dataesr/scientific-paragraphs-categorization huggingface dataset:
1. This is the purpose of the present section. In a previous study  [2]  of the same dataset we found that a (5, 5, 5, 5, 1) network with 3 hidden layers of 5 nodes each (for a total of 96 degrees of freedom) returns a distribution for the test statistic t which is well compatible with the target χ 2 96 distribution, for an appropriate choice of the weight clipping parameter. 12 The weight clipping selection is performed with the algorithm described in Section 3. 1, which iteratively reduces the window of potentially viable values of the weight clipping parameter. The last step of the selection process, where the window is already as small as the [2. 1, 2. 2] interval, is illustrated in Figure  9. A comparison with Figure  1  and Table  1  immediately reveals a number of differences between the univariate and the multivariate case.
For models with a good level of compatibility with the target χ 2 distribution, we sharply define a solution as "stable" by requiring the KS p-value not to vary more than 10% for at least 100 000 epochs. The memory is not a limiting factor. It does not exceed around 1 GB even for the most complex models we have considered. The training time is instead considerable, because of the large number of epochs that is typically required. For the present study we consider a neural network model "manageable" when a stable training (on a single toy dataset) takes less than 6 hours CPU time. This threshold takes into account   the need of repeating training on many toys (we use 100 toys to establish χ 2 -compatibility), of performing a scan on the value of the weight clipping parameter that ensures compatibility, and of exploring different architectures.
Conclusions and outlook We have proposed and validated a strategy for model-independent new physics searches that duly takes into account the imperfect knowledge of the Reference model predictions. The methodology is robustly based on the canonical Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing, which emerges as a completely natural and conceptually straightforward extension of the basic framework we proposed and developed in Ref. s  [1, 2]. Our findings open the door to real analysis applications, where a "New-Physics-Learning" Machine (NPLM) inspects the LHC data in search from departures from the Standard Model, with no bias on the nature and the origin of the putative discrepancy. The detailed study of the method in real LHC analyses will be essential in order to identify possible implementation issues, which might require further developments of the NLPM strategy itself or methodological advances in related domains.
e. without impacting the training execution time. 2. The generation of Reference-distributed toys. Our strategies for model selection and validation heavily rely on the availability of toy datasets, namely sets of unweighted data that mimic the outcome of the real experiment under the Standard Model hypothesis. Generating a large set of toys requires, in the first place, a large enough sample of Standard Model data. The potential issue is, as for item 1, that such large sample might not be available or it might be computationally too demanding to be generated. Furthermore, if the Standard Model data are weighted, producing unweighted events with the hit-or-miss technique can be highly inefficient in the presence of large weights, and conceptually impossible if some of the weights are negative as it is the case for simulations at Next-to-Leading order.
The time obviously increases with N R , potentially posing an obstruction to the data statistics we can handle, at fixed N R /N(R 0 ), or to N R itself, which on the other hand we might need to take large as per item 1. It should be noted that items 1 and 3, as well as item 4, are not absolute obstructions to the applicability of the NPLM strategy. They rather limit the integrated luminosity of the data (i. e. , N(R 0 )) that our algorithm can handle. Indeed item 1 can be addressed by lowering N(R 0 ), and item 3 as well because the impact of systematic uncertainties on the analysis is relatively smaller if the data statistics is lower. On the other hand an upper limit on N(R 0 ) does not prevent us from employing the full data luminosity for the analysis. One could indeed split the data in several independent datasets, run NPLM on each and combine statistically the corresponding p-values.
However, this necessarily entails a reduced sensitivity to new physics effects. We also see that most of the items listed above are not specific of the NPLM methodology. In particular the availability of sufficient samples of Standard Model data is a generic need of any LHC analysis, which will become more pressing with the high data statistics of the HL-LHC. Similarly, the generation of toy datasets is in principle a need for any un-binned analysis that can not rely on asymptotic formulas. Finally, learning the effect of nuisance parameters is methodologically identical to (and directly relevant for) the regression on the distribution dependence on parameters of interest, which is being studied extensively for other applications such as inference on new physics parameters. Potential limitations related with the training time are instead obviously specific of the NPLM methods.
It is not excluded that the training time could be substantially reduced by a better choice of the training algorithm or of its implementation, which is an aspect we did not investigate in great detail so far. A more radical solution is to trade neural networks with non-parametric Kernel models, which are radically faster to train  [29]. In summary, NPLM emerges as a promising option for the development of a new kind of model-independent new physics searches. The extensive deployment of this type of analyses might play a vital role in experimental programs where, like at the LHC, increasingly rich experimental data are accompanied by an increasingly blurred theoretical guidance in their interpretation. Furthermore, designing NPLM analyses and addressing the corresponding challenges might trigger developments in event generation and in likelihood-free inference techniques, with broader implications on LHC physics.
Therefore they should be kept distinct from the strict model-independent strategies as we defined them in Section 1. It should be noted that even the strict (in our definition) model-independent strategies  [1, 2, [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54]  are not "fully" model-independent because they rely on the prior choice of the feature variables x and possibly of the region X (x ∈ X) of the phase-space where they are considered to be of interest for the analysis. A fully model-independent test should ideally employ the entire ATLAS and CMS raw datasets (i. e. , the collection of detector hits) before the reconstruction of high-level objects, which is however unfeasible particularly because the trigger selection would be still an implicit bias.
On the other hand it is not hard to identify physically motivated features and search regions where new physics could emerge. For instance new heavy particles and shortdistance interactions would generically show up in final states with high-level reconstructed SM particles or jets with high transverse momentum. New light particles might instead be found in jets and alter their inner structure, and/or produce anomalous tracks to be found in the analysis of detector data of even lower level, etc. By restricting to the corresponding datasets one could perform new physics searches that are still way more model-independent than the search for one postulated new physics model. Clearly if many such model-independent searches were actually performed one should in principle include an estimate of the look-elsewhere effect in the assessment of the statistical significance of a putative excess.
However this effect would be present, and to a larger extent, also in an agnostic interpretation of the regular model-dependent LHC searches. In order to proceed to a finer characterization of the strict model-independent methods it is important to clarify the role and the origin of the Reference dataset R, which is an essential element of these strategies. The availability of sufficiently accurate SM background predictions is obviously a potential concern for any LHC analysis, and in particular it is a concern for the deployment of model-independent methods. In order to emphasize this issue, it was proposed in Ref. [55]  to accompany the regular notion of (signal) model-independence with the one of "background model independence" and to treat the two notions on equal footing. As mentioned above, to perform a new physics search up to the quantification of an excess significance it is necessary to have a trustable Reference Model.
The need of a Reference prediction is conceptually unavoidable in any strategy to search for "new" phenomena, as it provides the necessary notion of "old" phenomena. In absence of a Reference Model one can only perform the first step of a new physics search, as discussed for Anomaly Detection methods. We can identify "rare" events, but we can not say whether they are present in the dataset because of new or old physics. The need of a trustable Reference prediction is clearly not a new conclusion, it is a common requirement of any model-dependent or model-independent search ever performed. Our interpretation of the emphasis on "background model independence" given in  [55]  is the quest for methods with a built-in data-driven estimate of the background. We do not consider this aspect relevant, for two reasons.
From this viewpoint one would rank BumpHunter  [56]  and similar strategies  [55, [57] [58] [59] [60] [61] [62] [63] , which target resonant signals in a pre-specified variable, lower than methods with a broader target  [1, 2, [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [64] [65] [66] [67]. On the other hand, one should not employ these generic considerations to tell which one is the "right" strategy to pursue. That depends on aspects that are specific of the final state (features set and phase-space region) one is willing to explore, such as the availability (or not) of a trustable Reference sample and the actual perspectives of progress in the characterization of the data relative to more standard analysis techniques. This is why, ultimately, all these methods should be tried with data and their complementarities exploited to extract the most out of the LHC datasets.
Recruitment of individuals and data sharing Thirty individuals were recruited from four genetics centers belonging to the French network for rare diseases (CHU Dijon, CHU Nantes, CHU Rennes, APHP Paris) and carefully evaluated by our interdisciplinary clinical-biological team. Affected individuals with malformation syndromes and/or severe neurodevelopmental disorders, with negative trio exome sequencing and array comparative genomic hybridization results were enrolled. Informed consent was obtained from all subjects participating in the study.
Short-read genome sequencing Variants were identified using the FHU Translad computational platform, hosted by the University of Burgundy Computing Cluster (CCuB). Raw data quality was evaluated by FastQC software (v0. 11. 4). Reads were aligned to the GRCh37/hg19 human genome reference sequence using the Burrows-Wheeler Aligner (v0. 7. 15) and subsequently to GRCh38 for reanalysis. Aligned read data underwent the following steps: 1) duplicate paired-end reads were removed by Picard software (v2. 4. 1), and 2) base quality score recalibration was done by the Genome Analysis Toolkit (GATK v3. 8) Base recalibrator. Using GATK Haplotype Caller, Single Nucleotide Variants with a quality score >30 and an alignment quality score >20 were annotated with SNPEff (v4. 3). Rare variants were identified by focusing on nonsynonymous changes at a frequency of less than 1% in the gnomAD database.
RNA-sequencing Aberrant splice events and expression outliers were identified using the FHU Translad computational platform, hosted by the University of Burgundy Computing Cluster (CCuB). Raw data quality was evaluated by FastQC software (v0. 11. 4). Reads were aligned to the GRCh37/hg19 human genome reference sequence using the STAR2 Aligner (v2. 5. 2b) with the 2-pass mapping method using the human RefSeq genome annotation (Build GCF_000001405. 25). Read counts were also collected using STAR2. Uniquely mapped reads are counted when overlapping only one gene. Outlier expressed genes were detected using two parallel methods: DESeq2 (v1. 26. 0) and Outrider (v1. 4. 2). After a normalization step, the expression analysis was performed using the following analysis design: one versus the whole analysis batch, allowing computation of the expression variance for the whole cohort.
DNA methylation data analysis Briefly, methylated and unmethylated signal intensity generated from the EPIC array was imported into R 3. 5. 1 for normalization, background correction, and filtering. Beta values ranging from 0 (no methylation) to 1 (complete methylation) were calculated as a measure of methylation level and processed through the established support vector machine (SVM) classification algorithm for EpiSign disorders. The EpiSign Knowledge Database, composed of over 10,000 methylation profiles from reference disorderspecific and unaffected control cohorts, was used by the classifier to generate disorder-specific methylation variant pathogenicity (MVP) scores. MVP scores represent confidence of prediction for each disorder, ranging from 0 (discordant) to 1 (highly concordant).
Characteristics of the cohort The cohort was mainly composed of pediatric individuals (22/30; 73%), and the sex distribution was mostly female (19/ 30; 63%). Only two individuals (6%) came from consanguineous unions. The median age of our cohort was 13. 7 years (4 years and 6 months to 35 years and 1 month), including eight adult patients aged 18-35 years and 1 month. Phenotypic data were collected as Human Phenotype Ontology (HPO) terms. For each individual, at least two HPO terms and at most 11 HPO terms were collected, giving rise to a global dataset of 417 observations (Figure  1 ). The most represented terms, accounting for 66% of the available HPO terms, included abnormalities of the nervous system (41. 3%), head and neck (15. 3%), and skeletal system (9. 3%). Clinical data of the individuals are available in Supplementary Table  S2  and Supplementary Data.
Diagnostic rate from RNA sequencing data RNA-seq from whole blood was performed in 23 individuals (76. 3% of the cohort): 11 undiagnosed individuals, eight with candidate genes, and five with positive GS. For the remaining seven individuals (23. 3%), RNA-seq was not performed either because GS alone had already identified the causative variant (KMT2D, PURA, CYFIP2) or because the RNA was not available or did not pass the quality control standards (RIN ≥ 7). RNA-seq analysis confirmed the causal role of two variants in CASK and GATAD2B (2/23; 8. 7%). In particular, an aberrant splicing event was found in individual 1, who harbored a de novo deletion-inversion of 9. 4 kb in Xp11. 4 involving CASK, while the partial deletion of GATAD2B was identified in RNA-seq data as an expression outlier due to nonsense-mediated mRNA decay, accompanied by gene expression down-regulation.
She had facial dysmorphisms, including a prominent forehead, deep philtrum, and wide mouth with full lips. Previous genetic investigations, consisting of array CGH,  screening for Angelman syndrome (methylation and sequencing of UBE3A) and Fragile X Syndrome (FMR1), sequencing of FOXG1, CDKL5, STK9, RAI1, MECP2, MEF2C, and trio ES, had returned normal results. GS identified a pathogenic structural variant characterized by a de novo inversion of 2. 2 Mb in 5q14. 3 encompassing part of the regulatory region responsible for the neuronal expression of the MEF2C gene. This rearrangement was confirmed on qPCR. MEF2C expression with RNA sequencing data showed low expression of the MEF2C transcript in this individual, although there was biallelic expression in blood, confirming that the regulatory regions affected by the inversion were specific for the neuronal lineage (Figure  4 ).
Funding The study was performed within the framework of the  GAD (Génétique des Anomalies du Développement ) collection and approved by the appropriate institutional review board of  Dijon University Hospital  ( DC2011-1332 ). This work was supported by grants from  Dijon University Hospital , the  ISITE-BFC (PIA ANR ), the  European Union  through the  FEDER programs (PERSONALISE) , and the  Burgundy-Franche-Compté regional council (INTEGRA). The sequencing platform at the CNRGH was supported by the  France Génomique National  infrastructure, funded as part of the  "Investissements d'Avenir" program , managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The whole genome sequencing performed at the CNRGH was funded by the  Laboratory of Excellence GENMED (Medical Genomics)  Grant No.
Method Utilizing measurements from the Solar Wind Electrons, Alphas, and Protons (SWEAP)/SPAN-Electron (SPAN-E)  (Whittlesey et al. 2020 ) suite on the PSP (SPAN Ae and SPAN B), we analyze electron distributions inside switchbacks at heliocentric distances of 0. 125-0. 25 au. The SWEAP package consists of three electrostatic analyzers (SPANs). Two of the three SPAN sensors are dedicated to electron detection; SPAN Ae and SPAN B. SPAN Ae face forward (ram side), and SPAN B looks behind the spacecraft (anti-ram side). Together, the two electron sensors capture about 90% of the sky. The heat shield and other spacecraft obstructions block most of the remaining field of view (FOV). The sensors can also measure 3D electron velocity distribution functions (eVDFs) in the inner heliosphere. We utilized Level 2 (electron distributions) data from SPAN Ae and B to perform our statistical analysis.
Several bottom-up inventories report on the total CH 4 emissions for the USCB: according to the GESAPU database, the USCB emitted a total of 405 kt CH 4 in 2010  (Bun et al. , 2019). The E-PRTR (http: //prtr. ec. europa. eu/, European Pollutant Release and Transfer Register, 2018) collects emission reports of every individual mine, yielding an aggregated total of 507 kt CH 4 a -1 for the USCB. Dreger (2021)  reports hard coal mining emissions of 530 kt CH 4 for the USCB in 2018, and the Copernicus Atmosphere Monitoring Service regional emission inventory (CAMS-REG-GHG/AP) lists 632 kt CH 4 a -1  (Granier et al. , 2019; Fiehn et al. , 2020). EDGAR v4. 3. 2 (Emission Database for Global Atmospheric Research) gives emissions of 675 kt CH 4 a -1 (Janssens-  Maenhout et al. , 2017)  for fuel exploitation emissions (EDGAR abbreviation PRO) for the USCB in the year 2012.
The total USCB emission estimates of  Fiehn et al. (2020)  and  Kostinek et al. (2021) , based on airborne in situ measurements, are in broad agreement with the E-PRTR data for single flights. Using airborne imager data,  Krautwurst et al. (2021)  found some discrepancies between their estimates and the E-PRTR inventory for small groups of ventilation facilities. The isotopic CH 4 composition was measured by  Menoud et al. (2021)  with ground-based in situ instruments. Swolkień (2020)  discusses the short-term, shaft-wise CH 4 release in the USCB. Given the magnitude of emissions and the range of estimates, CH 4 in the USCB warrants further investigation. Here, we report on CH 4 emission estimates derived from measurements of four stationary, sun-viewing FTSs of the type EM27/SUN arranged in a network-like pattern enclosing the USCB during the CoMet campaign activities.
The respective downwind stations are located in the south (Pustelnik) and west (Raciborz) of the USCB. The northern station (Za Miastem) was identified as neither an upwind nor downwind station for any of the cases. The functioning of the EM27/SUN FTS and the data reduction techniques are described in detail by  Gisi et al. (2012) ,  Hase et al. (2015 Hase et al. ( , 2016)) , and with a particular focus on our setup by  Luther et al. (2019). Generally, the EM27/SUN instruments observe spectra of direct sunlight in the shortwave infrared spectral range from which the total column concentrations of CH 4 , carbon dioxide (CO 2 ), water vapor (H 2 O), molecular oxygen (O 2 ), carbon monoxide (CO), and other substances  (Butz et al. , 2017)   We run the software package PROFFIT  (Hase et al. , 2004 ) to retrieve the column concentrations [O 2 ] and  [CH 4  ] from the 7765 to 8005 and 5897 to 6145 cm -1 spectral windows, respectively, using absorption line parameters by  Toon (2017)  and  Rothman et al.
Further, we extract the a priori CH 4 profiles from a dedicated ECHAM/MESSy Atmospheric Chemistry simulation described by  Jöckel et al. (2016)  and  Nickl et al. (2020). For network deployments such as undertaken here, it is common practice to cross-calibrate the network nodes by side-by-side measurements  (Frey et al. , 2015 (Frey et al. , , 2019;; Chen et al. , 2016; Jones et al. , 2021; Dietrich et al. , 2021)  in order to exclude spurious gradients when conducting upwinddownwind analyses. We calibrated the four instruments through side-by-side measurements on 23 and 26 May 2018 at the southern location Pustelnik. Figure  2  shows the raw and calibrated XCH 4 records, and Table  1  lists the respective calibration factors. These instrument-specific empirical calibration factors have been applied to the XCH 4 campaign data discussed in the following.
Kostinek et al. (2021)  have identified a selection of parameter settings to obtain the best-guess parameter combinations for the same region, WRF domains, and time periods discussed here. There-fore, we adopt the same setup here and report on the overall WRF performance by comparing the simulations with the wind lidar observations. Figure  4  displays a comparison between modeled and observed wind speed (upper panel) and wind direction (lower panel) for altitude levels in the planetary boundary layer (PBL). PBL height is estimated by means of eddy dissipation rate gradients calculated from the wind lidar observations directly. The observation levels of the wind lidar do not match the WRF grid levels exactly. Thus, the comparison includes modeled and observed data if the WRF wind level is within the range of ±25 m around the level of observation.
Lagrangian methane dispersion via FLEXPART WRF wind fields drive the trajectory calculations in FLEX-PART. The model simulates trajectories for 50 000 particles for every USCB coal mining shaft reported by the E-PRTR and the CoMet database  (Gałkowski et al. , 2021)  with a total mass of 10 5 kg ĊH 4. The simulations do not consider background CH 4. The model releases particles in a 10 m ×10 m ×10 m box on the ground. The modeling period starts at 00:10 UTC the day of interest and continues until 17:50 UTC, which results in 17. 7 h simulation time. We chose the grid output option in FLEXPART with 100 × 100 boxes and a spatial resolution of roughly 1. 3 km stacked in 24 layers up to 3 km altitude. The simulated XCH 4 measurements are the sums of all boxes above each pixel enclosing an EM27/SUN location.
The 6 min FLEXPART output is interpolated to the observational time interval, which is generally one measurement per minute. After unit conversion, the sim- Comparison of WRF wind estimates and wind lidar observations for the observational period between 07:00 and 17:00 UTC for the three days of interest 28 May (blue), 6 June (orange), and 7 June (green). Shaded areas include roughly 80 % of the data points for ±2 ms -1 for wind speed in (a) and include roughly 50 % of wind direction measurements in a ±15 • range in (b). Wind direction simulations differ most from the observations for 7 June (green), a day with generally lower wind speeds than the other two discussed cases. This indicates wind information uncertainties when it comes to low wind speeds. For the wind direction comparison, note that values close to 0 and 360 • represent virtually the same wind direction but may introduce an error to the root mean square error (RMSE) calculations.