dataesr/scientific-paragraphs-categorization huggingface dataset:
Network The network module is designed to handle two different tasks. Its primary purpose is to combine results from different Baseline objects. Similar to the Baseline object, the Network object imports Baseline objects as attributes that may be invoked through the Network. In addition to this functionality, it can also be used to simulate cross-correlated data across a network of detectors. Both signal-only and signal and noise data can be simulated using a Network object. The network module handles all data generation by querying the module. The Network object can be initialized in two ways. By default, it is initialized through a list of Interferometer objects. from pygwb import network HLV_network=network. Network('HLV', [H1, L1, V1]) It is also possible to initialize a Network using a list of Baseline objects, to streamline the combination of results from different baselines that already contain final data products.
Subsequently, a baseline object is created using the pair of interferometer objects. Recall that the baseline module plays a central role in the pipeline and handles the computation of the various quantities of interest, including the (average) PSDs and CSDs of the baseline, relying on the spectral module. This is described in more detail in Sections 3. 2 and 4. 2. The delta-sigma cut is then performed, and optimally averaged spectra and an overall point estimate are calculated with the relevant Baseline methods. The delta-sigma cut is applied by default, but may also be calculated and applied at a later stage. Finally, the spectra, the overall point estimate, and the pickled baselines (if requested) are saved as the output. By default, the output is in numpy binary file format, npz. In realistic scenarios, we analyze year-long data sets, so running pygwb_pipe in series is suboptimal.
statistical_checks With the statistical_checks module, we provide a tool to perform initial statistical analyses of a pygwb run result set and to visualize them in preformatted plots. We identify five broad categories of checks. The first set calculates the running point estimates for W in the analysis and may not be changed at this point. The running averages are cumulative weighted averages of time-ordered segments, and do not take segment-by-segment correlation into account. In the case of a detection, these converge to a biased point estimate and σ, as proper postprocessing is not applied (see Section 3. 3). However, the visualization of running quantities is extremely useful to identify trends in the data, and ultimately will flag a possible detection. The module also provides a linear trend analysis, fitting the evolution of the parameters described above as a function of time.
Right: distribution of the deviations in ΔSNR i as a function of time before the delta-sigma cut (red) and after the cut (blue). but suboptimal  (Drasco & Flanagan 2003; Lawrence et al. 2023) , due to the intermittent nature of the signal, which is not taken into account in the search method. For more details on the time-domain characteristics of these two types of signals and the detection challenges these present, see, for example,  (Regimbau 2022). Finally, the O3 Advanced LIGO data set (data set 3) presents all the complexity of analyzing real GW detector data, including nonstationary noise, a large data volume, and expensive computational requirements. We handle large data sets by splitting the data into smaller pygwb_pipe jobs, assuming each job is independent; these are then combined using the pygwb_combine script (see Section 5 for details).
We then perform parameter estimation on the combined 100 days, presented in Figure  10  (right). We assume a loguniform prior from 10 -11 -10 -6 for Ω ref and a Gaussian prior with mean 0 and standard deviation 1. 5 for α. This shows a recovery within 1σ for Ω ref = 1. 06 × 10 -7 and within 2σ for the spectral index α inj = 0. The tests above illustrate that the simulator module is able to successfully inject a stochastic stationary signal and that the pygwb pipeline is able to recover this injection.
48%, as in  Abbott et al. (2021). Parameter estimation confirms Ω 25 is consistent with 0 and α remains unconstrained, as may be seen in Figure  14. These results agree with the previous parameter estimation carried out in  Abbott et al. (2021). Our results are quoted at the value of the Hubble parameter H 0 = 67. 9 km s -1 Mpc -1 , in line with published results. This is not the built-in value of H 0 , defined in Section 3. 3; however, rescaling is straightforward, as it is an overall multiplication factor, which may be changed when postprocessing the run with the pygwb_combine script or manually using the builtin functions of the OmegaSpectrum object, as explained in Sections 3. 3 and 5. 3 -1. 5 ± 0. 9 -1. 3 ± 0. 9 Note. On the left, three columns summarizing the point estimates from the weighted optimal statistic, at different spectral indices α.
B 1 0 f B A 2 1 2 2 2 ⎜ ⎟ ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ ⎤ ⎦ ⎥ ({ ˆ}| ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Marginalization of the calibration uncertainty is built into the pygwb_pe module, and this calculation is automatically triggered when passing a calibration error ò ≠ 0. Additional information on the treatment of calibration uncertainties can be found in  Whelan et al. (2014).
Acknowledgments The author list of this paper includes, in order: all pygwb code authors, in order of successful GitLab merge requests, at the time of writing; code reviewers; and testers, in alphabetical order. We would like to thank the  LVK stochastic group  for its continued support. Special thanks to  G. Cella  and  J. Suresh  for valuable comments on the manuscript. A. I. R. is supported by the  NSF  award  1912594. A. R. R. is supported in part by the  Strategic Research Program "High-Energy Physics"  of the  Research Council of the Vrije Universiteit Brussel  and by the  iBOF " Unlocking the Dark Universe with Gravitational Wave Observations: from Quantum Optics to Quantum Gravity " of the  Vlaamse Interuniversitaire Raad  and by the  FWO IRI  grant  I002123N  " Essential Technologies  for the  Einstein Telescope.
V. M. was supported in part by the  NSF  award  PHY-2110238. This material is based upon work supported by  NSFʼs LIGO Laboratory , which is a major facility fully funded by the  National Science Foundation. LIGO was constructed by the  California Institute of Technology and Massachusetts Institute of Technology  with funding from the  National Science Foundation , and operates under cooperative agreement  PHY-1764464. Advanced LIGO was built under award  PHY-0823459. The authors also gratefully acknowledge the support of the  Science and Technology Facilities Council (STFC)  of the  United Kingdom , the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of  Advanced LIGO  and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council.
These data allowed to individuate site-specific conformational variations occurring in the XcpH p region involved in the interaction with the other minor pilin XcpJ p. Successively, Qband DEER measurements have been performed to monitor the formation of the quaternary complex (XcpH p I p J p K p ). At this stage, these experiments have been performed to measure the inter-spin distances between XcpH p and XcpJ p in order to obtain more details about the organization of T2SS pilin complex at local level. NOÀ Gd ortho-SL strategies have been exploited in this study demonstrating the applicability of this biophysical approach for the investigation of multi-protein hetero-complexes. These measurements clearly showed the XcpH p /XcpJ p interaction between their α1 C helix domains, in agreement with the bottom position of the fourth pilin (XcpH) in the complex as reported in the available XcpHIJK structural model.
7 � 0. 2 to 4. 2 � 0. 2 ns) and hetero-trimeric complex (from 3. 7 � 0. 2 to 5. 3 � 0. 2 ns; Figures 3E and S2). Our results indicated that the flexibility of this region is slightly decreased during the interaction XcpJ p. These findings are in line with a previous study showing the α1 C region of XcpH p as one of the main XcpJ p binding sites. [7] EPR spectra of XcpH p53NO in the absence or presence of XcpJ p presented the same line shape (Figures  2A  and 3C , respectively). The simulations of these spectra showed analogue parameters Simulations of the experimental spectra (red) were performed individually by using SimLabel software  [36]  (a Matlab Graphical User Interface (GUI) using some of EasySpin  [37]  functions; see the Experimental Section). B) Rotational correlational time (τ C ) values obtained for each component (sharp components in pink, broad components in blue) from EPR spectra simulations.
These results agree with our cw-EPR measurements that detected conformational changes occurring in the top segment of α1 C helix of XcpH p (XcpH p53NO ) during the formation of the full minor pilins complex. The comparison between experimental DEER distance distributions with those predicted with MMM2019 software using the PDB of XcpHIJK complex revealed some discrepancies. Indeed, DEER data obtained for XcpH p46NO /XcpJ p53Gd (Figure  4F , black line) and XcpH p53NO /XcpJ p46Gd (Figure  4I , black line) showed peak maxima at higher distances compared with those extracted from the model (Figure4F and I, respectively, grey lines). On the other hand, shorter distances have been experimentally observed in the case of XcpH p30NO /XcpJ p53Gd (Figure  4C , black line) in contrast to those predicted (Figure  4C , grey line).
The fitting curve of each DEER trace has been superimposed in red. Distance distributions obtained with DEERAnalysis2019 for C) XcpH p30NO /XcpJ p53Gd , F) XcpH p46NO /XcpJ p53Gd and I) XcpH p53NO /XcpJ p46Gd alone (orange) and in the full XcpH p I p J p K p complex (black; the colour code for the accuracy of distances is reported below each distribution). NOÀ Gd distance distributions computed with MMM2019 software using the structural model of XcpHIJK complex (PDBDEV_00000086)  [7]  are shown in grey in (C), (F) and (I), respectively. J) Validation of the distance distributions for each couple of spin-labelled variants of minor pilins in the dimeric (XcpH p /XcpJ p complex, orange) and (XcpH p I p J p K p complex, black). labelled systems to probe hetero-oligomeric complexes and multiprotein interactions directly in native environments.
8 μM  [4]  ) and hetero-tetrameric XcpH p /XcpI p /XcpJ p /XcpK p (K D = 6. 7 μM  [4]  ) complexes. In order to probe the integration of the fourth minor pilins in the endopilus tip, XcpI p /XcpJ p /XcpK p complex was formed previously the addition of XcpH p. Cw-EPR spectra in the presence of 30 % v/v of sucrose were carried out maintaining the protein concentrations at 50 μM and using the same spectrometer parameters reported above. Simulation of the EPR spectra (Figure  S2 ) were obtained with SimLabel software  [36]  (GUI using some functions of Easyspin  [37]  ) in order to get the component percentages and the τ C values. Errors in τ C were calculated performing three independent simulations of the same spectrum. Four-pulse DEER spectroscopy (Q-band): Samples for DEER experiments were prepared with ~60 μM of final concentration for each minor pilins in 25 mM of sodium phosphate buffer (pH 6.
Successively, deep neural network (DEERNet) has been applied to correct the background function in a user-independent manner and the distance distributions were regenerate with TR without changing α values. At the end, the estimations of the distance profiles were performed using the validation tool included in DeerAnalysis2019 software (Figure  4J ). Distance distribution simulations were calculated with MMM2019 software using the available XcpHIJK structural model (PDBDEV_00000086). [7] rcular dichroism spectroscopy: Far-UV Circular Dichroism (195-250 nm) measurements (Figure  S1 ) were carried out using a Jasco CD-J-815 spectrometer operating a 25 °C. Wild-type (WT) and spin labelled variants of XcpH p and XcpJ p were prepared in 20 mM of phosphate buffer (pH 6. 5) with ~8-10 μM of final concentration.
Acknowledgements We thank  Dr. Annalisa Pierro  for valuable conversation leading to the initiation of this study. We also acknowledge  Prof. G. Jeschke  for the support provided about MMM2019 software. The EPR facilities at the  French Research Infrastructure INFRANALYTICS  ( FR2045 ) and the  Aix-Marseille University EPR Center  are acknowledged by all the authors. B. B. is supported by a  MESRI  fellowship. B. D. research activity is supported by the  National Research Institute for Agriculture, Food and Environment INRAE  and the  Université de Lorraine. T2SS research in R. V. 's laboratory is supported by the  SYNERGY_T2SS ANR grant  ( ANR-19-CE11-0020-01 ).
4. The largest previous genome-wide association studies (GWAS) of T2D have identified >500 risk loci  5, 6  , which demonstrated variable patterns of association with clinical features mediated via effector genes acting through distinct molecular mechanisms that are often cell-type specific  7, 8. Through the newly-established Type 2 Diabetes Global Genomics Initiative, we present findings from the largest T2D GWAS metaanalysis to date, comprising >2. 5 million individuals of diverse ancestry and a near three-fold increase in effective sample size over previous efforts  5, 6. We leverage the power afforded by this increased sample size with emerging single-cell functional genomics data derived from disease-relevant tissues to disentangle the aetiological heterogeneity of T2D. For the first time across multiple ancestry groups, we construct partitioned genetic risk scores (GRS) 9 and assess their association with T2D-related macrovascular outcomes and progression to microvascular complications in individuals with T2D.
9%); and a South Asian ancestry group (SAS, 3. 3%). The GWAS in each ancestry group were imputed to reference panels from the Trans-Omics for Precision Medicine Program (11. 8%), or whole-genome sequence data specific for and genetically similar to individuals in the study (31. 4%). Subsequent association analyses accounted for study-level population stratification and relatedness, and adjusted for age and sex, where appropriate, and additional study-specific covariates (Supplementary Table  3 , Methods). We analysed bi-allelic autosomal single nucleotide variants (SNVs) with minor allele frequency (MAF) ≥0. 5% in at least one of the five ancestry groups from the 1000 Genomes Project 13. Discovery of 145 previously-unreported T2D loci. We aggregated association summary statistics across GWAS via multi-ancestry meta-regression, implemented in MR-MEGA 14 , which allows for allelic effect heterogeneity that is correlated with ancestry.
These individuals were not included in the multi-ancestry meta-analysis, and so allowed us to avoid potential inflated type I error rates due to overlap between the discovery and testing datasets. To maximise sample size, we tested macrovascular outcomes in all individuals, adjusted for T2D status, and we tested microvascular complications only in individuals with T2D (Methods, Supplementary Table  18 ). We first conducted GRS association analyses within ancestry groups, and subsequently aggregated results across ancestry groups via fixed-effects metaanalysis (Methods, Supplementary Table  19 ). We also tested the association of an overall GRS, agnostic to cluster membership, to assess the evidence that mechanistic clusters in the partitioned GRS are differentially associated with T2D-related clinical outcomes.
Study-level analyses. Within each study, we assigned individuals to one of five ancestry groups using self-report and genetic background (Supplementary Tables  1  and 2 ). Any individuals that could not be assigned to a single ancestry group were excluded as population outliers. Within each ancestry group-specific GWAS, we conducted quality control of genotype data. Subsequently, we pre-phased individuals and imputed up to reference panels from the Trans-Omics for Precision Medicine Program , or population-specific whole-genome sequencing  [55] [56] [57] [58] [59] [60] [61]  (Supplementary Table  3 ). Studies imputed to reference panels mapped to GRCh38/hg38 FIGURE LEGENDS were lifted back to hg19 using the UCSC liftOver tool (https://genome. ucsc. edu/cgibin/hgLiftOver). We excluded SNVs with poor imputation quality and/or minor allele count (MAC) <5 from downstream association analyses (Supplementary Table  3 ).
In this expression, ߙ is an intercept, ߙ ாைே , ߙ ଷ்ோ , and ߙ ହ்ோ are log-fold enrichments of genic annotations, and ߠ is the log-fold enrichment of ATAC-seq peaks in the ݅th cell type. We conducted a test of enrichment of the ݅th cell type by comparing the deviances of models in which ߠ ൌ 0 and ߠ is unconstrained. We identified cell types with significant evidence of enrichment (P<0. 00023, Bonferroni correction for 222 cell types). All models were fitted using the R package logistf (https://cran. r-project. org/package=logistf). Difference in ancestry-correlated heterogeneity between mechanistic clusters. We tested for differences in Z-scores (beta/SE) for association of index SNVs in each cluster with the ݅th axis of genetic variation by comparing two linear models via ANOVA: (i) ݂ ିଵ ൫ܼ ൯ ൌ ߬ ; and (ii) ݂ ିଵ ൫ܼ ൯ ൌ ∑ ߬ ‫ܥ‬.
In these expressions: ݂ is the identity link function; ܼ is Zscore for the ݆th index SNV; ‫ܥ‬ is an indicator variable that takes the value "1" if the ݆th index SNV was assigned to the ݇th cluster and "0" otherwise; and ߬ and ߬ are regression coefficients. Regression models were fitted using the glm function in R. Cluster-specific partitioned GRS analyses. We tested for association of a cluster-specific partitioned GRS and an overall GRS with T2D-related macrovascular outcomes (CAD, ischemic stroke, and peripheral artery disease) and microvascular complications (ESDN and proliferative diabetic retinopathy) in participants from the All of Us Research Program (AoURP, multiple ancestry groups) and Biobank Japan (BBJ, East Asian ancestry). AoURP cohort description, sequencing, quality control, and phenotype derivation.
ACKNOWLEDGEMENTS Central analyses were supported by  Japan Agency for Medical Research and Development  ( JP21km0405213 ,  JP20km0405202 , and  JP21tm0424218 ),  NHGRI  ( HG011723 ),  American Diabetes Association Innovative and Clinical Translational Award  ( 1-19-ICTS-068 ),  American Heart Association Postdoctoral Fellowship  ( 15POST24470131  and  17POST33650016 ),  American Diabetes Association  ( 11-22-JDFPM-06 ),  Corporal Michael J Crescenz VA Medical Center Research, NIDDK  ( DK126194  and  DK105535 ),  Versus Arthritis  ( 21754 ),  NIHR Manchester Biomedical Research Centre  ( NIHR203308 ). A complete list of acknowledgments and funding appears in the Supplementary Note. We thank the  International Consortium of Blood Pressure (ICBP)  and the Meta- Analyses of Glucose and Insulin-related traits Consortium   (MAGIC)  for providing pre-.
considered 78 , 72. 7872 260 participants with whole-genome sequencing (WGS) and electronic health record (EHR) data from the AoURP Controlled Tier Dataset v5 71,Details of the generation and quality control of the genomic data can be found in the AoURP Genomic Quality Report release C2021Q3R5 (https://support. researchallofus. org/hc/enus/article_attachments/7002034709652/All_Of_Us_Beta_Release_Genomic_Quality_Repor logistf (https://cran. r-project. org/package=logistf), and mvmeta (https://cran. rproject. org/package=mvmeta).
• Finally, the survey not only tries to cover all possible advanced methods of polymorphism, metamorphism, and code transformation techniques but also provides a comparative explanation of the possible solutions or frameworks that occurred over one decade  (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021) (2022)  by multiple tables and pie charts, which is essential to understand the important objective of our study and motivate new researchers to provide a robust future research direction. The remaining sections of the paper are arranged in the subsequent order: Section 2 offers the required context for this research; evasion techniques and their detection on the Android smartphone platform. Section 3 covers smartphone malware identification techniques. The taxonomy of evasion techniques is presented in Section 4.
Android Application The phrases an application, APK file or an app are interchangeably used throughout this article, which refers to the Android app. Figure  2  illustrates an archive, where the unzip tool extracts the source code and associated permissions, images, and other files and directories from the compressed APK. We clarify the necessity of APK components and explain some critical terminology in this section. Before Android version 4. 4 KitKat, the Dalvik Virtual Machine (DVM) served as the virtualized environment in which the apps are executed. The later app versions use the Android Run-Time (ART); both the execution environments are analogous to the Java Virtual Machine (JVM). Various files and directories are compressed, which forms a. apk file. Classes. dex is the primary source file in-housing the executable bytecode of the Java classes declared in the source code.
Android Security Vulnerabilities and Existing Obfuscation Techniques Android apps are developed in semantically feature-rich Java language; adversaries such as malware authors, software plagiarists, and cybercriminals misuse reverse engineering tools to modify apps  [53]. Software companies deploy obfuscation techniques to protect their propriety as the first layer of defense against plagiarism or cyber-attacks  [37]. The authors thoroughly investigate the prevalence of obfuscation techniques used by adversaries, evasion methods that hide the app's real intent, and some practical app security challenges. During the development of this survey, we analyzed over 20,000 Google Play apps with more than ten thousand downloads. Surprisingly, less than a quarter of benign apps use obfuscation or app protection tools to save their software from cyber criminals.
In contrast, cybercriminals use these encryptions, protection, evasion, and obfuscation techniques to hide their behavior and circumvent anti-malware. Android IDE supports the basic version of ProGuard  [54] , thus assisting the developer in defending their proprietary code. Further, robust solutions such as DexGuard  [55]  are professional. However, ProGuard incurs additional costs. Since Android code is written in Java, obfuscation techniques are inherited from the parent platform. Basic obfuscation includes replacing the meaningful package name, class name, method name, or field(s) in a method with an unrelated character from a set of alphabets [a-zA-Z]. It is also possible to use 'NULL,' 'AUX,' or developer-defined strings such as 'ZERO'. Though Java-based obfuscation can be deployed in the Android SDK, the situation of deploying them is different.
For example, components of Android such as activity, broadcast receiver, service, or content provider must remain un-obfuscated for smooth communication with framework API and callbacks. The same applies to a method invoked via reflection API in the Android framework. At the same time, we have covered a variety of code-transformation approaches. Setting Android-based obfuscation tools involves more than selecting features  [56, 57]. In addition, many complicated circumstances make obfuscating specific parts of code complex or impossible to understand, and even if that code is obscured, the app will no longer operate.