dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Anderson 2001; Warton et al. 2012). When traits are highly correlated, the first axis of variance explains a large part of the total variation, and differences between groups along the other axes are not detected by distance-based approaches. Penalized-likelihood approaches (as well as conventional multivariate approaches), on the other hand, are able to detect such differences. In the phyllostomid bats, for example, the PL approach detected differences that occurred on axes other than the first axis, while the distance-based approach did not. Second, the phylogenetic structure of the data matters. Most available multivariate phylogenetic linear models are restricted to the Brownian motion process. However most empirical datasets will likely deviate from this BM assumption. In the phyllostomid bats dataset for example, we found a relatively low phylogenetic signal in the residuals of the different models, even though we could have expected a rather strong signal given that the group is thought to have experienced an adaptive radiation.
More generally, high-dimensional datasets likely accumulate measurement errors and uncertainties  (Goolsby et al. 2017 ) that may also lead to departures from the BM assumption. We have shown with simulations that methods relying exclusively on the BM process can have low power and/or high type I error rates when departures from the BM assumption occur. In the phyllostomid bats, differences associated with carnivory on the condylobasal length (a proxy for body-size) were not detected by the distance-based approach, even though these differences were observed along the main axis of variance, unless the tree was rescaled according to the corresponding PL estimate of phylogenetic signal. The PL approaches did detect these differences. Similarly, conventional (non-phylogenetic) methods have low power and/or high type I error rates when there is in fact a phylogenetic signal in the data.
e. when there is no signal in the residuals and p<n). Hence, multivariate tests can be performed with satisfactory power and type I error rates even when the predictors are phylogenetic clustered, provided phylogenetic structure in the residuals is properly accounted for; our regularized approaches allow doing so. Evolutionary biologists often reduce high-dimensional datasets using Principal Component Analyses before performing multivariate tests (e. g. simulation-based MANOVA) using either conventional PC axes or phylogenetic PC axes assuming Brownian evolution. We have shown that this approach has a reduced power and is prone to high type I error rates, regardless of whether conventional or pPC axes are used. Conventional PC axes are sometimes favored as they are more straightforward to interpret biologically  (Polly et al.
Finally, it would be useful to develop diagnostic tools for detecting outliers in multivariate phylogenetic regression analyses, similarly to what has been proposed for phylogenetic univariate  (Revell et al. 2018)  and conventional multivariate models  (Caroni 1987; Barrett and Ling 1992; Srivastava and von Rosen 1998; Barrett 2003). With datasets of increasing sizes, further efforts will also be needed to improve computational efficiency; they will be even more needed if regularization techniques with improved performances but more computationally demanding, such as those using quadratic penalties, are required  (Engel et al. 2017; Clavel et al. 2019). We already proposed efficient algorithms that can easily be parallelized on modern computing platforms and approximations to the cross-validated log-likelihood for computing the distribution of the statistics.
These computations (see Appendix 2) could be further improved by using rank-one update of the eigen-decomposition used in the pre-transformation for the LOOCV (e. g. ,  Mertens et al. 1995). For very high-dimensional datasets (p>1000), the computational burden for computing the distribution statistics can be avoided (or reduced) by using PL-based reduction techniques. We previously developed the PL-PCA and were able to apply it on a dataset of p=1197  (Clavel et al. 2019). In principle, one could use the ML or PL-MANOVA (with the model estimated when performing the PL-PCA) on a reduced set of these PL-PCA axes. This is a much less computationally demanding test. In this case, the appropriate phylogenetic structure is used in the construction of the pPC axes, and so this procedure is expected to perform well, although we did not directly assess its statistical performances.
CONCLUSIONS The development of multivariate phylogenetic comparative tools is urgently needed to study phenomic and more generally quantitative datasets of ever-increasing size across the tree of life. In this paper we identified several limitations of currently available approaches and proposed new tools -GLS based on regularization techniques -that show improved statistical performances in a wide range of situations. We focused here on phylogenetic structure in the residuals, but the proposed approach can be used to handle other forms of correlations in the residuals, such as in time series or spatial data analyses.
1995)  as it was more efficient than our own implementation of the Newton-Raphson algorithm based on both Equations (B2) and (B3). Although performing the n eigen-decompositions of the LOOCV procedure for the p by p covariance matrices may appear to be computationally prohibitive, this has to be done only once; repeated evaluations of the cross-validated log-likelihood (Eq. B1), and its derivatives (Eqs. B2-3) during the updating steps of the optimization is done very efficiently given that the dominant computations only involve vectors or scalar operations. It should be noted that this approach assumes that the phylogenetic covariance is known and fixed. For instance, with the Pagel's ߣ model considered here, we assume that for each of the permuted datasets the phylogenetic covariance is known and fixed to the penalized likelihood estimate obtained from the empirical data -that is ൌ ൫ߣ መ ൯.
ACKNOWLEDGEMENTS The authors wish to thank  Leandro Rabello Monteiro  for providing the phyllostomid bats dataset. Finally, we thank  Leandro Aristide ,  Carmelo Fruciano ,  Sophia Lambert ,  Odile Maliet ,  Benoît Perez ,  Ignacio Quintero ,  Ana C. Afonso Silva , and  Guilhem Sommeria-Klein  for helpful comments on the manuscript.
5 (Siemens, Germany) or transcription-mediated amplification (TMA): Aptima HIV-1 Quant assay (Hologic Inc. , San Diego, CA, USA). Alinity m HIV-1 assay: HIV-1 was isolated from 600 μL of sample per the manufacturer's instructions. The data were analyzed by the system software, version 1. 3. 1 or later. The dynamic range of quantification of the Alinity m HIV-1 assay is 20 to 10 7 copies/mL (1. 3-7. 0 Log 10 copies/ mL). The Alinity m HIV-1 assay primers and probes target the integrase and LTR regions of the viral genome. RealTime HIV-1 assay: HIV-1 was processed from 600 μL of sample using the m2000sp™ and the m2000rt analyzers for automated sample extraction and real-time PCR amplification and detection, according to the manufacturer's instructions. The data were analyzed with the Abbott RealTime m2000rt software, version 8.
Statistical analysis Descriptive statistics are reported as means ± SD. Relationships between quantitative variables were studied by means of Deming regression. Bland Altman analysis was performed to evaluate the differences in quantification between the assays. All statistical analyses were performed using PC SAS version 9. 3 (SAS, Cary, NC, USA). Additionally, the percentage of samples that differed by ≤/ > 0. 5 Log 10 copies/mL and ≤/ > 1. 0 Log 10 copies/mL were evaluated between methods at or near the clinical decision points of 200 and 50 copies/mL.
Analysis of Neural Activity The neural activity of single units was manually sorted from multi-units data on the basis of peak-to-trough amplitude and wave shape, and were further confirmed with PCA clustering and event interval analysis, using Spike 2 software (version 7, Cambridge Electronic Design Limited, Cambridge, United Kingdom). Units were included in the analysis if their spike widths (from peak to peak) were longer than 0. 6 ms and shorter than 1. 2 ms. Recordings longer than 2 min from non-paralyzed bees were included into the database. Customized Matlab (version 2016b, MathWorks, Natick, MA, United States) scripts were written for tracking bee locations from video frames, analyzing neural recordings and statistics. The raw neural data were first organized in a time resolution of 100 ms, and then instant animal locations and neural activity were synchronized by time stamps.
Next, bee trajectories were smoothed with three filters: first, single "sharp turns" were corrected. Most tracking errors were only one separated point within a sequence of correct tracking. When the trajectory showed a bee turning left or right >135 • , this turning start point (1st point) and the point after turning (3rd point) were directly connected with a line segment, and the 2nd point was re-located as the middle point on this line segment. Second, "jump and stay" were corrected. In rare cases, tracking points jumped to a wrong location and lasted 200-400 ms (2-4 points); therefore, we compared every point with the following 5 points and took the point nearest to the 1st point as the real bee location, the points in between the two real points are evenly re-located between them. In a last step, we applied robust smoothing with a well-established Matlab function SMOOTHN 1 to polish up the trajectory curves.
Spectra Using the selection criteria described in the previous subsection we selected more than 467 candidate stars to be studied spectroscopically. These close clusters have been studied by previous authors, and so we expected to find spectroscopic data for a large fraction of the selected stars. We queried the available public archives searching for high resolution spectra (R = λ/∆λ 45 000). We did not put any restriction on S/N a priori, so in many cases, we retrieved low S/N spectra of the same star and instrument that can be co-added to reach a S/N of ∼50. We retrieved, reduced, and calibrated spectra from the following instruments 3 : -UVES: a cross-dispersed echelle spectrograph installed at the second Very Large Telescope (VLT) unit, at Paranal Observatory. It covers part of the optical spectral region with a resolution R ∼ 45 000 or above, depending on the setup.
We L. Casamiquela et al. :  Differential abundances of nearby Open Clusters and their tidal tails selected the setups according to the wavelength range of our line list, those centered on 580 and 564 nm. We retrieved the spectra using the ESO Phase 3 spectral data webpage 4. -FEROS: a high resolution (R ∼ 48 000) echelle spectrograph providing an almost complete spectral coverage from 350 to 920 nm. It is installed at the 2. 2m MPG/ESO telescope at La Silla Observatory. We retrieved the spectra using also the ESO Phase 3 archive. -HARPS: a very high-resolution spectrograph (R ∼ 115 000) installed at the 3. 6m telescope at La Silla Observatory. The instrument was designed to obtain very high accuracy in radial velocity. The spectral range covered is 380 to 690 nm. We retrieved the spectra using also the ESO Phase 3 archive.
Method We used the public spectroscopic software iSpec  (Blanco-Cuaresma et al. 2014a; Blanco-Cuaresma 2019)  to analyze the spectra. This is a Python code designed to perform operations on stellar spectra and to compute radial velocities, atmospheric parameters, and individual chemical abundances using different available atmospheric models and radiative transfer codes. 0 2 G Bp -G Rp We employed the synthetic spectral synthesis method to compute atmospheric parameters and chemical abundances using the radiative transfer code SPECTRUM  (Gray & Corbally 1994) , the MARCS 7  atmospheric models  (Gustafsson et al. 2008) , and the solar abundances by  Grevesse et al. (2007). We used the line list from the Gaia-ESO survey  (Heiter et al. 2015b (Heiter et al. , 2019)). The spectral fitting was done comparing the observed fluxes weighted by their uncertainties with a synthetic spectrum for a set of spectral features.
To derive chemical abundances, an additional cleaning of lines was done discarding systematically discrepant lines of each element in most of the stars. This was done for three groups of stars according to evolutionary state: K giants (log g < 3. 5), G dwarfs (5000 < T eff < 5900 K), and F dwarfs (5900 < T eff < 6400 K) 8. For elements with few measured spectral lines, we used the flags included in the Gaia-ESO line list, indicating the reliability of the atomic data and their degree of blending. We tested that the obtained lines were consistent using stars observed with different instruments. The final selection of lines is in Table  2. In a final step, differential abundances were calculated line by line, subtracting the abundance values of a chosen reference star (see Sect. 6). This procedure was restricted to those lines present in the reference star.
03 dex. We added Poissonian noise to the spectra to degrade the S/N to between 100 and 200. We analyzed both sets of spectra using the same procedure as for the real data. Even if we introduce noise to the synthetic data, the experiment will not be comparable to the analysis of real data. For example, the effects of the continuum normalization and bad/missing atomic data are not taken into account and can contribute to the dispersion of a synthetic homogeneous cluster. To be more realistic, we added some possible sources of systematic uncertainties. To generate the spectra we used the radiative transfer code SYNTHE  (Kurucz 1993 ) and the ATLAS9  (Castelli & Kurucz 2004 )atmospheric models, instead of SPEC-TRUM  (Gray & Corbally 1994)  and MARCS  (Gustafsson et al. 2008) , which are used in the analysis pipeline.
Acknowledgements. We gratefully thank the anonymous referee for providing comments that have improved the quality of the work. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium). We acknowledge the  Gaia Project Scientist Support Team  and the  Gaia DPAC. Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. This research made extensive use of the SIMBAD database, and the VizieR catalog access tool operated at the  CDS, Strasbourg, France , and of  NASA Astrophysics Data System Bibliographic Services. This research has made use of Astropy (Astropy Collaboration et al.
Introduction The Debian distribution is one of the oldest free software distributions, providing today 60 000 binary packages built from more than 31 000 software source packages with an ocial support for nine dierent CPU architectures. It is one of the most used GNU/Linux distributions, and serves as the basis for some derived distributions like Ubuntu. A software package of Debian contains an archive of les to be placed on the target machine when installing the package. The package may come with a number of so-called maintainer scripts which are executed when installing, upgrading, or removing the package. A current version are completely or partially written by hand. These scripts are used for tasks like cleaning up, conguration, and repairing mistakes introduced in older versions of the distribution.
Since they may have to perform any action on the target machine, the scripts are almost exclusively written in some general-purpose scripting language that allows for invoking any Unix command. The whole installation process is orchestrated by dpkg, a Debian-specic tool, which executes the maintainer scripts of each package according to scenarios. The dpkg tool and the scripts require root privileges. For this reason, the failure of one of these scripts may lead to eects ranging from mildly annoying (like spurious warnings) to catastrophic (removal of les belonging to unrelated packages, as already reported  [39] ). When an execution error of a maintainer script is detected, the dpkg tool attempts an error unwind, but the success of this operation depends again on the correct behaviour of maintainer scripts.
There is no general mechanism to simply undo the unwanted eects of a failed installation attempt, short of using a le system implementation providing for snapshots. The Debian policy  [4]  aims to normalise, in natural language, important technical aspects of packages. Concerning the maintainer scripts we are interested in, it states that the standard shell interpreter is Posix shell, with the consequence that 99% of all maintainer scripts are written in this language. The policy also sets down the control ow of the dierent stages of the package installation process, including attempts of error recovery, denes how dpkg invokes maintainer scripts, and states some requirements on the execution behaviour of scripts. One of these requirements is the idempotency of scripts. Most of these properties are until today checked on a very basic syntactic level (using tools like lintian  [1] ), by automated testing (like the piuparts suite  [2] ), or simply left until someone stumbles upon a bug and reports it to Debian.
The bugs in Debian maintainer scripts that we attempt to nd may come at dierent levels: simple syntax errors (which may go unnoticed due to the unsafe design of the Posix shell language), non-compliance with the requirements of the Debian policy, usage of unocial or undocumented features, or failure of a script in a situation where it is supposed to succeed. The challenges are multiple: The Posix shell language is highly dynamic and recalcitrant to static analysis, both on a syntactic and semantic level. A Unix le system implementation contains many features that are dicult to model, e. g. , ownership, permissions, timestamps, symbolic links, and multiple hard links to regular les. There is an immense variety of Unix commands that may be invoked from scripts, all of which have to be modelled in order to be treated by our tools.
To address properties of scripts required by the Debian policy, we need to capture the transformation done by the script on a le system hierarchy. For this, we need some kind of logic that is expressive enough, and still allows for automated reasoning methods. A particular challenge is checking the idempotency property for script execution because it requires relational reasoning. For this, we encode the semantics of a script as a logic formula specifying the relation between the input and the output of the script, and we check that it is equivalent to its composition with itself. Finally, all these challenges have to be met at the scale of tens of thousands of scripts. The contributions of this work for this case study are: 1. A translation of Debian maintainer scripts into a language with formal semantics, and a formalisation of properties required for the execution of these scripts by the Debian policy.
Debian packages Three components of a Debian binary package play an important role in the installation process: the static content, i. e. , the archive of les to be placed on the target machine when installing the package; the lists of dependencies and predependencies, which tell us which packages can be assumed present at dierent moments; and the maintainer scripts, i. e. , a possibly empty subset of four scripts called preinst, postinst, prerm, and postrm. We found (Section 4. 2) that 99% of the maintainer scripts in Debian are written in Posix shell  [22]. Our running example is the binary package rancid-cgi  [31]. It comes with only two maintainer scripts: preinst and postinst. The preinst script is included in Fig. 1  We did a statistical analysis of maintainer scripts in Debian to help us design our intermediate language, see Section 4.
Managing package installation The maintainer scripts are invoked by the dpkg utility when installing, removing or upgrading packages. Roughly speaking, for installation dpkg calls the preinst before the package static content is unpacked, and calls the postinst afterwards. For deinstallation, it calls the prerm before the static content is removed and calls the postrm afterwards. The precise sequence of script invocations and the actual script parameters are dened by informal owcharts in the Debian policy [4,  Appendix 9]. Fig. 2  shows the owchart for the package installation. dpkg may be asked to: install a package that was not previously installed (Fig. 2 ), install a package that was previously removed but not purged, upgrade a package, remove a package, purge a package previously removed, remove and purge a package.
These tasks include 39 possible execution paths, 4 of them presented in Fig. 2. The Debian policy contains [4, Chapters 6 and 10] several requirements on maintainer scripts. This case study targets checking the requirements regarding the execution of scripts, and considers out of scope some other kinds of requirements, e. g. , the permissions of script les. The requirements of interest are checked by dierent tools of our toolchain presented in Section 3. For example, the dierent ways to invoke a maintainer script are handled by the analysis of scenarios (Section 3. 5) calling the scripts. Dierent requirements on the usage of the shell language are checked by the syntactic analysis (Section 3. 1), like the usage of -e mode or of authorised shell features that are optional in the Posix standard.
Some of the usage requirements can be detected by a semantic analysis; this is done in our toolchain by a translation into a formally dened language, called CoLiS (Section 3. 1). Finally, requirements concerning the be- haviour of scripts include the usage of exit codes and the idempotency of scripts. The last property is dicult to formalise since it refers to possible unforeseen failures (see discussion in Section 4. 4). Checking behavioural properties requires to reason about their semantics, which is done by a symbolic execution in our toolchain (Section 3. 4). We also check some requirements that are simply common sense and that are not stated in the policy, e. g. , invoking Unix commands with correct options. This is done by the semantic analysis (Section 3. 1).
Principles and workow of the analysis method Our goal is to check the above properties of maintainer scripts in a formal way, by analysing each script and the composition of scripts in the execution paths exhibited by the owcharts of dpkg. We call scenario either an execution path of dpkg, a single execution of a script, or a double execution of a script with the same parameters (to check idempotency); refer to Section 3. 5 for more details. The analysis should consider a variety of states for the system on which the execution takes place. Yet we assume the following hypotheses: the scripts are executed in a root process without concurrency with other user or root processes, the static content of the package is successfully unpacked, the dependencies de- the trees rooted at /etc coincide except on the child named rancid.
⊥ denotes the absence of a node. Finally, a leaf can be annotated by a property, e. g. , the annotation dir rooted at /etc/rancid/apache. conf. The diagram shows that the preinst script leads to an error state when the le /etc/rancid/apache. conf is a directory since the rm command cannot remove directories. Finally, another set of generated web pages provides statistics on the coverage and the errors found for the full set of scenarios of the Debian distribution. 3 Design and implementation of the tool chain The toolchain, as described in Fig. 3 , hinges on a symbolic execution engine which computes the overall eect of a script on the le system as a symbolic relation between the input and the output le system. This section details this execution engine, which is composed of (i) a front-end that parses the script and translates it into a script in a formally dened intermediate language called CoLiS, and (ii) a back-end that symbolically executes the CoLiS scripts to get, for each outcome of the script, the relation between input and output le systems encoded by a tree constraint.