dataesr/scientific-paragraphs-categorization huggingface dataset:
Mental disorders are very heterogeneous. Different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. This heterogeneity often results in bad diagnosis. To offer a rich signature for Major Depressive Disorder improving the understanding of the pathophysiological mechanisms underlying these mental disorders, learning representations from large databases of unlabeled videos using self-supervised approaches  [50]  may offer new perspectives for precision in medicine and deal with the scarcity and rareness of the labeled datasets.
Conclusion and future work In this paper, a proof of concept of a new computer-aided diagnostic system is proposed for depression relapse prediction based on audiovisual cues which are discriminant between healthy and depressed subjects. Low and high level features are extracted from clinical videos of interviews and then compared to deep representation models of depression and non-depression. A high correlation between the audiovisual encoding of a test subject and the depression model alert to a possible relapse. The proposed framework shows very promising results for depression recognition and depression relapse prediction. The proposed audiovisual based network reached an accuracy of 78. 97% for detecting depression in the DAIC-Woz dataset. The proposed correlation-based anomaly detection framework achieved an accuracy of 80.
99%, while the high-performing similarity measure-based approach achieved an accuracy of 82. 55% for predicting relapse depression. Both proposed approaches outperform the only existing approach in the literature of  Muzammel et al. (2021)  in the DAIC-Woz dataset. In future work, we are planning to learn others deep audiovisual representations of depression and non-depression and to study their performances in the proposed framework. We are planning also to integrate text modality in the proposed framework.
The fourth eligibility criterion (ie, having ever had sex with men) was assessed later, at the end of the questionnaire, to limit any unwanted disclosure. Demographic characteristics and HIV testing practice were collected before the sexual behaviours section. During the sexual behaviours section, the participants were asked if they had already had sex with women only, men only or both women and men. If some individuals reported having had sex only with women in their lifetime, the questionnaire ended at this section and these individuals were thanked for their participation. These individuals were then excluded from the analyses. For participants reporting at least one instance of sexual intercourse with a man in their lifetime, data related to MSM identity perceptions, access to MSM-CBTS and HIV testing site preferences were collected.
RESULTS Starting with 8 seeds, 568 additional individuals called the hotline to participate in the survey. Among these 576 individuals, 39 (6. 8%) were not reached after several call attempts following an appointment, and 3 (0. 6%) were not eligible. Of the remaining 534, 16 (2. 8%) were excluded since they reported never having had sex with a man. No withdrawal was recorded during the data collection. In total, among the 576 individuals registered in the hotline (including seeds), 518 (89. 8%) participated fully in the survey (figure  1 ). Among the 518 MSM who participated, 8 were excluded because of missing data on our variables of interest. Thus, 510 MSM were included in our analysis.
One of the limitations of our analysis is that we did not take into account community-based HIV testing outside fixed structures (eg, mobile testing). However, this form of testing remains less common among MSM in Côte d'Ivoire, with 11. 1% being tested outdoors (eg, outdoor testing campaign, home testing) during their last HIV test. 46 Moreover, whether outside or inside of fixed structures, community-based HIV testing takes place in MSMidentified places (eg, MSM NGOs, clubs and bars visited by MSM) and thus misses MSM who are far from the MSM community. These MSM who live far from MSM communities seem quite common; only 31% of our sample reported visiting bars or clubs where MSM meet each other, and only 27% knew of an MSM NGO. Other data related to pre-exposure prophylaxis or HIV self-testing uptake were not collected since both were not publicly available in Cote d'Ivoire during the survey implementation.
Multimode DAEs, or mDAEs, can thus be written in the Modelica language. This valuable feature enables describing large and complex cyberphysical systems with different behaviors in different modes. Unfortunately, multimode modeling has been the source of serious difficulties for non-expert users of the current generation of Modelica tools. Indeed, while many large-scale Modelica models are properly handled, some physically meaningful models do not result in correct simulations with most tools. As such problematic models are actually easy to construct, the likelihood of such bad cases occurring in large models is significant. It is unfortunately unclear which multimode Modelica models will be properly handled, and which ones will fail. As a consequence, quite often, end users have to ask Modelica experts, or even tool developers themselves, to tweak their models in order to make them work as expected.
Numerical methods for mDAEs is a highly relevant research topic, that falls outside of the scope of the works presented here. So-called symbolic-numeric methods  [12] [13] [14]  would be an interesting midway solution, although they are still unable to guarantee numerical nonsingularity. The paper is organized as follows. We first exhibit, in Section 2, three simple small-sized Modelica models that are not correctly handled by state-of-the-art Modelica tools, and we explain the reasons behind these failures. In Section 3, we go further by writing down a sensible physical model that would require extending the Modelica language. All four models are used throughout the article for either illustrating algorithms or assessing the performances of our implementation. Section 4 introduces the symbolic representations we are using to alleviate the need for mode enumeration during the handling of multimode models.
We also present two algorithmic building blocks, built on top of these representations, that constitute the basis for all of our contributions; namely, a multimode extension of the classical Dulmage-Mendelsohn decomposition of a system of algebraic equations  [15] , and a multimode extension of Pryce's Σ-method for the structural analysis of DAE systems  [4]. The design of these algorithms is fit for handling variable dimension models such as the one introduced in Section 3 so that they could be used for designing compilers for useful extensions of the Modelica language. Although these building blocks provide the bases for an efficient structural analysis of multimode systems, they are not quite sufficient for addressing the daunting challenge of scalability. Section 5 is dedicated to the CoSTreD (Constraint System Tree Decomposition) method, a novel generic approach for solving multimode constraint systems, that we apply to the building blocks introduced above.
Multimode Modelica Models Several constructs of the Modelica language enable the definition of switched or hybrid dynamical systems, often called multimode systems in the Modelica community. For instance, it is possible to use if-then-else conditional statements in equations, or equations can be themselves placed in such conditional statements. Hierarchical state machines  [5]  are also part of the Modelica language  [18] , enabling a higher-level, clearer modeling style for multimode systems. However, with all these constructs come several difficult issues. From a mathematical perspective, it turns out that the existence and uniqueness of solutions of a multimode DAE system is a much more difficult question than for pure (or single-mode) DAEs, as detailed in  [7]. As for the compilation of multimode Modelica models for the generation of simulation code, it is complicated by the fact that the structure of a multimode DAE system may depend on the mode, and may change at runtime whenever the system switches from one mode to another; as a result, convenient assumptions made by state-of-the-art Modelica tools for simplifying the compilation of such models can result in incorrect runtime behavior for many meaningful examples, as we shall see.
Equations el1 and el2 encode the second complementarity condition (between xxmin and yl) in a similar way. This model fails to simulate properly with both OpenModelica 1. 17. 0 and Dymola 2021; Figure  5  shows the output of Dymola 2021. Once again, the root cause of this behavior is that state-of-the-art Modelica tools perform an approximate structural analysis, disregarding the fact that the structure of the system is mode-dependent. For the Water Tank model, this analysis results in the DG shown in Figure  6. =(if bh then 0 else 1) × yh + (if bh then x -xmax else 0) Unknown yh can finally be isolated: yh = - if bh then x -xmax else 0 if bh then 0 else 1 (1) A problem is then bound to occur at runtime when Boolean variable bh is true. As a matter of fact, Equation (  1 ) is exactly the equation responsible for the division by zero exception shown in Figure  5 , which occurs at the initial time, when bh is true.
A Proposal for a Variable Dimension Extension of the Modelica Language A model of a possibly faulty transmission line is used to give flesh to a proposed extension of the Modelica language, enabling variable structure and variable dimension systems. It is a lumped model, consisting of the series interconnection of N instances of the same Modelica model, derived from an equivalent electrical circuit of the transmission line element shown in Figure  12  These equations can be regarded as plug equations: they are equations that result in the assignment of variables to a default value, with the sole purpose of keeping the same number of variables and equations in all modes. Such equations would be made unnecessary if variable v was defined only when open is false, and variable j was defined only when short is false.
This would be achieved by placing the corresponding variable declarations in if-then-else conditional statements, as shown in Figure  14. Although Modelica does not allow for this, at the time of writing of this paper, this could be a simple and handy extension of the Modelica language, that would enable the support of genuine variable dimension systems. The lumped model of the transmission line is given in Figure  15. The execution of this model by current Modelica tools such as Dymola and OpenModelica yields an exception at runtime. The analysis of this model, with N set to 1, by the IsamDAE tool, yields the conditional dependency graph shown in Figure  16 , which sheds light into the cause of this behavior: the set of leading variables of this model depends on the mode, which current industrial-strength tools fail to handle in all generality.
Constraint Dependencies follow Component Interconnections Because of the component-based design of large-scale Modelica models, such models are typically sparse, in that each component only interacts with a few other components. Hence, each model variable is only used in a few equations and each equation only involves a few model variables. Therefore, the resulting flat Modelica model (following the procedure described in Chapter 5 of the Modelica Language Specification  [18] ) is sparse. To formalize the notion of sparsity, in the context of wCSP and mwCSP, we use the notion of primal graph of a constraint system, that is, the undirected graph where two variables are related if and only if they appear in a common constraint. We emphasize that the notion of the primal graph should not be confused with the weighted adjacency graph used in the statement of Pryce's primal problem.
Structural Analysis of Long Modes in the IsamDAE Tool In Sections 4 and 5 above, we introduced the core building blocks, both in terms of data structure and algorithms, for the implementation of an efficient, genuinely multimode, compiler for the Modelica language. The development of the IsamDAE (https://team. inria. fr/hycomes/software/isamdae, accessed on 30 August 2022) tool was initiated in 2018 as a way of assessing the validity of our approach, especially in terms of computational times and memory consumption: our works not only aim at providing a compilation chain for multimode DAE models, but also at making it fit for large-scale models such as those designed in industrial settings. In this section, we present the structural analysis chain currently implemented in IsamDAE for the structural analysis of all modes of a multimode DAE model in an "allmodes-at-once" fashion, and we explain how the results produced by IsamDAE are reliable.
Structural Analysis Chain The whole analysis chain implemented in the IsamDAE tool builds on the idea of "dual" representation of multimode systems explained in Section 4. 1. Given a model of a multimode DAE system (The tool currently takes as inputs models declared in an ad hoc equational language inspired by the Modelica syntax. Its use for the compilation of Modelica code is obviously a major perspective of our works), this analysis chain can be summarized as follows.
Rescaling impulsive variables Focus again on System (51). Impulse analysis told us that λ, s both have impulse order ≤ 1. We thus rescale them accordingly: λ = def ∂ 1 ×λ and s = def ∂ 1 ×s (69) Modelica tools fail to execute. Recall that the root cause of this difficulty is that structural analysis methods implemented in these tools fail to yield the correct execution code for this model. In the present section, we demonstrate the generation of a target Modelica model under RIMIS form, resulting in a correct simulation of the model. Our approach is then formalized for its broad application to problematic multimode models. 10. 1. A Reduced Index Mode-Independent Structure (RIMIS) Form Using multimode structural analysis to transform a multimode Modelica model into a reduced-index model, that simulates correctly with state-of-the-art Modelica tools, is made difficult by the fact that the Modelica language does not permit to enable or disable an equation depending on the mode.
Based on this limitation, the basic principle of our model transformation is to evaluate all equation blocks of the CDG in a mode-independent fashion, irrespectively of the mode in which the system is. Of course, this leads to useless computations during simulation. However, this turns out to be a systematic way to ensure a correct simulation of multimode Modelica models. The model transformation is detailed below, in informal terms, then illustrated on a simple example. A mathematical definition of the transformation is detailed in Section 10. 4. Remark that models with either initial equations, or when, or reinit statements, are not covered in this paper. Further, note that models with non-scalar variables or class instances of any kind are not considered here. It is assumed that the models have been flattened according to the procedure described in Chapter 5 of the Modelica Language Specification  [18].
5. Replicate equations are generated from the CDG, which has two blocks of one equation each. From the block p : e → x, one replicate equation is generated by replacing variable x with its replicate x_2, then performing the partial evaluation  [39]  under the assumption that the Boolean condition p holds. In the context of the Modelica language, consider a Boolean expression q, and a real expression e. The partial evaluation of expression e, assuming q, is an expression e = π q (e), such that q implies e = e and free(e ) ⊆ free(e), where free(. ) is the set of free variables appearing in an expression. To define the partial evaluation operator π, and for the sake of clarity, we only consider the subset of the Modelica expression language defined by the following grammar, where p is a Modelica Boolean expression: Given a Boolean expression q and a real expression e, the partial evaluation of e, assuming q, is defined by induction on the structure of e:            π q (c) ≡ c π q (e 1 op e 2 ) ≡ π q (e 1 ) op π q (e 2 ) π q (v) ≡ v π q (v(e 1 ,.
Conclusions and Perspectives We reviewed several examples of multimode Modelica models that are currently not handled by the existing industrial-strength Modelica tools. We identified the reason for such failures as being an "approximate" structural analysis, that turns out to be incorrect in many sensible multimode examples. All models considered in this article are variable structure models, that is, their set of leading variables is mode-dependent, or their differentiation index is. Indeed, models that exhibit both features at the same time can easily be designed. In addition, models such as the faulty transmission line model presented in this article are arguably easier to describe as variable dimension models, which cannot be declared in the Modelica language at the time of writing. In our specific example, it is possible to complement the model with plug equations in order to comply with the current limitations of Modelica.
In addition, the modelling of magnetic anisotropy in finite element analysis software is presented. The second part presents a brief review of the electric motors using GOES magnetic circuits and their advantages compared to the classical motors. The third part presents a device for modelling the different junctions between the GOES teeth and the NOES yoke as in electric motors with concentric windings. Influence of the junction geometry is analysed with experimental measurements and FEA calculations. The fourth part analyses with FEA calculations the mixed motor performance compared to a full NOES reference motor in terms of electromagnetic torque, drag losses and iron losses.
++++ ++++ ++++ Existing commercial Finite Element Analysis (FEA) software applications models anisotropy as a diagonal permeability tensor (the two-axis anisotropic method) based on measurements of the magnetic characteristics in the main directions, i. e. the RD and TD for 2D calculation. The permeability in the Normal Direction (ND) is added for 3D models  [11]. 𝐵 = [ 𝜇 𝑥 0 0 0 𝜇 𝑦 0 0 0 𝜇 𝑧 ] × 𝐻 () For the two-axis method used in JMAG software application (version 19. 1), a vector relationship is applied between the flux density B and the magnetic field H for each main direction to determine B and H in the intermediate directions  (2). This software allows to consider both saturation and anisotropy of the magnetic circuit. In commercial FEA software, the most popular methods for calculating iron losses are the Steinmetz model and the modified Steinmetz model (Bertotti model)  [16].
In JMAG software application for anisotropic GOES sheets, the classical Steinmetz model approximation with bidirectional coefficients along the RD and TD is used, considering the harmonics presented in equation 3 where 𝑃 ℎ is the hysteresis losses, 𝑃 𝑒 the eddy current losses, i the harmonic rank, N the highest considered harmonic and 𝑓 the frequency. 𝐵 𝑅𝐷 and 𝐵 𝑇𝐷 are the maximum levels of flux density in the RD and the TD respectively. The coefficients 𝐾 ℎ 𝑅𝐷 , 𝐾 ℎ 𝑇𝐷 , 𝐾 𝑒 𝑅𝐷 , 𝐾 𝑒 𝑇𝐷 , 𝛼, 𝛽, 𝛾 and 𝛿 are determined with measurement data (iron loss curves as a function of flux density and frequency). 𝑃 𝑐𝑜𝑟𝑒_𝑙𝑜𝑠𝑠 = 𝑃 ℎ + 𝑃 𝑒 = ∑ { (𝐾 ℎ For the FEA simulations, the sheet steel data used for the estimation of the iron losses are shown in Table  III. These data were obtained with normalized SST measurements without sheet annealing.
A recent grand challenge of computational modelling in drug discovery, SAMPL7, 40 involves a set of β-CD host-guest pairs with a variety of structural and chemical features. Aside from the prototype β-CD, the other host molecules are β-CD derivatives with chemical modifications on either the primary or the secondary surface. These modified β-CD rings can still stably coordinate guest molecules, but various physiochemical properties including the binding affinity and the energetic preference of the directional guest coordination are altered. In this work, using this interesting β-CD host-guest dataset, we present a comprehensive modelling of β-CD host-guest binding, with a detailed evaluation of force-field accuracy and a correct treatment of the primary-secondary equilibrium. The obtained results would provide useful insights for both force-field development and practical considerations/guideline for molecular modelling of complex host-guest binding.
Aside from the regularization term, the objective function further includes the atomic force and the system energy. The atom-type symmetry defined in GAFF2 is applied, and the dihedral periodicity is fixed. The configurational ensemble used to generate reference data is obtained by 110 ns gas-phase unbiased sampling with a sampling interval of 10 ps. A high temperature of 600 K is employed to enhance the sampling efficiency. The obtained 11000 structures are then fed to ORCA  67, 68  for ab initio calculations. Due to the large sample size and the unconstrained exploration of configurational space at the high temperature, this reference set covers relevant regions and further adding samples leads to negligible changes of the refitting results. The target level used for the large host molecule is BLYP  69- 72  /def2-SVP with the latest D4 dispersion correction  73, 74  considering its good performance in modelling various properties (including non-covalent interactions) among pure functionals, while for the small guest molecules a higher-level method, the r 2 SCAN-3c  75, 76  composite scheme, is selected.
By averaging the contributions from all atoms, the RMSE of atomic forces is obtained. Upon FM refitting, the force RMSE is improved from ~18. 9 kcal/(mol·Å·atom) to ~12. 8 kcal/(mol·Å·atom). The time series of force errors for the drug-like guest G1 are shown in Fig. 3e-f , where we can see that the GAFF2 parameter set is generally not problematic for this molecule, and FM refitting only leads to minor improvements. The force RMSE is improved from the GAFF2 ~10. 8 kcal/(mol·Å·atom) to the FM-r 2 SCAN-3c ~7. 9 kcal/(mol·Å·atom). From both the analyses of energy and atomic forces, we know that the refitted parameter set reproduces the ab initio results in a better way than the transferable GAFF2. However, these numerical data provide no hints about the dynamic behaviors of the macrocyclic host produced by these parameter sets.
Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera. org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses. The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes.