate the sediment transport.
FLOW-3D FLOW-3D is a commercial CFD software, with a wide range of applications  [14]. The software uses a special method, called TruVOF, to track the free surface. This method is an improved form beyond the original volume of fluid technique which increases the accuracy of tracking interfaces at the boundary conditions  [30]. The FAVOR (fractional area-volume obstacle representation) method is also used to locate and simulate surfaces and rigid geometric regions  [31]. The continuity and momentum equations based on the Reynolds Averaged Navier-Stokes (RANS) equations are the governing equations used to compute the fluid motions. To solve the RANS equations, the software uses the finite volume method. The general form of the continuity equation is as following: V F ∂ρ f ∂t + ∂ ∂x ρ f u A x + ∂ ∂y ρ f v A y + ∂ ∂z ρ f w A z = R D I F + R SOR.
Confined Flow To assure that the two hydro-morphodynamic models, SedFoam and FLOW-3D, simulate correctly the water flow, the numerical results are compared to the experimental data through the dimensionless maximum velocity U m /U 0 where U m is the maximum velocity of the jet and U 0 is the initial mean velocity at the sluice gate calculated as U 0 = Q/(wb 0 ). A sluice gate with an aperture b 0 = 2 cm was used. The erodible particles bed was replaced with a smooth non-erodible bed. A flow rate Q = 8 l / min that gives an initial mean velocity U 0 equal to 33. 3 cm / s is considered. Experimentally, the vertical profile of the flow velocity was measured at several stations located at a distance x from the sluice gate. A velocity decay was observed at increasing distance from the sluice gate, with the dimensionless maximum velocity U m /U o.
Acknowledgements The authors very much appreciate the support of  Julien Chauchat  (associate professor) and  Cyrille Bonamy  (research engineer), whom represent the  SedFOAM  development team at  LEGI labs  and the  university of Grenoble Alpes , for providing tutorials and guidance. The authors would also like to thank with gratitude,  Marcelo F. Piva  (professor) from  Grupo de Medios Porosos at the University of Buenos Aires  and  Román G. Martino  (researcher) from the  Institute for Advanced Studies in Engineering and Technology, Universidad Nacional de Córdoba  and.
The present work was initially motivated by a recent study on a dimension reduction algorithm whose dominant step is the matrix multiplication  [5]. The study showed that a choice had to be made to perform the matrix multiplication: favor either performance, through a GEMM, or memory, through a SYMM. The new approach proposed in the present manuscript makes it possible to combine both these desirable properties. The remainder of the paper is organized as follows. We present related work in Section 2. We then analyze the AI of reference distributed 2D BC GEMM and SYMM algorithms as well as that of our proposals for new SYMM data distributions in Section 3. We present the implementation of our algorithms in Section 4 and assess the resulting communication volume and performance in Section 5.
SYMM task-based design Fully-featured distributed-memory dense linear algebra libraries such as ScaLAPACK  [10]  or Elemental  [21]  implement SYMM with a 2D BC data distribution. Such a regular data distribution makes it possible to easily set up MPI communicators along rows and columns and ensure collective communications. On the contrary, if we want to consider irregular data distributions, like those discussed in Section 3, it may be challenging to implement the corresponding code directly through the MPI interface. We therefore aim at designing a SYMM routine completely independent of the proposed mappings so that we can then effortlessly implement any non-trivial mapping. Task-based programming allows for such a separation of concerns  [16, 4]. In addition, we want to assess whether this is feasible with a code which is easy to write, read and maintain.
To this end, we decided, more specifically, to rely on the STF model  [13, 6]  introduced in Section 2. 3, which allows for writing a parallel code which resembles a sequential code one would naturally write. Finally, we do not want to trade off performance with elegance, and we thus rely on the latest developments for the scalability of the STF model  [4]  to design appropriate communication patterns while preserving the compactness of the expression. Without loss of generality, for the sake of conciseness, we restrict the presentation to the C ← AB + C case, where A is symmetric and only its lower part explicitly stored. We also assume blocks of size b-by-b, so that A is a M -by-M block matrix (m = M * b) and B and C are M -by-N block matrices (n = N * b). The sequential algorithm of the SYMM consists of three nested loops where the two innermost loops perform a matrix -block-column product (C * ,j ← C * ,j + AB * ,j ), while the outer loop goes through all N block-columns.
Other than that, we may observe that the STF pseudo-code is very similar to a sequential one. However, instead of being directly executed, GEMM and SYMM operations on blocks are inserted as tasks through the insert task primitive. The aim of such an insertion is to make the call asynchronous. Data dependencies between tasks are inferred by the runtime system thanks to the data access modes. The basic access modes being Read (R), Write (W), and Read Write (RW), the most straightforward implementation would be to associate a R access mode to blocks of A and B and a RW mode access to blocks of C. Such an approach, following the programming models of  [28, 3] , would be a valid STF code for distributed-memory machines. Indeed, the runtime system can not only infer the dependencies between tasks but it may also trigger the appropriate communications by moving around the data causing the dependencies.
Experiments The code designed in Section 4 allows us to assess all the mappings discussed in Section 3. We study their impact on the AI and performance. We have implemented Algorithm 3 on top of the StarPU  [6]  task-based runtime system and the NewMadeleine communication back-end, which, combined, support the dynamic detection of collective communications  [12]. In an applicative setting, where no synchronization is required between filling and computing the matrices, blocks of B are transferred through broadcasts transparently: the runtime detects them through dependencies in the DAG. In our benchmarking setting, artificial tasks are added to mimic the dependencies that allow a similar detection. We conducted our study in double precision on the Skylake partition of the Trs Grand Centre de Calcul (TGCC) computer.
Five test cases are assessed, ranging from S1 on 1 node (denoted S1 -1) to S5 on 132 nodes (denoted S5 -132). this observation applies to both MM1 and MM2 matrix multiplication steps and since they altogether dominate the RSVD algorithm, this directly translates into a significant improvement for the entire RSVD. As recalled above, MDS requires to compute the Gram matrix G before applying the RSVD itself. We also redesigned this step, which is mainly a reduction, using the DIST REDUX access mode introduced in  [4]. We have not reported detailed figures on the matter, however, the execution time of the entire RSVD-MDS algorithm (Gram computation and RSVD altogether) on the whole data set (S5 ) using 132 nodes (5,280 CPU cores) has been reduced from 70 seconds, with the original code of  [5]  (denoted (0) here), to 25 seconds, with TBC SYMM (denoted (4) here) together with the new Gram step design, while using about half the memory.
We complete the study with the illustration of the capability of task-based codes to exploit heterogeneous architectures. Without any change in the code (other than providing the CUDA cuBLAS kernels of single-GPU kernels), the runtime system may execute tasks on CPU or GPU  [6]. A subset of Jean Zay nodes have the exact same characteristics as described above in CPU-only case but are furthermore enhanced with four NVIDIA Tesla V100 SXM2 GPUs (32 GB). CUDA v. 10. 1. 2 is used. Bars denoted (4 * ) in Figure  8  correspond to the execution of the RSVD with GPUs enabled, relying on TBC SYMM for the matrix multiplication. The results show a considerable improvement over the CPUonly case in spite of the relatively low number of columns (n = 1, 000 only) of B and C, a typical set up for the application.
In the case where we allow SYMM to store an amount of memory equivalent to a full matrix as 2D BC GEMM does, we furthermore showed that 2. 5D TBC with s = 2 slices achieves a higher AI than 2D BC GEMM by a factor of √ 2. Our experimental study showed that the improvement of the AI translates into a compelling performance enhancement, up to the point of roughly matching GEMM performance. However, the highest AI does not always translate into the best performance. A preliminary analysis, not reported in this paper for a matter of conciseness hints that it is due to the scheduling of the communications that certainly must be reconsidered with the proposed new irregular data distributions. We plan to further investigate it in future work. The resulting code 3  has been integrated in a metabarcoding application  [5].
It consists in a MDS dimension reduction algorithm based on RSVD whose main computational steps are two dense matrix multiplications involving a symmetric input matrix. While one had to trade-off  [5]  between performance, with GEMM, or memory, with SYMM, we showed that, altogether, the proposed STF design and the new TBC distribution now achieve a performance competitive with GEMM. This study also showed that algorithms involving very irregular data and task distributions can now be implemented with a code easy to write, read and maintain thanks to the latest developments on the scalability of the STF model  [4] , while ensuring a competitive performance.
Acknowledgment This work was granted access to the HPC resources of TGCC under the allocation 2021-5063 made by  GENCI. This work was performed using HPC resources from  GENCI-IDRIS. This work was supported by the  SOLHARIS  project ( ANR19-CE46-0009 ) which is operated by the  French National Research Agency (ANR). This work has been supported by the  Rgion Nouvelle-Aquitaine , under grant  2018-1R50119  HPC scalable ecosystem. We thank the  StarPU and NewMadeleine development  teams for their great support all along this work.
Recent decades have seen the development of vision-based dietary assessment systems (VBDA). These systems generally consist of three main stages: food image analysis, portion estimation and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models, and the availability of high-quality training datasets. Food image segmentation still faces various challenges, while most existing research has focused mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes presentation with mixed-class food. This work focuses on the first stage of VBDA, where we introduce two notable contributions.
Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM attention mechanism after each extracted backbone features layers. Secondly, we present CamerFood10, the first food images dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms existing image segmentation benchmark models with an mIoU (mean Intersection over Union) of 65. 20%, representing more than +10. 74% improvement over the DeepLabv3+ with the same backbone.
The performance of the first two stages heavily relies on the effectiveness of artificial intelligence algorithms and the availability of good food datasets, while the final stage depends on a nutritional composition database. The food image analysis stage entails segmenting food regions from the background and recognizing each type of food present in the image. The next step involves evaluating the quantity or volume of each detected food item. Food image segmentation and recognition indeed pose significant challenges due to various factors. One of the primary challenges is the nonrigid structure of food, which differs from common objects. This characteristic makes it difficult to utilize shape as a reliable feature for machine learning models. Additionally, foods usually have high intra-class variation, meaning that the visual characteristics of the same food can differ significantly from one cook to another.
This variation is particularly pronounced in African foods, further complicating accurate food recognition. Furthermore, inter-class resemblance is another source of potential recognition issues, as different food items can appear very similar, as illustrated in Fig. 1. Some examples of generic food with such resemblances include brownie and chocolate cake, margarine and butter. Moreover, certain dishes may contain various ingredients, resulting in the same dish with distinct visual aspects. Another significant challenge in food image segmentation and recognition is the scarcity of publicly available datasets for model training. This lack of datasets hinders the development of accurate segmentation models. Current research on food image segmentation and recognition focuses mainly on images of Asian and Western foods.
Our method introduces an additional middle layer in the decoder path. This new layer, reintroduces more general extracted features which have potentially been lost in encoder path. This enhancement aims to improve the segmentation performance of the model. • CamerFood10 dataset is publicly available 1 and mid-DeepLabv3+ code is shared on Github 2. The remainder of this paper is organized as follows: Section II describes related work on food image segmentation datasets and techniques. Section III presents our dataset CamerFood10 and explains our proposed model architecture while Section IV describes the experimental results. The conclusion and future prospects are outlined in Section V.
Food image Dataset With advancements in deep learning models for computer vision, the field of food segmentation and recognition techniques is rapidly evolving. However, the performance of these techniques heavily relies on the availability of large and diverse well annotated image datasets. Collecting such datasets is a labor-intensive task, and the quality of annotations directly affects the performance of the models. While some publicly available food image datasets exist, only a few of them are annotated for image segmentation and detection tasks  [11, 12]. The annotation process for segmentation 2 https://github. com/babanthierry94/mid-DeepLabv3plus 2 https://drive. google. com/drive/u/1/folders/1MugfmVehtIjjyqtphs-4u0GksuHy3Vjz is particularly tedious and sensitive. Image segmentation datasets vary in terms of geographic origin of the food and the methods used for image collection.