dataesr/scientific-paragraphs-categorization huggingface dataset:
Platforms and processing Subgroup 3 concentrates on applying the methodologies developed within the other subgroups. Specifically, this relates to making available to a wider audience tools for the visualisation of the results for the inter-comparison of DEMs. This will be achieved by providing preferably open-source tools and performance tests on a variety of geographical regions around the world in order to demonstrate and verify the applicability of the DEM inter-comparison exercise. Furthermore, subgroup 3 will help to define the minimum standards and requirements regarding user access, data availability, programming interfaces and processing power, to which open candidate platforms should comply in order to participate in DEM benchmarking. Thereafter this subgroup will also be in charge of implementing and monitoring DEM benchmarking and comparison at global scale.
(2020)  found that telemetry and molecular data represent only 8. 4% of the studies and are much less used than presence data, that can be easily extracted from existing databases, especially in studies with operational objectives. Therefore, further validation procedures are required. Foltête et al. (2020)  found few papers that used biological data (regardless of data type) both a priori and a posteriori while the classical statistical procedure of validation consists in calibrating a model with a subsample of data and validating it with another subsample  (Fielding and Bell, 1997). The a priori use of biological data informs landscape connectivity approaches of species requirements (e. g. Du ot et al. 2018). The a posteriori use of biological data is based on the statistical inference of biodiversity indices such as species abundance  (Pinaud et al.
Species data and correction for sampling bias We extracted presence data for the period 2014 to 2019 from the French Bird Protection League's database: 653 presence records for Sitta europea, and 1156, 46 and 41 for Dendrocopos major, Dryocopos martius and Dendrocopos minor, respectively. We used this presence data to de ne the boundaries of the study area from a convex hull (extended by a buffer of 100 m) in order to exclude unprospected areas and avoid extrapolation. To correct for sampling bias, we followed Kramer-Schadt et al. 's (2013) spatial ltering method to remove nearby presence points according to a threshold distance (if too close, presence points could be redundant). This distance was de ned as 100 m based on species home-range size (Supplementary Information, Appendix S2). Spatial ltering retained 396, 665, 43 and 31 points for Sitta europaea, Dryocopos major,Dryocopus martius and Dendrocopos minor, respectively.
2003 ; Avon and Bergès 2016). These resistance values were also considered valid for forest birds. Approach 2, hereafter called "umbrella species", consisted in modelling functional connectivity with Maxent from presence-only data of Sitta europaea to predict a habitat suitability index (HSI). S. europaea is a secondary cavity nester and an umbrella species for woodpeckers  (Kotaka et al. 2002). This approach was based on the following assumptions: (1) umbrella-species habitat suitability is a good proxy for other species' presence, and (2) transformation of umbrella-species habitat suitability is a good proxy for matrix resistance. We calibrated the habitat suitability model using 198 presence data (50%) through the Maxent software. The landscape metrics were derived from the land cover map and calculated with the Chloe-4.
0 software (Boussard and Baudry 2017). We calculated the proportion of each land cover class using sliding windows (buffer radius of 100 m, 5 m between two adjacent windows). The windows were also used to calculate a landscape heterogeneity index: the Shannon If HSI ≥ threshold (in species habitat): resistance was assigned to 1. If HSI < threshold (in matrix): we used Eq. 1 to assign resistance. Resistance is inversely proportional to the permeability of the matrix. The function (eq. 1) assigned a resistance value of 1000 when HSI equalled 0 and of 1 when HSI was greater than or equal to the HSI threshold de ned by  Liu et al. (2013). Approach 3, hereafter called "multispecies", consisted rst in modelling functional connectivity separately for the three woodpecker species Dendrocopos major, Dryocopus martius and Dendrocopos minor using 50% of the presence data (respectively 332, 21 and 15 presence points) following the method presented in approach 2, then combining the three single-species connectivity maps (see next paragraph) and the Sitta europaea map to obtain a multi-species connectivity map.
Indeed, the relative importance of local and landscape-scale factors can depend on landscape context and species  (Howell et   In uence of species on the degree of validation We also tested corridor score sensitivity and performance against species and the current threshold we used to delineate corridors. The corridor score was the highest for Sitta europaea, certainly because the presence data of this species were used for calibration in two of the three models (umbrella and multispecies models). The median corridor score was the lowest for Dendrocoptes minor. The median corridor score for the habitat approach was negative for this species, indicating that the ecological corridor locations were less accurate than in a null model, and that our hypotheses on matrix resistance were globally inconsistent.
INTRODUCTION Sorting is a fundamental problem in computer science and a critical building block for many types of applicationssuch as, but not limited to, database servers  (Graefe, 2006) , image rendering engines  (Bishop et al. , 1998) , mining of time series  (Raoofy et al. , 2020)  or JPEG steganography with particle swarm  (Snasel et al. , 2020). This has pushed the research community to spend many efforts to provide efficient sorting libraries on new architectures. This research of performance is coupled to the changes in CPUs' designs and organization, which includes the vectorization capability. The performance of CPUs has improved for several decades by increasing the clock frequency. However, this approach has reached a steady-state due to power dissipation and heat effects. To go beyond this limitation, the manufacturers have used parallelization at multiple levels: by embedding multi-cores in a CPU, by allowing pipelining and outof-order execution at the instruction-level, by putting multiple computational units in each core, and by supporting vectorization.
In this context, vectorization consists in the capability of a CPU core of applying a single instruction on multiple data, a concept called SIMD by Flynn's taxonomy  (Flynn, 1972). The consequence of this hardware design is that it is mandatory to vectorize a code to achieve high-performance. On the contrary, the throughput can be reduced by at least a factor equivalent to the length of a vector compared to the theoretical peak of the hardware. For instance, vector sizes for single precision values are 8 in widespread CPUs (AVX2) and 16 (AVX-512) on many computing nodes. We can convert many classes of algorithms and computational kernels from a scalar code into a vectorized equivalent without difficulties. Besides, it can be done with autovectorization for some of them. However, some algorithms are challenging to adapt because of their memory/data access patterns.
In the current paper, we focus on the adaptation of a sorting strategy and its efficient implementation for the ARM CPUs with SVE. Our implementation is generic and works for any size equal to a power of two. The contributions of this study are: • Describe how we port our AVX-SORT algorithm  (Bramas, 2017)  to SVE; 1 The current study provides a translation of our AVX-SORT into SVE but also a completely new approach which works when the vector size is unknown at compile time. 2 The functions described in the current study are available at https://gitlab. inria. fr/ bramas/sve-sort. This repository includes a clean header-only library and a test file that generates the performance study of the current manuscript. The code is under MIT license. • Define a new Bitonic-sort variant using SVE and how runtime vector size impact the implementation 1 ; • Implement an efficient Quicksort variant using OpenMP (Board, 2013) tasks.
SORTING WITH SVE Overview Our SVE-QS shares similarities with the AVX-512-QS as it is composed of two key steps. First, we partition the data recursively using the sve_partition function described in 'Partitioning with SVE', as in the classical QS. Second, once the partitions are smaller than a given threshold, we sort them with the sve_bitonic_sort_wrapper function from 'Bitonicbased sort on SVE vectors'. To sort in parallel, we rely on the classical parallelization scheme for the divide-and-conquer algorithm, but propose several optimizations. This allows an easy parallelization method, which can be fully implemented using OpenMP.
PERFORMANCE STUDY Configuration We assess our method on an ARMv8. 2 A64FX -Fujitsu with 48 cores at 1. 8 GHz and 512-bit SVE, i. e. a vector can contain 16 integers and eight double floating-point values. The node has 32 GB HBM2 memory arranged in four core memory groups (CMGs) with 12 cores and 8GB each, 64KB private L1 cache, 8MB shared L2 cache per CMG. For the sequential executions, we pinned the process with taskset -c 0, and for the parallel executions, we use OMP_PROC_BIND =TRUE. We use the ARM compiler 20. 3 (based on LLVM 9. 0. 1) with the aggressive optimization flag -O3. We compare our sequential implementations against 4 It can be executed on any CPU using the Farm-SVE library (https://gitlab. inria. fr/ bramas/farm-sve) 5 This implementation is partially implemented in the branch optim of the code repository.
the GNU STL 20200312 from which we use the std::sort and std::partition functions. We also compare against SVE512-Bitonic, which is an implementation that we have obtained by performing a translation of our original AVX-512 into SVE. This implementation works only for 512-bit SVE, but this makes it possible to hard-code all the indices of the compare and exchange of the Bitonic algorithm. Moreover, SVE512-Bitonic does not use any loop, i. e. it can be seen as if we had fully unrolled the loops of the SVE-Bitonic. We compare our parallel implementation against the Boost (https://www. boost. org/) 1. 73. 0 from which we use the block_in direct_sort function. The test file used for the following benchmark is available online (https://gitlab. inria. fr/bramas/arm-sve-sort) and includes the different sorts presented in this study, plus some additional strategies and tests 4.
Comparison with the AVX-512 implementation The results obtained in our previous study on Intel Xeon Platinum 8170 Skylake CPU at 2. 10 GHz  (Bramas, 2017)  shows that our AVX-512-QS was sorting at a speed of ≈ 10 -9 second per element (obtained by T /N • ln(N )). This was almost 10 times faster than the STL (10 -8 second per element). The speedup obtained with SVE in the current study is lower and does not come from our new implementation, which is generic regarding the vector size, because the SVE512-QS is not faster, either. The difference does not come either from the memory accesses, because it is significant for small arrays (that fit in the L1 cache), or the number of vectorial registers, which is 32 for both hardware. Profiling the code reveals that the cycles per instruction is around 1.
This happens because, even in a restricted domain, each object might have relations with a huge number of other facts or objects, so that it would not be possible to encompass all these relations. As a consequence, the incompleteness of the description is a central feature of a knowledge-based system  [53] , and it represents a key distinction with respect to a database. Furthermore, incompleteness of a KB comes also from computational constraints as a complete reasoning might be very time-consuming. As a consequence of its incompleteness, a KB system might not be able to answer, for example, to the question if a statement is true or false, within a given time interval. Conversely, the need to provide an answer, while respecting a time constraint, typically results in an answer that is correct but only within a certain degree of reliability.
Some numerical results, obtained in  [50] , are reported in Fig. 4 a ) and b), referring, respectively, to an additive white Gaussian noise (AWGN) channel and to a Rayleigh fading channel. The performance is evaluated in terms of the similarity between the sentence emitted by the source and the sentence reconstructed by the semantic decoder at the receive side. The performance of DeepSC is compared with the following alternative methods: Huffman coding with RS (30,42) in 64-QAM; 5-bit coding with RS (42, 54) in 64-QAM; Huffman coding with Turbo coding in 64-QAM; 5-bit coding with Turbo coding in 128-QAM; an E2E DNN trained over the AWGN channels and Rayleigh fading channels, proposed in  [54]. As we can see from Fig. 4 , at low SNR values, where conventional schemes suffer from many errors at bit-level, the semantic decoder DeepSC significantly outperforms the alternative methods, exploiting the structure of a natural language.
Example of application: Edge online learning We show now a simple example of application of a goal-oriented system that takes into account the goal of the specific goal of communications. In this example, we consider the case of an automated image classification problem. The set-up consists of a sensor that collects data and sends it to an edge  server that trains a classifier. The proposed method minimizes a weighted sum of energy consumption and classification accuracy, under constraints on the average E2E delay. In particular, the method dynamically adjusts the number of quantization bits, the transmit power, and the CPU scheduling at the edge server. The method has been tested on the MNIST dataset  [67]  and on the Hydraulic System Monitoring (HSM) dataset  [68]. To increase the reliability of the classifier, an ensemble method is adopted, combining multiple classifiers (learners) that are trained in parallel on the same set of data.
Acknowledgment This work has been partly funded by the  European Commission  through the  H2020  project through the  RISE-6G, HEXA-X (Grant Agreement  no. 101015956 ) and  DEDICAT-6G  (Grant Agreement no. 101016499 ),  H2020 /  Taiwan  Project  5G   CONNI  (Grant Agreement no. 861459 ) projects and, by  MIUR  (under the  PRIN Liquid Edge contract ).
Numerical methods We combined lithospheric-scale numerical simulations of asymmetric extension by applying the thermo-mechanical code Flamar v12  (Burov and Yamato, 2008)  and the basin-scale stratigraphic forward modelling code DionisosFlow  (Granjeon, 2019). Our modelling approach enables the one-way joint run of Flamar and DionisosFlow (Fig. 1; Appendix A, B ). The thermo-mechanical code is particularly well suited to study the dynamics of lithospheric extension and topographic evolution. Depocenter geometries and total sediment loaded basement vertical motions were extracted from the thermo-mechanical model in 2D along a 2D dip line (X,Y) at each timestep. In two subsequent 3D stratigraphic simulations, these calculated basement subsidence and uplift values were uniformly extended in the third, Z dimension (models M1wet, M1dry) in the absence of any along-strike variations (Fig.
2 ) and deposition and the overall basin-scale sedimentary architecture and 3D facies distribution were simulated at high resolution (i. e. meter-scale vertical resolution). Both Flamar and DionisosFlow models use a dynamic timestep scheme, DionisosFlow uses a finer temporal resolution (i. e. hundreds of years) and, therefore, linear interpolation was made between the time steps (thousands of years) of the thermo-mechanical modelling results. Further model description and model limitations are shown in the supplementary information file (Appendix A, B). Despite the different numerical approach of the two software, the calculated syn-and post-rift sedimentary thicknesses are similar in the thermo-mechanical and in the subsequent stratigraphic numerical models. Nevertheless, the much finer temporal and vertical resolution of DionisosFlow enable a more detailed simulation and sedimentary interpretation for the stratigraphic architecture.
Acknowledgements We dedicate this study to the memory of  Ferenc "Frank" Horváth , one of the pioneers of sedimentary basin analysis. AB acknowledges financial support from the  ETH Zurich Postdoctoral Fellowship programme  and academic support of the  Hungarian Science Foundation  ( K 129279 ). Numerical modelling has been carried out at  Utrecht University. Fruitful discussions with  László Lenkey  on the thermal evolution of the Pannonian Basin are acknowledged. We thank  Piotr Krzywiec  and an anonymous reviewer for their detailed and constructive reviews that significantly improved the quality of the manuscript. The commercial stratigraphic numerical code used in this study can be requested by contacting  Beicip-Franlab  (www. beicip. com). We thank  Beicip-Franlab  for providing an academic license for DionisosFlow.
CT findings can indicate disease stage  [11] [12] [13]  and predict adverse outcomes  [14] [15] [16]. Patients with severe COVID-19 pneumonia tend to have more opacities, and the increase in lung consolidation that occurs most often later in the disease tends to be associated with more critical illness  [17, 18]. Despite two studies showing that lung disease extent in COVID-19 pneumonia assessed by visual scoring correlates with clinical disease severity  [19, 20] , visual estimation of disease extent even done by experimented radiologists may be a source of variability. Artificial Intelligence (AI) software developed to help radiologists in the quantification of lung involvement in COVID-19 may overcome this limitation  [21]. Some investigators developed their own AI system for accurate quantitative measurements and prognosis of COVID-19 pneumonia using CT  [22] [23] [24].
Study Design This retrospective study performed in a tertiary Parisian center was approved by the Ethics Committee of Foch Hospital, affiliated to the Versailles Saint-Quentin University, and the requirement for informed consent was waived for fully anonymized data analysis. Consecutive patients hospitalized for COVID-19 pneumonia (from 15 March to 15 December 2020), and having an abnormal chest CT scan performed at admission or within the 48 h after admission in Foch Hospital, Suresnes, France, were included. Patient charts were reviewed in terms of age, sex, and comorbidities (diabetes, systemic hypertension, COPD, heart disease, chronic renal disease, and immunodeficiency). We recorded the delay between the beginning of symptoms and the first CT scan performed at admission or within the 48 h after admission and collected all biological data, including inflammation indicators that have been previously reported to be associated with the prognosis of COVID-19  [10].
Statistical Analysis Receiver operating characteristics (ROC) was plotted, and the area under the curve (AUC) was calculated with 95% confidence intervals for the prediction of severe outcome by the percentages of lung volume occupied by all opacities and by high-density opacities only. Discriminant thresholds were obtained using the Youden index (Sensitivity + Specificity -1). Univariate logistic regression models were performed, followed by multivariate models using the variables associated with the severe outcomes (p value < 0. 2). The final model was obtained using the stepwise descending procedure. All tests were bilateral with a 5% degree of significance. Statistical analyses were performed with the SAS 9. 4 software.
In accordance with Lassau et al. [22] , we found an association of clinical severity with increased neutrophils count and neutrophilslymphocytes ratio. Few studies have combined clinical and biological data with chest CT for prognostication in COVID-19 pneumonia  [14, [21] [22] [23]. Like previous investigators, we demonstrate here that a model integrating AI-based CT scan information and clinical and biological parameters improves the prognosis performance of the score. The main limitation of our study is the retrospective design. As a result, some clinical and biological data were missing. Particularly lactate dehydrogenase and procalcitonin were measured only in 60% and 47% of our patients, respectively. This explains why we did not include these biomarkers in our prediction models despite both were significantly different between patients who experienced deterioration or death compared to those who did not.
Encoding Student Models for Evolution The evaluation of NEMOKD in Section 4. 3 uses two baseline student architectures: FlexStudent and Resnet8x4. We encode certain features of these model into genotypes to evolve their hyper-parameters. We encode the FlexStudent model with 11 genes. Genes 1 and 2 determine the number of convolutional and fully connected layers respectively. Genes 3-7 determine the number of output channels in convolutional layers. Genes 8-11 encode the number of fully connected layers. Figure  3  shows how NEMOKD decodes the genotype representation of the baseline FlexStudent model into a CNN architecture. For the Resnet8x4 student we encode hyper-parameters to evolve the number of layers in the output channels from convolutional layers. The number of layers are fixed.
3. Latency and accuracy is measured on the VPU device to asses the fitness of population members. This evaluation data is fed into the evolutionary NSGAII algorithm. Accuracy after 30 epochs is indicative of performance should full training later be performed. Therefore to save time and energy costs of fully training all evolving student variants, a developer might select a fully evolved partially trained model that meets latency requirements, then subject it to more training with 200+ epochs. We say NEMOKD's evolutionary optimisation is hardware-aware , because fitness is measured on the processor architecture (Intel's Movidius VPU) intended for deploying the model. This is based on the idea that a model's latency performance depends on the processor architecture used, and that optimising a model for one architecture may be ineffective if deploying to another  [27].
Hardware Platforms This section evaluates quantisation for programmable hardware, and our NEMOKD evolutionary algorithm for the fixed VPU architecture. The dataset and neural network model for the experiments are shown in Table  2. For the programmable hardware experiments we target the mid-range Xilinx Zynq Z7020 140 mm × 87 mm FPGA on the Xilinx PYNQ-Z2 development board which uses ≈13. 8 W energy. This FPGA has 53 k Lookup Tables (LUT), 106 k Flip Flops (FF) and 560 KB of Block RAM (BRAM) memory. Of the 64 quantised neural networks in Section 4. 2, only four fit on this FPGA. This validates the need for aggressive compression approaches such as quantisation, on small to medium sized FPGA devices. For the fixed hardware experiments we use a USB-based Intel Neural Compute Stick 2 (NCS2) accelerator using a 72.
2. Relative performance comparison of accuracy and hardware resource costs, compared with the other 63 quantised models (Section 4. 2. 2). The training was done using 50,000 images from the MNIST dataset. A validation dataset of 10,000 images was then used to minimise overfitting. Accuracy was measured using a testing dataset, to test how well the model generalised to new data. FINN's backend converted the model to a binary weight file and a synthesisable C++ implementation for hardware.