dataesr/scientific-paragraphs-categorization huggingface dataset:
9 Ma. The stem age of neotropical Lyciscidae that are nested within an Australian clade is estimated at 33 Ma, which is a very late time to cross Antarctica. Only a few dating analyses have been performed for groups of chalcid wasps. Variability in mean age estimates were noted depending on the study, data sets and methods used (differences up to 12 Myr). Our estimates for the age of the planidial clade ] and ] are close to previous estimates  (Murray et al. 2013; Zhang et al. 2022). The mean age for crown Agaonidae at 60. 7 Ma (50. 7-70. 9) is 15 Myr younger than previous estimates of 75 2)  (Cruaud et al. 2012 ) but this difference is likely due to the reduced set of taxa used in our study. Nevertheless, ages of all other groups of wasps that are strictly associated with Ficus (Epichrysomallidae, Sycophaginae, and other pteromaline fig wasps) postdate the age of Agaonidae.
CONCLUSION Chalcidoidea may represent one of the largest radiations of any insect group. The superfamily has deep origins in the Middle Jurassic (95%CI for stem age = 167. 3-180. 5 Ma) followed by a tremendous diversification in the Paleogene concomitant with the radiation of plants and the insects that feed upon them. We have assembled the largest phylogenomic data set ever to address relationships within 356 genera based on 1007 exons and 1048 UCE loci. By reducing saturation, the results of the independent analyses converged in clade support and also became more congruent with our morphological support for certain family group relationships. Conversely, morphological convergences are highlighted. The combined analysis of the exonsAA and UCEs90-25 data sets (2054 loci and 284,106 sites) produced a generally well supported hypothesis.
CITED REFERENCES FIGURE LEGENDS Saturation was assessed by calculating the R squared of the linear regression of uncorrected p-distances against inferred distances in individual gene trees. A. IQ-TREE tree obtained from the combined exonsAA+UCEs90-25 data sets (see also Figure  S1 ). Monophyletic families are in grey, para-or polyphyletic families are in color. Higher level groups/clades discussed in text are highlighted with boxes. Statistical support for backbone nodes are shown with single (SH-aLRT 80% or UFboot 95%) or double stars (SH-aLRT 80% and UFboot 95%) B. Contribution of the exonsAA and UCEs90-25 data sets to the combined tree. Gene concordance factor (gCF); gene discordance factor due to polyphyly (gDFP); site concordance factor averaged over 100 quartets (sCF). Points: raw data (Table  S2d ) C.
Askew R. R. 1980. The biology and larval morphology of Chrysolampus thenae (Walker) (Hymenoptera, Pteromalidae). Entomologist's Monthly Magazine 115:155-159. Zhu Q. 2014. AfterPhylo. A Perl script for manipulating trees after phylogenetic reconstruction. Available from https://github. com/qiyunzhu/AfterPhylo/. Version 0. 9. 1.
ACKNOWLEDGEMENTS JYR and AC are grateful to  Audrey Weber  ( INRAE, France ) for sequencing of the UCE libraries and to the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées, France  for providing computing resources. We thank  Gary Gibson  ( Agriculture and Agri-Food, Canada ),  Paul Hanson  ( Univ. de Costa Rica ),  Christopher Darling  ( Univ. of Toronto, Canada ),  Nicole Fisher  ( CSIRO, Australia ),  Michael Gates  ( USDA, USA ),  Michael Haas  ( Univ. of Marburg, Germany) ,  Christer Hansson  ( Museum of Biology, Sweden ),  Jason Mottern  ( USDA, USA ),  John D. Pinto  ( UCR, USA ),  Stefan Schmidt  ( ZSM, Germany ),  Christine Lambkin ,  Chris Burwell  and  Susan Wright  ( QM, Australia ) for providing specimens and for helpful discussion. AC and JYR acknowledge the Queensland parks and wildlife services for collecting permits ( WITK18278817-WIF418664617 ).
Samples from fifty-five surface water resources and twenty-five drinking water treatment plants in Europe, Africa, Asia, and USA were used to analyse the fluorescence composition of global surface waters and predict aromaticity and treatability from fluorescence excitation emission matrices. Nine underlying fluorescence components were identified in the dataset using parallel factor analysis (PARAFAC) and differences in aromaticity and treatability could be predicted from ratios between components H ii (λ ex /λ em = 395/521), H iii (λ ex /λ em = 330/404), P i , (λ ex /λ em =290/365) and P ii (λ ex /λ em = 275/302). Component H ii tracked humic acids of primarily plant origin, H iii tracked weathered/oxidised humics and the "building block" fraction measured by LC-OCD, while P i and P ii tracked amino acids in the "low molecular weight neutrals" LC-OCD fraction.
, 2014a; Stedmon and Markager, 2005b). A number of prior studies have used PARAFAC to analyse fluorescence datasets in the context of water treatment. Shutova and colleagues  (Shutova et al. , 2014)  reported linear correlations between between ratios of humic-like PARAFAC components vs percent DOC removal in treatments involving coagulation. However, in that study as in many others (e. g. Murphy et al. , 2011; Vines and Terry, 2020) , PARAFAC models of drinking water resources were site-specific with only modest statistical similarities obtained when comparing models from different treatment plants. This reflects a serious limitation of the PARAFAC approach, namely that selecting a different model tends to produce systematically different fluorescence intensities and ratios. This has greatly limited the generality of conclusions about DOM behaviour and treatability drawn from PARAFAC analyses  (Ishii and Boyer, 2012).
Analytical methods DOC analysis was performed in the Suez Lab using a benchtop analyzer (Aurora 1030W, OI instruments) that uses persulfate wet oxidation to quantify organics in water samples. UV absorbance at 254 nm, representing chromophoric dissolved organic matter (CDOM), was measured using a Nanocolor UV/Vis II (Macherey Nagel) and was used in combination with DOC to calculate specific UV absorbance (SUVA = UV 254 /DOC). A spectrophotometer (Aqualog, Horiba-Jobin Yvon) was used to measure fluorescence excitation emission matrices (EEMs) on the filtered water samples. EEMs were made from scans across the selected range of excitation wavelengths (excitation 210-620 nm with 5-nm bandpass), with emission spectra simultaneously recorded by CCD detector (220-800 nm at 4-nm increments). Fluorescence data exported from the AquaLog were fully corrected for instrumental and samplerelated biases.
Statistical methods Parallel Factor Analysis was used to mathematically decompose the global fluorescence EEM dataset according to well-established methodologies  (Bro, 1997). PARAFAC identifies the unique underlying spectra of a fixed number of independent components that sum to reproduce the measured EEMs with least error and determines the relative concentrations (F max ) for each component. Modelling was implemented using the drEEM toolbox in MATLAB  (Murphy et al. , 2013). Extensive modelling iterations confirmed that a nine-component model provided the best fit to the dataset. However, only post-membrane samples from a pilot plant contained the ninth component; and this component had spectral properties indicating a whitening agent (Section 3. 3). After excluding outlier samples affected by whitening agents, only eight components remained in the model.
The 8-component model was validated both by split-half analysis (S2T1), and by separately modelling short-wavelength (protein-like) and longer wavelength (humic-like) regions of each EEM and fusing the resulting spectra. After the model was finalized, the fluorescence EEMs from challenge samples and outliers were projected on the model, producing F max for every sample in the study. F max ratios were determined by dividing F max for one PARAFAC component by F max for another. Regression equations relating F max ratios with other measurements were determined in MATLAB (2020a) using either a standard linear model for small sample sizes (N ≤ 5) or a robust linear model determined by iteratively reweighted least squares regression (N ≥ 10)  (Holland and Welsch, 1977). To determine prediction errors (RMSE p ) for regression analyses, regression models were tested on a prediction dataset consisting of randomly selected samples that had not been used to develop the regression model.
In the case of regressions involving PARAFAC components, the prediction dataset consisted of the five challenge samples together with 19 other samples that were collected after the model had been finalized. For these samples, PARAFAC scores were determined by projecting the EEMs on the existing PARAFAC model, then regression fits were determined by projecting those scores on the existing regression equations. In the case of fluorescence ratios, the prediction samples were selected randomly by algorithm, then projected on the regression equation derived from the remaining samples.
Ferric chloride coagulant, SA Super powder activated carbon and Amberlite IRA458Cl ion-exchange resin were selected due to their specific capacity for organic matter removal. The same doses were administered to each synthetic and natural water sample. Since the synthetic samples each had similar DOC concentrations, they each received similar effective doses, making removal a function of only DOM source and mineral matrix. DOC concentrations in the natural water samples varied between 2. 5-5. 7 mgL -1 , making removal a function of both DOM source and effective dose. Two natural samples had nearly identical DOC concentrations (Sarthe, Loire) and received similar effective doses. 3  lists their similarity with published spectra (Tucker congruence coefficient, TCC) according to the OpenFluor database  (Murphy et al.
, 2014; Yamashita et al. , 2010). In PARAFAC models with only three or four components, H i and H ii are often combined indicating stable relative abundances across many datasets  (Wünsch et al. , 2019). However, H ii has more humic character and represents are more aromatic and higher-molecular weight fraction than H i  (Cuss and Guéguen, 2015). Component H iii is reported to represent soluble microbial products other than proteins  (Wells et al. , 2022)  and is typically abundant in wastewater DOM and at agriculturally-impacted and eutrophic sites  (Stedmon and Markager, 2005a; Yamashita et al. , 2010). Its emission peak overlaps with the position of the microbial "M" peak  (Coble, 1996; Stedmon and Markager, 2005b; Wünsch et al. , 2019) , although "M" also captures emission wavelengths associated with H i and H iv.
S2 ) where several pulp mills are located upstream of the sampling location. The other (W ii ) matches a spectrum isolated from wastewater  (Cohen et al. , 2014)  and has a multi-peaked emission spectrum characteristic of optical brighteners that are used widely in textile and paper industries (Fig. S1 ). In this study, W ii was the ninth component and observed only in some samples of membrane permeates and not in any challenge test samples. The isolation of these components from a diverse drinking water dataset highlights the sensitivity of fluorescence spectroscopy in combination with PARAFAC for detecting the potential organic contamination of drinking water samples.
Surface water aromaticity SUVA in the dataset ranged between 1. 0 and 5. 5 Lmg -1 m -1 and thus spanned a range from low to very high aromaticity. Fig. 2  shows correlations between established and newly derived predictors of surface water aromaticity. In the first two subplots, raw water samples accounted for most measurements in the upper right portion of each subplot (SUVA 1. 7 -5. 5, mean 3. 1 ± 0. 8 Lmg -1 m -1 ) and partially or fully treated water samples accounted for the lower left portion (SUVA 1. 0 -3. 7, mean 1. 8 ± 0. 6 Lmg -1 m -1 ). However, a single linear regression line fits all samples equally well, with no indication that treatment altered the underlying relationship between SUVA and fluorescence. A better predictor of SUVA discovered in this study was the ratio between F max for the longest emitting PARAFAC component (H ii , λ em =520 nm) versus the shortest emitting component within the "humic" region (H iii , λ em =404 nm), hereafter referred to H ii /H iii or "PARIX" standing for "PARAFAC Index".
Note that H iii fluorescence overlaps spectrally with component H iv (λ em =420 nm), although these two signals have distinct sources and behave differently during water treatment (see further below). In Fig. 2 , circular markers indicate the calibration dataset, i. e. , 166 samples from Cameroon, China, France, Italy and USA that were included when building the PARAFAC model, of which 53 had matching LC-OCD measurements. Red stars indicate the prediction dataset, consisting of samples that were excluded from PARAFAC modelling. The prediction dataset consists of 24 samples from Spain, USA and France (including the five natural Challenge Samples) of which 10 samples had LC-OCD measurements. For prediction samples, PARAFAC scores were estimated by projecting the sample EEMs on the model. With the one exception described below, no data points were omitted from any graph, so the depicted correlations are robust across continents and ecosystems with visible outliers that show the range of departure to be expected due to combined sampling, measurement, and modelling errors.
The single exception was the HIX subplot, where six extreme outliers from France and China were removed for visual clarity (HIX in the range of 0. 15-0. 6 and PARIX below 0. 5). Fig. 2a  correlates PARIX with SUVA measurements made according to the traditional (USEPA) method and by LC-OCD. A much stronger linear correlation is seen with the LC-OCD dataset than with the traditional SUVA dataset, indicating that much of the scatter among grey points in Fig. 2a  is attributable to measurement error in the traditional SUVA dataset. For this reason, and because it is typical to favour the LC-OCD dataset in cases of disagreement  (Hutchins et al. , 2017) , the LC-OCD dataset was used to derive equations to predict SUVA from fluorescence. Fig. 2a-b  and Table  4  show that SUVA in the calibration dataset could be estimated with good accuracy by simply multiplying PARIX by a constant equal to four (RMSEc = 0.
64 Lmg -1 m -1 ). This applied to surface water samples in various stages of water treatment and was the case even for the prediction dataset, i. e. , when this ratio was estimated by projecting new EEMs upon the pre-existing PARAFAC model (RMSEp = 0. 55 Lmg -1 m -1 ). Note that the magnitude of the constant in a regression equation involving PARAFAC components depends on their spectral ranges and shapes. Thus, this constant may vary between datasets if fitted with slightly different PARAFAC components. Conversely, a general result e. g. , a linear relationship between SUVA and the ratio of components representing H ii and H iii , would be expected to hold for two different PARAFAC models. It is relevant to compare the predictive power of PARIX with the predictive power of FIX and other well established fluorescence indices.
Predicting treatability from FDOM ratios Fig. 5  shows treatability results for the five French rivers. Since there were nine different treatments to apply, only a small number of different raw waters could be tested, however SUVA in these samples spanned most of the normal range observed in freshwater systems (approx. 1. 6 -6)  (Kellerman, 2015; Massicotte et al. , 2017; Weishaar et al. , 2003)  and in total Fig. 5  shows data from 40 separate pilot tests. Treatability, assessed as percent DOC removal, was generally well predicted from the fluorescence composition of raw river, when represented as ratios between H iii and either H ii , P i or P ii. Especially, since PARIX was highly correlated with SUVA, it was also an excellent predictor of water treatability by any process in which aromaticity plays a deciding role, especially coagulation and ozonation and combinations thereof.
Fluorescence surrogates of LC-OCD fractions Correlations between fluorescence and LC-OCD measurements were explored after first normalising LC-OCD concentrations to humic substance (HS) concentration, and normalising fluorescence parameters to H ii intensity. This avoids apparently strong correlations driven by concentration artefacts, i. e. , higher DOM concentrations tended to produce higher signals for both fluorescence and LC-OCD parameters. Additionally, eight samples with HS concentration below 600 μg/L were deleted due to low signal to noise, leaving 60 samples in the dataset. The aromaticity index was strongly correlated to nominal molecular weight, as would be expected from its relationship with SUVA (Fig. 6 ). The inverse of PARIX was strongly positively correlated to (HS-normalised) concentrations of building blocks.
The molecular basis of PARIX Recent studies have attempted to deduce the molecular origin of DOM fluorescence by linking fluorescence spectroscopy with ultrahighresolution mass spectrometry. The usual approach is to obtain a collection of samples, subsampled and measured by each of the two analytical techniques, and search for post-hoc correlations between the two datasets  (Herzsprung et al. , 2012). Although the specific molecular structures responsible for specific fluorescence components can't be identified this way, this approach has been used many times to indicate the relative proportions of carbon, hydrogen and oxygen in the ionizable molecular formulas that covary with PARAFAC components. In two prior studies that both identified components similar to H ii and H iii and mapped post-hoc correlations in van Krevlan space, each was associated with a broad range of molecular formulae properties with no apparent differences between the two  (Kellerman et al.
, 2015; Wünsch et al. , 2018a). However, Wünsch and colleagues also tested a different approach whereby the fluorescence and mass spectrometry datasets were simultaneously decomposed using a coupled tensor factorization model. In that analysis, H ii and H iii mapped in distinct regions of van Krevlen space, with H iii (H/C=1. 2-1. 4, O/C=0. 5-0. 6) more saturated and less aromatic than H ii (H/C=0. 9-1. 3, O/C>0. 6), which is consistent with the interpretation of PARIX in this study. The same analysis indicated that H i encompassed nearly the full range of molecular formulae correlated with H iii , as well as additional formulae of more aliphatic character. This is consistent with FIX and PARIX each representing the ratio between a condensed, aromatic humic fraction compared to a relatively aliphatic humic fraction.
In this chapter, we present the various categories of Face Manipulation and their use within different remote operational systems. We then use the example of remote identity document onboarding systems to illustrate how each category can be used in practice to compromise such a system. After a definition of the different Face Manipulation categories and the common algorithms used to produce them, we go through the various manipulation detection algorithms and common image and video forgery datasets. We then introduce some known counter-forensics methods that can be used by an attacker to avoid detection. Knowing the detection methods and the counter-forensic, we present how we can build up a safer system by using the correct methods at the correct time. But also how knowledge about the tampering process could be used to design the user experience to make the systems harder to compromise.
We will start by introducing a typical remote identity document onboarding system and explain which part is most likely to be attacked. We will discuss about the different types of Facial Manipulation attacks and how they fit into our particular system. We will then give a definition of each attack and present the common technologies and methods to create those forgeries. After, we will present common face manipulation detection methods and more general image manipulation detection algorithms. We will also introduce datasets used to study those attacks and to train the detection algorithms. Then, we will discuss some typical counter-forensic methods and how one can design his/her system to reduce the chances of forgeries. Finally, we will conclude with a discussion of the remaining challenges and perspectives for better use of nowadays detection methods in practical usage.
Landmark-based face manipulation Even though deep learning-based face manipulation algorithm performs extremely well, there is still use cases where a more classical method is appropriate. One advantage of classical methods is that they do not need training data and can most of the time produce convincing results in real time. Landmark-based methods usually come down to three simple steps. First, a face detection algorithm is applied. Then salient face features, often called landmarks, are extracted. And finally, the manipulation is performed. In general, classical face manipulation algorithms used common landmark detection techniques like  [11]. In such cases, those methods would only be used in simple 2D cases, i. e. fixed portrait tampering. More advanced methods exist to perform 3D face alignment.
They first detect facial expressions in a source and the target video. Then, they generate a 3D model of the face of both the target and the source video. Next, they transfer the facial expressions from the source to the target 3D model. Finally, they blend the 3D model on the target video. This produces very convincing results. Today, it is also possible to give life to a still image  [17]  by transferring facial expressions from a pilot video and optionally the voice. At the time of writing, those more advanced methods tend to be much more difficult to implement. Because of that, AI-based methods are usually preferred for their ease of use. And thanks to the numerous existing face databases, many powerful methods have been proposed.
Some methods have been proposed to detect both deepfakes by face swapping and deepfakes by face reenactment. In ref. [30] , the authors trained two convolutional neural networks to detect both swapped face videos and reenacted face videos. Despite encouraging results, their methods are dependent on the training database and, therefore, do not work very well on unseen types of deepfakes. In ref. [31] , the authors also propose a method based on deep learning to automatically detect deepfakes by looking at the facial regions. Their method is robust to different levels of compression. However, it is also very dependent on the training database. A model able to detect with a high level of accuracy a type of deepfake can easily be fooled by a deepfake that has been generated with another method.
Think of the production of a deepfake. As we mentioned, CNNs tend to produce blurry results. A counterfeit might then want to enhance the quality of his deepfake by applying a sharpening filter. Such post-processing is performed by the widely used DeepFaceLab software for instance. Whatever the processing use, it will leave traces. Most of the time, the detection of splicing forgeries relies on camera-based methods. In fact, in such forgeries, a large portion of the image may carry a significant difference with respect to intrinsic camera properties. It is not unreasonable to suppose that the spliced part might have gone through several post-processing such as sharpening and Gaussian blurring. In those cases, some methods  [43, 44]  aim at exposing those post-processing as a trace of image forgery.