dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead, it aims at indicating the clustering in the data least expected from the background-only hypothesis and thus to initiate more specific investigations. Applied to ANTARES data recorded between 2007 and 2012, this analysis found a large structure with a post-trial significance of 2. 5σ. This result is consistent with a random fluctuation of the background of atmospheric neutrinos. Using this method to analyse public data from IceCube resulted in an excess located within the overlap between the cluster from the ANTARES data and the field of view of IceCube. This observation has a significance of 2. 1σ. Even though a general, model-independent analysis cannot be as sensitive as a dedicated search due to the high trial factors, this method provides a good way to become aware of the most prominent and even unforeseen structures in data and can be regarded as a trigger for more specific investigations.
Despite the high trial-factor, it can outperform standard pointsource and two-point correlation analyses in a scenario of unknown extended structures. Figure  21 : Top: Sum of all evaluated scales (corresponding to Figure  14 , not directly to the neutrino flux) resulting from ANTARES data, in galactic coordinates and before segmentations. As only upgoing events are used in this analysis, the field of view of ANTARES does not cover the whole sky. This results in the large, homogeneously blue area in the upper left of the skymap. Bottom: For comparison, an example for a random dataset with the same colour code as the ANTARES results. The observed maximum values of U , see Equation  4 , are similar, while the clustering of the overfluctuations is more pronounced in the recorded data.
27 (À0. 29 to 0. 85) hPa decade À1 ] is not. The HadCRUT4 dataset  (Morice et al. , 2012)  indicates that global mean annual air temperature increased at a rate of 0. 17 C decade À1 over the same 1979-2019 interval, meaning that the air pressure trends equate to temperature sensitivities of 2. 10 (1. 38-2. 85), 1. 39 (0. 58-2. 14), and 1. 58 (À1. 73 to 5. 03) hPa C À1 for the annual mean, maximum, and minimum pressures, respectively. The ensemble of CMIP5 simulations  (Taylor et al. , 2011 ; Table  S1 ) offer an alternative estimate of the sensitivity of summit pressure, isolating the impact of climate warming by filtering out interannual variability through the application of running 30-year means. The results show that the largest increases should be in wintertime pressure as global mean annual air temperature rises, with minimum values most sensitive to warming (Figure  5C ).
Data and Code Availability The observational data from the automatic weather stations on Mt. Everest are available from National Geographic: https://www. nationalgeographic. org/projects/perpetual-planet/everest/weather-data/ Hourly ERA5 reanalysis data on pressure levels can be obtained freely from the Copernicus Climate Data Store, available here: https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-pressurelevels?tab=form. The CMIP5 data (see Supplementary Information Table  S1  for the models used) can be accessed freely through the Earth System Grid Federation (https://pcmdi. llnl. gov/mips/cmip5/data-access-gettingstarted. html). The reconstructed summit air pressures for all oxygen ascents are available here: https://docs. google. com/ spreadsheets/d/14IAtgzZt36YudrhHN0tAPWS4-ddyWf_9IfZx-Yy8kQQ/edit?usp=sharing.
Air Pressure and Oxygen Availability During Summit Climbs The Himalayan Database  (Hawley and Salisbury, 2007)  provides a comprehensive history of Mt. Everest mountaineering. We used it here to identify successful climbs without supplemental oxygen over the period 1979-2019, extracting reconstructed summit air pressure for the hour that each climber reached the peak. For 19 of these 208 ascents, the exact time was not recorded, so we estimated the summit pressure at the time of ascent using a Gaussian weighted average (with a standard deviation of 3. 5 h) centred at 12:00 Nepal Time (NPT) on the day of the successful climb. These choices reflect the mean and standard deviation of summit times across the 189 records that recorded this information.
Notes 1. Other studies have failed to replicate this facilitatory influence of neighbourhood density in the lexical decision task (e. g. Carreiras et al. , 1997). 2. This hypothesis and all methods for this experiment were pre-registered in the Open Science Framework repository (https://osf. io/mt5zb) prior to double-blind data collection. Exploratory analyses that were not preregistered are indicated as such. Pseudoword analyses were also conducted and included in response to a request from an anonymous reviewer. 3. A power analysis for data with random participants and items was used to determine the number of participants  (Green and MacLeod, 2016). As suggested by  Brysbaert and Stevens (2018) , we ran 200 Monte Carlo simulations using the SIMR package in R on the data reported by  Meade et al.
(2019). The results showed that power was 86% to observe the main effect of neighbourhood density with 28 participants. The present study is the first in which the interaction between neighbourhood density and flanker context is investigated, so there was no dataset available to directly estimate power for the interaction. 4. All materials, data, and analysis scripts for both experiments have been made publicly available (https://osf. io/tjm53). 5. We also note that such single mechanism accounts of lexical decision cannot account for the present findings. See  Grainger (2018)  for a proposal that integrates an accumulative word evidence mechanism within the framework of MROM.
This article introduces a Matlab© code to implement the General Differential Split Sample Test (GDSST) (Dakhlaoui et al. [5]). As an illustration, the GDSST is applied to five catchments in northern Tunisia over 30year reference period and compared to three benchmark Split Sample Test (SST) methods. The techniques are compared as regards to the number of validation exercises and to the differences in temperature ( T) and precipitation ( P) between the sampled sub-periods, whose length was set to 8-year. The GDSST allows a larger number of discontinuous periods to be sampled, and is computationally more effective than the basic bootstrap to identify the most climatically contrasting conditions. In addition, the GDSST offers a larger continuum of climatic conditions and a better spread of validation periods than the benchmark techniques, which is essential to test the parameter transferability of hydrological models.
As supplementary material, a package file containing MATLAB© scripts to run the three benchmark SSTs and the proposed GDSST, as well as an application example on the five catchments, can be freely downloaded. • An enhanced split-sample test based on an oriented bootstrap to assess transferability of hydrological models. • The proposed split-sample test is computationally more effective than the basic bootstrap to identify the most climatically contrasting conditions. • MATLAB© code of the proposed GDSST and four benchmark SST, with application example.
Introduction This article introduces a transferable package (see supplementary material) of the General Differential Split Sample Test (GDSST) to select sub-periods of discontinuous years gathering similar to different conditions in terms of precipitation and temperature. The GDSST was originally proposed in Dakhlaoui et al. [5]  to assess the transferability of conceptual rainfall-runoff models under past and future climate variability. In this paper, we showed that compared to three existing benchmark techniques, the GDSST allowed a larger number of climatically contrasted discontinuous periods to be sampled, and was computationally more effective than the basic bootstrap to identify the most contrasted periods. When applied to three hydrological models in five catchments in northern Tunisia, the GDSST provided clear transferability limits of the models under changing precipitation ( P ) and temperature ( T ) conditions towards drier and hotter conditions.
Three benchmark SST techniques The SST methods included in the package and selected for comparison with the proposed GDSST are ( Fig. 1  ): (i) a sliding-window SST  [2]  ; (ii) a random bootstrap SST  [1 , 3]  ; and (iii) a 4-sub-period DSST  [4]. These three techniques were selected because they enable simultaneous investigation of the effect of T and P on model transferability under climate variability. The sliding-window SST technique  [2]  consists in using calibration-validation tests on independent sub-periods of equal length, considering all possible pairs of sub-periods. The sampling method used to generate sub-periods is based on sliding windows applied over the reference period. The technique enables the identification of n -l + 1 calibration sub-periods, where l is the number of years composing each sub-periods and n is the number of years of the reference period.
MATLAB code of the split sample tests The routine "SST. m", presented below and contained in the GDSST package (see supplementary material), allows generating sub-periods by GDSST  [5]  and by three benchmark Split Sample Tests: (i) sliding-window SST  [2]  ; (ii) random bootstrap SST  [1 , 3]  ; and (iii) 4-sub-period DSST  [4]. The arguments of the routine are: OptSST: variable used to set the SST to be used. It must be set to 'GSST' for GDSST, 'Mobile' for sliding-window SST, 'Rand_part' for random bootstrap SST, and '4PDSST' for 4-sub-period DSST. AnnualPrecip: array of 2 x n dimension. The first column is for years and the second column for annual precipitation. n the number of years of the reference period. AnnualTemp: array of 2 x n dimension. The first column is for years and the second column for mean annual temperature.
Application example The GDSST package (see supplementary material) includes an application example of the four splitsampling methods using the climatic data from five catchments in northern Tunisia (PTfile. mat), it can be run via the Matlab script (MainSST. m). The data file: "PTfile. mat" This matlab file contains the dataset of an application example from five catchments in northern Tunisia (Rhezala, Melah, Maaden, Joumine and El Abid). See Dakhlaoui et al. [5]  for more details about the catchments. It contains an array PT of 30 × 11 dimension. The first column of the PT array contains the years, for each catchment two columns are reserved, one for the annual precipitation and the second for mean annual temperature. The reference period is from 1st September 1970 to 31st August 20 0 0.
It was based on the hydrological years (from the 1st of September to the 31st of August). The PT array of the "PTfile. mat" of the application example of the five catchments of the northern Tunisia, is presented below. The first column contains the years (1971-20 0 0), the second and third columns are reserved respectively to annual precipitation and mean annual temperature of the catchment 1, the fourth and fifth columns are reserved respectively to annual precipitation and mean annual temperature of the catchment 2, etc. The users can implement the new and benchmark splitsample approaches with their own data, by adapting the dimension of this table this to the number of their study catchments and years. The main program: "MainSST. m" The main program allows reading the needed data for the SST "PTfile.
m" and running the SST by calling the "SST. m" routine. Three variables need to be set by the user: OptSST: Variable that define the SST to be used. It must be set to 'GDSST' for GDSST, 'Mobile' for sliding-window SST, 'Rand_part' for random bootstrap SST, and '4PDSST' for 4-sub-period DSST. nsousperiod: number of sub-periods to be generated by GDSST or random bootstrap SST durationSubP: Duration of the sub-periods expressed in years. The code give the following outputs: Echantillon2: a four column array containing all the independent validations exercises. The first column contain the order of calibration period, the second the order of validation period, the third contain the change in temperature between calibration and validation period T and the last contains the relative change in precipitation P.
Validations exercises from the first catchment are ranged in the first lines, then the second catchment, etc. The order of catchments is the same as PTfile. m Combination2: contain the years composing the generated sub-periods. Each line contains one subperiod. The first nsousperiod lines contain sub-periods from the first catchment, then the second catchment, etc. The order of catchments is the same as PTfile. m figure. m: Scatter representing the calibration-validation exercises generated by the selected SST expressed in term of T and P. The MATLAB© code of the main program "MainSST. m" is presented as follow:.
For sake of fair comparison, the same number of randomly selected sub-periods was set with the GDSST. Note that the number of sub-periods for the two other techniques is already limited by their design: 23 sub-periods for the sliding-window SST and four for the 4-sub-period DSST. Fig. 3  shows the scatter plots generated by the MATLAB© code with the different sampling techniques according to the differences in mean annual temperature and precipitation between the validation and calibration sub-periods ( T and P ). The figure allows the spread of the sample provided by each sampling technique to be evaluated in terms of T and P. Fig. 3  a shows the sample offered by the sliding-window SST technique when applied to the five study catchments. It provided 1 495 possible validation exercises for 115 (23 × 5 basins) calibration exercises.
Conclusion We present in this paper the MATLAB© code and an application example of the GDSST proposed by Dakhlaoui et al. [5]. The code allows to user to generate subperiods and corresponding calibrationvalidation exercises by the new GDSST and three benchmark Split Sample Tests. The code allows the user to visualize the generated subperiods in a scatter plot representing the calibration-validation exercises expressed in term of the differences in mean annual temperature and precipitation between the validation and calibration sub-periods ( T and P ). This figure allows evaluating the spread of the sample provided by the sampling technique, before to use it in a DSST exercise. In the provided application example, the GDSST was compared to three other existing techniques to select sub-periods over a 30-year past period on a set of five basins under semi-arid conditions in northern Tunisia.
Acknowledgments This work is part of the Postdoc research of the first author at the  HSM laboratory ,  Montpellier France , which was supported by an  Erasmus Mundus Alyssa  mobility scholarship. It was also carried out as part of the  ENVI-Med CLIHMag ( Changement cLimatique et Impacts Hydrologiques au Maghreb  ) project funded by the  program INSU-MISTRALS  ( 2014-2015 ). The first author have benefited also of Erasmus + MIC short stay support in 2018. The authors are grateful to  INM  (  Institut National de la Météorologie  ) and to DGRE (  Direction Générale des Ressources en Eau ) in Tunisia  for providing the necessary hydro-climatic data for this study.
Using this approach, data from 211 lesional mania patients was extracted from 114 reports. Among 201 cases with focal lesions, more patients had lesions involving exclusively the right (60. 7%) than exclusively the left (11. 4%) hemisphere. In further analyses of 56 eligible lesion images, while findings should be considered cautiously given the potential for selection bias of published lesion images, right-sided predominance of lesions was confirmed across multiple brain regions, including the temporal lobe, fusiform gyrus and thalamus. These, and several frontal lobe areas, were also identified as preferential lesion sites in comparisons with control lesions. Such pooled-analyses, based on the most comprehensive dataset of lesional mania available to date, confirm a preferential association with right-hemisphere lesions, while suggesting that several brain areas/circuits, relevant to mood regulation, are most frequently affected.
Data extraction, data items and risk of bias Two researchers extracted data separately according to PRISMA guidelines. For each paper, author name, title and journal, publication year, study type, and number of reported and eligible cases was recorded. For each eligible case, we noted age at first episode of lesional mania, gender, hand dominance, time-interval between brain lesion and mania onset, availability of lesion image (MRI, CT, SPECT, drawing on a standard brain atlas, or photographs of autopsy specimens), lesion location and nature as described by original authors, previous history of depression, personal or family history of other neuropsychiatric disorders, and mania symptoms mentioned in the case description. Additional clinical information was extracted, namely medication at the onset of mania, medication used to treat the manic episode, duration of manic symptoms, length of follow-up and subsequent affective episodes.
bic. mni. mcgill. ca/ServicesAtlases/ICBM152NLin2009), using MITK software v2014. 10. 00 (http://mitk. org/wiki/MITK). We only traced tissue damage that was clearly visible in the available images and did not extrapolate to juxtaposed brain slices. Two of the authors, including a neuroradiologist, performed this task jointly, and a third author, who is a neurosurgeon, independently reviewed lesion traces. When tracing, each case report matched a single individual, except for 5 case series  [9] [10] [11] [12]. In these case series, representing a total of 54 patients (n = 7; n = 7; n = 11; n = 12; n = 17) individual lesions were fused into a single conjoint lesion tracing, from which the original individual lesions were impossible to disentangle. For these series, we considered each group tracing as a single case, hence comprising a total of 5, rather than 54, cases.
Because data did not follow a normal distribution, a nonparametric test was necessary. Since visual inspection of the distribution plots of the left-right differences in lesioned voxels showed that the distribution of these differences around 0 was not always symmetrical, we opted for the more conservative Sign Test, where the symmetry assumption is not required  13. Furthermore, we compared lesion distribution among our tumor sub-sample with data for 169,934 adult-onset brain tumours reported in a database published by Ostrom et al. , not selected for any particular clinical outcome  14. We considered this sample as the best approximation to the normative anatomical distribution of tumors in the general population. To perform this comparison, we classified tumors in our database according to the criteria used by Ostrom et al.
Specifically, for each scan in the right-hemisphere stroke group, we found the axial slice with the maximum extent of damage, and considered this slice, as well as up to four neighboring lesion-englobing slices, at 8 mm intervals above and/or below the main slice  15. The proportion of scans from the right-hemisphere stroke group contributing with 1, 2, 3, 4 or 5 slices was determined according to the equivalent proportions in the lesional mania case-reports, so as to render the groups comparable, and with the number of slices extracted from each subject reflecting lesion extent (please see Matlab Script supplementary file for full details). Because the proportion of lesioned voxels per area per individual did not follow a normal distribution, we compared the two groups using the Wilcoxon ranksum test, a non-parametric test for two independent samples  13.
To further confirm the relevance of specific brain regions, we then compared lesion distribution among specific sub-samples of our database with that of patients with lesions of similar etiology, but not selected for any particular symptom. Compared to the distributions of brain tumors described by Ostrom et al. 14  , tumors associated with lesional mania were more frequently located in the temporal lobes, brain stem, and cerebrum, which includes the thalamus and basal ganglia (Fig. 2d ). Among lesional mania patients with traced right-sided vascular lesions, a more detailed comparison with MRI scans from 439 right-hemisphere stroke patients described by Sperber and Karnath  15  showed that lesional mania patients had a higher median proportion of lesioned voxels in the orbital part of the superior frontal gyrus, medial orbitofrontal cortex, rectus gyrus, hippocampus and parahippocampal gyrus, amygdala, superior and middle temporal poles, inferior temporal gyrus, fusiform gyrus and thalamus (Table  2  and Supplementary Tables  S7  and S8 ).
Discussion The main aim of this work was to test the hypothesis that lesional mania is more often associated with righthemisphere than left-sided lesions. Towards this aim, we performed a systematic literature review of case-reports and case-series, followed by pooled analysis of individual patient and lesion data from eligible cases. We retrieved 211 cases of lesional mania, occurring at a much older age than the typical age of onset reported for primary mania, probably reflecting the cumulative age-related increase in the likelihood of suffering a brain lesion associated to cardio-vascular diseases and tumors. For most of these cases, right-hemisphere focal brain lesions (84. 1%) were reported, with only 35. 3% of patients having left-sided lesions. This difference was statistically significant and was conserved across different disease subtypes as well as in analyses restricted to patients without personal or family history of neuropsychiatric disorder.
Yet, in several neuropsychiatric lesion studies lesions were left-skewed or equally distributed across both hemispheres  151, 153, 154  , demonstrating that a systematic exclusion of aphasic patients either did not occur or did not lead to such a lateralization bias. Finally, it is possible that a small number of patients included in the tumor or vascular lesion control populations may have developed manic symptoms, as none of the two databases collected data regarding behavioral manifestations, including systematic neuropsychiatric assessment. However, the rarity of post-lesional mania and the large size of the two control populations mean that any false-negative controls will have a diluted effect. Furthermore, rather than biasing our results in favor of a spurious association, the occurrence of a small minority of manic episodes among the two control populations will, if anything, result in an under-estimation of the true effect size of the associations reported here.
Acknowledgements JBB-C, GC, RMC and AJO-M conceived and designed the work; GC, RR, AV, VCS, CS,  H-OK  and SS acquired the data; JBB-C, GC, CS, H-OK, SS and AJO-M, analyzed and interpreted data; JBB-C, GC and AJO-M drafted the work; RMC, RR, AV, VCS, CS,  H-OK  and SS revised the manuscript critically for important intellectual content; all authors approved the final version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
e. , while receiving eMBMS broadcast video additional programme information and ondemand content can be requested and served over unicast. New functionalities have been integrated into the testbed as a result of the 5G-Xcast project. The existing radio network was upgraded several times from its initially 3GPP Release-11 compatibility up to 3GPP Release-14 compatibility. The existing core deployment, which was dedicated lab test software, was replaced by a commercial product, the Nokia Micro Core Network which is a unique integrated -SW only -solution that provides all the Core virtualized network functions (VNFs) in addition with management tool that can be deployed on only one server. The modular architecture allows integration into experimental set-ups. New functionalities in the context of eMBMS have been provided by Expway with the novelty of integrating MBMS Operation on Demand (MooD) capabilities.