dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation settings To evaluate our recommendation method, we apply Ad-visIL to four test datasets and eighteen scenarios. Test datasets We ran experiments on four test datasets denoted by INRand 2 , FOOD100, INAT100 and LAND100. INRand 2 is obtained by randomly sampling 100-classes of ImageNet  [11]. The three other test datasets FOOD100, INAT100 and LAND100 contain 100 classes sampled from FOOD101  [6] , iNaturalist  [49]  and Google Landmarks v1  [35] , respectively. These three datasets are thematic and fine-grained since they were designed for the classification of images of food, natural concepts and tourist points of interest, respectively. Test scenarios. The memory budgets are the same three as those used for generating reference configurations. We consider six new distributions of classes across steps, whose settings are presented in the "Test" row of Table  1.
Evaluation protocol As in the case of reference configurations, in our evaluation, model performance is measured in terms of average incremental accuracy (Equation  1 ). We assess the recommendations provided by AdvisIL as follows. Given a test dataset and a test scenario, we compare the average incremental accuracy of the models trained according to: i) AdvisIL's recommended pair (a,b). The model built with this pair is called the recommended model. ii) Oracle pair: an upper-bound for AdvisIL which selects by brute force the best-performing pair of algorithm and backbone network for each test configuration. It is not usable in practice since it assumes the access to whole stream of data from the beginning. In addition, the extensive computations it requires are costly in terms of time and computational power.
iii) Baseline pairs, which are three fixed combinations: b 1 : (FeTrIL, ResNet), b 2 : (DSLDA, ShuffleNet) and b 3 : (SPB, MobileNet) whose corresponding models are called baseline models. These pairs were selected according to their aggregated rank on reference datasets (Equation 5), i. e. that they have the highest rank according to their accuracy on all reference experiments. In practice, we present in Table  2  the performance gap between these baseline models and the recommended model. These gaps are denoted ∆ b1 , ∆ b2 , and ∆ b3.
On average, across all four test datasets and eighteen test scenarios, the recommended model outperforms the best fixed model by 1. 29% (Table  2 ). The accuracy of the recommended models is below the oracle, with an average gap of 1. 04%. The gap between the average classification performance of the recommended model and of that of the oracle is stable across scenarios, regardless of the number of steps and class distribution among the steps, and regardless of the memory budget. It is also stable across test datasets. Therefore, the recommendations are relevant whatever the scenario and dataset. Results per scenario are presented in Table  3    mance improvement obtained by AdvisIL is explained by the fact that its recommendations follow the trends observed in the experiments corresponding to the reference configurations, whereas no fixed combination of algorithm and backbone network outperforms all others in all scenarios.
Ablation study AdvisIL recommends an algorithm-backbone combination based on pre-computed experiments on several reference datasets. In what follows, we study the individual contribution of the different components of the method: choice of the backbone network, choice of the algorithm and choice of the number of available reference datasets. Using a single backbone network. We present results with a fixed backbone network and different algorithms in the left part of Table  4. Each column corresponds to the average classification performance of models trained according to AdvisIL recommendations when the set of reference configurations is reduced to consider a single type of backbone network. The results indicate that AdvisIL's recommendations are better when we use multiple backbone networks rather than just one.
Using a single CIL algorithm. Similarly, we present in the right part of Table  4  results obtained when only a single algorithm is available. Each column corresponds to the average classification performance obtained with AdvisIL recommendations when the set of reference configurations is reduced to consider a single algorithm and multiple backbone networks. When we reduce the set of configurations to DSLDA or FeTrIL, the performance drop is rather low, because these two algorithms have similar performance, and tend to perform much better than the others tested, so that they are often recommended by AdvisIL. On the contrary, when using only SPB, the third best-performing algorithm, performance drops significantly. Using fewer reference datasets. AdvisIL's recommen-dation are computed by ranking the classification performance of candidate algorithm-backbone pairs on the reference datasets.
Here, we measure how using less reference datasets impacts the relevance of the recommendations. Results obtained by using only one (∆ #1 ), two (∆ #2 ), three (∆ #3 ) and four (∆ #4 ) out of five reference datasets are presented in Table  5. They are averaged over all possible combinations of reference datasets. Using less reference datasets is damageable to recommendation relevance, with up to 1. 88% loss in the averaged classification performance when using only one reference dataset. Interestingly, when using only two reference datasets, AdvisIL already surpasses the baselines presented in Table  2. The impact of the ablation fades when the number of reference datasets grows, and becomes negligible when four of them are used. Overall, the results of Table  4  show that both the choice of an algorithm and the choice of a backbone network contribute to the relevance of AdvisIL's recommendations.
This entailed a consequent computation effort. We may generate the reference configurations more efficiently, taking inspiration from hyperparameter search approaches, e. g. [53]. Regarding the recommendation process, we could take into account the similarity between the user's dataset and the datasets used to build the set of reference configurations  [1]. From a practical point of view, we note that this similarity can only be computed using the first batch of the user's dataset as it is the only one we may have access to. We focused on memory constraints and expressed them as the number of parameters of the neural networks. It may be possible to take other constraints into account, such as the training complexity (the maximum number of computations for each incremental step) and inference time.
Classification performance for the three baseline models and for the recommended model. Performance is averaged over the four test datasets. For each scenario (m, k, α, β), the best result is in bold and the combination of algorithm (either DSLDA (DS) or FeTrIL (FT)) and backbone ( MobileNet (Mob) , ResNet (Res) or Shufflenet (Shu) ) recommended by AdvisIL is provided.
S-HDAg can bind GSTP1 mRNA, thus decreasing the GSTP1 protein expression, leading to ROS accumulation and subsequent rise in cellular apoptosis and DNA damage in proliferative cells. 76  More recently, Yu et al  77  performed a sophisticated study based on microarray dataset analysis, comparing cancerous and para-cancerous specimens from individuals with CHB or CDH-related HCC. Seven genes closely involved in mitotic cell cycle and DNA replication (CDC6, CDC45, CDCA5, CDCA8, CENPH, MCM4, MCM7) were found to be differently expressed (mostly upregulated) only in the CDH-driven HCC subgroup; therefore, the alteration of pathways in which these genes are involved seems selectively mediated by HDV. Overall, these findings show that the molecular profile of CDH-related HCC is characterized by an overexpression of genes involved in cell cycle and DNA replication/ repair, highlighting genomic instability as a key mechanism of hepatocarcinogenesis.
To do so, information on stone artefact variability, production technologies and design choices (henceforth: lithic data) is vital for our understanding of early human evolution as stone artefacts are abundant, durable and yield rich insights into fundamental dimensions of human behavior  17, 18. We here describe an open-access dataset  19  compiled by a concerted effort of lithic experts across Europe to help overcoming this problem and to begin asking pan-European questions on synchronic and diachronic techno-cultural variability, and to facilitate critical interrogations of the scope and quality of current knowledge and the significance of higher-level taxonomic denominations to organize the record (e. g. Final Magdalenian, Hamburgian, Azilian, Federmesser, Ahrensburgian, Swiderian, Belloisian, etc.
)  20, 21. The dataset  19  was constructed with the aim of 1) rendering long-standing hypotheses and narrative framings of the Late Glacial amendable to systematic empirical scrutiny, and 2) breaking away from terminological and semantic discussions and instead to open new possibilities for data-driven investigations of millennial-scale patterns and trends in human technological behavior beyond labels and categorizations largely contingent on research history. Two complementary but interrelated concerns motivate our effort: a) the growing recognition of scale in archaeology and cultural evolutionary studies with the resulting need to take serious hitherto largely neglected macro-archaeological processes and phenomena  22, 23  , and b) the impeding effects of regional research traditions and culture-historical frames of reference for regional and continental-scale comparison  20, 24  , to address both cross-sectional and longitudinal questions in Late Glacial archaeology and beyond.
Our efforts resonate with calls for synthesis and higher-level archaeological analysis  21, 25  using 'big data' or at least unprecedentedly large datasets, while also duly accounting for the fragmentary and complex nature of the available material registers  [26] [27] [28]. The latter draws attention to a nascent transformation in knowledge ecologies based on an ethos of sharing and collaborative inquiry  25, [29] [30] [31]  necessary to develop data infrastructures promoting broad-scale comparative investigations at cross-regional scales. The here-presented dataset  19  is a first attempt to work towards such a shared data infrastructure for Late Glacial Europe, to build a scalable higher-order data framework for lithic analysts and to probe into the untested utility and potential of such integrated collaborative work.
NACs are situated in space and time: we employ macro-regions reflecting geographic and ecological differences but also scholarly expertise and predefined time slices (binned in equidistant 1k-year intervals) to construct the data framework. Geographic macro-regions (n = 16) and employed time slices (n = 4) are shown in Fig. 1  (cf. Supplementary Table  1 ). Time slices I to IV span the period between 15 and 11 ka BP, including the terminal part of the Pleniglacial (Late Upper Paleolithic), the Late Glacial (Final Paleolithic) and the incipient Holocene (earliest Mesolithic). The individual NACs (n = 86) were assembled from the literature based on the current state-of-the-art of lithic research in a given macro-region and then assigned to a single or multiple time slices depending on the latest relative, stratigraphic and/or chronometric dating evidence.
For each NAC, a broad range of lithic variables pertaining to different levels of technological organization were then extracted from the currently available literature by expert editors acquainted with a given macro-region. Collaborative data compilation and recording process. The workflow of collaborative data compilation, preparation and synthesis is outlined in Fig. 2. Each macro-region was handled by an expert editor intimately familiar with the regional lithic evidence and the latest archaeological debates and interpretations. In a first step, regional lithic experts carefully reviewed the literature and identified the latest culture-historical framework accepted for a given macro-region, thereby assembling a regional inventory of NACs. Expert editors were then confronted with a first draft recording scheme integrating a broad range of lithic variables and asked to synthesize and test-record the published lithic data for each inventoried NAC within their macro-region accordingly.
Initial insights and encountered problems were subsequently discussed by all research participants on a two-day online workshop to adjust and improve the data infrastructure and to align expectations and basic definitions as well as to address open questions  21. Based on the critical feedback received, the lithic data structure was updated, and all initial entries thoroughly checked by the CLIOARCH team. Datasets were then sent back to the expert editors, who had to review and revise their provided data and address any identified issues and inconsistencies. Expert editors were supported by CLIOARCH core members throughout the entire process to ensure overall data coherence and to provide feedback and/or a second opinion if needed. The revised macro-regional datasets were then submitted for final data integration and preparation.
Dataset design and data collection principles. The 1511NAC dataset  19  is designed to facilitate macro-archaeological analyses of lithic diversity, complexity and cultural evolution in Europe between 15 and 11 ka BP  21. Part of this endeavor is the examination of potential offsets between different lithic data domains and/or dimensions of lithic production technologies and toolkits. The overall structure of the dataset  19  reflects this analytical aim (Table  1 ): the first data domain (I) gathers NAC-level information on the structural composition of lithic toolkits based on a broad distinction of lithic morphotypes divided into 'armatures' and 'domestic' tools respectively (Tables  2, 3 ); the second data domain (II) draws together NAC-level lithic information on the organization of production technologies in the form of discrete traits, focusing especially on the laminar component (Tables  4 5 6 7 8 9 ); the third data domain (III), finally, consists of a library of 2D outline shapes of key tool groups (armatures, endscrapers and borers/perforators) extracted from the literature and digitally prepared for analysis with site-level resolution.
Data Records Folder structure. The complete 1511NAC dataset is openly available at Zenodo  19  and structured as shown in Fig. 4. The data repository comprises four principal folders: 1511NAC_Dataset, 1_data, 2_scripts and 3_output. Generally speaking, the folder 1511NAC_Dataset contains all data entries relevant for researchers who are interested in using the dataset  19. The folders 1_data, 2_scripts and 3_output contain the raw data and R scripts that were created and used to generate the dataset  19  and the figures in this paper. Each principal folder is described in more detail below.
1511NAC_Dataset 1_data This folder contains both the raw and the final data as contained in the 1511NAC_Dataset, as well as the data required to reproduce the figures and tables included in this paper. The files and the dataset  19  included here are primarily provided for full transparency. They allow researchers to trace the entire process of data processing leading to the final dataset  19  supplied. • The artefact_images_by_region subfolder is identical as the above-described. • The raw subfolder contains the original, unprocessed. csv files holding the site-specific metadata as well as the trait-data of data domains I and II as submitted by the macro-regional expert editors. • The prepared subfolder contains all data derived from the raw master tables and the prepared lithic artefact images, etc.
RE_diverse_spec Focus on a diverse raw material base with evidence on specialization on (a) selected raw material(s) RE_uniform_nospec Focus on uniform raw material base yet no obvious specialization, i. e. , adaptation of the knapping strategy to the given raw material (properties, forms) RE_uniform_spec Focus on a uniform raw material base with evidence for specialization, i. e. , adaptation of knapping strategies to raw material conditions/properties RE_highqual_spec Global focus on high quality raw material with evident adaptation of knapping strategies to the properties of the raw material Table  6. Categorical NAC-level raw material economy data recorded as systematic presences/absences in data domain II (Technology). analytical units and data domains in detail. The general trait-structure of the dataset  19  is shown in Fig.
3. The primary analytical units are the macro-regional NACs and in the case of outline data, both NACs and individual archaeological key sites. We provide each data domain as a separate. csv file for easy and flexible implementation. Linked metadata provides further contextual information on the key sites respectively attributed to macro-regional NACs, such as lithic assemblage provenance, site quality, radiometric dating, and the underlying bibliographic sources. Cultural taxonomic units. The dataset  19  comprises 86 separate NAC entries. Supplementary Table  3  shows how these are spread over time slices I to IV and the 16 captured macro-regions. These NACs cover a large portion of Northern and Western Europe and circumscribe the following geographic macro-regions: Atlantic Iberia, Cantabrian Iberia, Mediterranean Iberia, Western France, Britain, Northern France, the Low Countries, Switzerland, Southern Germany, Northern Germany, Southern Scandinavia, Northeastern Italy, Austria/ Slovakia/Hungary, Moravia/Bohemia, Poland, and Lithuania.
NAC naming conventions follow common literature practice, but priority was always given to the state-of-the-art understanding of regional experts. NACs were either recorded as bounded units (e. g. , 'Magdalenian' , 'Federmesser' , ' Ahrensburgian' or 'Belloisian') or as chronological sub-units (e. g. , 'Final Magdalenian' , 'Havelte' , 'Early Azilian' or 'Late Laborian'), depending on the importance of the latter in the archaeological discourse and the prevailing opinion/consensus in the respective body of expert literature. Individual NACs identified and recorded in this way are linked to a portfolio of key archaeological sites anchoring them in space and time and exemplifying the lithic information assembled in the toolkit and technology data domains (n = 350). These sites tend to be the best-studied site exemplars of a NAC in       2 ).
The domestic tools group sports the following morphotypes (n = 10): endscrapers, burins, borers (including perforators, becs and other atypical forms), Zinken (borers with a typical, strongly bent nose), notched pieces, denticulated pieces, combinatory tools/multitools, large blade knives/heavily retouched larger blades, (other) LCTs (Large Cutting Tools) and flake adzes/axes (cf. Table  3 ). Presence data (1) indicates NAC-level recurrence and thus inter-assemblage importance of these morphotypes. Technology data. Discrete binary data on technology (presence/absence) for different NACs is recorded in 54 individual techno-typological traits, organized in five sub-domains which together make up data domain II. Again, these data operate on the aggregate level and the recorded information refers to NAC-wide trends and patterns not necessarily reducible to observations pertaining to individual archaeological sites.
2D artefact outlines. The 2D outline library (data domain III) hosts 5986 individual lithic tool outlines, compiled and grouped in three broader categories: armatures (AR), endscrapers (ES), and borers (BR) with reference to the categories listed in the toolkit data domain. Burins were not collected because of the huge variability of artefact shapes associated with this tool group and because the so captured variability is shaped by extreme levels of blank-derived noise. The AR group comprises 3512 artefact outlines linked to tool morphotypes classified as 'armatures'. The ES group contains 1930 outlines identified as 'endscrapers'. The BR group contains 544 outlines identified as 'borers' and perforators. AR and ES outlines were collected from complete lithic artefacts only, or artefacts for which the missing part (for example, the apex of a point) could unambiguously be reconstructed.
In addition, AR outlines come with supplementary information on point axiality, distinguishing between distal ('d') and proximal ('p') lithic points if possible. A breakdown of the spread of outlines across time slices and macro-regions is provided in Fig. 5. The outline datasets  19  also harbors metric artefact-level information, even though this data is only available at the sub-sample level and was not systematically collected. Where scale bars were available on the submitted images (AR: n = 2966; ES: n = 1628; and BR: n = 498), a pixel-to-centimeter ratio/scaling factor was created for each image separately using tpsUtil and tpsDig2 in R  41. This scaling factor was then implemented in the outline extraction process  39  using the outlineR package  42  to automatically associate a scaling factor -where applicable -with individual outlines, allowing for the quick and ready extraction of a multitude of user-defined artefact-specific metric measurements.
The dataset  19  supplies the following artefact-level measurements extracted via the Momocs package  38  : length (cm), width (cm), area (cm 2 ), perimeter (cm), calliper (cm), circularity and eccentricity (see  38  for a definition and description of these specific measurements). Metadata. Contextual and supplementary information on the selected archaeological key sites (Supplementary Tables  2, 5 ) anchoring regional NACs in the dataset  19  is organized according to three sub-domains. The first harbors information on site location (geographic coordinates), dating range, dating method, dating quality, the type of site, the nature, context, and coherence of the lithic assemblage(s) in question, faunal preservation as well as the original year of excavation (Table  10 ). Sub-domain two addresses the quality and reliability of the key sites listed by calculating quality scores (1 to 6) and ranks (1 to 4) based on information provided in the first sub-domain (Supplementary Tables  4, 6 ).
Lower quality scores broadly indicate less reliable archaeological contexts expressed in lower ranks. The third and last sub-domain is a register of processed tool outlines stored in the outline library so that analysts can quickly identify the distribution of available outlines across sites (Supplementary Table  7 ). Dataset coding and abbreviations used. Metadata (NACs, macro-regions, expert editors, etc. ), tool groups, technological traits, 2D artefact outlines and archaeological site contexts are coded as abbreviations. All abbreviations used in the dataset  19  are listed in the foregoing tables or are explained in the Supplementary Information.
technical Validation Data quality and reliability. Quality scores were calculated for each archaeological site context based on the associated metadata provided (Supplementary Table  4 ). The score was derived by summing all positive entries (1s) for the following data categories: dating quality = reliable, site stratigraphy = stratified, assemblage position = primary/in-situ, assemblage coherence = homogeneous, and excavation year = 1980-2000 (Supplementary Table  6 ). Recorded years of excavation = after 2000 (1s) were double weighted, resulting in a possible score of 6 for the most dependable archaeological contexts. Figure  6a  shows the geographic distribution of quality scores across Europe, pointing to north-south and east-west quality gradients with Western and Southern European sites scoring generally higher.