dataesr/scientific-paragraphs-categorization huggingface dataset:
Efficiency of path evaluation We now study the efficiency of data path computations over the graph. Tab. 2 and Tab. 3 show, for each dataset and entity type pair, T 0 is the time to evaluate the corresponding path queries without the view-based optimization of Sec. 4. 2 and 4. 3, referred to as VBO from now on. |Q T O | is the number of queries whose execution we stopped (timeout of 30s) without VBO. |Q NV | is the number of queries for which Algo. 1 did not recommend a view. T R is the time to evaluate the rewritten queries on the data graph,  while T Q NV is the time to evaluate the non-rewritten queries Q NV ; T = T R + T Q NV is the (total) execution time to evaluate queries using VBO. Finally, s = T 0 /T is the speed-up due to VBO. We do not report times to materialize views because they were all very short (less than 0.
01s). All times are in seconds. The evaluation time T 0 without VBO ranges from 100s to 2000s; these path queries require 5 to 9 joins, on graphs of up to more than 200,000 edges (Tab. 1). |Q NV |, the number of queries that could not make use of any views, is rather small, which is good. The number of candidate views, respectively, materialized views depend on the complexity of the dataset, and thus on the complexity of the paths. The total path evaluation time T is reasonable. Finally, the VBO speed-up is at least 2× and at most 137×, showing that our view-based algorithm allows to evaluate path queries much more efficiently.
Complementary to these, we focus on identifying, and efficiently computing, all paths between certain extracted entities, to give a first global look at the dataset content, for graphs obtained from multiple data models. Our view selection problem is a restriction (to path-only queries) of those considered, e. g. , in  [13, 14, 17, 19]. This allows for its low complexity, while being very effective. Our algorithms rely on collection graphs built by Abstra  [8, 9] , the interactive path enumeration approach, including VBO, is novel; it has been demonstrated in  [7].
15 ,181 in the PubMed dataset. (τ 1 , τ 2 ) N root min(S p ) max(S p ) (τ P , τ (τ 1 , τ 2 ) N root N gen min(S p ) max(S p ) O ) 21 0 13,988 (τ P , τ O ) 99 1 0 629 (τ P , τ L ) 21 0 15,181 (τ P , τ L ) 95 5 0 137 (τ L , τ O ) 21 0 5,054 (τ L , τ O ) 97 3 0 603 (τ P , τ P ) 21 0 389 (τ P , τ P ) 97 3 0 89 (τ L , τ L ) 21 0 1,214 (τ L , τ L ) 97 3 0 3,050 (τ O , τ O ) 21 3 3,090 (τ O , τ O ) 97 3 0 8,960 Fig. 2. Entity paths in PubMed (left) and Nasa (right). PubMed: min(L p ) = 5; max(L p ) = 8. Nasa: min(L p ) = 5; max(L p ) = 9. (τ 1 , τ 2 ) N root max(L p ) min(S p ) max(S p ) (τ 1 , τ 2 ) N root max(L p ) min(S p ) max(S p ) (τ P , τ O ) 41 7 0 651 (τ P , τ O ) 48 7 0 2,593 (τ P , τ L ) 33 7 0 193 (τ P , τ L ) 39 7 0 760 (τ L , τ O ) 21 5 0 1,412 (τ L , τ O ) 39 5 0 258 (τ P , τ P ) 28 7 0 35 (τ P , τ P ) 36 7 0 207 (τ L , τ L ) 15 5 2 158 (τ L , τ L ) 15 5 0 674 (τ O , τ O ) 21 5 0 1,232 (τ O , τ O ) 21 5 0 4,889 Fig.
In recent years, computer science has made major advances in understanding drawing behavior. Artificial intelligence, and more precisely deep learning, has displayed unprecedented performance in the automatic recognition and classification of large databases of sketches and drawings collected through touchpad devices. Although deep learning can perform these tasks with high accuracy, the way they are performed by the algorithms remains largely unexplored. Improving the interpretability of deep neural networks is a very active research area, with promising recent advances in understanding human cognition. Deep learning thus offers a powerful framework to study drawing behavior and the underlying cognitive processes, particularly in children and non-human animals, on whom knowledge is incomplete.
Classification The most popular application of deep learning is classification. Classification plays a major role in computer vision in tasks as varied as classifying Alzheimer's disease from magnetic resonance images  (Wen et al. , 2020) , identifying fish species  (Li X. et al. , 2015) , and recognizing malware images  (Yue, 2017). Classification is also a preliminary step in other tasks, such as segmentation. CNNs are mostly used for image classification in a supervised learning paradigm, where a model is trained to classify images into categories predefined by the user, by learning from a dataset of labeled images (i. e. , images for which the category is known). Once trained, the model is used to predict the categories of new, unlabeled images. The first CNN developed for sketch classification was Sketch-a-Net  (Yu et al.
Generation Pallavi  (2019)  devised SuggestiveGAN, a generative adversarial network (GAN;  Karras et al. , 2019). A GAN is an unsupervised algorithm in which two neural networks compete. Fundamentally, one neural network (the discriminator) is a classifier to distinguish real images from fake images; the other neural network (the generator) tries to generate the most 'realistic' fake images (according to the real dataset). SuggestiveGAN is able to reconstruct incomplete drawings (with missing strokes). The proposed model grasps the structure of the drawings at the expense of the details.
Nonetheless, segmentation could be of great interest in this kind of analysis. For example, segmentation can be used to analyze body proportions, which are indicative of the emotional state in children [e. g. , disproportionally large hands can express aggression  (Leo, 2015) , and the relative size of the head and trunk varies with age in children  (Thomas and Tsalimi, 1988) ]. In addition, annotated sketch databases are not common, and annotating sketches will lead to bias, depending on the perception of the person doing the annotation. For this reason, SSS should be studied in depth through unsupervised stroke-level segmentation or by using temporal sequence algorithms  (Gharghabi et al. , 2019) , which also consider the time spent not drawing. Applying SSS to scribbles could lead to semantic segmentation, not necessarily obvious to human perception.
As orangutans are more dexterous with their hands than chimpanzees and gorillas  (Mackinnon, 1974) , the first layer could separate humans and orangutans as well as the two other species. Another way to interpret CNNs is by using the model parameters proposed by  Chen et al. (2016) , who developed InfoGAN, a generative model for interpretation that maximizes mutual information to discover latent features. This method has been evaluated using various datasets, such as the MNIST dataset (LeCun, 1998), a database of handwritten digits. In this case, the generator was able to discover latent features describing, for example, digit type, width, and rotation of the digits. InfoGAN has also been used on the CelebA face dataset  (Liu et al. , 2015) , revealing encoded features like the azimuth, the presence of glasses, hairstyle, and emotion.
From these results, we anticipate that InfoGAN would have a high appeal in studying sketches, to explore the development of perception and representation in children by identifying features that are common and those that are discriminant between children and adult drawings. 2018 ) collected drawings from young children, older children, and adults to understand how object representation develops with age. They used a method called transfer learning, where a model trained on one task is reused for another task. Transfer learning saves computing resources and allows for high performance with a relatively small number of datasets because it exploits the fact that some properties learned by a model to solve one task are useful for many other related tasks. In the study by  Long et al. (2018) , sketches were encoded by a pre-trained CNN; features were extracted from layers across several depths; and representational dissimilarity matrices (RDMs) were calculated for each of the three-age classes and compared.
Available datasets for drawing(s) analysis As a result of the widespread availability of touch-screen devices, drawings and in particular, sketches, can now be more easily collected and analyzed. Moreover, scholars can also collect drawings online through crowdsourcing or online drawing games. However, these datasets have been rarely used in psychological or anthropological studies, possibly because of the lack of associated metadata on the participants, such as their age, location, gender, culture, or drawing skill level. This metadata can be difficult to collect because they may require ethical approval. Datasets can be organized into two families: amateur and expert datasets. In this review, amateur datasets mostly collate data on sketches and drawings without associated metadata on the person who did the drawing (in particular the drawing skills).
Amateur datasets A major -and one of the first -sketch datasets is QuickDraw by Google. This dataset includes more than 50 million sketches belonging to over 345 object categories  (Jongejan et al. , 2016). QuickDraw is an online game where participants are asked to draw an object in 20 s, and a network is trained to recognize that object. For each sketch, the category is stored, as well as a Boolean indicating if the category was recognized by the game, the timestamp of the sketch, the country where the drawing was made, and the spatial and temporal data of the strokes. Despite some limitations (e. g. , the lack of metadata such as the sex and age of the person who did the drawing), QuickDraw is a highly promising tool for investigating cultural differences in drawing-based object representations.
The second important amateur dataset is the TU-Berlin dataset, which provides more than 20,000 sketches of 250 categories of common objects drawn by 1,350 unique participants  (Eitz et al. , 2012). TU-Berlin sketches were collected via Amazon Mechanical Turk (AMT), a crowdsourcing marketplace where requesters hire crowd-workers to perform particular tasks (in our case, drawing an object from a given category). Furthermore, each drawing is associated with a second category, for which other participants are asked to identify the drawn object. The temporal order of the strokes is available for each drawing; however, the personal data on the participants are not available. A third dataset is the Sketchy database, which consists of 12,500 photographs of 75,471 sketches belonging to 125 object categories  (Sangkloy et al.
, 2016). Each sketch is paired with a photograph, and each photograph is linked to a number from 1 to 5, characterizing the ease of sketching. Temporal data on strokes are available for each sketch. As most of the datasets are constructed by asking the participants to draw a particular object, there may be a large variability with respect to the drawn object and its features. For example, when asked to draw a dog, two participants may think about completely different breeds, which can be undesirable for the analyses. For this reason, datasets containing sketches representing photographs can lead to a decrease in variability, which can be an asset for this type of data.
Experts' datasets Among expert datasets, Manga109  (Fujimoto et al. , 2016)  provides 109 Japanese comic books drawn by 94 professional creators with each book containing 194 pages on average. These books date from 1970 to 2010 and several genres are illustrated. Each page is annotated with rectangular areas characterizing the position of metadata, such as frames, text, and character (face, body), through software developed for this study. Hicsonmez et al. (2017)  collected more than 6,500 pages from a total of 24 children's book illustrators, with the goal of recognizing the authors using deep learning. The list of datasets in this review is not exhaustive, only the main datasets are described. Other sketches datasets exist, such as COAD  (Tirkaz et al. , 2012) , SPG  (Li et al. , 2018 ), SketchyScene (Zou et al.
Methods have been developed to interpret these results, such as heatmaps and similarity matrices, that are relevant to sketches. Another approach uses generative modeling (e. g. , GANs) to generate drawings, to analyze the generative process, and eventually infer the underlying behavior. However, while drawing ontogeny is known to critically depend on various factors such as culture, age, and sex, the large datasets of drawings and sketches, currently used to train CNN and other AI algorithms, usually lack this kind of information. Thus, it is important to develop new datasets, methods, and criteria to advance our understanding of drawing behavior. A dataset with many ancillary variables could, for example, allow cultural analysis. By unraveling the extraordinary predictive capacity of models and through ongoing research to make these models more transparent, AI will undoubtedly significantly contribute to improving our understanding of the fundamental behavior of drawing, for humans and their relatives.
Numerical results Algorithms were run on an Intel(R) Core(TM) i7-4770K CPU and 16GB of RAM running Fedora 36 Linux distribution. The algorithm was coded with g++ (gcc version 12. 1. 1) and uses Kv-0. 4. 50 for Interval Arithmetic. Kv uses boost libraries. Algorithms were compiled with -O3 -DNDEBUG -DKV_FASTROUND options. For the Linear Programming, we use PNL 1. 10. 4 compiled with -DCMAKE_BUILD_TYPE = Release and -DWITH_MPI = OFF, as a C++ wrapper to 5. 5. 2. 0-28. LPsolve is free but it is slower than other commercial solvers  2. PNL uses by default set_break_numeric_accuracy(lp, 5e-7) in lp_lib. c and sets default parameter values and tolerances to ProductionPARAM in lp_lib. h. In order to reduce the computational time of LPSolve, we set its verbosity to "SEVERE", and time out after 1 s.
VI. ACKNOWLEDGEMENTS We thank  Peter Zoller  for inspiring discussions and comments on the manuscript. BV acknowledges funding from the  Austrian Science Fundation  (FWF,  P 32597  N), and from the  French National Research Agency  ( ANR-20-CE47-0005 ,  JCJC  project  QRand ). AE acknowledges funding by the  German National Academy of Sciences Leopoldina  under the grant number  LPDS 2021-02  and by the  Walter Burke Institute for Theoretical Physics at Caltech. SN and SM acknowledge support from the  EU Horizon 2020 research and innovation program  under grant agreement no. 817482  ( PASQuanS ), the  Quan-TERA QuantHEP  project and the  Italian PRIN 2017. Project  QuantHEP  has received funding from the  Quan-tERA ERA-NET Cofund in Quantum Technologies implemented within the European Union 's  Horizon2020 program.
The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a variable number of images as well as variable image size without loss of performance. In addition, we define a set of constraints to allow the generation of a relevant synthetic dataset to train convolutional neural networks for the PS problem. Our proposed dataset is much larger than pre-existing ones, and contains many objects with challenging materials having anisotropic reflectance (e. g. metals, glass). We show on publicly available benchmarks that the combination of both these contributions drastically improves the accuracy of the estimated normal field, in comparison with previous state-of-the-art methods.
Existing training datasets Regardless of its architecture, a neural network needs to be trained on a proper dataset to perform well. In practice though, it is very difficult to acquire a large dataset of real images with 3D ground truths of photographed objects. For this reason, deep photometric stereo networks proposed in the literature often rely on training datasets of synthetic 3D objects, notably the Blobby and Structure datasets introduced in  [6] , and CyclePS in  [11]. The Blobby dataset is composed of 10 geometric shapes, each one observed from 1296 distinct viewpoints. As the name suggests, the shapes in Blobby are rather smooth and regular (Fig. 2a ). The Structure dataset consists in objects with complex geometry containing fine details (Fig. 2b ). It is composed of 8 objects, rendered in 3D from 1387 to 6874 viewpoints.
To simulate surfaces with non-Lambertian light reflectance, a material from the MERL  [28]  dataset is randomly drawn and applied in each rendering, providing a total of 25920 samples for Blobby and 59292 for Structure. In both cases, each sample is rendered under 64 different light directions, randomly selected on the hemisphere (Fig. 3c ). Finally, the CyclePS  [11]  dataset is also composed of complex objects, but contains only 18 objects rendered from 10 views (Fig. 2c ). However, the number of materials available is substantial because Disney's principled BSDF  [3]  parametric reflectance model is used. It allows the variation of the base colour, roughness, proportion of specular reflectance, etc. , thus the objects can be rendered using a near infinite number of materials. The training dataset presented in the present paper will also feature the possibility to generate as many materials as needed, while also considering much more geometric shapes than in existing sets.
(a) Blobby  [15]  (b) Structure  [15]  (c) CyclePS  [11]  (d) DiLiGenT  [34]  (e) DiLiGenT10 2  [31]  Figure  2 : Samples from existing datasets. The first three  [15, 11]  are synthetic ones, used for training the neural networks. Both the last ones  [34, 31]  are real-world datasets used for benchmarking. Our proposed multi-scale architecture is evaluated on both benchmarking datasets, and trained on a new synthetic training set, which contains much more objects with non-Lambertian reflectance.
Existing benchmarking datasets To validate the relevance of the training datasets, as well as to verify that the models trained on these synthetic data are able to generalize to real images, two real-world datasets exist: DiLiGenT [34] and DiLiGenT10 2  [31]. The DiLiGenT dataset comprises 10 different objects, taken from the same viewpoint under 96 different illuminations (Fig. 3a ). The reflectance of the objects in this dataset goes from quasi-Lambertian to moderately specular. For each photographed object, the ground truth normal map is provided, as well as the calibrated lighting directions and intensities. Therein, the ground truth geometry was acquired by manually registering laser scans with the images. The DiLiGenT10 2 dataset contains 10 different objects. Each object was explicitly fabricated with 10 different materials and photographed under 100 calibrated illuminations (Fig.
3b ). The ground truth was not obtained by scanning the objects, but from the 3D digital models used to machine the objects. This real dataset is particularly interesting for evaluating performances on highly specular materials and translucent ones. Indeed, it contains metallic materials, such as aluminium or steel, and a translucent one (acrylic). This dataset also contains diffuse and slightly specular materials, hence most of real-world material characteristics are present. The diversity of object shapes is also high as it contains objects with simple geometry like balls but also complex ones like turbines. It offers the opportunity to test the impact of diverse inter-reflection, shadow and shading effects. Today, it is the most complete dataset composed of real images available in PS.
PROPOSED LEARNING DATASET As discussed in Section 2, the existing Blobby and Structure synthetic datasets lack of diversity in terms of geometry and textures. For example, although the Structure dataset is composed of complex objects, all these objects are statues. Similarly, the number of different materials in the MERL material base is only 100. This is clearly not enough to model the huge diversity of materials present in the nature. The CyclePS dataset partially solves this issue, by allowing to generate infinitely many materials by randomly selecting parameters from a parametric BSDF model. Still, it remains limited in terms of geometry. Overall, a greater diversity of shapes and materials in the images of the training dataset would be beneficial for training networks for photometric stereo.
For these reasons, we propose here a new dataset, which includes a large variety of shapes and materials. In order to create this dataset, we implemented our own image data generation pipeline. We used the Blender  [8]  software with the Cycles rendering engine. As a result, our new dataset is composed of two parts: • Our Blobby contains objects with smooth surfaces; • Our Object contains objects with complex geometry: strong discontinuities, edges, corners, textures details, etc. Samples from our training dataset are shown in Fig. 6. Our Blobby has 3000 distinct objects, generated by the sum of random Gaussian potentials, followed by iso-surface extraction using the Marching Cubes algorithm  [27]. Our Object contains 76 detailed objects which are 3D meshes from the Sketchfab [2] website. Moreover, to allow the learning of non-Lambertian surfaces, more than 1100 different real materials, extracted from the ambientCG  [1]  website, are randomly applied to the objects, much more than the 100 materials of Structure and Blobby.
7a. Yet, many real-world objects exhibit a spatially-varying reflectance, which is a known limitation of existing PS techniques  [6]. To solve this problem in our generation pipeline, we rather incorporated a few spatially-varying material maps, as in the example of Fig. 7b. This technique was used for 50% of the renderings. It allowed us to create both objects with uniform reflectance, and others with spatially-varying one, as illustrated in Fig. 6. Finally, to generate data having realistic lighting conditions, we rendered all the images with both random illumination direction (Fig. 3c ) and random intensity. In total, 15 000 blobby samples and 45 000 object samples were generated this way. Table  1  summarizes the characteristics of the existing datasets, versus the ones we propose. In order to ensure the reproducibility of our results, the code and these learning datasets will be made publicly available online.
EXPERIMENTS In this section, we demonstrate the effectiveness of our proposed multi-scale architecture on publicly available benchmarks, namely DiLiGenT  [34]  and DiLiGenT10 2  [31]. To evaluate the impact of our new training dataset, we trained our network both on the pre-existing training datasets Blobby and Structure (this training is referred to as "DS1" in the following) and on our new training dataset ("DS2" in the following). In the rest of this section, "Mono (DS1)" will thus refer to the mono-scale architecture trained on the pre-existing dataset, "Multi (DS1 + DS2)" to the multi-scale architecture trained on both the pre-existing and the new datasets, etc. We will first provide a few qualitative results to illustrate the importance of the two building blocks of our contribution, and then provide a thorough quantitative evaluation on the two benchmarks.
Qualitative evaluation Let us start by showing two illustrative results on the DiLiGenT10 2  [31]  benchmark, on challenging metallic objects (the copper golf ball and the copper hexagon). As we shall see, both the new training dataset and the new multi-scale architecture contribute to improving the estimation performances on such objects exhibiting an anisotropic reflectance. Since we do not have access to the ground truth normals, for visual purpose we show as "ground truth" the result we obtained with our Multi (DS1+DS2) approach, applied to the same object but fabricated in PVC (a matte material). The example of Fig. 8  shows that, independently from the training set, the multi-scale architecture largely contributes to improving the results on metals. In this example, the same dataset is used for training both the mono-scale and multi-scale architectures, and the latter offers visually more accuate results.