dataesr/scientific-paragraphs-categorization huggingface dataset:
An important preprocessing step before selecting the proxies is to remove in each proxy record from P2k+ (see Methods) the forced variability of NASST, using linear regression starting in 1870. This is a novel step that allows us to produce an AMV reconstruction that exclusively reflects the internal variability signals. The forced component is estimated using a signal-to-noise maximizing empirical orthogonal function technique 2 (Methods, Extended Data Fig. 1 ) from historical simulations of 37 CMIP5 models (Extended Data Table  2 ). Reconstruction methods applied in this study require that the predictors (i. e. , the proxy records) have no temporal gaps. To use the maximum number of proxies available to reconstruct each temporal window we follow a nested reconstruction approach  34  with moving windows of one year.
2c-d. We identify three main clusters of records with fairly distributed weights: central Asia, Europe and western North America. It is worth noting that proxies from Asia and western North America are highly represented in the PAGES 2k database  35  , which basically explains their relatively large number used for the reconstruction. Interestingly, there are a number of highly weighted proxy records of annual and boreal summer (June-July-August) temperatures over the Eastern Pakistan/Tibetan Plateau. This link has been supported in a couple of recent studies which have highlighted the role of AMV variations for spring and summer temperatures in this region  41, 42  , notably by affecting large scale pressure gradients in the Eurasian sector. We also use a large number of proxy records from eastern Asia/northern China, for which climate conditions have also been shown to be significantly affected by the AMV variations through atmospheric Rossby wave propagation and altered heat advection in the western Pacific  43.
Given the large-scale AMV teleconnections shown by these studies  [41] [42] [43]  and the large weights we find for Asian proxy records, we justify their inclusion in our new AMV reconstruction, the first one to date to include them  18, 34. Western North American proxy records are mostly sensitive to summer and yearly variations of temperature and precipitation (Fig. 2c-d , Extended Data Table  3 ). It appears that Fig. 1  does not show a consistent relationship between the AMV and instrumental summer and annual temperatures in this region, although it is observed for some precipitation time series over the historical period (Fig. 1 ). Finally, the fact that only five proxy records from Europe are used is mostly due to their relatively reduced presence in the proxy record database we use (<10%).
Data and model-based validations As for the previous reconstructions  18, 34  , most of the proxy records used in this study are terrestrial, which might be surprising given that the AMV is an oceanic mode. Our method indeed selected a poor amount of ocean proxies from P2k+ (1 out of 55), essentially because those with a resolution lower than annual have been excluded (Methods). To evaluate if our AMV reconstruction is in line with existing ocean proxy records, we consider a posteriori those not used in the reconstruction. Since some of these proxies have very low temporal resolution, which might lead to spurious significant correlations with the AMV, we use a significance test that takes the time series autocorrelations into account, similarly to previous studies  17, 38  (see Methods). We find that 37 ocean records (23 from the North Atlantic including the Mediterranean Sea) from the Ocean 2k database  35  are significantly correlated at least at the 90% confidence level with our AMV reconstruction (Fig.
It exhibits a significant correlation with the AMV reconstruction (r=0. 64; p<0. 01, Fig. 6a ). This suggests that more than 40% of the variability of NASST over the last millennium can be explained by internal variability only, the rest being related to external forcing. Strikingly, validation scores obtained by this optimal regression approach are much higher than for the AMV reconstruction (med(CE)=0. 44 and med(CE)=0. 25, respectively). A plausible explanation for these discrepancies in skill scores between NASST and AMV reconstructions is that the way the AMV is constructed accompanied with external forcing removal from proxy records might in turn partly decorrelate NASST and proxies by removing their common responses to the same forcings. Using a recent reconstruction of volcanic activity  46  , we further perform a superposed epoch analysis  47  (Methods) on both the NASST and the AMV reconstructions, to characterize the response to the 10 largest eruptions of the last millennium (Extended Data Table  4 ).
4 ). Regarding solar forcing, neither the 10-year or 30-year filtered time series from the PMIP3 TSI reconstruction  48  is significantly correlated with our 10-year filtered AMV reconstruction, even when solar forcing leads by a few years (r=0. 23, p>0. 2, lag=12; r=0. 32, p>0. 2, lag=13; respectively, Extended Data Fig. 5 ). Both the 10-year and the 30-year filtered time series of the TSI reconstruction a modestly significantly correlated with the NASST reconstruction (r=0. 5, 0. 1<p<0. 2, lag=13; r=0. 52, 0. 1<p<0. 2, lag=14; respectively). This result therefore further supports the idea that our reconstruction represents only internal variability of the North Atlantic and can be thus considered as an additional independent validation of our reconstruction. The wavelet analysis in Fig. 7  shows that the AMV reconstruction exhibits important multidecadal variations (Fig.
impacts of the AMV over the historical era. a) Historical evolution of AMV indices investigated in this study for the period 1870-2017 calculated using the HadISST dataset36 (cf. Methods). b,d,f,h,j) Map of averaged regression coe cients between the 10-years smoothed composite of the three AMV indices from a) and CRUTS430 precipitation data for the period 1901-2017. Maps are respectively relating Annual, DJF, MAM, JJA and SON regression coe cients. c,e,g,i,k) Map of regression coe cients between the composite of the three AMV indices from a) and CRUTS430 surface temperature data for the period 1901-2017. Maps are respectively relating Annual, DJF, MAM, JJA and SON regression coe cients. For ce-), white grid points indicate that regression coe cients are not signi cantly different than 0 at the 90% con dence level, using a two-tailed student test with corrected degrees of freedom17,38 (Methods).
Methods: Instrumental AMV indices: Historical AMV indices have been calculated using annually-resolved values. The AMV reconstructions compared and presented in this study are then smoothed with a ten year kernel filter. We have computed, over the period 1870-2019, the three instrumental AMV indices  2, 8, 28  of this study using the Hadley Center Global Sea Ice and Sea Surface Temperatures (HadISST) dataset  36. The three indices are based on the spatially averaged SST over the North Atlantic (between latitudes 0° and 60°N) and differ in the way the externally forced signal is removed. The different approaches used assume that external forcings factors have different implications for temperatures and precipitation over time. The first index uses the global averaged SST anomalies as a proxy for the externally forced signal that is subtracted from NASST, resulting in the   index 8 (Fig.
Methods and Data Data sets provided by U. S. Departement of Transportation (2018) report platooning experiments of five vehicles driving in the same lane, with the platoon leader 0 applying scenarios of consecutive plateau of speeds 0, 20 or 26 m/s. Each experiment lasts 6 to 12 minutes with about five leader's speed changes in each. The first group of experiments, conducted in 2016, presents a lot of missing data. The second group of platooning experiments, performed in 2018, only includes the CACC mode. We aim to analyse the response time of the follower vehicle to a speed change of the leader. Corresponding author: christine. buisson@univ-eiffel. fr 1 These experiments were realized in the Volpe Center test track. Each Cadillac SRX vehicle was equipped with the CARMA system and was driven by a professional driver.
The data collection procedure was identical for each platoon experiment. Data available here: http://doi. org/10. 21949/1504485. Based on the vehicle's speed profile and the setting time when speed changes, we examine if such speed changes surpass a threshold given by the total variance of the vehicle's acceleration. With this detection method, we identify the instants at which speed transitions occur (see Figure  1 ). Thus, by comparing the speed change instants of two successive vehicles, we compute response times 2.
Overcrowded hospitals in its different process's levels is a very common issue all over the globe. Research through different works have studied this problem from different angles. Most of those focused on a very specific part of the internal flows. In this study, we present a mathematical model aiming to reflect patient flow and resources usage. This model presents a new perspective on how it is possible to organize a hospital flow for inpatients of different pathologies. It takes into consideration the patient's pathology, survival function and the current beds distribution and discharges. To model the different variations of a patient's flow, we used a non-homogenous discrete time Markov chain. Then, we tested our model using discrete event simulation using the National Health Service (NHS) dataset.
4% patients with a response time < 8 minutes. In the Chilean community, a team of experts had let a cohort study  [14]  including patients with non-prioritized conditions while registration in hospitals. The data included a waiting list of 987497 patients in 77 healthcare institutions from 2008 to 2015. The study showed that despite geographical disparities and health centers differences, waiting time variability have significant impact on death rate. In the other hand, as the efforts to decrease the length of stay are getting bigger, the concerns of rising the readmission rates are also worrying. Premature discharge can be one of leading causes to readmission as shown by many studies based on cohort and longitudinal approaches  [15] [16]. Recently, the coronavirus outbreak first appeared in Wuhan China in the 31 st December 2019 had made all healthcare institutions and administrations under scope and huge pressure.
Our approach uses a non-homogenous discrete time Markov chain and queuing theory properties. The core of our approach is based on dividing the treatment process into three main levels: the first level which is preliminary diagnosis then the second: treatment process (prescriptions, laboratory analysis, physical monitoring, inpatient's further diagnosis) and then in the thirdplace discharge. The main objective in every level is to theoretically decide the number of served patients and overflowing patients and the hospital's capacity to serve all patients. In the following, we organize the paper in three main sections; the first presents related works and techniques used in healthcare processes management. And the second depicts our proposed model with all used concepts and recommendations. While the third section includes a case study based on NHS database to validate our model.
The study used hospital internal logistic data of two years. Using an (Markovian patient's arrival distribution and finite resources) M/M/m and (Markovian distribution of patient's arrival, general resources usage, infinite resources) M/G/∞ to reflect the probability distribution of total patients in each ward, the study confirmed the Little's law of queuing theory. Two years later, in 2018,  [25]  used different queuing theory models' variations to determine operation performances like beds utilization rate, waiting times and demand behavior assessment. The model was based on six months data recorded in University hospital and concluded as results that the balance between admissions and type of pathologies can be a possible way to optimize resources usage. In the same year, a team management and chief medical officers in Lehigh Valley Health Network  [26]  confirmed the value of queuing model in theory as it can statistically provide alignment and focus on future insights.
1) DATA DESCRIPTION Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at the National Health Service (NHS) hospitals in England. It is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. This same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. All data used in the current work is collected from the NHS's official digital platform  [4].
9 million) of which included at least one procedure or intervention, and 4. 7 million of which were day cases. A study by  [37] , has demonstrated a high correlation between bed occupancy and longer Emergency department waiting times. As with a nearly 100% bed occupancy, the percentage of patients in ED waiting longer than four hours increased by 9%. The study also listed: Hospital length of stays over 21 Patients arrival rate in one of the NHS hospitals per hour days, higher emergency admissions and lower discharges to be main factors of inconveniently higher waiting times. The study included 138 English NHS healthcare providers of which daily situation reports (Sitrep), hospital episode statistics and electronic staffing records data over 90 days between December 2016 and February 2017 were extracted.
V. CONCLUSION We have demonstrated how Markov chains and queuing theory can be an effective to model patient's flow and to forecast needed beds in different hospital's wards. The study used a Nonhomogeneous Discrete time Markov chain to represent the different flow variations. Thus, in order to validate this model, we used discrete event simulations that gives as results the frequencies of a server and resources utilization. The results of the simulation were then compared to the average bed occupancy in the National Health Services database using the chi-square technique. The methodology we have developed enables estimation of the main characteristics of access to service of patients and hospital managers as well as medical care providers: forecasting of the arrival rate of patients and bed occupancy in function of patient's arrival and their priority.
Acknowledgements The authors would like to express their gratitude toward  Professor Bengt Lindgren  of  Uppsala University, Sweden , for fruitful discussions and support with the ellipsometric characterization of TbCo thin-film materials. The excellent support and infrastructure of the MyFab facility at the  Ångström Laboratory of Uppsala University  is also highly appreciated. The authors acknowledge support from the  Knut and Alice Wallenberg Foundation  project " harnessing light and spins through plasmons at the nanoscale"  (project no. 2015. 0060 ), the  Swedish Research Council  (project no. 2019-03581 ), the  Swedish Foundation for International Cooperation in Research and Higher Education  (project no. KO2016-6889 ), and the  Swedish National Infrastructure for Computing (SNIC). This work was part of a project which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no.
PET/CT imaging PET/CT studies were performed with 3 types of scanners. In the CHU of Liège, 89 studies were acquired using a Philips Gemini TF or BB (scanner A), and in the CHU of Brest and ICO St Herblain 17 and 34, respectively, were acquired using a Siemens Biograph mCT (scanner B). In addition, 18 studies performed with a General Electric Discovery ST (scanner C) at the McGill University Health Center were used as an external validation set. A mean activity of 306 MBq of [ 18 F]FDG was injected before image acquisition with a mean uptake time of 66 min. The acquisition and reconstruction protocols are described in Table  1  of the supplementary data A.
Radiomic features and intensity discretisation Two hundred and fifteen features were extracted from the segmented volumes using the Oncoradiomics research toolbox. These features included first-order grey level statistics, geometry, fractals, texture matrix-based features and others. The detailed description of the features can be found in supplementary data B. For those standardised by the IBSI (Imaging biomarkers standardisation initiative), the implementation follows IBSI benchmark. We also studied the ratio of the features' values calculated in the tumour and in the liver (TLR), except for the shape features. In addition to the radiomic features, data also included 4 clinical parameters i. e. FIGO stage (IB to IVA), histological types (squamous cell carcinoma (SCC) or not), age and presence of lymph node (LN) metastasis.
We used fivefold cross validation in our training data and chose the number of features to be used according to the best mean fold area under the curve (AUC) value, F-score (with Beta 1 and 2) and AUC of precision recall curve (AUCpr). Patients who recur were oversampled using the random oversampling method in the training data to help in the learning process  [39]. We repeated this procedure for each of the models. We used for each classifier the default hyperparameters values in their respective R packages. Finally, for each of the different models with distinct selected features, all training data were bootstrapped with 1000 repetitions, in order to get confidence intervals for each performance metric, and tested on one test set independent of model/feature selection training set. We measured AUC, F 1score and F 2 -score, precision, recall, AUCpr and its corresponding percentile confidence intervals of the distinct models.
Different methods have been developed to decrease radiomic features variability across clinical centers. Harmonising acquisition and reconstruction parameters, for instance through the EANM EARL initiative might contribute reaching such goal. A key finding of the present study is that the TLR perform better across scanners than the native radiomics features. As each patient acts as his own control, it should not come as a surprise finding the ratios as more stable than the native features. ComBat is another method that was previously successfully used for that purpose. It was shown to align features distributions across different clinical centers as well as to improve predictive ability of radiomic features namely in cervical cancer DFS prediction  [50]. In our dataset, the best model  was not obtained when applying ComBat harmonisation before FS.
Indeed, in lung cancer for instance, Parmar et al. [21]  and Sun et al. [19]  compared different FS methods and classifiers and reached very different conclusions. Deist et al. [22]  also observed that when training the model with different datasets, the performance of classifiers is significantly different. We similarly observed that the performance of our models is highly dependent on the data used to train and test the models, which can be related to the relatively small sizes of the datasets, compared to other ML applications where thousands or tens of thousands of samples are available for training the models. We also found that the radiomic features or models selected and developed by other researchers were not transposable to our population i. e. could not predict recurrence in our patients, with the caveat that the previous works did not evaluate DFS, but rather LRR  [8].
I. I N T R O D U C T I O N Deep learning (DL) networks provide state-of-the-art performance for many machine-learning tasks  [1, 2]. Their ability to achieve good generalization is often explained by the fact they use very few priors about data  [3]. On the other hand, their strong dependency on data may lead to selection of biased features of the training dataset, resulting in a lack of robustness in classification performance. Here robustness refers to the ability of a classifier to infer correctly even when the inputs (or the parameters of the classifier) are subject to perturbations. These perturbations can be due to general factors -such as noise, quantization of inputs or parameters, and adversarial attacks -as well as application specific ones -such as the use of a different camera lens, brightness exposure, or weather, in an imaging task.
This means that the network function can create significant deformations of the space (small distances in the original domain map to larger distances in the final layers) and explains how an adversarial attack with small changes to the input can lead to class label changes. Our proposed regularizer penalizes big changes at the boundaries between classes. By forcing boundary deformations to evolve smoothly across the architecture, and at the same time by maintaining a large margin, the proposed regularizer therefore favors smooth variations instead, leading to better robustness. We will empirically demonstrate this claim on classical vision datasets. The proposed regularizer is based on a series of graphs, one for each layer of the DL architecture, where each graph captures the similarity between training examples given their intermediate representation at that layer.
Another example is  [21]  where the authors exploit graph convolutional layers  [22] , smoothing the latent representations of the inferred images using images from the training set, in order to increase the robustness of the network. Note that this could be described as a denoising of the inference (test) image, using the training ones. This differs from our study, which focuses on generating a smooth network function. In Section III we demonstrate, using readily-available image classification datasets, the robustness of the proposed regularizer to common perturbations studied in the literature: (i) noise  [7, 10] , for which we show reductions in relative error increase, (ii) adversarial attacks  [4, 5] , for which the median defense radius [14] is increased by 50 in comparison with the baseline and by 12 in comparison with another method in the literature  [11] , and (iii) implementation defects, which result in only approximately correct computations  [23] , for which we increase the median accuracy by 48 relative to the baseline and 26 relative to another method in the literature  [11].
I I I. E X P E R I M E N T S In the following subsections, we evaluate the proposed method using various tests. We use the well-known CIFAR-10 dataset  [26]  as a first benchmark and we demonstrate that our proposed regularizer can improve robustness as defined in Section II. A. In summary, in subsection A) we first verify that the proposed regularizer favors Definition 1. We then show in subsection B) that by using the proposed regularizer we are able to increase robustness for random perturbations and weak adversarial attacks. In subsection C), we challenge our method on more competitive benchmarks. Finally, in subsection D) we extend the analysis to CIFAR-100  [26]  and Imagenet32x32  [27]  to validate the generality of the method. These experiments demonstrate that DNNs trained with the proposed regularizer lead to improved robustness.
To measure accuracy, we average over 10 runs each time, unless mentioned otherwise. In all reports, P stands for Parseval  [11]  trained networks, R for networks trained with the proposed regularizer, and V for Vanilla (i. e. baseline) networks. Note that the network architecture is the same for all networks and that Vanilla refers to the network architecture trained without any regularizer. The network architecture is depicted in Fig. A1. Training hyperparameters and details of our implementation of the Parseval method can be found in the Appendix. The corresponding code is available at https://github. com/cadurosar/laplacian_networks.