dataesr/scientific-paragraphs-categorization huggingface dataset:
In a Monte Carlo experiment, we have compared the forecasting performance of the damped trend model in MSOE form, estimated using our new method, to that of the SSOE damped trend model. Simulation results show that the out-of-sample forecasting performance of the damped trend model estimated in this fashion is at least comparable to that of its SSOE counterpart. We have further illustrated the usefulness of our estimation method in two empirical applications dealing with the annual time series from the M3-competition database  (Makridakis and Hibon, 2000)  and with quarterly time series on total credit-to-GDP data published by the Bank for International Settlements. This exercise has highlighted that the damped trend model in MSOE form competes very well with the damped trend model in SSOE form, which is considered a benchmark model difficult to beat in empirical studies of forecast accuracy  (Gardner and McKenzie, 2011).
e. crop succession and cropping practices) that reconcile crop production, reduced herbicide use and biodiversity conservation. Decision support systems can help to tackle this challenge. Various Decision Support Systems (DSS) exist to help farmers to take strategic or tactical decisions to manage weeds in their fields. DSS are of many forms, ranging from websites to learn about weeds and get weeding advice such as InfloWeb  (Terres Inovia et al. ) , to complex computer-based software to test different herbicide treatments for weed management, e. g. Weed Manager  (Parsons et al. , 2009)  or WeedSOFT®  (Neeser et al. , 2004). However, these DSS focus on one particular technique or weed species and, to date, no DSS assesses the impacts of a combination of multiple and detailed cultural practices on many weeds at the long term cropping-system scale, and none considers the multicriteria impacts of weeds on crop production and biodiversity  (Holst et al.
, 2012). Hence, based on  Lefèvre et al. (2014) , we proposed a workshop to design cropping systems while using the prototype of the DSS with crop advisors (Figure  3 ). The DSS was a preliminary decision tree built from a cropping-system database for a similar production situation (in terms of pedoclimate) of the workshop's participants  (Colas et al. , submitted; Colas, 2018)  and simulated with FLORSYS during step ii (Figure  1 ). Different formats for presenting the "leaves" were tested for the decision tree (Figure  2 ). The workshop was organized over two days, with time in-between to simulate the cropping systems designed during the first day. The vocabulary used by the crop advisors was put in writing to ensure that it corresponded to the one used in FLORSYS. Moreover, the crop advisors chose a set of weed impact indicators among the ones available in FLORSYS to evaluate the tested cropping systems (section 2.
4. 4). The prototype consisted of the decision tree shown during the workshop and a metamodel built from cropping-system database  (Colas et al. , submitted; Colas, 2018)  during step ii (Figure  1 ). This metamodel quickly and easily predicts the weed impact indicators from synthetic cropping system descriptors, using a fast predictor (see section 2. 4. 4). To evaluate their experience with the DSS prototype, a short online survey with a semi-structured question assessed: (1) the ease of use of this prototype, i. e. of entering new data, (2) the user's confidence in the cropping system descriptors ranks and in the results of the prediction and (  3 ) what improvements users would like to have.
Visual guides for pinpointing pertinent changes in cultural practices Based on the results of step i (section 3. 1), the visual guides aim to support users when choosing which cultural practices to change and how to combine them in order to reach a given weed-impact goal. These visual guides are based on multivariate classification and regression (CART) trees  (Breiman et al. , 1984)  fitted on a large and diverse cropping-system database in step ii  (Colas et al. , submitted; Colas, 2018). To simplify, CART trees are referred as "decision trees" thereafter. To take account of the local constraints of farmers, we developed a series of different decision trees corresponding to contrasting pedoclimatic conditions as well as for different combinations of weedimpact indicators  (Colas et al,. submitted; Colas, 2018).
The R-shiny application  (Chang et al. , 2017)  of the DSS prototype (see screenshots in supplementary material online section D) consisted of two tables, the first listing all the synthetic cropping system descriptors as proxies of meta-decision rules. The cropping systems descriptors were ranked according to their importance on the variation of the weed impact indicators, corresponding to the VIP estimated with the random forest. This table was pre-filled with the mean values of the descriptors from the cropping-system database, and users could change one or several of these descriptors at a time. A simple click runs the random forest (hence "fast predictor") and the second table shows the resulting weed-impact indicators, together with the minimum and maximum values recorded in the database.
For the display of weed impact indicator results in both the visual guide and FLORSYS's fast predictor, the users preferred a display showing values for all weed impact indicators, highlighted with colour codes, instead of bars or aggregated scores. Combining the same ensemble values that need to be maximized with values to be minimized was always harder to be understood by crop advisors and farmers in our workshops and meetings. This is why polar diagrams or bar plots were not selected. The colour code was judged helpful to visually and fast extract the results, even though multivariate decision making based on colour seems to be only useful when the level of data complexity is low and mostly for females  (Stella and Malcolm, 2002). Indeed, the red/green dichotomy is not adapted to colour-blind people, as we observed with one of the crop advisors in the workshops.
Towards the future decision support system Users' advice is essential to design a decision support system but not all the suggestions of future users are relevant or possible to follow. For instance, the content of the DSS was derived from the research model FLORSYS, with a sensitivity analysis performed on FLORSYS identifying the inputs essential for predicting weed impact on crops  (Colas et al. , submitted; Colas, 2018). These very influential inputs must be included in the DSS, even if they are difficult to handle by the users. To help future users utilise those difficult cropping system meta-decision rules, we need to work on a common language logical for all users  (Van Meensel et al. , 2012). Helping users with easier-to-handle inputs applies even more to FLORSYS, which is intended for finetuning cropping systems.
Acknowledgements The authors would like to thank the farmers and their crop advisors from the watershed management local authority SAGEBA and the consulting company  PERI-G in Picardie  and from the  GRCETA de l'Aube  for participating in the group meetings. The crop advisors of the  Chambre d'Agriculture de Haute-Marne and Bruno Chauvel (INRA ) helped us to find crop advisors for the workshop. We would like to thank  Órla McLaughlin , who proofread the English The present work was financed by  INRA  (EA and MIA divisions), the  French  project  CoSAC  ( ANR-14-CE18-0007 ) and the  Burgundy Region.
Modeling of χΤ(T,H) A good approximation of the χΤ(T,H) function can be obtained from dc experiments. If fits to the dc data have been possible, then the χΤ(T,H) function can be parametrically reproduced for any point in the (T,H) space using the spin Hamiltonian parameters and tools such as Easyspin  [31] , Phi  [32] , MAGPACK  [33]  or other. Thus, χΤ(T,H) can be calculated for the temperature and magnetic field domains of the ac experiment, even if these are different from those of the dc experiment, which is usually the case. However, fits to the dc data may not have been possible, e. g. , in the case of very large Hamiltonian matrices, or in the case of molecules with strong spin-orbit couplings that preclude the use of the spin Hamiltonian approach, thus complicating analysis. In that case, χΤ can be calculated for the T and H domains of the ac experiment by interpolation of the dc experimental data.
While this method yielded overall correct results, it was prone to produce discontinuities which, as previously noted [4], require additional treatment, such as smoothing. On the contrary, numerical solution using the Runger-Kutta method implemented by Matlab's embedded solvers (ode45 in particular), consistently produced realistic curves without additional treatment. Although this adds a slightly higher computational overhead with respect to the previous method, the higher stability and accuracy it affords make it an uncontested choice for this application. Up till now, it is assumed that each interval ΔH of the ascending magnetic field is swept a rate κ = dH/dt common for all intervals, which is equivalent with assuming a constant field-sweep rate. It has been commented  [50]  that this numerical solution is applicable to methods that measure the magnetization continuously as the field is swept, such as with micro-SQUID or VSM magnetometers, in which case the experimental sweep rate coincides with the numerical one.
IMPLEMENTATION In order to benefit from additional functionality, the tool has been written in Matlab and can make use of Easyspin's curry function to parametrically calculate dc susceptibilities and magnetizations. However, any experimental dataset or any calculated curve generated by Phi or any other program can also be used instead. In that case though, care must be taken in defining the T and H domains so that they overlap with those of the ac/hysteresis data to be simulated by the tool. SMM-ET will then create new dc datasets through interpolation with Matlab's cubic spline function. This capability may also be useful when a parametric calculation is possible but computationally expensive. In its current version, SMM-ET outputs the calculated curves in a series of self-explanatory figures.
The data of each curve will appear on the workspace under self-explanatory names. However, users can also extract the data from the figures using the Matlab command line. More detailed instructions and the tool executable (along with a sample parameter file) are provided at http://chiralqubit. eu/ SMM-evaluator-tool. Alongside, are included indicative files of χM vs T and M vs H experimental data  [48]  of Dy III SMMs.
over large sample numbers it could provide just as valuable Quantum Mater Res. 2020;1:e200004. https://doi. org/10. 20900/qmr20200004 conclusions as these more laborious and costly studies, in the way that large datasets of magnetic exchange couplings helped magnetochemists set the basis for an empirical understanding of magnetostructural correlations. Park K, Beedle CC, Hendrickson DN, Myasoedov Y, Zeldov E, et al. The effect of uniaxial pressure on the magnetic anisotropy of the Mn12-Ac single-molecule magnet. EPL Europhys Lett. 2013 May 1;102(4):47008.
over large sample numbers it could provide just as valuable Quantum Mater Res. 2020;1:e200004. https://doi. org/10. 20900/qmr20200004 conclusions as these more laborious and costly studies, in the way that large datasets of magnetic exchange couplings helped magnetochemists set the basis for an empirical understanding of magnetostructural correlations. Park K, Beedle CC, Hendrickson DN, Myasoedov Y, Zeldov E, et al. The effect of uniaxial pressure on the magnetic anisotropy of the Mn12-Ac single-molecule magnet. EPL Europhys Lett. 2013 May 1;102(4):47008. Alternating current susceptibility, high field magnetization, and millimeter band EPR evidence for a ground S = 10 state in [Mn12O12(CH3COO)16(H2O)4]•2CH3COOH•4H2O. J Am Chem Soc. 1991 Jul;113(15):5873-4. 2. Sessoli R, Gatteschi D, Caneschi A, Novak MA.
This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic 18 F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age 6 SD, 27 6 7 y; weight, 70 6 10 kg) underwent a test-retest 18 F-FDG PET/MRI examination of the brain (n 5 20). The imaging protocol consisted of a 60-min PET listmode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function  (AIF). Training of the cGAN was performed using 70% of the total datasets (n 5 16, randomly chosen), which was corrected for motion using MR navigators.
The resulting cGAN mappings (between individual frames and the reference frame [55-60 min after injection]) were then applied to the test dataset (remaining 30%, n 5 6), producing artificially generated lownoise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was 1.
Imaging Protocol All examinations were conducted in the afternoon; subjects were asked to keep their eyes open without performing any task. Before each scan, the glucose concentration (mmol/L) in blood was measured, and a venous line was established for the injection of the 18 F-FDG tracer. In addition, an arterial line was established in the contralateral arm for manual arterial blood sampling. To ensure a high signal-tonoise ratio in the MR images, a head and neck coil was used. After positioning the brain in the center of the field of view, a 60min PET list-mode acquisition was initiated with the bolus injection of 18 F-FDG (352 6 66 MBq). Contemporaneously with the PET data FIGURE 1. Schematic representation of cGAN methodology for motion correction of dynamic PET frames. [1]  Motion vectors from the MR navigators are applied to align the PET data.
PET list-mode data were rebinned into a dynamic frame sequence (24 3 5 s, 1 3 60 s, 1 3 120 s, 11 3 300 s) and were reconstructed (e7 tools; Siemens) into a 344 3 344 3 127 matrix (voxel size, 2. 08 3 2. 08 3 2. 03 mm) using the ordinary Poisson ordered-subset expectation maximization 3D algorithm (3 iterations, 28 subsets, 2 mm gaussian filter). Attenuation and scatter correction were performed using CT-based attenuation-corrected maps corrected for motion in each PET frame.
3D-cGAN Generative Adversarial Networks (GANs) are generative algorithms, which belong to the field of unsupervised learning  (24). The architecture of a GAN consists of 2 convolutional neural networks that together constitute an opponent-component system: a neural network (termed the Generator G) that generates artificial data based on a training dataset, and a neural network (termed the Discriminator D) that classifies the artificially created data as being either real (i. e. , belonging to the training dataset) or being artificially generated. Conditional GANs (cGANs) are a supervised extension of the GAN framework  (25). Although GANs typically perform a mapping operation from a random noise vector (z) to an output vector (y) expressed as (G: z ! y), cGANs perform a mapping operation from an observed image (x) and a random noise vector (z) to an output image (y), expressed as (G: {x, z} ! y).
Here, the mapping operation is the process of linking 2 image patterns together and is "learned" from a training set that defines the true correspondence between pairs of input and output images (x!y). In short, the training dataset provides a general mapping of 2 images with different noise characteristics (e. g. , a low-count to a high-count image). It is a generic mapping operation that accounts for broad features in the 2 images but does not account for subject-specific attributes. Accordingly, such mapping is representative for the transformation of any 18 F-FDG uptake image obtained at a particular time (e. g. , 2-3 min after injection) to any 18 F-FDG uptake image at a later time (e. g. , 55-60 min after injection). In this 3D-cGAN implementation, corresponding pairs of low-count (early) and high-count (late) PET frame images were used to define the mapping operation (G) by minimizing a loss function expressed as: ん cGAN ðG, DÞ 5 E x,y ½log Dðx, yÞ 1 E x ½log À 1 2 D À x, GðxÞ ÁÁ , Eq.
cGAN-Aided Motion Correction A random 70%-to-30% data split of the full data was used for cGAN training (14 scans) and testing (6 scans). Initially, all studies in the training set (14 measurements with 37 frames each) were corrected for motion using motion vectors obtained from the contemporaneously acquired MR navigators (23) (Fig. 1 ). Real-time data augmentation (rotation, translation, shearing, additive gaussian noise, brightness, contrast) was performed on the training datasets, resulting in 21,000 synthetic datasets. Subsequently, cGAN mapping was performed between the last high-count PET frame (reference frame 37, representing tracer accumulation at 55-60 min after injection) and all other PET frames, resulting in 36 mappings (x!y) with variable quality (Fig. 2 ). The obtained mappings were then applied to the test datasets to obtain artificially generated high-count images (using cGAN) that imitate the distribution of the reference frame from the original low-count images.
After the generation of the cGAN-based high-count images for the test datasets, motion correction was performed by considering the 55-60 min postinjection PET frame as reference frame (F ref ), and all other frames were subsequently registered to the F ref using a standard multiscale mutual information (MI) routine (Greedy module ITK 1. 2. 4, Insight Segmentation and Registration Toolkit). For each frame of the dynamic sequence, this approach resulted in a 6-parameter motion vector.
Standard PET Frame-Based Motion Correction To evaluate the added value of cGAN-aided motion correction, this methodology was compared with a standard PET frame-based motion correction. PET image frames were aligned using the same multiscale MI-based coregistration routine as described above (Greedy module ITK 1. 2. 4). This routine performs alignment between images starting at a coarse scale, which is then used to initialize registration at the next finer scale, a process repeated until it reaches the finest possible scale. As for the early images (,3 min after injection), the applied multiscale MI coregistration approach failed due to insufficient count statistics, thus we summed the first 3 min of the dynamic sequence to create a reference frame with sufficient statistics. Subsequently, all later frames (.
3 min after injection) were rigidly aligned to this summed frame. It is important to point out that this approach (summing of early frames) is frequently implemented in dynamic studies when low-count images that do not contain sufficient data that would allow extraction of an accurate motion vector are analyzed. Because robustness of this coregistration procedure can be improved by low-level smoothing (The ITK Software Guide; Kitware Inc. ), our standard registration approach therefore consisted of applying a heuristically chosen 4-mm gaussian filter to the images before registration. However, to assess the performance of cGAN methodology when processing the original (low-count) images, this smoothing step was omitted when testing cGAN-processed images.
PET Emission Data and Attenuation Map Alignment To account for misalignment between the PET emission data and the attenuation map, we used a dual-reconstruction approach. Specifically, PET attenuation correction was performed on the basis of an attenuation map derived from a CT image acquired immediately after the PET/MRI protocol. This CT attenuation map was coregistered to the first MR sequence of the study protocol (TOF-MRA sequence), and this static map was then used to perform attenuation correction for the whole PET dynamic sequence. However, this approach does not take into account PET interframe motion. Therefore, non-motion-corrected PET frames were initially used to derive a motion vector (using either MR navigators for the training data or cGAN-processed images for the test data), and once the motion vector was determined this information was used to align the CT attenuation map to each PET dynamic frame.
Generation of Simulated Test Datasets Given the low number of the original test datasets (6 scans), additional test datasets were generated on the basis of the original 6 test datasets. These simulated datasets were used to further investigate the potential of the cGAN method to address the problem of interframe motion. Excessive interframe motion was simulated by adding to each dynamic frame (except the reference frame) an arbitrary translation (0, 1, or 2 voxels in each direction) and rotation (0 , 0. 5 , or 1 for each Euler angle) vector (SimpleITK 1. 2. 4, Supplemental Fig. 3 ). Ten repetitions were performed for each dataset with different motion vectors added, resulting in a total of 60 synthetically created test datasets, each consisting of 37 dynamic frames. These datasets were then either preprocessed using cGAN methodology or were directly coregistered to the reference frame (standard PET frame-based motion correction) using the normalized MI alignment routine as described above.
Moreover, to assess the potential of cGAN methodology to aid in the detection of intraframe motion in routine clinical static studies, we partitioned the list-mode data of the reference frames (#37 from 55-60 min after injection) into a series of short subframes (10 subframes of 30 s and 20 subframes of 15 s). cGAN was used to produce artificially generated high-count images from these subframes. This procedure was applied for each measurement of the test dataset (6 scans). Improvement in image quality was assessed on the basis of the increase in MI after cGAN processing. In addition, to further demonstrate the ability of cGAN processing in accurately accounting for intraframe motion, we selected a representative reference frame, which we partitioned into 15-s subframes and introduced random motion (translation 3-5 mm, rotation , 1 ) to the subframes.
cGAN-Based IDIF To assess the clinical performance of the cGAN method for motion compensation of dynamic PET frames, we extracted the IDIF from the test dataset (both original and simulated) and compared the IDIF with the AIF. For this, we replaced the MRI navigator-based motion correction in our previously developed IDIF pipeline with the developed cGAN-aided motion correction. This analysis pipeline was described in detail previously  (9, 10). In brief, it entails automated segmentation of the petrous region of the internal carotid arteries (ICA) from the corresponding TOF-MRA images followed by an automatic multiscale intermodal NMI coregistration (Greedy ITK 1. 2. 4) of the TOF-MRA volume and the reference frame (frame #37) for each study. The arterial blood-pool region was defined on the basis of the MR angiography image, clearly identifying the ICA region.