dataesr/scientific-paragraphs-categorization huggingface dataset:
MP acknowledges financial support from  JAE-Doc program  of the  Spanish National Research Council (CSIC) , co-funded by the  European Social Fund. PAM acknowledges support from  ERC StG  Grant  DEGAS-259586  and from the  Science and Technology Facilities Council  grants  ST/K003305/1  and  ST/L00075X/1. This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure. BA dedicates this paper to the memory of  Javier Gorosabel , whose wisdom and sense of humour will always be missed.
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the Hi line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
To minimize the chances of getting stuck in local optima during model fitting, researchers choose the initial parameter guesses to lie as close to the global optimum as possible. In previous and current surveys, these initial guesses are provided manually, effectively using the pattern-recognition skills of humans to identify the individual components within the blended spectra. This manual selection process is time consuming and subjective, rendering it ineffective for the large data volumes in the SKA era. Automatic line finding and Gaussian decomposition algorithms can solve these problems. However, the available algorithms for automatic line detection are either unlikely to scale to the data volumes of SKA, or lack the flexibility to fit complex spectra. The Bayesian line finder by  Allison et al.
(2012)  searches parameter space using the nested sampling algorithm  (Skilling 2004) , and uses Bayesian inference to discover the optimal number of spectral components. However, it has only been applied to simple spectra with few components, and has not been tested on complex Galactic 21 cm data. Procedural algorithms like those of  Haud (2000)  or  Nidever et al. (2008)  iteratively add, subtract, or merge components based on the effects these decisions have on the resulting residuals of least-squares fits, and have been used to interpret large datasets from, e. g. , the Leiden-Argentina-Bonn (LAB) All-Sky Hi survey  (Kalberla et al. 2005). However, the initial parameters for each fit are adopted from previous solutions in adjacent sky positions, thereby limiting the use these algorithms to densely-sampled emission surveys.
The locations in the data satisfying the conditions from Equations 2-5 are identified with blue circles, with blue line segments showing the guessed ±1σ widths from Equation  7. The positions and widths indicated by the blue circle and line segments represent the guesses that AGD would produce for this example spectrum. we describe the Python implementation of AGD called GaussPy; in Section 4, we discuss AGD's performance in decomposing real 21 cm absorption spectra; and in Section 5, we present a discussion of results and conclusions.
The optimal value of log α is that which maximizes the accuracy (Equation  9 ) between AGD's guessed components and the true answers in the training data. This non-linear optimization process is performed using gradient descent and is described in detail in Appendix B. GaussPy is the name of our Python 12  /C implementation of the AGD algorithm. This lightweight python module is easy to deploy on high-throughput computing solutions like HTCondor 13  (see Figure  3 ) or Hadoop 14  /MapReduce  (Dean & Ghemawat 2004) , allowing for rapid decomposition of very large spectral datasets, e. g. , the spectral data products of the SKA. AGD may also be suitable for deployment on the Scalable Source Finding Framework  (Westerlund & Harris 2014). GaussPy is maintained by the author and will be publicly available through the Python Package Index 15  upon publication of this manuscript.
g. ,  Heiles & Troland 2003). If negative amplitudes are found, then the fit is remade using the Limitedmemory bound constrained Broyden-Fletcher-Goldfarb-Shanno (L-BFGS-B)  (Zhu et al. 1997; Nocedal & Wright 2006 ) algorithm for which we can enforce the constraint that all amplitudes be non-negative. In GaussPy, we minimize the functional R[u] (Equation 8) using the quasi-Newton algorithm "BFGS2" from the GNU Scientific Library 16  Multimin package and achieve computation-time scalings of O n 1. 95 , where n is the number of channels in the data, and O α -0. 4. The relative scaling between log α and the minimum preserved scale in the data is found numerically to be approximately δ chan ≃ 3. 7 × 1. 8 log α , (13) where δ chan is the spatial scale in channels (see Figure  2 ). By plugging in an estimate of the expected component widths to Equation  13 , one obtains a rough estimate of the appropriate regularization parameter log α.
PERFORMANCE: 21 CM ABSORPTION We test AGD by comparing its results to humanderived answers for 21 spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey  (Murray et al. 2014b, in prep. ). 21-SPONGE spectra cover a velocity range from -100 to +100 km s -1 , tracing Galactic Hi gas. 21-SPONGE's 21 cm absorption spectra are among the most sensitive ever observed with typical optical-depth root-meansquare (RMS) sensitivities of σ τ 10 -3 per 0. 4 km s -1 channel  (Murray et al. 2014b, in prep. ). This combination of sensitivity and spectral resolution will stay among the best obtainable through the SKA era. The survey data come natively in units of fractional absorption (I/I 0 ), we transform the data into optical depth units (τ = -ln(I/I 0 )) for the AGD analysis because only in τ -space will a single component produce a single peak in curvature (i.
In Figure  5 , we show a scatter plot of the best-fit FWHMs and peak amplitudes for all AGD and humanderived Gaussian components for the 21-SPONGE data. There are 118 and 120 components detected by AGD and human, respectively. We performed a 2-sample Kolmogorov-Smirnov test on the amplitudes, FWHMs, and derived equivalent widths (EW = τ (v)dv) of the resulting components from AGD vs. human results and find that in each case, the AGD and human distributions are consistent with being drawn from identical distributions. Thus, AGD results are statistically indistinguishable to human-derived decompositions in terms of the numbers on components, the residual RMS values, and the component shapes. Figure  6  shows the AGD guesses, AGD-best fits, and human-derived best fits for all 21 spectra in our data set.
Robustness to varying observational noise Regularized derivatives (Section 2. 2) are insensitive to noise on spatial scales less than that set by the regularization parameter log α (Equation  13 ). Because the observational sensitivity of 21-SPONGE data is uniform and very high, we next demonstrate that AGD is robust to varying noise properties by characterizing the guessed position and FWHM of a Gaussian component with fixed shape in data with increasing noise intensity. Figure  8  shows that ∼ 100% of component guesses remain within ±1σ distance of the true component positions for noise intensities ranging from 1-16 × RMS. Over the same range in noise, the guesses FWHMs varied by ±20%. Therefore, varying the noise properties has little effect on AGDs performance, making AGD a robust tool to analyze heterogeneous datasets with varying sensitivities.
2014, in prep. ). For this test, we train the algorithm on results from the independent Millenium survey  (Heiles & Troland 2003). We find that AGD performs comparably to humans when decomposing spectra in terms of number of components guessed, the residuals in the resulting fit, and the shape parameters of the resulting components. AGD's performance is affected little by varying observational noise intensity until the point where components fall below the S/N threshold (i. e. , completeness). Combined with Monte Carlo simulation, we use AGD to measure the Hi line completeness of 21-SPONGE data as a function of Hi peak optical depth and velocity width. Thus, AGD is well suited for helping to interpret the big spectral data incoming from the SKA and SKA-pathfinder radio telescopes. The time required GaussPy to decompose a spectrum varies with the number of channels and complexity of components.
Similarly, in  (Perrinet, 2010) , we preferred to retrieve an approximate solution to the coding problem to have a better match with the measure of efficiency Eq. 14. 4. doi:10. 1002/9783527680863. ch14 Such an algorithmic framework is implemented in the SHL-scripts package 2. These scripts allow the retrieval of the database of natural images and the replication of the results of  (Perrinet, 2010)  reported in this section. With a correct tuning of parameters, we observed that different coding schemes show qualitatively a similar emergence of edge-like filters. The specific coding algorithm used to obtain this sparseness appears to be of secondary importance as long as it is adapted to the data and yields sufficiently efficient sparse representation vectors. However, resulting dictionaries vary qualitatively among these schemes and it was unclear which algorithm is the most efficient and what was the individual role of the different mechanisms that constitute SHL schemes.
, 2004; Fischer et al. , 2007a). Such a representation is implemented in the LogGabor package 3. This transform is linear and can be performed by a simple convolution repeated for every edge type. Following  Fischer et al. (2007b) , convolutions were performed in the Fourier (frequency) domain for computational efficiency. The Fourier transform allows for a convenient definition of the edge filter characteristics, and convolution in the spatial domain is equivalent to a simple multiplication in the frequency domain. By multiplying the envelope of the filter and the Fourier transform of the image, one may obtain a filtered spectral image that may be converted to a filtered spatial image using the inverse Fourier transform. We exploited the fact that by omitting the symmetrical lobe of the envelope of the filter in the frequency domain, we obtain quadrature filters.
Higher and smaller values of α were also tested, and gave representation results similar to those presented here. In summary, the whole coding algorithm is given by the following nested loops in pseudo-code: 1. draw a signal I from the database; its energy is E = I 2 , 2. initialize sparse vector s to zero and linear coefficients ∀j, a j =< I, Φ j >, 3. while the residual energy E = I 2 is above a given threshold do: (a) select the best match: i = ArgMax j |a j |, where | • | denotes the modulus, (b) increment the sparse coefficient: s i = s i + α • a i , (c) update residual image: I ← I -α • a i • Φ i , (d) update residual coefficients: ∀j, a j ← a j -α • a i < Φ i , Φ j >, 4. the final set of non-zero values of the sparse representation vector s constitutes the list of edges representing the image as the list of couples π i = (i, s i ), where i represents an edge occurrence as represented by its position, orientation and scale and s i the complex-valued sparse coefficient.
This class of algorithms gives a generic and efficient representation of edges, as illustrated by the example in Figure  14. 3-A. We also verified that the dictionary used here is better adapted to the extraction of edges than Gabors  (Fischer et al. , 2007a). The performance of the algorithm can be measured quantitatively by reconstructing the image from the list of extracted edges. All simulations were performed using Python (version 2. 7. 8) with packages NumPy (version 1. 8. 1) and SciPy (version 0. 14. 0) (Oliphant, 2007)  on a cluster of Linux computing nodes. Visualization was performed using Matplotlib (version 1. 3. 1) (Hunter, 2007 ). 4 Measuring the ratio of extracted energy in the images, N = 2048 edges were enough to extract an average of approximately 97% of the energy of images in the database.
Such a behavior is also observed in V1 of monkeys as the size of the stimulation is increased from a stimulation over only the classical receptive field to 4 times around it  (Vinje and Gallant, 2000). Note that by definition, our representation of edges is invariant to translations, scalings, and rotations in the plane of the image. We also performed the same edge extraction where images from the database were perturbed by adding independent Gaussian noise to each pixel such that signalto-noise ratio was halved. Qualitative results are degraded but qualitatively similar. In particular, edge extraction in the presence of noise may result in false positives. Quantitatively, one observes that the representation is slightly less sparse. This confirms our intuition that sparseness is causally linked to the efficient extraction of edges in the image.
, 2010). These examples illustrate the different applications of sparse representations and in the following we will illustrate As we tested different parameters for the filters, we measured the gain in efficiency for the algorithm as the ratio of the code length to achieve 85% of energy extraction relative to that for the default parameters (white bar). The average is computed on the same database of natural images and error bars denote the standard deviation of gain over the database. First, we studied the effect of the bandwidth of filters respectively in the (A) spatial frequency and (B) orientation spaces. The minimum is reached for the default parameters: this shows that default parameters provide an optimal compromise between the precision of filters in the frequency and position domains for this database.
Figure14. 3-A, shows the list of edges extracted on a sample image. It fits qualitatively well with a rough sketch of the image. To evaluate the algorithm quantitatively, we measured the ratio of extracted energy in the images as a function of the number of edges on a database of 600 natural images of size 256 × 256 5 , see Figure14. 3-B. Measuring the ratio of extracted energy in the images, N = 2048 edges were enough to extract an average of approximately 97% of the energy of images in the database. To compare the strength of the sparseness in these full-scale images compared to the image patches discussed above (see Section 14. 2), we measured the sparseness values obtained in images of different sizes. To be comparable, we measured the efficiency with respect to the relative 0 pseudo-norm in bits per unit of image surface (pixel): This is defined as the ratio of active coefficients times the numbers of bits required to code for each coefficient (that is, log 2 (M ), where M is total number of coefficients in the representation) over the size of the image.
For different image and framework sizes, the lower this ratio, the higher the sparseness. As shown in Figure14. 3-B, we indeed see that sparseness increases relative to an increase in image size. This reflects the fact that sparseness is not only local (few edges coexist at one place) but is also spatial (edges are clustered, and most regions are empty). Such a behavior is also observed in V1 of monkeys as the size of the stimulation is increased from a stimulation over only the classical receptive field to 4 times around it (Vinje and Gallant, 2000). Note that by definition, our representation of edges is invariant to translations, scalings, and rotations in the plane of the image. We also performed the same edge extraction where images from the database were perturbed by adding independent Gaussian noise to each pixel such that signalto-noise ratio was halved.
Figure14. 4: Effect of filters' parameters on the efficiency of the SparseLets framework As we tested different parameters for the filters, we measured the gain in efficiency for the algorithm as the ratio of the code length to achieve 85% of energy extraction relative to that for the default parameters (white bar). The average is computed on the same database of natural images and error bars denote the standard deviation of gain over the database. First, we studied the effect of the bandwidth of filters respectively in the (A) spatial frequency and (B) orientation spaces. The minimum is reached for the default parameters: this shows that default parameters provide an optimal compromise between the precision of filters in the frequency and position domains for this database. We may also compare pyramids with different number of filters.
Figure14. 5: Histogram equalization From the edges extracted in the images from the natural scenes database, we computed sequentially (clockwise, from the bottom left): (A) the histogram and (B) cumulative histogram of edge orientations. This shows that as was reported previously (see for instance (Girshick et al. , 2011) ), cardinals axis are over-represented. This represents a relative inefficiency as the representation in the SparseLets framework represents a priori orientations in an uniform manner. A neuromorphic solution is to use histogram equalization, as was first shown in the fly's compound eye by (Laughlin, 1981). (C) We draw a uniform set of scores on the y-axis of the cumulative function (black horizontal lines), for which we select the corresponding orientations (red vertical lines).
Discrete Event Simulations Discrete Event Simulation software  [17, 18]  (DES) was written in Python, using the SimPy package. All delay distributions and (if possible) distribution fits per indication and workflow step were measured and modeled into a database. Analytical fitting results (gamma distributions) for IRTs were taken from  [11]. Simulation results were evaluated in steady state, simulating several years. Simulations were performed to assess (a) conventional scheduling: raising the occupancy by overbooking and thus creating waiting times for patients in the waiting room just before treatment, (b) ConWIP workflow: WIP level, occupancy, and the time between CT and treatment.
Conclusions An organizational change was introduced, changing the organization from conventional scheduling to a Work-In-Progress (WIP)-controlled environment. We went through the process of negotiations, measurements, simulations, and implementation with supporting workflow management software (RT-Flow). An output increase of 32% was obtained, leading to a current mean of 8. 9 fractions per worked day (mean 10 h per workday), of which was 29% lung, 21% liver, 13% intracranial, 11% head and neck, and 7% prostate treatments. This new hybrid ConWIP workflow is recommended in the case of a high number of liver and lung treatments, representing long IRT, high variability, and high rate of cancellations. An added advantage of ConWIP workflow is the possibility of easy prioritization up to last minute.
The need to transfer knowledge gained from single species to a more generalized ecosystem-based approach has led to the development of categorization methods in which species with similar traits (morphological, phenological, physiological, and ecological characteristics) and associated life strategies are classified into ecological groups such as functional groups/types or guilds. Plant-trait approaches can be used to rank species' strategies for capturing resources and abilities to coexist, e. g. , in relation to plant architecture: niche complementarity vs. facilitative interactions. These approaches often group species according to their adaptive strategies when faced with changes in their environments (response traits) and/or their function in the ecosystem (effect traits). While taxonomic classification does not indicate the ability of organisms to provide specific ecosystem services (unless exhaustive and accurate databases are available, which is not the case to date), functional compositions of communities are directly related to effect traits  (Lavorel and Garnier 2002)  and, in turn, to ecological functions.
g. , nature of competitive and facilitative interactions). This may allow plant sequences and species mixtures to be designed, as well as enlarge the scope towards more numerous trophic levels to account for the soil food web. This direction may also be chosen for "push-pull" technology, a strategy for controlling pests using repellent "push" plants and trap "pull" plants (  Khan et al. 2011)  to generalize it to a greater number of organisms or production situations. Most of all, to ensure legitimacy, we think it necessary to develop "interactive" knowledge databases that can incorporate farmerexperience feedback in a wide range of farming conditions. Such interactive approaches are already used for agrobiodiversity conservation via seed exchanges among farmers  (Pautasso et al. 2013). A second type of knowledge-based approach for dealing with complexity consists of using an inferential method for qualitative hierarchical multiattribute decision modeling, to cope with complexity while searching for operational outputs.
2013). This innovative approach, combining databases and qualitative modeling helps to bridge the gap between scientific knowledge and implementation of biodiversity-based agriculture. It is already used for teaching in French agricultural engineering schools and international training sessions on modeling for sustainable management of crop health. We think that other ecological features and interaction data with abiotic factors for organisms, provided respectively by researchers and users, could be used in such a method. Model-based games involving scientists, farmers, and other stakeholders combined with biophysical simulation models can be used to perform iterative design and assessment of spatiotemporal distributions of crops, livestock, and seminatural habitats potentially promoting input services.
Acknowledgments This paper emerged from work and discussions in four key projects:  Locally Adapted Organisms and Organizations  ( O2LA ) funded by the  French Agency for Research  ( ANR-09-STRA-08 ), Modeling for Integrated Crop Management, Assessment and Cropping system design (MICMAC design) funded by the  French Agency for Research  ( ANR-09-STRA-06 ),  Crops and ANimals TOGETHER (CANTOGETHER)  funded by the  Seventh Framework European Programme  ( Food, Agriculture and Fisheries, Biotechnology ), ( FP7 , Grant agreement no. :  289328 ), and  Tata-Box  ( Territorial Agroecological Transition in Action : a tool-box for designing and implementing a transition to a territorial agroecological system in agriculture) funded by the  French Agency for Research  ( ANR-13-AGRO-0006 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.