dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Results Performance Improvements Table  3  shows the performance improvements of approximated code w. r. t. libm based implementations of our benchmarks. We show improvements for the following settings: for small, middle and large target errors with an equal error distribution, and the table index width set to 8 bits (which enables table-based methods), approximating each elementary function call separately for large target errors with a derivative-based error distribution (and table index width 8) and individual approximation for large target errors with an equal error distribution, but with no table (table index width set to 0), with individual approximation for large target errors, approximation of compound functions with depth 1 for large target errors, approximation of compound functions with as much depth as is possible (until the expression is no longer univariate) For benchmark 'ex2_3', Metalibm timed out for all elementary function calls.
For the compound approximations, we only report performance improvements for functions which actually have compound calls. We observe that our tool can generate code with significant performance improvements for most functions. As expected, the improvements are smallest for the tightest target error bounds, and largest for the largest ones, on average 2. 7% to 14. 4%. If we consider only those cases where the approximation was successful, i. e. the performance is better than a libm implementation (otherwise we can just use libm), the average improvements are between 9. 2% and 16. 2%. We further note that for several benchmarks, improvements of more than 20% (up to 27%) are possible. Comparing the performance improvements to our estimates of the portion of running time spent in library calls (see Table  1 ), we see that we can recuperate much of the overhead (in exchange for lower accuracy, see Somewhat surprising, we did not observe an advantage of using the derivativebased error distribution over the equal one.
This is the result of discrete decisions concerning the approximation degrees and the domain splittings inside Metalibm. For those benchmarks which contain compound functions, we observe that it is generally beneficial to approximate 'as much as possible'. However, for some benchmarks, like sinxx10, we observe that Metalibm cannot provide any improvement. In general, we noticed that it has trouble in approximating sine functions and its powers. Finally, we also considered implementations of our benchmark set in single floating-point precision, even though Metalibm does not support implementations in single precision (but can target large errors). We considered the following setting: program target errors are scaled to single precision, inputs/outputs and arithmetic operations are in single precision and approximation code is accurate just enough to guarantee that casting of its double result to single precision is accurate enough.
On average we observe that a slight performance improvement is still possible, sometimes even reaching 18%. However, to achieve performance improvements comparable to those of double-precision code, we need a singleprecision code generation from Metalibm. and maximum execution times. For large target errors the maximum execution time of our code is even often smaller than the best timings of libm-based implementations. For instance, for the forwardk2jY benchmark, our the derivativebased is not only 17% faster on average, but its maximum execution time is also considerably smaller than the one of the libm-based code.
Elementary Function Approximation The problem of evaluation of elementary functions is highly dependent on the technology. The theoretical support followed the needs of evolving hardware and software but was proceeding in a rather function-by-function manner. A good overview of the existing approaches for can be found in  [29]. However, to the best of our knowledge, Metalibm is the first and so far the only project aiming at automation of the libm development and providing strong accuracy guarantees. Approximate Computing Approximate computing operates on the premise that many applications are tolerant to a certain amount of noise and errors and thus high accuracy is not always needed. Many techniques which trade accuracy for efficiency have been developed in the recent past and are for instance surveyed in  [37].
Such a method has previously been used to characterize the occurrence of heavy metal bearing mineral phases in polluted soils and sediments  (de Boer & Crosby 1995; El Samrani et al. , 2004)  or to support the analysis of light-scattering features of particles in water bodies  (Peng et al. , 2009; Pinet et al. , 2017). The aim of this paper is to introduce a method that determines the mineralogical composition of suspended sediment in continental waters. This method involves a simple sampling protocol combined with the robustness of powerful statistical techniques, making it an ideal tool for processing a significant amount of water samples collected across a watershed. The main purpose is to take advantage of the expertise in mineralogical determination, then to reinforce it by the ability of machine learning tools to identify statistical patterns in large datasets, and finally to supply an efficient tool to characterize suspended sediment in lakes or rivers.
Machine learning The particles were partitioned into homogeneous groups based on their elemental composition using the Partitioning Around Medoids (PAM) unsupervised classification  (Kaufman & Rousseeuw, 1987). The PAM method is based on a simple to use k-medoids algorithm that does robust classifications  (Breitkreutz & Casey, 2008). The elemental content of particles representing 99% of the total of elements detected by the EDXS analysis was considered to derive the mineralogical groups. These elements are sodium (Na, 2. 2%), magnesium (Mg, 2. 7%), aluminum (Al, 25. 7%), silicon (Si, 57. 7%), potassium (K, 2. 8%), calcium (Ca, 0. 7%), titanium (Ti, 1. 2%), and iron (Fe, 6. 9%). The randomForest supervised learning classification algorithm  (Breiman, 2001)  was applied on the same training dataset to test the robustness of the groups and to be compared with the classification resulting from the PAM method.
The randomForest classification was then used on all the remaining sampling stations to extend the mineralogical determination. The randomForest classification computes hundreds of classification trees, selecting randomly m variables (i. e. elements) that lead to the split of the dataset at each tree node. A 'vote' is assigned to each tree built during the classification process, and then the majority vote over the set of trees built in the process designates the most consistent and recurrent classification as the final result. In the current study, the package used was "randomForest" ported in R by  Liaw and Wiener (2002)  from the original Fortran program by Breiman and Cutler (available at http://www. stat. berkeley. edu/users/breiman/). The sampling stations used for the training and the validation of the two classifications are described in Table  1  by their dates, locations, depth, and the corresponding numbers of SEM images and particles analyzed.
a single mineralogy) for each group while keeping a reasonable number of groups and minimizing the possible randomForest misclassifications. Such tests required an expertise in earth sciences in support of the statistical analysis of the classification results that was accomplished in collaboration with mineralogists. In order to reach the best agreement between PAM and randomForest classifications, various parameterizations for the randomForest classification were tested on the training dataset. (left) image used in grain size analysis and (right) the same image with the particles analyzed by EDXS and manually tagged using green numbers.
e. the training of the randomForest classification). The classification reveals an increase in the Al/Si ratio from group 1 (defined as pure quartz with Al/Si close to 0) to the most developed and mature sediments of group 9 (defined as kaolinite with Al/Si close to 1). The classification also detected two poorly represented groups that were very distinct from the others, with major contents in iron or titanium (respectively, iron oxides, and titanium dioxides). Except for these two groups representing less than 3% of the dataset, the classification distinguished 8 groups of clays (represented by chlorite, smectite, illite, and kaolinite) corresponding to 65% of the classified particles, 3 groups of quartz (22% of the dataset) and 2 groups of feldspars (orthoclase and albite, 3% and 7% of the dataset, respectively).
Supervised randomForest classification By varying the number of trees, N tree , built during the classification and the number of variables, N var , sampled at each split, the minimum error rate was obtained for N tree = 500 and N var = 2. The most representative tree of the forest having the majority 'votes' and corresponding to the best split of the dataset is shown in Fig. 3. This tree highlights the importance of Si, Al, K, Fe, and Na contents in the classification process. On the other hand, the 3 remaining elements (Mg, Ti, and Ca) did not significantly contribute to the repartition of the chemical analyses into distinct groups. These results were confirmed by the internal calculations of variables importance of randomForest called MeanDecreaseAccuracy and MeanDecreaseGini  (Breiman, 2002) , which assign the lowest values (i.
Comparison of the classifications The resulting tree and the corresponding supervised randomForest classification were compared to the PAM partitioning. Table  3  shows a confusion matrix where the misclassification between PAM and randomForest are detailed and quantified via the classification error rate. The entire process was repeated because of the random aspect of the variables used during the classification. All the confusion matrices thus obtained showed similar results, and the classification error rate between both classification methods was always lower than 4% using the combined (N tree = 500 and N var = 2) parametrization. The randomForest classification was used on the remaining dataset, which included stations sampled during the campaigns of  April and December 2014 (i. e. , 17,916 particles).
Conclusions The new PAM-randomForest procedure presented in this paper shows robust results and offers an easy method to characterize suspended sediments. The determination technique of the mineralogical assemblages in rivers is highly representative of the total sediment, even though just a few milliliters of water were sampled. It remains a powerful procedure even in the presence of a large dataset, which is essential in a spirit of maximum representativeness of the measurements. It also allows a more precise characterization of the suspended sediment sampled compared with an estimate of the total mass of the bulk particles. This method is particularly interesting for intensive sampling programs, where a series of samplings are made and where it is easier to keep filters instead of several litters of water or kilograms of sediments.
Acknowledgements The authors are grateful to the  French Centre National d'Etudes Spatiales (CNES)  and the  French company NOVELTIS  for co-funding the first author's Ph. D. This work was also supported by the  Brazilian Agência Nacional de Águas (ANA)  for funding the field campaigns. The authors thank also  Thierry Aigouy  of the  French Laboratory Géosciences Environnement Toulouse  ( UMR 5563 ),  Sophie Gouy  from the  French Centre de Micro.
Biogeochemical Argo floats, profiling to 2,000-m depth, are being deployed throughout the Southern Ocean by the Southern Ocean Carbon and Climate Observations and Modeling program (SOCCOM). The goal is 200 floats by 2020, to provide the first full set of annual cycles of carbon, oxygen, nitrate, and optical properties across multiple oceanographic regimes. Building from no prior coverage to a sparse array, deployments are based on prior knowledge of water mass properties, mean frontal locations, mean circulation and eddy variability, winds, air-sea heat/freshwater/carbon exchange, prior Argo trajectories, and float simulations in the Southern Ocean State Estimate and Hybrid Coordinate Ocean Model (HYCOM). Twelve floats deployed from the 2014-2015 Polarstern cruise from South Africa to Antarctica are used as a test case to evaluate the deployment strategy adopted for SOCCOM's 20 deployment cruises and 126 floats to date.
, 2011). These 12 SOCCOM floats were deployed on the Polarstern PS89 cruise in 2014-2015  (Boebel, 2015). This was one of our largest deployments and provides us with three annual cycles to assess our strategy. Although the SOCCOM array now exceeds 108 floats (of 126 deployed), many do not yet have even one full annual cycle, and many are still near their deployment locations. PS89 deployment choices were based on the water mass, air-sea flux, and circulation structure from historical hydrographic data and particle trajectory analysis using existing Argo floats and two models. We evaluate this one set of deployment choices by comparing its 3 years of float trajectories with the ensemble OSSE and Argo trajectories (section 5. 2) and briefly describe the water mass/circulation regimes covered thus far by these 12 floats in comparison with our original expectations (section 5.
Elements of the SOCCOM Float Deployment Strategy All SOCCOM floats are BGC-Argo floats. SOCCOM uses two types of enhanced Argo floats: UW-modified Teledyne Webb Apex floats and Seabird Navis floats. Each includes core Argo sensors (temperature, conductivity, and pressure) and, to the greatest extent possible, all BGC sensors (oxygen, nitrate, pH, and optical sensors-either chlorophyll fluorescence and backscatter on Apex or these plus fluorescence by color dissolved material on Navis floats), and ice avoidance software to enable winter profiling. SOCCOM follows Argo protocols for core properties (temperature, salinity, and pressure), cycling (parking at 1,000 m, profiling every 10 days from 2,000 m to the sea surface;  Riser et al. , 2018; Roemmich et al. , 2009) , and data management. SOCCOM core and BGC data are posted through the public real-time and delayed mode Argo data stream (http://jcommops.
About half of the cruises are GO-SHIP, and about two thirds of the floats have been deployed from these usually longer GO-SHIP cruises. Only two cruises thus far, on transits of opportunity, have been mostly SOCCOM-supported and planned. Therefore, deployment locations must be planned in advance based on as much prior information as possible and cannot be adjusted based on in situ sampling during given cruises; this will also be the case for large-scale expansion of biogeochemical float deployments going forward. We have endeavored to deploy in all sectors of the Southern Ocean without concentrating floats in any one region. Covering gaps in the Indian and Atlantic sectors is a focus in the remaining years. Sustaining the SOCCOM float array beyond 6 years, as floats complete their lifetimes, will require additional resources, similar to the continuously replenished core Argo float array and, assuming funding sources for the floats, should be achievable as long as the international GO-SHIP program and routine national Southern Ocean ship-based programs continue into the future.
HYCOM performed better than SOSE for the northernmost STZ 5904475 and SAZ 5904474 and for the southernmost ASF 5904472. HYCOM, unlike SOSE, shows float trapping at Maud Rise. The importance of high spatial resolution at high latitudes is relevant as SOCCOM and the new UK ORCHESTRA program are planning future Weddell Sea float deployments, and models such as HYCOM and SOSE are used to inform deployments. The purpose of the D rms calculation was to evaluate the use of Argo and modeled synthetic floats in predicting dispersion of our individual floats. This dispersion quantity (equation (  1 )) is related to the Lagrangian relative diffusivity, which requires averaging (1) over many individual floats, but which we are not doing here. Davis (2005)  mapped the diffusivity using WOCE profiling floats; magnitudes were similar to those from surface drifters  (Zhurbas & Oh, 2004).
The same stacking was predicted for a monoclinic 4-layer polytype C2/c11 by Christy  28. The predicted symmetry is the highest that can be reached with this (CO 3 ) stacking. T[K] a[Å] b[Å] c[Å] V[Å 3 ] d [g. cm - Dynamical refinement. Two refinements were carried out against PEDT data using both "kinematical" and "dynamical" refinements. The results of the kinematical refinements are not shown, since the dynamical refinement provided better results its results are used in the discussion. The refinements had to account for the possible microtwinning already observed (Fig. 1c )  16. New features added in the program JANA2006 and Dyngo allow us to take into account both modulation and the twinning in the dynamical procedure. Given the strong pseudo-hexagonal lattice geometry, the 3-fold rotations around this axis (±120 degree) were added and improved significantly the results in terms of reliability factors and structural parameters (atomic positions and displacements).
The possibility to have the 6-fold twinning is not supported by our data at 300 K because it would produce supplementary satellites that are not observed. The improvement of the R factors is more pronounced for the satellite reflections with a decrease around 10 percent. The parameters used to select the reflections involved in the dynamical refinement were chosen following the suggestions in Palatinus et al. 38, 39  =. RS max ( ) 0 4 g at 100 K =. RS max ( ) 0 8 g at 300 K, =. S matrix ( ) 0 01 g max ( ) Å -1 , =. g 1 6 max ( ) Å -1. At 100 K, one data set was used in the dynamical procedure (crs. 2) (Table  2 ). At 300 K, the crystals are more beam sensitive (incomplete data sets) and present weaker reflection intensities, especially very weak second order satellite reflections. Consequently, two datasets were combined (crs.
All theoretical sequences represented in the Fig. 8  are obtained from the ab initio procedure in the Superflip program using the data of the 4-layer polytype and attributing the intensities of the satellite reflections to another γ value. The same stacking sequences can be built up from the model initially designed for the 4M polytype by adjusting γ to another value. In the literature two stacking sequences were suggested as potential 2-layer polytype: "00" (2O: Cmcm) and "+-" (2M: C12/c1). The later 2M is used as the basic brick in the description using the OD theory by Makovicky  27. However, using one of the two methods to predict the sequence using our unified modulated model, the derived stacking of the 2-layer end member came out to be "00". For specific γ values like 1 3 , 1 4 or  2 5  , the stacking sequences are not a combination of other sequences and are also used as basic bricks together with the 4M or 2O to obtain all the other sequences.
For transmission electron microscopy (TEM) investigations, the white powder was slightly dispersed in ethanol and a drop of the suspension was deposited and dried on a copper grid with a thin film of holey amorphous carbon. Electron diffraction investigations were performed on a Philips CM120 electron transmission microscope (TEM) (Vacc = 120 kV, LaB 6 ) with the precession device Nanomegas Digistar and a side-mounted CCD camera Olympus Veleta with 14 bit dynamic range. PEDT data sets of non-oriented patterns were recorded at 100 K, 225 K, 290 K and 300 K on several thin crystals or clusters of crystals (Fig. S6 ). The precession angle was set to 1 degree with a tilt step of 1 degree. A condenser aperture of 10 μm and low illumination setting (spot size 8) were used to produce a semi-parallel beam of 60-85 nm in diameter on the sample to reduce the electron dose.
Fifteen data sets were analysed to determine average lattice parameters and symmetry of the crystals but only two data sets collected at 100 K (crs. 1 and crs. 2) and two at 300 K (crs. 3 and crs. 4) were used for the structure solution and refinement (see experimental details in Table  S1 ). PEDT data were analyzed using the computer programs PETS  45  and JANA2006 37 following a procedure described elsewhere  46. For each data set the result is a list of hklm indices with associated intensities and estimated standard deviations based on counting statistics. The refinements were performed using both kinematical and dynamical approaches ("kinematical refinement" and "dynamical refinement") implemented in JANA2006  38, 39. X-ray powder diffraction (XRPD) diagrams were measured from 300 K to 100 K with 50 K steps.
Acknowledgements We would like to thank  Iryna Andrusenko  and  Enrico Mugnaioli  for their advices and the scientific exchanges. The crystallographic part was supported by the project  18-10504S  of the  Czech Science Foundation  using instruments of the  ASTRA laboratory established  within the  Operation program Prague Competitivenessproject   CZ. 2. 16/3. 1. 00/24510.
In this paper, we will describe in detail the setting up of a Design of Experiments (DoE) applied to the formulation of electrodes for Li-ion batteries. We will show that, with software guidance, Designs of Experiments are simple yet extremely useful statistical tools to set up and embrace. An Optimal Combined Design was used to identify influential factors and pinpoint the optimal formulation, according to the projected use. Our methodology follows an eight-step workflow adapted from the literature. Once the study objectives are clearly identified, it is necessary to consider the time, cost, and complexity of an experiment before choosing the responses that best describe the system, as well as the factors to vary. By strategically selecting the mixtures to be characterized, it is possible to minimize the number of experiments, and obtain a statistically relevant empirical equation which links responses and design factors.
Introduction A Design of Experiments (DoE) is a fascinating and incredibly useful tool to strengthen any study dealing with varying parameters by giving weight to all observations and trends with a minimal number of experiments to run. It allows to look at factors' individual and combined impacts as well as to optimize inputs for maximum performance. This paper is a methodological publication and conclusions on the case study will be reported in another manuscript for concision reasons. Although known since the mid-1930s and widely used in the industry, DoE are not often taken into consideration in academia  [1] [2] [3] [4]. This is likely because they could appear intimidating and overwhelming without the right tools and knowledge of mathematics and statistics. In recent years, many software solutions appeared that can remove most of that burden from the user.
All factors will be detailed in Section 2. 3. Although the planning of a DoE is closely linked to the system under study, the methodology described in this paper is adaptable to other designs. This paper illustrates the setup of an OCD designed to understand the effect of formulation on the capacity of Li-ion batteries at high currents. The mathematics on which the DoE are based will not be discussed here, as there are already numerous resources on this topic  [5] [6] [7]. As these calculations are complex, software assistance in the planning and analysis of the design is highly recommended. Here, Design Expert (Stat-Ease Inc. ) was used as a data analytics tool, in conjunction with Matlab (Mathworks) to store and manipulate experimental data. Standard DoE studies are typically articulated around seven or fewer steps  [3, [5] [6] [7].