dataesr/scientific-paragraphs-categorization huggingface dataset:
G. Assessment of Moderators Because biometric systems should be robust against human factors, we performed the calculation of EER, Zero-FMR, FMR1000, and AUC with confidence intervals separately for subgroups of female and male participants. Moreover, we divided the sample into 3 age groups:   [27]  included as a dataset The oldest group was not older than the oldest sample used in previous work, that is above 76 years, because this is also the maximum age in our subsample. In addition, since the data set used in this study was collected in a larger study involving clinical populations, we assessed whether the pathological changes in the EEG, which are prominent in patients with temporal lobe epilepsy, might affect the result. Therefore, we analysed the following subpopulations. Healthy controls (HC), patients with temporal lobe epilepsy (TLE), and patients with mild cognitive impairment or subjective cognitive complaints (MCI/SCC).
This calculation was performed on mated scores, since we did not want to base it on some specific metric or threshold such as the EER or AUC. The non-parametric MANOVA requires metric data, but allows for non-normality and variance heterogeneity  [75]. This method is implemented in the R-package MANOVA. RM  [76]. We used it with the parametric bootstrap (1000 iterations) which showed the most favorable performance in unbalanced designs and was therefore generally recommended  [75]. The Wald-Type statistic allowed us to address our multivariate hypotheses on between-and withinsubjects factors and their interactions. All mated scores of all single epochs were submitted to this statistic, but the model was informed about the participant to which the several epochs belonged. Thus the epochs were an additional factor that was not modeled.
H. Experimental Data 1) Sample: We recruited a total sample of 70 participants at the Department of Neurology, Paracelsus Medical University Salzburg, Austria, from May 2012 to December 2015 within a larger study focused on memory disorders. After exclusion of participants who did not undergo both EEG-examinations (two TLEr, one TLEl, three HC) or whose EEG was of poor quality (one SCC, one TLEl, two HC) 60 participants remained for this analysis. Poor quality of the EEG was defined as less than 4 sec in at least one of the two recordings after excluding segments of 500 ms according to the automatic data inspection (see Section II-H3). Table  II  gives an overview of the demographic characteristics of patients included in the subgroups. We obtained 8 participants that were younger than 36 years, 27 participants that were up to 62 years old, and 27 participants that were aged 63 years or older.
2) Data Registration: EEG was recorded in two sessions separated by two weeks and took place in the same setting, that is, in a quiet room. Participants were instructed to close their eyes and stay awake. Eyes closed is a condition that allows to reduce artefacts from blinking, and thus variability of the recorded EEG. Recordings lasted for 2-3 min. We used a BrainCap with a 10-20 system and a BrainAmp 16-bit ADC amplifier (Brain Products GmbH, Germany). The sampling rate was 500 Hz. Of the 32 recorded channels, one was used to monitor the lower vertical electrooculogram, and one was used to measure electrocardiographic activity. Two were positioned at the earlobes for re-referencing, which was conducted in order to remove the bias of the original reference, which was placed at FCz. Data analysis was conducted for data collected from the remaining 27 electrodes F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, TP9, and TP10.
1592 s, 48 dB/oct), and an additional notch filter (50 Hz) was applied. An automatic artefact detection was carried out in order to exclude highly contaminated datasets. Please note that the automation of this procedure ensures objectivity, which means at the same time that it is reproducible. Nevertheless, the nature and number of artefacts surely depends on the specific recording and participant. Maximal allowed voltage step per sampling point was 50 μV (values which exceeded this threshold were marked within a range of ±100 ms); maximal allowed absolute difference on an interval of 200 ms was 200 μV and lowest allowed absolute difference during an interval of 100 ms was 0. 5 μV (values which exceeded this were marked with a surrounding of ±500 ms). The result of this artefact detection was reviewed visually in order to determine whether the automated detection yielded reasonable results and whether poor data quality was due to noise on the reference electrodes, which led to exclusion of the dataset.
Thus, the effect could be confounded by the relative age of this group. The difference between the age groups in Figures  5  and 6  does not appear to be significant, since the confidence intervals overlap completely. However, the confidence interval for the youngest subgroup is larger for both the AUC and the EER. The statistical analysis of the mated scores is given in Table  IV. The Wald-Type statistic is larger with a larger effect or interaction of effects. p-values below. 025 are considered to yield a significant effect or interaction after Bonferroni correction (represented in bold font). The interactions between sex and session as well as pathology and session suggest that the handling of multiple sessions was significantly affected by the sex and neurological conditions of the subjects in the dataset.
D. Outlook to Future Work The results for AF may be seen as an example. It is most likely that the effect of age, sex, and pathology in interaction with session is even stronger for those features with greater variation in the feature subset selection across the three cross validation scenarios, which may be subject to further investigation. We used a data set with EEG recordings from two different days. This is the minimum for cross-session crossvalidation, but more sessions can result in better performance of the biometric system because enrolment can be based on multiple sessions. However, by excluding one session for testing, the cross-validation is still valid  [11]. It is thus supposed that the use of multiple sessions for enrolment could aid the selection of robust features for the biometric system  [22].
Additional factors can be hypothesized to play a role in multi-session evaluation. For example, we controlled time of the day, but it would be important to assess the robustness of an EEG system to these factors. Also intake of caffeine and tobacco could be an issue in biometric systems, as well as alcohol and any neurologically active medication. Moreover, future applications need to consider the possibility of using user authentication via EEG in mobile communication systems, such as smartphones  [12] ,  [83] ,  [84]. It is not likely that multiple channels are available for such an application, so that biometric feature vectors based on measures of interaction need to be optimized for low channel numbers. Finally, a publicly available dataset that allows for the analysis of age, sex, and pathology in relation to EEG biometric features would be highly warranted.
V. CONCLUSION In this work, we pointed the community towards a serious problem that is present in the majority of published articles on EEG biometric features. The stability of EEG biometric features depends on factors such as age, sex, and pathology. This result emphasizes the need for adequate datasets that involve at least two sessions when a biometric system should be tested, and accurate reporting of the factors that moderate stability. Furthermore, we demonstrate that the previous results obtained with autoregressive coefficients can be improved by deriving frequency-dependent measures.
ACKNOWLEDGMENT We want to thank  Eugen Trinka ,  Julia Höfler ,  Judith Dobesberger ,  Giorgi Kuchukhidze ,  Markus Leitinger ,  Margaritha Kirschner ,  Elisabeth Schmid ,  Daniela Sinadinoska ,  Susanne Grinzinger ,  Martin Scherr , and  Wolfgang Staffen  for recruitment of patients, and all participants for their time.
Trigger selection For the 2016 data set, events are selected by the standard diphoton trigger, requiring transverse momenta (p T ) larger than 42 and 25 GeV for the leading and subleading photons, respectively. Loose identification criteria are imposed on the photon shower width in the ECAL and on the ratio of the energies recorded in the ECAL and HCAL to reduce the rate of background from jets misidentified as photons. For the 2017 data set, a dedicated HLT algorithm was developed to select events with a single photon satisfying requirements consistent with production at a displaced vertex. Such photons tend to strike the front face of the barrel ECAL at a non-normal incidence angle, resulting in a more elliptical electromagnetic shower in the η-φ plane  [26]. In addition to standard requirements on the shower width and electromagnetic to hadronic energy ratio, requirements on the major and minor axes of the shower are also imposed.
This allows the identification of the elliptical shower shape, described in greater detail in Sec. 4. 2. Loose requirements on the amount of energy around the direction of the photon in the CMS subdetectors (isolation) are also imposed on trigger photon candidates, and the photon p T is required to exceed 60 GeV. Electrons misidentified as photons are suppressed by requiring the candidate photon to be geometrically isolated from charged-particle tracks. Relaxing the trigger requirement from two photons to only one photon increases the background rate, and in order to reduce the trigger rate to a level acceptable for the operation of the HLT the scalar p T sum of all jets (H T ) is required to exceed 350 GeV. For signals with neutralino proper decay length larger than 10 m, the signal acceptance is improved by about a factor of two compared to the 2016 data set.
Photons are required to satisfy |η| < 2. 5 and to not fall in the transition region between the barrel and endcap of the ECAL (1. 444 < |η| < 1. 566), where the photon reconstruction is not optimal. For the 2016 data set, photon candidates that share the same energy cluster as an identified electron associated with the primary vertex are vetoed following the procedure detailed in Ref. [45]. To remain consistent with the HLT selection, photons matched geometrically to charged-particle tracks are vetoed for the 2017 data set as well. Because of algorithms designed to reject noise and out-of-time pileup, the default photon reconstruction vetoes photons delayed by more than 3 ns. To evade this veto, a second set of out-of-time (OOT) photons is therefore defined, in which the clustering starts from ECAL deposits whose signals are delayed by more than 3 ns.
The timestamp for each photon is recorded, and the mean and RMS parameters of the resulting distribution are extracted as a function of the photon energy. The time response mean is adjusted to zero for both data and simulation, and the timestamps in the simulated events are smeared by an additional Gaussian-distributed random variable such that the resolution in simulation matches that measured in data. The calibrated photon arrival time is denoted as t γ. These calibrations are applied to simulated signal samples in order to accurately predict the signal response, and their uncertainties are propagated to the predicted shape of the t γ distribution for the signal as a systematic uncertainty. The time resolution of a single photon candidate is roughly 400 ps. The resolution is constant up to a photon timestamp of 25 ns, the upper boundary of t γ used during the signal extraction.
In order to remain compatible with the respective HLT selection, slightly different event selection criteria are imposed on the 2016 and 2017 data sets. For the 2016 data set, triggered by a diphoton HLT, a second photon with p T larger than 40 GeV is required to match the analogous HLT requirement. For the 2017 data set, the first category, referred to as the 2017γ category, requires events with no subleading photon or events where the subleading photon does not pass the photon identification criteria. The second category requires events to have a subleading photon satisfying the photon identification criteria, and is referred to as the 2017γγ category. The second-photon requirement helps to reduce background by one to two orders of magnitude, while the signal yield remains high for low to intermediate lifetimes.
Finally, for the 2017 data set, the H T is required to be larger than 400 GeV in order to match the requirements of the HLT and to reach the plateau of the trigger efficiency. For the 2016 and 2017γγ analyses, for a given neutralino proper decay length, the signal yield increases as a function of the SUSY breaking scale, Λ, by roughly a factor of two over the range considered for this analysis (Λ from 100 to 400 TeV). The product of signal efficiency and acceptance for the lowest Λ is roughly 10. 0 ± 0. 1% and 0. 15 ± 0. 01% for neutralino proper decay lengths of 0. 1 and 100 m, respectively. For the 2017γ analysis, the product of signal efficiency and acceptance varies as a function of Λ from 5. 5 ± 0. 1 to 10. 4 ± 0. 2% for a neutralino proper decay length of 0. 1 m, and from 0. 22 ± 0. 03 to 0.
Summary A search for long-lived particles that decay to a photon and a weakly interacting particle has been presented. The search is based on proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment in 2016-2017. The photon from this particle's decay would enter the electromagnetic calorimeter at non-normal impact angles and with delayed times, and this striking combination of features is exploited to suppress backgrounds. The search is performed using a combination of the 2016 and 2017 data sets, corresponding to a total integrated luminosity of 77. 4 fb -1. Both single-photon and diphoton event samples are used for the search, with each sample providing a complementary sensitivity at larger and smaller long-lived particle proper decay lengths, respectively.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Nine datasets derived from cells and tissue, isolated from different regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified. Comparison of individual microglia clusters showed 249 genes highly conserved between them. This core gene signature included all known markers and improves upon published microglial signatures. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression.
Comparison of published microglial signatures Ten publications that defined microglial signatures, four in human and six in mouse, were identified (Table  1 ). To compare across studies, genes from each signature were converted to a common identifier i. e. HGNC  (Povey et al. , 2001)  or MGI  (Shaw, 2009)  for human and mouse, respectively, using the online tool g:Profiler  (Reimand et al. , 2016). Subsequently, the tool was also used for interspecies comparison based on the MGI homology database, identifying human orthologues of mouse. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 26, 2018.
Transcriptomics data acquisition and pre-processing Tissue and cell transcriptomic datasets derived from the CNS were acquired for the derivation of the human microglial signature. These included data from the Genotype-Tissue Expression (GTEx) project  (Lonsdale et al. , 2013) , Allen Brain Atlas (ABA)  (Hawrylycz et al. , 2012) (http://www. brain-map. org/) and from a study by  Zhang et al. (Y. Zhang et al. , 2016). The GTEx data comprised of two datasets, one generated on Affymetrix microarrays (n = 207) and a second by RNA-Seq (version 6, n = 1,259). In both cases, tissue samples were isolated from thirteen regions of the CNS at post-mortem, from individuals with no known neuropathology. The ABA data, generated on the Agilent microarray platform, consisted of 3,702 tissue samples taken from six individuals with up to 411 unique anatomical regions of the brain.
Data from  Zhang et al. (Y. Zhang et al. , 2016)  consisted of RNA-Seq data (n = 41) generated from different human CNS cell types (neuronal, glial and endothelial). For downstream analysis, data (n = 132) from immune (myeloid and lymphoid) and brain cell types (neuronal and glial) was downloaded (GSE49910) from the Gene Expression Omnibus (GEO)  (Mabbott, Baillie, Brown, Freeman, & Hume, 2013). Lastly, for the analysis of microglia in aging and Alzheimer's, data was derived from post-mortem samples of Alzheimer's patients and controls from four cortical and hippocampal brain regions of 85 individuals (n = 235)  (Berchtold et al. , 2013). Further details of these datasets are provided in Table  S1. Transcriptomics data was downloaded from the appropriate sources and underwent stringent quality control.
The unprocessed microarray data from GTEx (GSE45878) and the Alzheimer's dataset (GSE48350), were downloaded from GEO. Data quality was assessed using the ArrayQualityMetrics package  (Kauffmann, Gentleman, & Huber, 2008)  in Bioconductor, and samples failing more than one of three metrics (between arrays comparison, array intensity distribution and variance mean difference) were removed. Subsequently, data was normalized using robust multiarray average from the oligo package  (Carvalho & Irizarry, 2010)  and where multiple probesets represented the same gene, the probeset with the highest average expression across donors was selected to represent the respective gene. GTEX and ABA preprocessed RNA-Seq data was downloaded directly from the GTEx portal. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted April 26, 2018. ; https://doi. org/10. 1101/308908 doi: bioRxiv preprint and the ABA website, the latter consisting of six pre-normalized datasets from the brains of separate donors. Furthermore, quality control was conducted by inspecting sample-to-sample correlation networks using Graphia Professional (Kajeka Ltd, Edinburgh, UK), revealing outlier samples or batches effects. Evident from the GTEx RNA-Seq data, early batches (LCSET-1156 to LCSET-1480) poorly correlated with other samples, forming a highly connected group separate from other samples within the network. For downstream analysis, genes were filtered from the Affymetrix microarray, with a normalized expression level <20 and <1 FPKM or RPKM for RNA-Seq.
Gene annotation through coexpression networks analysis To define a core microglial gene signature, the tissue and cell transcriptomics datasets described above were analyzed using the coexpression network analysis tool Graphia Professional. For each dataset, Pearson correlations (r) were calculated between all genes to produce a gene-to-gene correlation matrix. From this matrix, a gene coexpression network (GCN) was generated, where nodes represented genes and genes correlating greater than a defined threshold were connected by edges. Coexpressed genes formed highly connected cliques within the overall topology of the graph, which were defined as clusters using the Markov clustering algorithm (MCL), with the default inflation value of 2. 2  (Van Dongen, 2000). All Pearson threshold values used for individual datasets were above r ≥ 0.
7 and thereby graphs included only correlations that were highly unlikely to occur by chance (Figure  S1 ). For each dataset, the threshold for correlations was further adjusted to achieve a single microglial cluster containing the three canonical marker genes for microglia, CX3CR1, AIF1 and CSF1R  (Elmore et al. , 2014; Mittelbronn et al. , 2001). The final microglial gene signature was defined by genes present in at least three of the nine dataset derived microglial signatures (Table  S2  and S3 ).
Validation of the core human microglial signature Various lines of evidence were investigated to validate the conserved nature of the derived human microglial signature. Firstly, the average expression of signature genes was compared between myeloid and other cell types from an atlas of primary human cells  (Mabbott et al. , 2013) , using the Mann-Whitney U test. Similarly, the average expression of signature genes in the GTEx RNA-Seq data and donor one of the microarray ABA data was also compared with the microglial cell densities in. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 26, 2018.
; https://doi. org/10. 1101/308908 doi: bioRxiv preprint mouse, for comparable regions  (Lawson et al. , 1990). Where available, immunohistochemical (IHC) staining of proteins encoded by signature genes were examined in the Human Protein Atlas (HPA)  (Nilsson et al. , 2005)  across different regions of the human CNS. Enrichment analysis for Gene Ontologies (GO), pathways and transcription factor binding sites were conducted using ToppGene (Table  S4 )  (Chen, Bardes, Aronow, & Jegga, 2009). In order to annotate the function of signature genes with relevance to myeloid and immune cells, the GeneCards database and literature were consulted (Table  S3 )  (Safran et al. , 2010). Signature genes were then compared with other published mouse and human microglial signatures. The largest signature reported by  Galatro et al.
(Galatro et al. , 2017) , comprising of 1236 genes, included many genes from other microglia signatures and largely overlapped with the proposed signature. Therefore, to evaluate the specificity of the two signatures, we examined the expression of their respective genes in the GTEx RNA-Seq dataset. A GCN constructed (r ≥ 0. 7) from the GTEx RNA-Seq dataset revealed five gene clusters enriched in Galatro et al. signature genes, representing various region-specific expression profiles (Table  S5 ). Subsequently, for each cluster, the average expression of  Galatro et al. genes was compared between the region with the highest expression versus the remaining samples, using the Mann-Whitney U test.