dataesr/scientific-paragraphs-categorization huggingface dataset:
The demands of each application of the batch for CPU, storage, memory and throughput are uniform independent random variables. The mean value of such variables is dictated by the nominal value we measured for our benchmark application. That application, as recalled in the previous section, is a plate-recognition application packaged as a two-modules microservice. The second microservice module can process the video stream either in the cloud or on a fog node. The resulting distribution values for the application batches are enlisted in Tab. II; symbol u 0 refers to the nominal values we measured on FogAtlas for the plate recognition app. Finally, the probability that an application belongs to region k ∈ {1,. , K} follows a truncated Pareto distribution of parameter α, i. e. , P{R u > k} = k -α /γ, where R u is the random variable representing the index of the region assigned to the application u and normalization constant γ = K h=1 h -α.
A. Experimental Results In Fig. 4a  we have depicted the number of deployed applications for increasing batch size. The upper graph reports the results averaged on 10 instances of a scenario with parameter β = 1. 5 (top) and β = 2. 5 (bottom), respectively. The red line is the optimal solution obtained by the Gurobi ILP solver  [13] , the blue line is FPA, whereas the green one is the variant of FPA implemented in FogAtlas, namely FPA-R. It considers region-wise aggregated resources and delegates the intra-region, per-server deployment to Kubernetes schedulers using a randomized placement policy 1. As seen in Fig. 4a , up to U = 50, the deployment of the batch of applications is complete. In the last part of the curve, communication constraints dominate, saturating around 100 deployed applications in the optimal case.
VIII. CONCLUSIONS In this paper, we have introduced an optimization framework for microservice scheduling over fog infrastructures, where different configurations are used to orchestrate fog computation modules to the edge or in cloud. The problem combines a multi-comodity flow and a placement problem, but can be reduced to a m-dimensional knapsack problem by introducing throughput proportionality. We proposed a greedy algorithm, namely FPA, which performs efficiently with respect to the optimal solution by performing placement using a gradient approach. We have tested numerically our framework under realistic dimensioning, leveraging our platform FogAtlas. Extensive numerical experiments have confirmed the scalability properties of the proposed fog orchestration technique.
A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO 2 ) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO 2 climatology, and (2) the reconstruction of pCO 2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO 2 Atlas (SO-CAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO 2 and the ocean predictors.
, 2009) ; therefore, the FFNN output is kept close to realistic values. Furthermore, the reconstruction of monthly climatologies during the first step allows for a potential change in seasonal cycle in response to climate change to be taken into account when applied to time slices or to model output providing the drivers, but no carbon cycle variables. The remainder of this paper is structured as follows: Sect. 2 introduces datasets used during this study and describes the neural network; Sect. 3 presents results for its validation and qualification, as well as its comparison to three mapping methods as part of the Surface Ocean pCO 2 Mapping intercomparison (SOCOM) exercise  (Rödenbeck et al. , 2015). Finally, results and perspectives are summarized in the last section. 2 Data and methods.
At the basin and regional scale, however, adding the SSH improved the spatial pattern of reconstructed pCO 2 and the accuracy of our method. For the first step, the reconstruction of monthly climatologies, the  Takahashi et al. (2009)  monthly pCO 2 gridded climatology (1 • ×1 • ) was used as the target. The original climatology was constructed by an advection-based interpolation method on a 4 • × 5 • grid. It was interpolated on the 1 • × 1 • SOCAT grid, which is also the resolution of the final output for the FFNN. For the second step, the target was provided by the SOCAT v5 observational database  (Bakker et al. , 2016). We used a gridded version of this dataset that was derived by combining all SOCAT data collected within a 1 • × 1 • box during a specific month. SOCAT v5 represents global observations of the sea surface fugacity of CO 2 (f CO 2 ) over the period from 1970 to 2016.
It includes data from moorings, ships and drifters. These data are irregularly distributed over the global ocean with 188 274 gridded measurements over the Northern Hemisphere and 76 065 over the Southern Hemisphere. In order to ensure a satisfying spatial and temporal data coverage, we limited the reconstruction to the period from 2001 to 2016, which represents ∼ 77 % of the database (Fig. 1a ). c The following formula is used to convert f CO 2 to pCO 2  (Körtzinger, 1999) : where f CO 2 and pCO 2 are in micro-atmospheres (µatm), p is the total pressure (Pa), R = 8. 314 J K -1 is the gas constant and T is the absolute temperature (K). Parameter B (m 3 mol -1 ) is estimated as B = (-1636. 75 + 12. 040T -3. 27957 × 10 -2 T 2 + 3. 16528 ×10 -5 T 3 ) × 10 -6. The parameter δ is the cross-virial coefficient (m 3 mol -1 ): δ = (57.
7 -0. 118T ) × 10 -6. The total pressure is from the Jena database (6 h, 5 • × 5 • ; http://www. bgc-jena. mpg. de/ CarboScope/?ID=s, last access: 13 September 2017) based on the National Center for Environmental Prediction (NCEP) reanalysis  (Kalnay et al. , 1996). f CO 2 = pCO 2 exp p B + 2δ RT , (1) Monthly global reprocessed products of physical variables from ARMOR3D L4 distributed through the Copernicus Marine Environment Monitoring Service (CMEMS; 0. 25 • × 0. 25 • ; http://marine. copernicus. eu/services-portfolio/access-to-products/?option=com_ csw&view=details&product_id=MULTIOBS_GLO_PHY_ REP_015_002, last access: 27 October 2017) were used for SSS, SST and SSH  (Guinehut et al. , 2012). The GlobColour project provided monthly chlorophyll (Glob-Colour "CHL" data) distributions at a 1 • × 1 • resolution (http://www.
globcolour. info/products_description. html, last access: 31 October 2017). For MLD, daily data from the "Estimating the Circulation and Climate of the Ocean" (ECCO2) project Phase II (Cube 92), at a 0. 25 • × 0. 25 • resolution  (Menemenlis et al. , 2008)  were used. For atmospheric xCO 2 , the 6h data from Jena CO 2 inversion s76_v4. 1 on a 5 • × 5 • grid were selected (http://www. bgc-jena. mpg. de/CarboScope/?ID=s, last access: 13 September 2017). Finally, an ice mask based on daily "Operational Sea Surface Temperature and Sea Ice Analysis" (OSTIA) data with a gridded 0. 05 • × 0. 05 • resolution  (Donlon et al. , 2011)  was applied. MLD and CHL data were log-transformed before their use in the FFNN algorithm because of their skewed distribution. In regions with no CHL data (high latitudes in winter) log(CHL) = 0 was applied.
It does not introduce discontinuities, as log(CHL) is close to zero in the adjacent region. All data were averaged or interpolated on a 1 • × 1 • grid and, depending on the resolution of the dataset, averaged over the month. It is worth noting that all datasets have to be normalized (i. e. , centered to zero-mean and reduced to unit standard deviation) before their use in the FFNN algorithm, for example, SSS n = SSS -SSS SD (SSS). Normalization ensures that all predictors fall within a comparable range and avoids giving more weight to predictors with large variability ranges  (Kallache et al. , 2011). As surface ocean pCO 2 also varies spatially, geographical positions, latitude (lat) and longitude (long), after conversion to radians were included as predictors. In order to normalize (lat, long) the following transformation is proposed: lat n = sin lat • π/180 • long n, 1 = sin long • π/180 • long n, 2 = cos long • π/180 • Two functions sin and cos for longitudes are used to preserve its periodical 0 to 360 • behavior and thus to consider the difference of positions before and after the 0 • longitude.
Network configuration and evaluation protocol In this work, we use Keras, a high-level neural network Python library ("Keras: the Python Deep Learning library", Chollet, 2015; https://keras. io) to build and train the FFNN models. The identification of an optimal configuration is the first step in the FFNN model building. This includes the choice of the number and size of hidden layers (i. e. , intermediate layers between input and output layers), the connection type, the activation functions, the loss function and optimization algorithm, as well as the learning rate and other low-level parameters. Based on a series of tests and their statistical results (RMSE, correlation and bias) a hyperbolic tangent was chosen as an activation function for neurons in hidden layers, and a linear function was chosen for the output layer.
1. Step 1: reconstruction of monthly climatologies. FFNN reconstructs a normalized monthly surface ocean pCO 2 climatology as a nonlinear function of normalized SSS, SST, SSH, Chl a, MLD climatologies and geographical position (lat and long): pCO 2, n = SSS n , SST n , SSH n , Chl n , MLD n , long n , lat n. (2) Surface ocean pCO 2 from  Takahashi et al. (2009)  provided the target. The dataset was divided into 50 % for FFNN training and 25 % for its evaluation. This 25 % did not participate in the training. This set is used to monitor the performance of the training process and to drive its convergence. The remaining 25 % (each fourth point) of the dataset was used after training for the FFNN model validation. More details about the FFNN training process can be found in  Rumelhart et al. (1986)  and  Bishop (1995).
Validation and evaluation datasets were chosen quasi-regularly in space and time to take all regions and seasonal variability into account. In order to improve the accuracy of the reconstruction, the model was applied separately for each month. We have developed a FFNN model with five layers (three hidden layers). Twelve models with a common architecture were trained. Tests with one model for 12 months showed a slight decrease in accuracy (not presented here). About 17 500 data were available for each month to train the model, resulting in monthly FFNN models with about 1856 parameters.
2. Step 2: reconstruction of anomalies. During the second step, normalized pCO 2 anomalies were reconstructed as a nonlinear function of normalized SSS, SST, SSH, Chl a, MLD, xCO 2 and their anomalies, as well as geographic position: pCO 2, anom, n = SSS n , SST n , SSH n , Chl n , MLD n , xCO 2, n , SSS anom, n , SST anom, n , SSH anom, n , Chl anom, n , MLD anom, n , xCO 2,anom, n , long n, 1 , long n, 2 , lat n (3) Surface ocean pCO 2 anomalies were computed as the differences between colocated pCO 2 values based on SOCAT observations and monthly pCO 2 climatologies reconstructed during the first step that provided the targets: pCO 2, anom = pCO 2, SOCAT -pCO 2, clim, FFNN. (4) The set of target data was again divided into 50 % for the training algorithm, 25 % for evaluation and 25 % for model validation.
K-fold cross-validation was used for the evaluation and the validation of the FFNN architecture. Cross-validation relied on K = 4 different subsamples of the dataset to draw 25 % of independent data for validation (Fig. S1  in the Supplement). Each sampling fold was tested on five runs of the FFNN for each month. Each of these five runs was characterized by different initial values that were randomly chosen. From these five results, the best was chosen based on rootmean-square error (RMSE), the r 2 and the bias. The final model architecture at step 2 had three layers (one hidden layer). About 10 000 samples were available for training for each month; thus, a model with 541 parameters was developed. Note that a higher number of parameters did not show a significant improvement in the accuracy.
Reconstruction of surface ocean pCO 2 The previous section presented the development of the "optimal" architecture of a FFNN model for the reconstruction of global surface ocean pCO 2 and the estimation of its accuracy. This FFNN model was used to provide the final product for scientific analysis and comparison with other mapping approaches. In order to provide the final output, the selected FFNN architecture was trained on all available data: 100 % of data for training, 100 % for evaluation and 100 % for validation. The network was executed five times (different initial values) and the best model was selected based on validation results considering the root-mean-square error (RMSE), the r 2 and the bias computed between the network output and the SOCAT-derived surface ocean pCO 2 data. The final model output is referred to as the LSCE-FFNN product.
Validation The subset of data used for network validation, which was 25 % of the total, represented independent observations, as they did not participate in training during model development (see Section 2. 2. 1). The skill of the FFNN to reconstruct monthly climatologies of surface ocean pCO 2 was assessed by comparing colocated reconstructed pCO 2 and corresponding values from  Takahashi et al. (2009). The global climatology was reconstructed with satisfying accuracy during step 1 with a RMSE of 0. 17 µatm and an r 2 of 0. 93. The model output of step 2 was assessed using K-fold crossvalidation as previously presented: K = 4 different subsets of independent data were drawn from the dataset and the network was run five times on each subset. From these 20 results the best one was chosen based on the RMSE, the r 2 and mean absolute error (MAE) (the bias is presented in Table  S1  in the Supplement).
, 2013). Results for these biomes are presented in Fig. 5. Biome 6 is relatively well-covered by observations and represents a key region for testing the skill of the model to reproduce the observed strong IAV linked to ENSO. El Niño events are characterized by positive SST anomalies, reduced upwelling and decreased surface ocean pCO 2 values. These episodes could be identified in all model time series (Fig. 5a ) with reduced pCO 2 levels in 2004/05 and 2006/07 (weak El Niño), 2002/03 and 2009/10 (moderate El Niño), and 2015/16 (strong El Niño). JMA-MLR (blue curve in Fig. 5a ) tended to underestimate pCO 2 during weak El Niño events; it was also underestimated during the La Niña 2011-2012 event by Jena-MLS13. LSCE-FFNN and ETH-SOMFFN, both based on a neural network approach, yielded similar results despite differences in network architecture and predictor datasets.
Sea-air CO 2 flux variability Sea-air exchange of CO 2 was estimated using the same gasexchange formulation (Eq. 5) and wind speed data (6-hourly NCEP wind speed) for each mapping data  (Rödenbeck et al. , 2005). It is worth noting that the sea-air flux is sensitive to the choice of the wind speed dataset  (Roobaert et al. , 2018). Figure  6a  presents the global 12-month running mean of the sea-air CO 2 flux for four mapping methods. All models showed an increase in CO 2 uptake in response to increasing atmospheric CO 2 levels, albeit with a strong betweenmodel variability in multi-annual trends. There is less agreement between the methods compared to reconstructions of surface ocean pCO 2 variability (Fig. 4b ). This results from the contribution of uncertainties in sea-air CO 2 flux estimations over regions with poor data coverage (mostly in the South Hemisphere: the South Pacific, the South Atlantic, the Indian Ocean and the South Ocean; see Fig.
Summary and conclusion We proposed a new model for the reconstruction of monthly surface ocean pCO 2. The model is applied globally and allows a seamless reconstruction without introducing boundaries between the ocean basins or biomes. Our model relies on a two-step approach based on feed-forward neural networks (LSCE-FFNN). The first step corresponds to the reconstruction of a monthly pCO 2 climatology. It allows for the output of the FFNN to be kept close to the observed values in regions with poor data cover. In the second step, pCO 2 anomalies are reconstructed with respect to the climatology from the first step. The model was applied over the period from 2001 to 2016. Validation with independent data at the global scale indicated a RMSE of 17. 57 µatm, an r 2 of ∼ 0. 76 and an absolute bias of 11.
Acknowledgements. The authors would like to thank the two referees,  Christian Rödenbeck  and  Luke Gregor , for their helpful comments and questions, as well as  Frederic Chevallier  and  Gilles Reverdin  for their suggestions. MV acknowledges funding from the  CoCliServ  and  EUPHEME projects (ERA4CS program ). Financial support. This study was funded by the  AtlantOS  project (EU  Horizon 2020 research and innovation program , grant agreement no. 2014-633211 ) and the CoCliServ and EUPHEME projects. Review statement. This paper was edited by  James Annan  and reviewed by  Christian Rödenbeck  and  Luke Gregor.
CO 2 fluxes and trends. Code and data availability. Python code for the pCO 2 climatology reconstruction (the first step of LSCE-FFNN model) and Python code for reconstruction of the pCO 2 anomalies (the second step of LSCE-FFNN model) are provided at the end of the Supplement. Time series of reconstructed surface ocean pCO 2 and CO 2 fluxes are distributed through the Copernicus Marine Environment Monitoring Service (CMEMS), http://marine. copernicus. eu/ services-portfolio/access-to-products/, search keyword: MULTI-OBS. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-12-2091-2019-supplement.
Results: In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manuallyannotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ( 91.
01% counting accuracy) but also is more than an order of magnitude faster than TasselNet (13. 82 fps on 912 × 1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions: This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity, and improving efficiency without sacrificing accuracy. It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy.
Background In agricultural production, crop yield is one of the key factors when monitoring crop growth status. Wheat is one of the top three cereal crops in the world. Its grain yield is mainly associated to spike number m -2 , grain number m -2 and thousand grain weight  [1]. Among these traits, spike number m -2 is the most important index  [2, 3]. Conventional manual approaches to counting wheat spikes are tedious and labor-intensive. The counting results are also errorprone and unrepresentative due to small sampling areas used. To meet the need of large-scale analyses in the era of intelligent agriculture and to obtain the index of spike number m -2 accurately in real time, counting wheat spikes must be automated in a reliable way, and possibly with low cost. With the rapid development of recent deep learning technologies, large-scale visual databases and cost-effective graphical processing units, image-based approaches appear to be promising alternatives to automate the task of wheat spikes counting.
In particular, we created a large-scale Wheat Spikes Counting (WSC) dataset to validate the effectiveness of TasselNetv2. Extensive experiments show that, TasselNetv2 reaches 91. 01% relative counting accuracy and achieves the state- of-the-art performance on the WSC dataset, and notably, can process images 13. 21 times faster than TasselNet (13. 82 fps for TasselNetv2 vs. 1. 05 fps for TasselNet). Further experiments demonstrate that TasselNetv2 also reports state-of-the-art counting performance on the Maize Tassels Counting (MTC) and ShanghaiTech Crowd Counting datasets  [5] , which confirms a good generality of TasselNetv2. Several interesting ablative studies are conducted to justify the effectiveness and necessity to include the context for better counting performance. Overall, the main contributions of this paper are:.
Image acquisition Field wheat images in the WSC dataset are collected from three experimental fields of Gucheng, Hebei, Zhengzhou, Henan, and Tai'an, Shandong, containing seven sequences from 2011 to 2013. Due to the different local geology and climate conditions, three cultivars were planted, respectively, including Zimai No. 24 in Taian, Jimai No. 22 in Gucheng, and Zhengmai No. 366 in Zhengzhou. Figure  4  shows the image capturing device, main components include a high-resolution CCD digital camera (E450 Olympus), a low-resolution monitoring equipment, a 3G wireless data transmission system, and several solar panels for power supply. The CCD digital camera is set with a height of 5 m above the ground, and the focal length is fixed to 16 mm. From 8 a. m. to 17 p. m. , images are captured from a perspective oblique to the ground once an hour.
Wheat spikes counting dataset There are tens of thousands of wheat spikes in the wheat images, and they present a high degree of similarity when the time interval is short, which makes the annotations for all of the captured images costly and needless. This means only a subset of images is essential to build the dataset, but this subset should be large enough to cover wheat spikes in various scenarios. We pick out this subset with a two-stage selection strategy. At the first stage, we choose images according to the date, after the heading stage of wheat. Before obvious emergence of spikes, the sampling interval is set to 3 days. After wheat spikes emerge, the number of wheat spikes changes rapidly, and thus the sampling interval is shortened to 2 days. At the second stage, 10 candidate images collected in each day (from 8 a.
m. to 17 p. m. ) are taken into account. Considering the illumination characteristics in one day, three images are chosen from three time periods, i. e. , morning (8 a. m. to 11 a. m. ), noon (12 a. m. to 14p. m. ), and afternoon (15 p. m. to 17 p. m. ), to maintain the diversity of the dataset. Finally, a total of 196 images, with the resolution of 3648 × 2736 , were chosen. The number of wheat spikes varies from 0 to over 10, 000. Since the image resolution is very high, and wheat spikes are extremely dense (it brings tremendous difficulties for the annotation process), each original image is cropped to 9 sub-images with a resolution of 1216 × 912. Thus, 1764 images in all are used to construct the dataset. Table  1  presents the information of each sequence in the dataset. With seven sequences in the WSC dataset, the training set, validation set and test set are divided, as shown in Table  2.