dataesr/scientific-paragraphs-categorization huggingface dataset:
For abstract models with concepts that are highly connected by various relations, this conversion can be quite challenging with such systems since the implemented structure and code are both dependent on the cardinality of the relations. While graphs allow to link a nodal element of a set to another element by an edge, hypergraphs generalize them by allowing to materialize sets (hyperclasses) and subsets (classes) of elements, corresponding to concepts using nodes, and edges representing the potential semantic relations of one to one or more classes, hyperclasses, elements they gather  [9]. A rarely explored but suitable approach in health domain is to convert such an abstract model into a property graph database dealing with such concepts of vertices and edges  [4, 5]. The PREDIMED project has chosen then to model all the data useful to the CDW as an Entity-Association hypergraph based data model and to implement it in a property graph database translating model concepts to graph vertices respectively without loss of semantics.
This original approach allows the deployment of graphical tools to interactively query the data warehouse  [2]. Its implementation in such NoSQL database offers a possibility of model's flexible modifications unlike rigid relational systems. A formal definition language is associated with this graph model. The compilation of this language allows exports to commonly-used models such as I2B2 or OMOP, star models being poorer than the hypergraph model we have defined. Hypergraph based models have been used for several decades by complex topological GIS systems  [11]  and have proven to be a powerful tool. A query represents a path within a hypergraph with possible constraints on the attributes of vertices or edges  [10]. These queries are generated from a graphical interface to textual query language.
The chosen model also allows for complex attributes (vectors, matrices, methods and execution parameters, etc. ) at the various levels of classes and links, constituting an accurate reference catalog of the information collected in the data lake. We have made this model directly operational by interfacing it with the functionalities of a NoSql property-graph database, natively optimized to express the information of complex, highly connected data structures. We have chosen a property graph database  [14]  suited to store information within named collections of nodes and directed edges, natively based on the JSON standard. Within these collections of nodes implementing concepts, we differentiate classes derived from the same set of concepts (or hyperclass) according to a specific high-level attribute from which all their elements inherit, allowing to simply implement classes and hyperclasses whatever their level in the data structure.
These collections are indexed according to proven technologies in order to operate as close as possible to the "in memory" performances of the infrastructures that compute them, which opens the way to unprecedented interactive exploration possibilities compared to the usual performance of huge conventional databases. In addition to accessing the data via the user interfaces provided by the system, we have developed a generic high-performance RESTful API around this database in the form of microservices running on the server side. This API enables other applications to interact with the data lake using the standard REST/JSON protocol. Among these applications, a graphical interface, directly derived from the conceptual model, was developed in order to navigate in the data lake and explore it.
The design of the model is thus implemented in a spatial information management system (QGIS  [13] ) that we use to describe both graphically and formally the arcs and nodes of the model, as well as their attributes. This dual structure allows us to use GIS as a development framework to quasi-automatically generate the core environment for navigating through the data lake, as well as expressing its structure as a language  [12]. In particular, the interactive webmapping functions are spontaneously adapted to select elements of the graph and access their attributes, without new developments being required to integrate the evolutions of the model.
Data exploration, visualization (Fig 3, 4, 5) The set of objects selected at each stage of a navigation in the data lake can be easily projected on the fly in different application environments whose various complementary investigation functionalities are suited to explore their different facets. We thus offer possibilities of visualization and fine analysis of the information. For instance, we can consider only data related to each patient of a cohort, which allows to precisely control the inclusion of these patients in each clinical study. We can also produce interactive dashboards generated on the fly and linked to a Lucene  [17]  library based search engine, which can provide a statistical description of subsets of information. For this, we currently rely on the Elasticsearch/Kibana  [15]  package and the possibilities it offers to browse different information, visualize it and optionally export it to CSV text delimited format.
The result is nonetheless accurate and reproducible. The extraction of cohort related datasets observed in depth by health specialists, allows, via a random analysis of patient data, to validate the projects datasets, to select and extract only relevant features to datasets of reasonable dimensions to be used by machine learning algorithms. ( ) 1 (1) (1) (3) (2) The general, highly connected and extensive structure of the model aims at progressively describing all the elements of interest to materialize the patients' long-term health trajectories. The ability to gather and explore multimodal and multisource information within PREDIMED CDW allows it to be effectively involved in various clinical studies. For instance, first uses of PREDIMED enables the efficient provision of relevant data for a wide variety of projects such as: x making predictions on emergency interventions around chronic hematology patients, x daily regional cartographic tracking of the COVID-19 pandemic and of the emergence and evolution of clusters, x identification of risk factors and their consequences in farmers, with analysis of their exposome, in particular their occupational activities and environment, x study on the life quality at work for the network of care providers working in the emergency department during the Covid-19 pandemic.
All of the information in the data lake is described using various standard terminologies and high-level referentials, such as ICD10 for diseases coding or the Geonames  [18]  repository for geographic locations for instance. Our approach makes it possible to parameterize the interoperability and data exchange process of our data lake with the outside world. Significant data transformation procedures are required, first to populate the data lake with information corresponding to the target model, and then to export parts of it as an interoperable standardized data set. These procedures involve an Extract Transform Load (ETL) software brick (Fig  3 ). The currently chosen ETL software  [16]  allows us to define different jobs to reshape and align the parametrized structures at the input and output of each of these processes, and to periodically run the validated and compiled version in an industrialized manner.
Impact The interactivity in the ability to analyze intermediate results while navigating the graph aims at empowering clinicians to observe and select variables and events of interest that can potentially be used then as features to create datasets, which can be then efficiently analyzed by complementary machine learning based algorithms. As the basic structure of the model remains invariant even when the model evolves, the implementation variables only depend on the content of the class of classes (and hyperclasses), that of the class of links and their respective attributes, which constitute the primitive deployment parameters of our application architecture. The model is thus potentially extensible without any impact in terms of development on the implementation, independently of the potentially increasing number of classes of a hyperclass (to integrate new types of processing for instance) or the possibilities of links to new ontologies or other graphs.
This flexibility, made possible by a highly generic code, makes it virtually automatic to implement the coherence of the means of expressing information within the various software bricks of the platform, which constitute an application continuum driven by the model. This approach favors the implementation of complementary automatic and human control routines for the quality of data and services produced by the application platform. Thus, traversals of different paths supposed to lead to the same result as a response to a complex request can be used to evaluate the quality of the data. The code developed to implement the original functionalities provided by PREDIMED platform is built around different interchangeable software bricks oriented towards "in memory, JSON Restful" technologies involved in the different layers, whether it is the property graph database, the search and visualization engine or the ETL, for which other Open Source alternatives exist.
Conclusions The graph approach allows us to move forward in a rapid iteration cycle to provide tangible answers to clinicians' objectives in terms of visibility on complex and massive patients' data. This was achieved in the context of a pandemic that catalyzed the transition from a proof of concept to a production platform. We have worked particularly on the maturation of its architecture, its implementation and its human organization while being vigilant to remain in conformity with the various legal aspects. This dynamic acceleration of the PREDIMED platform's possibilities is sparking new initiatives in terms of research projects, which are also fertile for new methods of data exploration, offering clinicians new in-depth, configurable and interactive views of patients' medical data. PREDIMED allows non-IT specialists to use complex and massive data mining tools to effectively and rapidly explore and design patient information features for research projects.
[2] , to solve the nonlinear equations of the Oxley model and determine the optimal set of internal variables C 0 , δ, φ (defined later) of the Oxley analytical model. Compared to the original models, the approach proposed in this paper allows us to much more efficiently find the solution of the nonlinear system of equations of the Oxley model by allowing a significant gain on the computation time and much higher precision at the level of the equilibrium condition of the efforts of the analytical model. The structure of this paper consists of a brief synthesis of the orthogonal section and a presentation of the basic equations of the Oxley model (we refer the reader to the numerous publications in the literature for more details concerning the definition of this analytical model). We then present the basics of the Extended-Oxley analytical model proposed by Lalwani  [3] , then our approach to its numerical implementation in Python with the help of the LMFIT library.
Recently, Xiong et al. [23]  proposed an original method to replace the three for loops nested in each other, using a combination of constrained optimization function, the genetic algorithm GA, the Patternsearch function, and the multivariate constraints as well as the fmincon optimization algorithm provided by Matlab 2010b to solve this problem. This approach, if it allows for obtaining faster and more accurate results, nevertheless poses the problem of being relatively complex and difficult to implement. The solving methodology proposed in our work is similar to the one proposed by Xiong et al. [23]  but differs radically in the numerical means used to solve the system of nonlinear equations. As presented in Section 3, it is relatively simple, close to Oxley's original algorithm, and can be extended to all the derived forms of Oxley's original model proposed in the literature insofar as it is external to the equation blocks.
Implementation of the Extended-Oxley's Model Using Python In order to solve the system of nonlinear equations shown in Equation  (15) , we propose here a more efficient approach based on an optimization method implemented in Python language  [26]. We have chosen the LMFIT library  [2]  for the core of the optimization algorithm because it proposes an efficient and easy-to-use implementation of the Levenberg-Marquardt algorithm  [27, 28].
Implementation of the Model Using Python All of the equations presented above defining the Extended-Oxley model were programmed using the Python language, an extended interpreted language with an expressive syntax that some have compared to executable pseudo-code. All development was done using native Python functions, with the only need to import the math module for trigonometric functions. The Python module matplotlib was also used to automatically produce all the graphs presented below. The main function of the program computes the four quantities σ N , σ N max , τ int , and k chip from the three internal parameters φ, C 0 , and δ. It also computes, at the same time, all the required results, such as cutting and advancing forces F C and F T , temperatures T AB and T int , and geometric quantities such as h, l AB , and t 2.
Solving Algorithm Based on LMFIT The original part of this work concerns the method used to solve the system of equations defined by  (15). Instead of a system similar to the one originally proposed by  Oxley [5, 7, 8]  and still used many years later by Lalwani  [3] , we chose to solve this system using an optimization procedure based on the Python library LMFIT proposed by Newville et al. [2]. The module LMFIT provides a high-level interface for nonlinear optimization and curve fitting problems for Python and provides an access point to the Levenberg-Marquardt algorithm  [27, 28]  of scipy. It can also support most of the optimization methods of scipy. optimize. The main advantage over the standard implementation of scipy is the ability to provide a useful parameter object whose value can vary during fitting, be fixed, or have upper and/or lower bounds.
Our program therefore imports the module LMFIT version 1. 0. 3, which provides the minimization function used to solve the system of equations defined by Equation  (15). Figure  4  presents the general flowchart of the proposed algorithm and shows that two optimization algorithms encapsulated in each other and referred to hereafter as internal optimization algorithm and global optimization algorithm, respectively, are used to obtain the solution of the system in  (15). The internal optimization algorithm is in charge of solving the first two equations of the system in  (15) , corresponding to the Equations (  12 ) and (  14 ), whereas the global optimization algorithm is in charge of finding the minimum value of the cutting force, i. e. , the last equation of the system in  (15). Thus, due to the interlocking of the two algorithms, the final solution is the one for which the equilibrium condition defined by Equations (  12 ) and (  14 ) is first satisfied by the proposed objective function ∆ F , given by the following equation: ∆ F = τ int -k chip 2 + (σ N -σ N max ) 2 , (16) and then the cutting force F C is minimized in a second step.
The virtual neutron scattering experiment at BWAVES was performed using a D2O-hydrated protein powder sample of HIV-1 protease, loaded in a cylindrical sample holder, 5 mm in height and 5 mm in diameter, with the packing density to give the total incoherent scattering cross-section for the sample of 0. 2 cm -1 (that is, a 10% incoherent scatterer with the BWAVES design sample geometry). The higher-energy data in Fig. 3  show vibrational spectrum, with the C-H stretch modes visible near 300 meV. The lower-energy part of the spectrum exhibits what appears to be two components, with the positions independent of Q, indicative of the localized relaxational dynamics. It should be noted that development of yet another QENS component, due to the water solvent-driven protein dynamics, could be expected at higher measurement temperatures in this system.
QENS and INS provide critical information on transitions between the metastable states as well as a broad range of collective and individual dynamics in soft matter systems, including liquids, lipid assemblies and polymers. QENS/INS also helps unravel detailed mechanisms of proton/ion transport and elucidate many other aspects of soft matter dynamics. As a typical example, a combination of separately measured scattering spectra from polymer systems is usually necessary for thorough characterization of the polymer dynamics (Fig. 5 ,  [15] ), but unfortunately this relies on arbitrary matching of the data from different spectrometers summed over all Q values. In contrast, with its broad dynamic range BWAVES will be an ideal instrument to enable unequivocal analysis of polymer dynamics, including the geometries of molecular motions via the Q dependence of the scattering signal over the broad energy range.
Fig. 6  shows the obtained elastic incoherent structure factor (EISF) representing the geometry of the lipid motion. It is likely that the lipid dynamics is best described as motion within a cylinder, yet the gap in the Q-range between the NSE and BASIS data leaves uncertainty regarding the effective cylinder dimensions. BWAVES, due to its capability of selecting long final wavelength (thus, lower Q and higher energy resolution) when operated in a "booster" mode, will be able to close the gap in the Q-range between NSE and BASIS and allow unequivocal determination of the geometry of the lipid motion. While understanding cooperativity and heterogeneity in the dynamics of liquids and polymers remains a great scientific challenge, the dynamics of water, especially in confined space and at interfaces, is critical for a wide range of industrial and bio-medical applications and is a topic of equally active discussion  [19] [20] [21] [22].
QENS is ideally matched to probe the dynamics of fast ions (~GHz), complementing measurements of THz lattice dynamics with INS, and BWAVES will be able to simultaneously measure both regimes in a single instrument setting, a unique feature not accessible with existing neutron spectrometers. Example QENS measurements across the superionic transition in CuCrSe2 are shown in Fig. 10. The broadening of the QENS peak upon warming into the superionic phase (T > 365K) is the hallmark of increasing diffusion. The QENS data analysis will  directly estimate the ion diffusion and jump length from the Q dependence of the signal, and these results can be also combined with the analysis of phonons in the same measurements. Thus, BWAVES will open unprecedented opportunities for studying SICs. The discovery of a novel class of 2D MXene materials  [74] [75] [76]  has revolutionized many applications and lead to an exponential growth in research on MXenes for electrochemical devices.
INTRODUCTION When crystallized from a solution, many organic compounds can form multiple solid phasessolvent-free (including polymorphs) or multiple component phases such as hydrates, solvates, salts, co-crystals and other phases  1. A recent survey 2 on organic solvates deposited in the Cambridge Structural Database (CSD) categorized 325 104 unique organic structures. Of these, 83. 8% were solvent-free phases (including multi-component crystals), 8. 3% were single-organic solvent solvates, 6. 4% were hydrates, and only 1. 5 % of the structures contained at least two solvents, including solvate-hydrates (1. 1 %) and heterosolvates (0. 4 %). Studies on solvate, solvate-hydrate and hydrate formation, occurrence and crystal structure analysis have been conducted on compounds having rich solid-form landscape, for example, prolific solvate formers olanzapine  3  , galunisertib  4  , axitinib  5  and sulfathiazole  6  as well as solvatehydrate formers such as bosutinib  7  and 3,5-dihydroxybenzoic acid  8, 9.
Single-crystal X-ray diffraction. SCXRD data for the solvates, excluding DAN S DXN , S DMF and S DMSO , were collected on a XtaLAB Synergy-S Dualflex diffractometer (RIGAKU Oxford Diffraction) equipped with a HyPix6000 detector and a microfocus sealed X-ray tube with Mo Kα radiation (λ = 0. 7107 Å). A single crystal with dimensions of <0. 1 × <0. 1 × <0. 1 mm  3  was fixed with oil in a nylon loop of a magnetic CryoCap and set on a goniometer head. The sample was cooled to 150 -170 K, and ω scans were performed with a step size of 0. 5°. Data collection and reduction were performed with the CrysAlisPro 1. 171. 40. 35a software. Structure solution and refinement were performed with AutoChem3. 0 and SHELXL  34  software that are parts of the CrysAlisPro suite. Non-hydrogen atoms were refined anisotropically.
Hydrogen atoms were added geometrically and refined using a 'riding' model. SCXRD data for FUR polymorphs were measured with a Nonius Kappa CCD diffractometer (Bruker AXS) with Mo Kα radiation (λ = 0. 71073 Å). Data were collected at 190 K. Data reduction was performed with the DENZO/SCALEPACK. Crystal structures were solved by direct methods with SHELXS-97, refinement was performed by SHELXL-97  35. SCXRD data for DAN solvates SDXN, SDMF and SDMSO were collected on an Oxford Diffraction SuperNova CCD diffractometer equipped with the microfocus X-ray source (Cu Kα, λ = 1. 54184 Å). Single-crystal X-ray measurements on DAN II were performed using a Rigaku XtaLAB MM7HFMR diffractometer equipped with the 'quarter-chi single' goniometer, the rotating anode generator (graphite monochromated Cu Kα radiation), and the Pilatus 200K detector.
In all measurements the CrysAlis Pro 1. 171. 40. 45a program was used for data collection, cell refinement and data reduction (Rigaku Oxford Diffraction, 2019)  36. The intensities were corrected for Lorentz and polarization effects. A multi-scan absorption correction was applied. The structures were solved using the direct methods implemented in the SHELXS-97, and refined with the SHELXL-18/3 program 34 , both operating under WinGX 37. All non-H atoms were refined with anisotropic displacement parameters. Hydrogen atoms attached to imidazolidine N1 atoms were found in the difference Fourier maps and refined with the isotropic displacement parameters. All remaining Hatoms were positioned geometrically and refined using the riding model with Uiso(H) = 1. 2Ueq (CH and CH2) or Uiso(H) = 1. 5Ueq (CH3).
Training strategy In this paper, we propose a two-stage training method to jointly optimize the proposed ANFL and MEFL modules with the backbone and classifier in an end-to-end manner. In the first stage, we train the backbone with the ANFL module, aiming to learn an AFG block that produces node features containing both AU activation status and their associations for each facial display. A priori, we notice that existing AU datasets usually have imbalanced labels, where some AUs occurred less frequently than others, and most AUs are inactivated for the majority of face images. To alleviate such issues, we propose a weighted asymmetric loss to compute the loss value between the ground-truth and predictions generated by the FGG block. It is inspired by the asymmetric loss  [Ridnik et al. , 2021] , but has a unique weight for each subtask (each AU's recognition) as well as fewer hyperparameters.