dataesr/scientific-paragraphs-categorization huggingface dataset:
Candidate services partitioning based on k-meansThe k-means clustering method is one of the most commonly used unsupervised clustering methods [27]. This method allows dividing a set D of n data samples into k homogeneous clusters CL 1 ,. , CL l ,. , CL k , which are also called Algorithm 1 -The k-means clustering method. Inputs: D = {p1,. , pj,. , pn} a dataset of n samples. Z = {1,. , l,. , k}, k is the total number of clusters. J (b) is the objective function at iteration b. : a threshold value. 1: Begin 2: b ← 0 ; 3: J (b) = ∞ ; Choose the initial set of clusters centroids C (b) = {µ if l = arg min l∈Z ||pj -µ Update the set of clusters centroids C (b+1) = {µ (b+1) 1 ,. , µ (b+1) l ,. , µ (b+1) k }; 10:for l = 1 to k do 11:.
4. 1. 1. 4 : 1 9 : 11419 Candidate services partitioning based on k-meansThe k-means clustering method is one of the most commonly used unsupervised clustering methods [27]. This method allows dividing a set D of n data samples into k homogeneous clusters CL 1 ,. , CL l ,. , CL k , which are also called Algorithm 1 -The k-means clustering method. Inputs: D = {p1,. , pj,. , pn} a dataset of n samples. Z = {1,. , l,. , k}, k is the total number of clusters. J (b) is the objective function at iteration b. : a threshold value. 1: Begin 2: b ← 0 ; 3: J (b) = ∞ ; Choose the initial set of clusters centroids C (b) = {µ if l = arg min l∈Z ||pj -µ Update the set of clusters centroids C (b+1) = {µ (b+1) 1 ,. , µ (b+1) l ,. , µ (b+1) k }; 10:for l = 1 to k do 11:.
The pose is estimated based on the movement of the vehicle between two consecutive frames. We developed a system for computing visual odometry in real time and producing a sparse point cloud of 3D points on the fly  (Nawaf et al. 2016 (Nawaf et al. , 2017)). The second goal is to perform an offline 3D reconstruction of a high-resolution metric model. This process uses the high-resolution images to produce a dense model, scaled based on baseline distances. We developed a set of tools to bridge our visual odometry software to the commercial software Agisoft Photoscan/Metashape in order to use the densification capabilities. After this step we obtained a dense pointcloud and a set of oriented high-resolution photographs describing accurately the entire site. This is enough to produce a high resolution orthophoto of the site (1 pixel/0.
The development architecture of these ontologies was performed with a close link to the Java class data structure, which manages the photogrammetric process as well as the measured items. Each concept or relationship in the ontology has a counterpart in Java (the opposite is not necessarily true). Moreover, the surveyed resources are archaeological items studied and possibly managed by archaeologists or conservators in a museum. It is therefore important to be able to connect the knowledge acquired when measuring the item with the ontology designed to manage the associated archaeological knowledge. The modelling work of our ontology started from the premise that collections of measured items are marred by a lack of precision concerning their measurement, assumptions about their reconstruction, their age, and origin.
The present implementation is based on a double formalism, JAVA, used for computation, photogrammetric algorithms, 3D visualization of photogrammetric models, and cultural heritage objects, and then for the definition of ontologies describing the concepts involved in this photogrammetric process, as well as on the surveyed artefacts. To implement our ontological model, we opted for OWL2 (Web Ontology Language), which has been used for decades as a standard for the implementation of ontologies  (McGuinness and Harmelen 2004). This web ontology language allows for modelling concepts (classes), instances (individuals), attributes (data properties) and relations (object properties). In fact, the main concern during the modelling process is the representation of accurate knowledge from a measurement point of view for each concept in the ontology.
On the other hand, the same issue presides over the elaboration of the JAVA taxonomy, where we have to manage constraints involving differences in the two hierarchies of concepts within the engineering software side. For this purpose, we developed a procedural attachment method for each concept in the ontology. This homologous aspect of our architecture leads to the fact that each individual of the ontology can produce a JAVA instance since each concept present in the ontology has a homologous class in the JAVA tree. Note here that the adoption of an automatic binding between the ontology construction in OWL and the JAVA taxonomy cannot be produced automatically in our case. Hence, we have abandoned an automatic mapping using JAVA annotation and JAVA beans for a manual extraction, even if this is a common way in literature  (Horridge et al.
Although some work has already yielded results  (Hiebel et al. 2010; Xueming et al. 2010) , it is not enough to support the close-range photogrammetry process, from image measurement to artefact representation. Linking our ontology to CIDOC-CRM can provide more integrity between cultural heritage datasets and will allow more flexibility for performing federated queries cross different datasets in this community. Being a generic ontology, however, the current state of CIDOC-CRM does not support the items that it represents from a photogrammetric point of view, a simple mapping would not be sufficient and an extension with new concepts and new relationships would be necessary. Our extension of the CIDOC-CRM ontology is structured around the triple <E18_Physical_Thing, P53_ has_former_or_current_location, E53_Place>, which provides a description of an instance of E53_Place as the former or current location of an instance of E18_Physical_Thing.
Application in Nautical Archaeology The applications to query, visualize, and evaluate survey data acquired through photogrammetric survey methods have the potential to revolutionize nautical archaeology (as evidenced by many chapters in this volume). Even the simplest utilization of off-the-shelf photogrammetry software combined with consumer-grade portable computers and without the swrlArp:isCloseTo(?a, "IdTargetAmphora", 6. 2) ^ hasTypologyName(?a, "Pitecusse_365") -> sqwrl:select(?a). Means select all amphorae with the typology Pitecusse_365 and at a maximum distance of 6. 2 m from the amphorae labelled IdTargetAmphora need for special graphics hardware, can greatly simplify and expedite the recording of underwater archaeological sites  (Yamafune et al. 2016). The development of a theory of knowledge in nautical archaeology will certainly change the paradigm of this discipline, which is hardly half a century old and is still struggling to define its aims and methodologies.
This in turn will improve the conditions to the development of broader anthropological studies, for instance, evaluating and relating cultural change in particular areas and time periods. The study of the history of seafaring is the study of the relations of humans with rivers, lakes, and seas, which started in the Palaeolithic. An understanding of this part of our past entails the recovery, analysis, and publication of large amounts of data, mostly through non-intrusive survey methods. The methodology proposed in GROPLAN aims at simplifying the collection and analysis of archaeological data, and at developing relations between measurable objects and concepts. It builds upon the work of  Steffy (1994) , who in the mid-1990s developed a database of ship components. This shipbuilding information, segmented in units of knowledge, tried to encompass a wide array of western shipbuilding traditions-which developed through time and space-and establish relations between conception and construction traits in a manner that allowed comparisons between objects and concepts.
Around a decade later Carlos Monroy transformed Steffy's database into an ontological representation in RDF-OWL, and expanded its scope to potentially include other archaeological materials  (Monroy 2010; Monroy et al. 2011 ). After establishing a preliminary ontology, completed through a number of interviews with naval and maritime archaeologists, Monroy combined the database with a multi-lingual glossary and built a series of relational links to textual evidence that aimed at contextualizing the archaeological information contained in the database. His work proposed the development of a digital library that combined a body of texts on early modem shipbuilding technology, tools to analyse and tag illustrations, a multi-lingual glossary, and a set of informatics tools to query and retrieve data  (Monroy 2010; Monroy et al.
Then we propose to use a transfer learning process to fine-tune our model over the Xlendi shipwreck amphorae. This approach allows us to train the model using a small part of the Xlendi database. Underwater objects are rarely found in a perfect state. Indeed, they can be covered by sediments or biological growth, or by another object, and they are often broken. It is common that amphora necks are separated from an amphora's body. We want to detect all the amphora pieces by performing a pixel segmentation which consists of adopting a pixel-wise classification approach on the orthophoto  (Badrinarayanan et al. 2015; Shelhamer et al. 2016). To improve the model, we define three classes: the underground, the body of the amphora and the head of the amphora; which are the rim, the neck and the handles respectively.
We propose to learn the relevant sketch using a machine learning process on a part of the Xlendi orthophoto. Then we propose to use an architecture similar to the previous one to learn the sketch process. The sketch of Xlendi created by the CNN is given on Fig. 9. 7 (bottom right). The weakness of this approach is evident when objects that are not known by the Fig. 9. 3 Pixel segmentation on the testing part of the Xlendi orthophoto. On the probability map, the wither the pixel, the higher the prob-ability to be an amphora is. On the object detection map, the green circles represent the correctly detected amphorae and the red circles the false positive detections model are present, because they are not in the training database. Figure  9. 5 is focused on a grind stone with a yellow starfish above.
2003; Jardim and de Figueiredo 2010; Judd et al. 2007; Raskar 2001; Roussou and Drettakis 2003; Tao et al. 2009; Xie et al. 2014). An interesting open source approach, called Suggestive Contour Software (DeCarlo and Rusinkiewicz 2007), allows visualization of 3D models built with mesh by NPR and gives several options to modify final results with various parameters (occlusion contours, suggestive contours, ridges and valley, etc. ). The link between accuracy and sketch is a question considered by several scientists in the field of computer vision and virtual reality  (Bénard et al. 2014). More recent research in this field now uses deep learning and AI  (Bylinskii et al. 2017; Gatys et al. 2016). Concerning cultural heritage and archaeological artefacts, some morphological properties of amphorae are well represented with NPR rendering (Fig.
This visualization is made of both photorealistic and semantic representations of the observed objects including amphorae and other archaeological material. A user can freely navigate the site, switch from one representation to another, and interact with the objects by means of dedicated controllers. The different representations that are visualized in the tool, can be seen as queries mixing geometry, photorealistic rendering, and knowledge (for example: display the amphorae colorized by type on the site). Although software rendering packages such as Unity, Sketchfab and Unreal Engine, or APIs such as OpenVR, HTC Vive, SDK already exist, we have chosen to develop our own solution. The work presented here is based on the  Arpenteur project (2018; Drap 2017 ) that provides geometrical computation capabilities, photogrammetric features and representation and processing of knowledge within ontologies.
For more details on the total focus process, we refer to  Perwass and Wietzke (2012). Depth map computation-a brute force stereo points matching is performed among neighbouring micro-lenses. The neighbourhood is an input parameter that defines the maximum distance on the MLA between two neighbouring micro-lenses. This process can be easily run in parallel. This is achieved using Raytrix RxLive software which relies totally on the GPU. The total processing time does not exceed 400 milliseconds per image. Next, using the computed calibration parameters, each matched point is triangulated in 3D in order to obtain sparse point cloud. Dense point cloud and surface reconstruction-in this step, all pixel data in the image are used to reconstruct the surface in 3D using iterative filling and bilateral filtering for smoother depth intervals.
weights of the CNN. On the Xlendi Image we have only used 20 amphorae as training examples. Results are given on Fig. 9. 2 where we can see that all the amphorae in the testing image are detected. The false positives are mainly located on the grind stones. This error is due to the small size of the training database. Indeed, during the pre-training step there are not grinding stone examples in the used images, then during the tuning step only a few grind stone examples are represented.
Acknowledgements This work is partially done in the framework of the project  GROPLAN (2018 ,  ANR-13-CORD-0014 ) funded by the  French Agency for Scientific Research (ANR). We would like to thank the artist  Cosette Nigon  for providing sketches in the deep learning, style transfer approach (Fig. 9. 4). We would like to thank also  Gina De Angelis  and  Alessandra Sprega , two architects, graduates from the  University of Rome III, Rome, Italy  for the archaeological design made from an orthophoto of underwater wreck for training the CNN (Fig. 9. 7, top right).
Development of the MOVE toolbox The basic idea is to detect the targets put on the can by contrast with the rest of the image. For its convenience and with a view to process each image as a matrix, we have chosen MATLAB as our main developing tool. Positions of the pendulum are extracted from mp4 videos, either with 480 Â 320 or 640 Â 480 resolution and acquired at the rate of 30 fps. Each movement has its own processing because we have to extract different physical quantities with different issues. The video in color is treated as a four-dimensional array which is transformed into a three-dimensional array to consider only images in shades of gray. Each frame of the movement is a two-dimensional array. The MOVE algorithm dedicated to linear movement is based on the comparison between the frame processed and an associated background  [8].
Frequency analysis of the Wilberforce effect According to (15) and (  16 ), the movement of the Wilberforce pendulum is made of the two frequencies v + and v À which can be revealed by spectral analysis. The MATLAB fast Fourier transform (FFT) algorithm proposed by MATLAB  [10]  has been used for that purpose, and the result is displayed in Figure  7. This figure proves that translation and rotation oscillate as predicted with almost the same frequencies. Those frequencies are written down in Table  2. They are calculated for each motion (translation and rotation), and the measurement uncertainty is due to the accuracy on the discrete frequencies. We can then combine these two results in order to obtain more precise values  [11]. The pendulum movement is thus a superposition of two simpler periodic motions, each one composed of only one frequency: these are the normal modes of the Wilberforce pendulum.
Acknowledgements We are grateful to  Troy Lee ,  Mohammad Mahmoody-Ghidary ,  Miklos Santha  and  Robin Kothari  for insightful discussions, to  Krzysztof Pietrzak  for pointing out the O(N log N )-time algorithm that classical Alice can use in Protocols 1 and 2, and to  Dominique Unruh  for pointing out the "practical" difficulty (and possibly fundamental inefficiency) arising from the need to use quantum memories to implement Protocols 1 and3. G. B. is also grateful to  Ralph Merkle  for his most inspiring Distinguished Lecture at Crypto ' 05 , which sparked this entire line of work. G. B. is supported in part by  Canada's Natural Sciences and Engineering Research Council (Nserc) , the  Institut transdisciplinaire d'informatique quantique (Intriq) , the  Canada Research Chair program , the  Canadian Institute for Advanced Research (Cifar)  and the  Institute for Theoretical Studies at the ETH Zürich.
Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of traditional ML, we introduce a "topologyinformed ML" paradigm-wherein some features of the network topology (rather than traditional descriptors) are used as fingerprint for ML models-and apply this method to predict the forward (stage I) dissolution rate of a series of silicate glasses. We demonstrate that relying on a topological description of the atomic network (i) increases the accuracy of the predictions, (ii) enhances the simplicity and interpretability of the predictive models, (iii) reduces the need for large training sets, and (iv) improves the ability of the models to extrapolate predictions far from their training sets.
Nature of the data set To establish our conclusions, we rely on the database developed by Hamilton et al. ,  24, [26] [27] [28]  which comprises the forward dissolution rate (DR) of a series of aluminosilicate glasses with varying compositions under varying pH conditions. In details, the database comprises dissolution data for two families of glasses, namely, (i) the "Glasses A" series (Na 2 O) 25 (Al 2 O 3 ) y (SiO 2 ) 75-y , with y = 5%, 10%, 15%, 20%, and 25% and (ii) the "Glasses B" series (Na 2 O) x (Al 2 O 3 ) x (SiO 2 ) 100-2× , with x = 12. 5%, 16. 7%, and 25%. As such, the glass compositions cover both the tectosilicate and peralkaline domains, with varying fractions of non-bridging oxygen (BO) atoms. The dissolution kinetics of these glasses is systematically studied in unsaturated aqueous solutions over a wide domain of pH, ranging from pH 1 to 12.
The DR is here quantified in terms of the SiO 2 -leaching rate (expressed in units of mol/cm 2 /s). In total, the database comprises 200 data points. 26 ore details can be found in the Methods section. Note that simple metrics (e. g. , the fraction of non-BO atoms) do not offer any good correlation with the DR (see ref. 5  ). In particular, all the glasses from the series B are fully charge-compensated and, hence, present a theoretical zero fraction of non-BO atoms and yet exhibit varying DRs. This justifies the use of more complex descriptors as presented in the following.
Blind ML We first assess the ability of "blind ML"  8, 10, 12  (that is, which does not embed any physics/chemistry about the dissolution process) to offer realistic prediction of the dissolution kinetics of the aluminosilicate glasses considered herein. To this end, we first consider as inputs the glass composition (i. e. , the molar fractions of Na 2 O and Al 2 O 3 ) and the solution pH, whereas the DR is used as output. We then adopt the polynomial regression technique to infer the relationship between inputs and output. 9,10 Indeed, although our previous work on the same DR data set has shown that more complex ML algorithms (e. g. , artificial neural network) offer improved performance, 5 such complex algorithms do not yield any analytical, easily usable function relating the inputs and output of the model and are poorly interpretable.
In contrast, the polynomial regression method eventually yields an analytical model expressing the DR as a polynomial function of the inputs: Model I : DR ¼ f ðpH; Na 2 O; Al 2 O 3 Þ (1) In the following, we refer to this model as "Model I. " To avoid any overfitting, we divide the database into (i) a training set, which is used to train the model, (ii) a validation set (10% of the data points of the database generated by the cross-validation method  9, 10  ), which is used to validate the performance of the model and identify the optimal polynomial degree, and (iii) a test set, that is, some data that are kept fully invisible to the model and that are used to assess its ability to predict unknown data. The test is here chosen by randomly selecting 30% of the data points from the database.
e. , linear model). b Predicted dissolution rate for p = 1 as a function of the measured dissolution rate permitted by the fact that, here, the training set homogeneously covers all the range of the possible glass compositions. More generally, these results exemplify how an interpretable ML model can offer some physical insights into the relationship between inputs and output-which would not be possible with a less interpretable model (e. g. , artificial neural network). We now assess whether the models considered herein can be used to extrapolate predictions, that is, to predict the dissolution rate of glasses with compositions that are different from those used during the training of the model. To this end, rather than randomly choosing data from database to serve as a test set, we purposely select the data from the Glasses A series as a training set and those from the Glasses B series as a test set.
Here, the dissolution data of Glasses A are used as a training set to predict the dissolution kinetics of Glasses B (test set). Note that, the symbol shape (square or diamond) represents "training set" or "test set", whereas the color (red or blue) denotes the glass family, namely, "Glasses A or B" larger number of descriptors usually decrease the interpretability of the model, and (vi) the use of a large number of descriptors can result in some degree of overfitting, which, in turn, tends to decrease the extrapolability of the model. In contrast, adopting a topological fingerprint of the atomic network filters out some of the structural details. As such, the use of topological descriptors only may not fully capture some of the fine details of the relationship between composition and dissolution kinetics, but, nevertheless, we find here this level of simplification/filtering to be key in enhancing the extrapolability of the trained models.
Experimental dissolution rate data For each glass composition and pH, the dissolution tests conducted by Hamilton et al. were carried out on glass powders comprising grain sizes ranging from 74 to 149 μm. These experiments were conducted under static conditions at a surface area to solution volume ratio (SA/V) of ~1. 4-2. 0 cm -1. 26 For each pH, the extent of dissolution was assessed from the concentration of leached SiO 2 (as measured by ICP-AES and ICP-MS) in solution at 5-to-7 regular intervals (for example, 24, 49, 96, 168, and 336 h) of solvent contact. In each case, the pH was recorded before any dissolution and at the time of the dissolution measurement. All the experiments were conducted at 25 °C and ambient pressure. The experimental data present an uncertainty of ± 1. 5% of the logarithm of the dissolution rates-as estimated from the standard deviation of the dissolution rate data associated with different measurements conducted on the same glass and at constant pH.
RRMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P n i¼1 DR predicted i À DR measured i 2 n v u u t P n i¼1 DR measured i n (9) The intrinsic uncertainty of the dissolution data is here directly embedded within the ML framework by incorporating in the training set all the dissolution data obtained for the same glass composition and solution pH (rather than only their average value). This imposes a lower bound of RRMSE = 1. 5%, which corresponds to the intrinsic degree of uncertainty of the DR data set measured in experiments.