dataesr/scientific-paragraphs-categorization huggingface dataset:
To ensure high mating success, the group is usually composed of around four to a dozen of DPQs (personal communication from the French royal jelly producers: GPGR, and island mating in the Beebreed dataset  [3] ). Virgin queens brought to the mating station are then mated by the drones on the mating station. We call this PS mating. The other alternative, artificial insemination, allows for greater mating control. In particular, drones used to mate a virgin queen can be taken from a PS composed of very few sister-DPQs or even from a unique DPQ (for example in  [4] ). In the latter case, we call it single sire (SS) mating. Compared to PS mating, SS mating generates more related female offspring in the colony. When properly accounted for in the pedigree, SS mating should enable estimations of genetic parameters and breeding values with lower standard errors than PS mating for which the precise origin of drones cannot be distinguished among the sister-DPQs and has to be probabilistically derived  [5, 6].
Here we investigate the impact of the mating strategy and the sire modeling on the bias and standard error of genetic parameter estimates and breeding values, using simulation. We focus on datasets with colony phenotypes from both inseminated breeding queens and open-mated DPQs. First, for inseminated breeding queens, we quantify the reduction in the standard error of estimated genetic parameters when the sire was a single DPQ (SS mating) compared to a group of DPQs (PS mating). Second, with the same simulation datasets but assuming that only the dams of DPQ(s) were known in the sire pedigree, we explored how assuming that drones came either from a PS or a SS impacted the genetic evaluation. Lastly, we investigated two alternative strategies to accommodate phenotypic records of open-mated DPQs in the genetic evaluation: either by adding dummy PS in the pedigree, or by adding a non-genetic effect to the evaluation model to account for the origin of the mates of the DPQs.
Genetic parameters were used to obtain BLUP estimated breeding values (EBVs)  [24]. The true genetic trend was calculated as the regression coefficient of true breeding values (BVs) of BQs over years, from the fifth generation of the breeding program (when the selection nucleus became closed) to the last tenth generation of selection. The estimated genetic trend was derived similarly based on EBVs instead of BVs. Starting values used for the AIReML algorithm  [25]  are shown in Additional file 2: Table  S1.
Last, the standard error of estimated rWQ was lower in genetic parameter sets with a negative rWQ than in those with a null rWQ. This was consistent with findings in simulated honeybee datasets  [16] , and is also in agreement with theoretical predictions  [26]. For given genetic variances for worker and queen effects, a negative rWQ also lowers the phenotypic variance, leading to higher heritabilities for the worker and for the queen effect. Another reason for this observation is that we forced the estimated rWQ to be within the parameter space of [-1; 1], resulting in smaller errors for true values nearer to the bounds.
Funding This work is supported by a public grant overseen by the  French National research Agency (ANR)  as part of the  « Investissements d'Avenir » program , through the " ADI 2021 " project funded by the  IDEX Paris-Saclay  ( ANR-11-IDEX-0003-02 ) and also by the  CARNOT France Future Elevage  (project  BeeMuSe ).
Availability of data and materials The R simulation code used during the current study is not publicly available while the authors continue to perform active software developments. They may be available from the corresponding author upon reasonable request. The R code to obtain the inverse of honeybee-specific relationship matrices is available at the AINVhoneybees GitHub repository https://github. com/Tristan-Kistler/AINV-honeybees/. DOI: 10. 5281/zenodo. 7951334. The version used in this study is v19. Further development will also be available on this repository.
II. METHODS (1) Literature search and systematic review A systematic approach was used to search for and synthesise the available literature. Literature was searched on the Scopus (www. scopus. com) and Dimensions (www. dimensions. ai) databases using the following Boolean search string: ('Clock genes' OR 'Clock' OR 'Adcyap1') AND ('Birds' OR 'Avian') AND ('Migration' OR 'Flying'). Search results were exported in the comma separated value format and the literature was subsequently summarised, guided by citation networks visualised using CitNetExplorer 1. 0. 0 and VOSviewer 1. 6. 16 (van Eck & Waltman, 2017). The results retrieved from Scopus were converted into the appropriate format using the R package Scopus2CitNet 0. 1. 0. 0 in RStudio 1. 4. 1106 (RStudio Team, 2021) , running R version 4.
0. 5 (R Core Team, 2020). Due to the diverse array of species in which these studies were conducted, the literature was organised by year of publication followed by species, with taxonomic grouping based firstly on order (passerine or non-passerine), followed by family. Families were grouped, based on higher taxonomic classifications, into superfamilies and parvorders for the sake of a concise and cohesive comparison (see Section III); warblers, which is a paraphyletic group, are discussed together as they share significant overlaps in morphology. (2) Species Species for comparative analysis were selected based on the existing literature for either the Clock or Adcyap1 gene in relation to migration phenology and/or for which genomic or transcriptomic studies have been conducted. This included unpublished data from eight species: American redstart, Setophaga ruticilla (Linnaeus); common chiffchaff, Phylloscopus collybita (Vieillot); common yellowthroat, Geothlypis trichas (Linnaeus); hermit thrush, Catharus guttatus (Pallas), magnolia warbler, Setophaga magnolia (Wilson); Swainson's thrush, Catharus ustulatus (Nuttall), and white-throated sparrow, Zonotrichia albicollis (Gmelin).
Residents were defined as species that do not follow an annual cycle of migration (although they may follow a pattern of altitudinal migration within their resident range); species with a single migratory population that was not sampled were treated as resident. Inclusion in subsequent analyses was based on data availability in two categories: (i) the availability of genetic data as either within-population-level data on individual alleles for Clock or Adcyap1 or records of the most common allele for the species from the literature or online databases, as well as species availability for the phylogenetic tree from the Bird Tree website (see Section II. 3); (ii) the availability of migration data including migration dates for each migratory season as well as shapefile data for GIS to compute different parameters related to migration (see Section II.
4). As far as possible, species were only included if they share one of the major migratory flyways with more than one other included species and therefore experience similar Zeitgebers for annual synchronicity. (3) Genetic data Population-level allele data were retrieved for 40 species (39 for Clock and 37 for Adcyap1) from either the supplementary material of the article or the online data repositories Dryad (www. datadryad. org), Figshare (www. figshare. com), or directly from the article authors (see Section VII, Acknowledgements). Clock data were transformed to represent the actual number of poly-Q repeats, as different studies used different primers resulting in variable lengths in the raw data. Adcyap1 was consistently amplified and sequenced using the same primer set or region, facilitating between study comparisons.
, 1990)  against reference genomes and available databases, including PopSet and Nucleotide  (Agarwala et al. , 2018) ; where no sequence data were available a further BLAST search was carried out against the Sequence Read Archive (SRA) for specific species. The tree used for the phylogeny was generated from the 'Ericson' phylogeny  (Jetz et al. , 2012)  by sampling 5000 trees from the Bird Tree website (www. birdtree. org) for available species from the list of 76. The trees were summarised to a 60% consensus tree by maximum clade credibility using TreeAnnotator 2. 6. 3, part of BEAST 2. 6. 3  (Bouckaert et al. , 2014) , with a 10% burn-in. (4) Migration and range data estimates Migration data were computed using QGIS 3. 16. 15 (QGIS Development Team, 2022) from shapefiles extracted from the geodatabase of distribution maps compiled by BirdLife International, version 2021.
1 (BirdLife International & Handbook of the Birds of the World, 2021), supplemented with shapefile data from eBird  (Fink et al. , 2021). Centroids were computed for the non-breeding and breeding ranges to determine the average coordinates by latitude and longitude (in degrees) for each. The average migration distance between these centroids was calculated (in metres). Geographic distance matrices for the breeding and non-breeding coordinates of each species were generated with the java application Geographic Distance Matrix Generator 1. 2. 3  (Ersts, 2012)  in degrees. Species classified as partial migrants, for which breeding and non-breeding range data were available, were treated as migrants, while data-deficient partial migrants were treated as resident birds. For resident species, the calculations were based on their full range.
(5) Population genetics Population genetics analyses were done for the 40 species for which individual allele data was available. POPGENE 1. 32  (Yeh et al. , 1997)  was used to test for Hardy-Weinberg  (Hardy, 1908; Weinberg, 1908)  equilibrium using chisquared (χ 2 ) tests (with significance measured at α = 0. 02), to calculate the observed (H o ) and expected (H e ) heterozygosity, and to create a genetic distance matrix using fixation index (F ST ) values. Python for Population Genetics (PyPop) version 0. 7. 0  (Lancaster et al. , 2007)  was used to test for selection and neutrality as well as linkage disequilibrium. Neutrality was assessed using Slatkin's implementation Biological Reviews (2023) 000-000 © 2023 Cambridge Philosophical Society. Time trees and clock genes  (Slatkin, 1994)  of the Ewens-Watterson  (Ewens, 1972; Watterson, 1977)  test, with the probability values expressed as the relative degree at which the observed F-value occurs in a sample distribution for a simulation run with 10,000 repeats.
31, the geographic distance matrices, generated with Geographic Distance Matrix Generator 1. 2. 3 (for both breeding and non-breeding coordinates), the taxonomic distance matrices, generated using the R package vegan 2. 5-7  (Oksanen et al. , 2020) , and the divergence times matrix. Divergence times between pairs of species were retrieved from the Time Tree resource  (Kumar et al. , 2017)  website (www. timetree. org) using Python Automated Retrieval of Time Trees (PAReTT version 1. 0. 1) and exported as a vectorised matrix. P < 0. 02 and Z > 1. 96 (or <-1. 96) is considered significant. (7) Phylogenetic generalised least squares analysis Phylogenetic generalised least square (PGLS) models were fitted for the full list of 76 species for which the most common allele and migration data were available.
Each test was run independently using the R package caper 1. 0. 1  (Orme et al. , 2018)  to avoid error from repeat sampling. PGLS was used to relate both Clock and Adcyap1 length to breeding and non-breeding latitude (as distance from the equator in degrees), as well as to total migration distance between regions and to the normalised dates for the beginning, middle, and end of spring and autumn migration, assuming Brownian motion and a lambda (λ) = 1. 0. The phylogenetic tree used as input for PGLS was retrieved from the Bird Tree website (www. birdtree. org). The phylogenetic signal for each gene was measured using the R package phytools 0. 7-90  (Revell, 2012)  to compute both lambda and kappa for the gene and tree data and verify the presence of Brownian motion.
The strength of the phylogenetic signal for each gene was also assessed by estimating lambda and kappa. *, P < 0. 10; **, P < 0. 05; ***, P < 0. 02. Caprioli  et al. , 2012; Kuhn et al. , 2013)  whilst others found no evidence for an association  (Peterson et al. , 2013; Contina et al. , 2018; Parody-Merino et al. , 2019). Additionally, several transcriptomic studies on related species have detected no differences in expression levels of key clock genes  (Jones et al. , 2008a; Franchini et al. , 2017; Brown, 2019)  used in candidate gene association studies. It should be noted that pitfalls within study design or data analyses could confound the results. For example, most early studies relied on the Mantel test  (Mantel, 1967)  to identify correlations between genetic distance and geographic distance, as is a common practice in studies of spatial genetics and ecology.
The full data set for Clock revealed a limited number of genotypes with a small number of alleles and widespread homozygosity while Adcyap1 was considerably more diverse with many more genotypes per species and more heterozygotes. A general trend was found for higher observed than expected heterozygosity among nearly half of the species (Table  S1 ) while the whole data set analysis showed only marginally less heterozygosity (Table  3 ). Population genetics analyses found most tested species to be in Hardy-Weinberg equilibrium, with only five species failing equilibrium assumptions. For the willow warbler and common chiffchaff, this was anticipated as their data included individuals of known subspecies. For the remainder, it is possible that a similar population substructure exists or that there are heterozygote deficiencies or advantages potentially arising from a variety of factors such as inbreeding, hybridisation or recent population bottlenecks  (Hedrick, 1987; Lade et al.
VII. ACKNOWLEDGEMENTS The authors would like to thank Chris Guglielmo, Yolanda Morbey and Ivan Maggini for support with field data collection at Long Point Bird Observatory (Canada), and Andrea Galimberti, Cristina Possenti, Fernando Spina and the late Nicola Saino for support with genetic analyses and field data collection in Ventotene Island (Italy). We would like to thank BirdLife International for supplying the species range data used to calculate migration phenology traits. Images were created in BioRender. com. This work is based on the research supported wholly/in part by the National Research Foundation of South Africa  (Grant Number: 112062).
The demand for (near) real-time stream processing has been increasing over the years since many modern applications (e. g. , Internet-of-Things) generate massive amounts of streaming data over time and analytical insights from such data can bring several benefits, e. g. , for real-time decision making  [14]. As its name implies, (near) real-time stream processing needs to immediately deliver and analyse data streams upon their arrival. Since streaming data grow bigger, faster, and become more complex by the time, there exist several inherent problems which are still challenging issues, such as (i) the unbounded size of streaming data, (ii) time-varying model, concept drift, or dataset shift, and (iii) uncertainty and imperfection, etc. We refer the readers to  [14] ,  [15]  for good surveys on data stream analysis.
Computer Vision We begin this section with one of the earliest and most popular applications of tensor tracking: visual tracking which is an important task in computer vision  [120]. Naturally, video datasets can be represented as 4-th order streaming tensors of dimensionality, width × height × channel × time. Accordingly, there are several studies devoted to developing tensor-based visual trackers for better modeling the appearance of target objects, such as  [80] ,  [121] -  [123] , to name a few. For example, Hu et al. in  [80]  proposed the so-called IRTSA tracker using incremental tensor subspace learning to capture the appearance of objects. Zhang et al. in  [121]  introduced another visual tracker called DTAMU which stands for dynamic tensor analysis with mean update. Weiming et al.
Oblique projection and instrumental variable-based techniques can handle colored noises. Therefore, it is desirable to extend these approaches from subspace tracking to tensor tracking. b) Outliers and Missing Data. They are now becoming more and more ubiquitous in modern datasets. Outliers are data points that appear to be inconsistent with or exhibit abnormal behaviour different from others. Missing observations are often encountered during the data acquisition and collection. Both outliers and missing data can cause several issues (e. g. , they introduce bias in estimation) for knowledge discovery from data in general and data streams in particular  [166]. Accordingly, dealing with them is an essential task in the analysis of corrupted datasets which has been still a hot topic in data mining for decades.
The submodular knapsack can be reformulated as a BSOCP problem, which is an acceptable formulation to CPLEX  [44]  and SCIP  [45]. These solvers implement LP outer approximation-based branch-and-cut (LP-B&C) algorithm  [46]  to solve the BSOCP or general Mixed-Integer Second-Order Conic Programming (MISOCP) problems. The LP outer approximation is sometimes called the polyhedral outer approximation (or polyhedral relaxation). In fact, any second-order conic program (SOCP) is polynomially reducible to a linear program  [47]. As for a submodular knapsack constraint, general solvers will linearize it into an intersection of a set of classical knapsack constraints, thus, inefficiency arises if too many linearizations are applied. On the other hand, there are alternative exact approaches to solve some classes of nonconvex MINLPs using PWL relaxations  [48, 27].
Finally, we perform computational experiments on many instances to evaluate the proposed algorithms. The computational results show that our tailored branch-and-price algorithms for DW reformulation outperform the conventional branch-and-cut algorithm for BSOCP formulation implemented in a state-of-art commercial solver; and the PWL-B&C algorithm can be a standalone algorithm for submodular knapsack. The source code and benchmark are released on our project website https://github. com/lidingxu/cbp.
MBQCP formulation We present a non-convex Mixed Binary Quadratically Constrained Programming formulation for the submodular knapsack problem (with conflicts). Although we do not use this formulation to solve the price subproblems, this formulation inspires PWL relaxation and the PWL-B&C algorithm. Here, we introduce a slack variable w to define the sum i∈N ′ a ′ i x i. Then our MBQCP formulation becomes the following non-convex MINLP program: max i∈N ′ π ′ i x i , (15a) s. t. i∈N ′ a ′ i x i = w, (15b) σ 2 i∈N ′ b ′ i x i ≤ (c -w) 2 , ( 15c ) x i 1 + x i 2 ≤ 1, ∀(i 1 , i 2 ) ∈ D ′ , (15d) x i ∈ {0, 1}, ∀i ∈ N ′ , (15e) w ∈ [0, c]. (15f) Although the program contains a concave quadratic constraint (15c), the nonlinearity is only a univariate quadratic function compared to the |N ′ |dimensional nonlinear SOC function f in (10b).
, 2018) , or automatic sleep staging  (Chambon et al. , 2018; Perslev et al. , 2021). Even though the regression context has been less explored in the literature, it has been shown to be successful for biomarker learning, e. g. , focusing on brain age as an application  (Al Zoubi et al. , 2018; Engemann et al. , 2020; Sun et al. , 2019). In the following, we focus on methods for regression modeling in the particular case of statistical discrepancies between datasets, for example, due to different populations, acquisition devices, or tasks performed during the recording. In other words, we aim to fit a regression model on one dataset and apply it on another. Different approaches have been explored to predict cognitive-behavioral or biomedical outcomes from M/EEG data. Methods like Common Spatial Filtering (CSP)  (Koles, 1991)  or Source Power Comodulation (SPoC)  (Dähne et al.
, , 2013) )  to define non-linear feature transformations that facilitate statistical learning with linear models. These techniques perform remarkably well given their simplicity  (Congedo et al. , 2013; Nguyen et al. , 2017; Sabbagh et al. , 2020)  and are competitive with methods exploiting anatomical information or endto-end deep learning approaches  (Engemann et al. , 2022). As the field of Riemannian geometry applied to M/EEG is beginning to expand and consolidate, many opportunities remain unexplored. In this work, we focus on investigating the utility of the Riemannian framework for defining dataset-harmonizing transformations. The recent emergence of large public databases and advances in ML have led to promising prediction models. Yet, these models can be sensitive to shifts in the data distribution and may perform poorly when applied to datasets from other clinical or research contexts.
We refer to these gaps between datasets as dataset shifts  (Dockès et al. , 2021; Quinonero-Candela et al. , 2008). This issue has also been referred to as batch effects  Li et al. (2022). In this work, we focused on domain adaptation techniques that attempt to deal with these shifts. We aim for a predictive model to not only perform well on the data it has been trained on, called source domain, but also when applied to data from a distinct statistical distribution, called target domain. Many domain adaptation methods exist, ranging from simple approaches minimizing the difference between the second-order statistics of source and target domains  (Sun et al. , 2017) , to more sophisticated models measuring the distance between deep representations of the source and target domains based on optimal transport  (Damodaran et al.
, 2018). In the context of brain data analysis, Canonical Correlation Analysis (CCA) and multiway CCA (MCCA) have been largely applied to find common brain activity and combine data across subjects when the same stimulus is presented to them  (de Cheveigné et al. , 2019; Dmochowski et al. , 2018; Lankinen et al. , 2014 Lankinen et al. , , 2018)). The experimental setups we are interested in do not meet these assumptions, for example, we consider recordings at rest and from different datasets with different subjects for which CCA or MCCA are not adapted. As we wish to work with M/EEG covariance matrices as basic signal representations for machine learning, we focus on techniques that explicitly use the geometry of SPD matrices to model the statistical distributions of distinct source and target datasets.
One first ap-proach proposed in the BCI context is to re-center the distributions to a common point of the SPD space  (Li et al. , 2021; Yair et al. , 2019; Zanini et al. , 2018). To get a better alignment of the distributions,  (Bleuzé et al. , 2021; Maman et al. , 2019; Rodrigues et al. , 2019)  propose to complement this re-centering step by adding a re-scale and a rotation correction. These covariance-based methods were initially developed to solve classification problems and are not necessarily applicable to regression without modification. In addition, most of them require labels in the target domain  (Bleuzé et al. , 2021; Rodrigues et al. , 2019)  for alignment. We focus on unsupervised alignment methods that can be readily used for regression modeling. In this paper, we develop a model-based approach for tackling dataset shifts in M/EEG data in which we consider re-centering, re-scaling, and rotation techniques from previous research on classification  (Bleuzé et al.
, 2021; Maman et al. , 2019; Rodrigues et al. , 2019)  to regression contexts, while assuming that no labeled data is available in the target domain. We build on top of the conceptual framework from  (Sabbagh et al. , 2020)  The article is organized as follows. In Section 2, we extend the generative model from  (Sabbagh et al. , 2020)  to express and decompose dataset shifts into distinct factors, which motivates the three steps we use to compensate for dataset shifts: re-centering, re-scaling, and rotation correction. In Sections 3 and 4, we assess the robustness of these alignment steps using simulations and real-world M/EEG data.