dataesr/scientific-paragraphs-categorization huggingface dataset:
We also lack data on the effect of morphs on mate choice in Lacertidae, as we only found some information on mate choice relative to morphs in Podarcis muralis (Pérez i de  Lanuza et al. , 2013; Sacchi et al. , 2015; 2018 , who found mixed support for colour assortative mating) while indirect genetic results also suggest some possible assortative mating in Podarcis melisellensis  (Huyghe et al. , 2010). One main limitation of our work lies in the data we used to score the colouration. Neither photographs nor literature sources account for variation of ultraviolet (UV) colouration. Lacertids frequently use UV Thomas de Solan et al. colouration for mate choice or to signal their fighting capacity  (Olsson et al. , 2011; Pérez i de Lanuza et al. , 2014). Thus, in principle, we could have underestimated the frequency of conspicuous colouration and polymorphism in our dataset.
In the future, it would be interesting to test this hypothesis by investigating other taxa in which speciation is mainly driven by post-zygotic isolation. We also wish to stress a previously neglected inherent difficulty with the detection of the morphic speciation model through comparative methods in empirical datasets. The diversification analyses we used test for differences in diversification rates associated with the presence of polymorphism. Yet, the morphic species model predict that "polymorphic lineages should be ancestral and monomorphic lineages should be derived"  (Corl et al. , 2010b) , as morphic speciation generates monomorphic species from polymorphic species. The signature of the morphic speciation model in phylogenies should thus be an excess of monomorphic species sister to a polymorphic species, not an increased diversification rate in clades where all species are polymorphic.
In this paper, we present a new approach to improve the thermo-mechanical performance of the HEMT (high electron mobility transistor) technology. This study aims to solve two optimization problems. The first one is the optimization of the thermal behavior of the HEMT, through the optimization of its maximum operating temperature which influences the electrical characteristics such as electron mobility, and also influences the mechanical behavior of its structure. While the second problem will be the optimization of the mechanical behavior of the same technology, through the optimization of the stresses distribution that also influence the electrical characteristics and reliability of the HEMT structure. The resolution of these two optimization problems will be done, by the multi-objective optimization approach thanks to numerical tools such as Comsol multiphysics and Matlab software, which allows to solve these two problems simultaneously by taking into consideration the imposed constraints.
In this paper, we will treat the HEMT technology using a new optimization method. We will develop a complete multiphysics model, which allows simulation of the electrothermal and the thermo-mechanical behavior. Thanks to the modeling step, we can study the influence of the operating conditions on the HEMT structure behavior, and identify the different parameters that impact the performance of the component. In the next step, we will adjust the multi-objective optimization approach to the HEMT technology. We will couple the finite element model (FEM) using Comsol multiphysics software, to the multi-objective optimization model using Matlab software, which allows us to optimize the HEMT multiphysics behavior by the determination of the optimal variables. To achieve this objective, our paper will be composed of several parts.
The precedent equation links the dissipated heat with different thermal parameters. In addition, it allows describing the thermal behavior of the component structure, as a function of the operating conditions, especially the dissipated power. To develop the electrothermal model we need the thermal properties of the material of the layer, which we have classified in Table  1. For some material properties, the thermal conductivity is temperature dependent  [3, 23, 24]. We have developed the finite element model using Comsol multiphysics software as a function of boundary conditions. The first condition is the linear dissipated power (P diss ) in (W/mm) applied in the active area of the HEMT and the second is the reference temperature (T ref ) in the substrate. The results of the numerical simulations for the electrothermal model are presented in Figure  2 , and we have compared them with the experimental results.
Numerical simulation of stresses in the HEMT The FEM was developed using Comsol software, With thermal and mechanical boundary conditions, the mechanical one has o forms of displacement applied in the SiC layer. This model allowed observing the von Mises stress distribution on the device structure. Based on Figure  3 , This distribution is very high in the substrate layer that represents the growing base of the transistor, on both sides of the gate and in the AlGaN layer. These stresses exceed 4 GPa in these critical areas. Therefore, the functioning conditions of the HEMT technology have an important mechanical effect on the critical parts of its structure. The same finite element model allows observing the stress evolution according to the different applied loads, we have carried out numerical simulations for the various values of the dissipated power, and we have fixed the value of the reference temperature.
Our optimization process requires the development of a coupling between the finite element model using Comsol multiphysics software and the multi-objective optimization model coded in Matlab software. The flowchart in Figure  5  shows the multi-objective optimization coupling process. The optimization work was entirely automated via a main script written in MATLAB. The script starts the Multi-objective optimization process by generating an initial population using a uniform random distribution function. Then the main script submits multiple sets of decision variables and waits for each individual objective function's values before proceeding further. MATLAB calls Comsol software for changing the input parameters to compute the thermomechanical responses taken as objective values and returns back those values to the optimization process for executing the multi-objective algorithm and displaying the optimal results.
Results and discussion In this section, we will present the results of the solution to our optimization problem. This problem is solved by the multi-objective optimization approach, and the objective function was the maximum operating temperature and the von Mises stresses. Concerning the deterministic constraints, we have chosen as deterministic variables three design parameters of the three layers that represent a great impact on the multiphysics behavior of the component. These parameters are the AlGaN, GaN, and SiC(Silicon Carbide) layers. In Table  3 , we have classified the initial, maximum, and minimum values that we will use during the resolution of this optimization problem. The multi-objective optimization in this part is carried out by using BSAMO and NSGA-II algorithms coupled with the FEM that we have developed by Comsol multiphysics software, according to the optimization process presented in Figure  5.
Conclusion In this paper, we have proposed an approach for improving the thermomechanical performance of the HEMT, using a multi-objective optimization method. The application of this approach is ensured due to the coupling of two models: firstly, the finite element model developed using Comsol multiphysics software, and the second one is the multi-objective optimization model using Matlab software, it is used to minimize two objective functions, the temperature and the von Mises stress with well-determined design variables. The application of this approach to the HEMT technology has allowed the exploration of new parameters of design satisfying a Pareto front that gives a good compromise of minimum values of temperature and von Mises stress. The multi-objective optimization method has shown its efficiency to solve optimization problems compared to the deterministic and classical methods.
Antecollis should be evaluated with the patient seated in a chair without armrests and external supports. Their back should be kept straight to measure the contribution of cervical tract to antecollis, without compensation of the thoracic and the lumbar spine. Besides the degree of trunk flexion, knee angles should be evaluated because of coexisting postural misalignments (E). Images should be taken horizontally from a distance of at least 3 m from the patient with the lens approximately at waist height. The image should display the patient fully filling the screen. We recommend calculating posture degrees by analyzing images with the measurements proposed in the NeuroPostureApp, a freeware software-based tool (https://www. neuroimaging. uni-kiel. de/Neu roPostureApp/). 40  Although the finding of a monomorphic myopathy in the camptocormia of PD patients can be considered corroborated,  35  the clarification of its cause remains an important task.
The need to achieve statistically relevant results in electrical simulations requires a large number of iterations under different operating conditions. Moreover, the nature of parametric simulations makes the collection and filtering of the results non-trivial. To tackle these issues, scripts are normally used to control all the parameters. Still, this approach is usually ad-hoc and platform dependent, making the whole procedure hardly reusable, scalable and versatile. We propose a generic, opensource framework to generate complex stimuli and parameters for electrical simulations, together with a programmable Spiceand Verilog-A-based module capable of observing and logging internal states of the circuit to facilitate further result analysis.
Moreover, there is also the problem of incompatibility between simulators and licenses. Even if the tools and license requirements are met, the aggregation of results after complex parametric simulations is cumbersome and takes a big part of the project time just to prepare the data. In this paper, we show an open-access and open-source framework to carry out complex parametric electrical simulations and analyses. This framework is a wrapper to commercial tools, which allows researchers to perform and repeat a large 979-8-3503-3277-3/23/$31. 00 ©2023 IEEE number of simulations, no matter the underlying simulation environment. The framework is implemented in Python and Verilog-A and it is licensed under the MIT License. The code and documentation can be found online  [2] ,  [3]. This paper is organised as follows: the current state of the art is summarised in section II, followed by the motivation of this project in section III; In section IV the two parts of the framework are described in detail and some use cases are provided in section V.
II. STATE OF THE ART The main format used to describe electrical netlists is called Spice  [4] , which stands for Simulation Program with Integrated Circuit Emphasis. An electrical simulator will take the spice netlist, parse it and simulate it. The same netlist can be simulated under different compatible simulators as it is just a description of the circuit. All of the available tools for electrical simulations provide a graphical user interface to generate circuits based on a drag-and-drop mechanism which is very user-friendly. The graphical user interface can be also used to customise every aspect of the simulation to provide the necessary parameters to the simulator. However, not every available tool provides a mechanism to easily save this configuration to be used later, or on a different computer.
An example of an automated CAD tool is the AWR Design Environment (AWRDE), provided by Cadence, working in a custom language called SAX. This tool works via a simple interface where the user can control any aspect of the environment through code. Users have also access to powerful tools that allow them to modify part of the circuit and generate custom reports programmatically, and even perform analysis in Python. However, this tool requires an expensive licence that not everyone is able to afford and requires some time to learn to properly use its functionalities. Other tools like PySpice  [5]  allow the generation of netlists and the launch of simulations directly with Python. In this way, both stimuli and configuration live under the same block of logic, which reduces the time between iteration cycles.
A. Netlist and simulation configuration tool The main objective of the Python tool is to facilitate the generation of complex electrical parameters as well as inject them inside predefined spice netlists and simulation configuration files, in a similar manner to a text macro system. This tool, which from now on will be referred to as builder, exploits the way electrical components are defined in spice netlists. For example, a MOSFET transistor is defined as shown below. The syntax can slightly change between different spice implementations, but the definition of the parameters is identical. This builder exploits the textual nature of netlists in order to generate the necessary stimuli for the simulations. A set of values can be generated, inserted into the stimuli templates and feed to the simulator.
To achieve this, the user needs to define the different stimuli files that will be used as templates and define the functions to generate the values for each iteration. The different input files are treated as templates and processed by the Jinja [6] template engine. An advantage of using a template engine like Jinja is that logic can be embedded directly into the templates, which means that certain parts of a template can be rendered or discarded depending on the values of some variables. The builder can be configured through a YAML or TOML config file. This file contains metadata, user-defined properties, the command to execute, the list of files to use as templates and the number of iterations to execute. These variables can be accessed directly in the templates. In order to generate values, the user needs to define one or multiple ValueFactories in Python.
Source code 2 shows a small section of the spice netlist that has been converted to a Jinja template. This part generates a chain of inverters where the number of stages is determined by the user variable nstages. In this example, the variability is induced by altering the length and the V th of each PMOS and NMOS. The values are generated using a Factory that draws the values from normal distributions, as seen in the ParamFactory shown in the examples in  [2]. Thanks to the Jinja templates, the number of inverters for each Ring Oscillator can be embedded in the logic and configured by the user. Once the schematic is created, the parameters to simulate the process variability are defined in the same way they have been described in section IV-A and are inserted into the netlist.
The VTEAM memristor model  [10]  provides several parameters to simulate the behaviour of memristors. Being an open Verilog-A description model, it can be easily modified to add new terminals and provide information about interesting internal variables. For this application, it is important to obtain the internal resistance and the distance of the threshold of the doping region. The state observer saves that data, allowing to have a detailed description of the behaviour that can be used to drive in real-time the evolution of the simulation and the proper parametric settings. For all of the memristors that are connected to the state observer, the absolute time of measurement, the internal resistance and the distance of the doping region are written to a parameterised CSV file. Source code  [3]  shows an example of the state observer used to save the state of two memristors under the execution of Logic-in-Memory (LIM) operations.
While still allowing one to visualize temporal networks in a low-dimensional space, TGNE additionally allows one to gauge the uncertainty around the latent positions in a natural and rigorous manner. Our contributions can be summarized as follows. • We propose TGNE: a variational approach to inference in the CLPM model that allows one to calculate trajectories of Gaussian distributions for node embeddings in a latent space, given a history of interactions. • We develop model-based statistical analysis of simulated and real-world datasets using the obtained dynamic embeddings. • We conduct a novel and rigorous evaluation of the uncertainty learned through the variational approximation of the posterior. • We assess to what extent the TGNE can be used to reconstruct missing events in the temporal network.
C. EFFECT OF THE HYPERPARAMETERS The proposed method has four hyperparameters: the dimension d, the number of change points K, the initial scale τ 0 , and the scale τ. The number of change points K controls the time resolution of the latent trajectories. It should be adapted to how frequently we expect the nodes' states to change in our dataset. The initial scale τ 0 controls the scale of the initial latent positions z i. Finally, the scale τ is a temperature parameter that controls the deviation of the latent positions between frames, namely ||z (k+1) i -z (k) i ||. To illustrate its effect, in Figure  2  it can be seen that for τ = 50. 0 the frames are not constrained to be close to each other, and the latent positions can change drastically between frames. On the other hand, for τ = 1. 0, the latent positions are constrained to be close to each other, and the frames are more similar to each other.
D. IMPLEMENTATION We implemented our method in Pyro, a Pytorch-based probabilistic programming language  [2]. This effect handleroriented programming language allows one to define the model as a Python function. The execution trace of the function can then be read and decorated by effect handlers, allowing one to define high-level probabilistic operations such as conditioning, or performing Stochastic Variational Inference. To optimize the variational parameters ϕ and the bias term β, we use the ADAM algorithm  [17]  with learning rates γ = 0. 01 and γ = 0. 00001 respectively.
E. SCALABILITY We discuss two strategies to scale the method to networks with a large number of nodes: node-batching and negative sampling. As the log likelihood term is a sum of terms over all source nodes, node-batching can be implemented by computing the loss and gradients on a subset of the nodes at each iteration, and then averaging the gradients over the whole dataset. The log-likelihood decomposes as a sum of contributions from positive node pairs (interacting at least once) and negative node pairs that never interact. However, most of the node pairs in the network never interact, and thus the information conveyed by the negative pairs is redundant. This opens up the possibility of negative sampling which may dramatically speed up inference on networks with many nodes. We propose the following strategy, akin to the casecontrol approximate likelihood introduced in  [26] : for each node i, we sample K nodes j such that (i, j) never interact in the network.
We denote P(i) the set of nodes that connect with i at least once in the event history, and N (i) a random subset of the set of nodes that never connected with i. The log-likelihood can be approximated as: log(p(Y | z)) ≈ i∈U   j∈P(i) 1 0 λ ij (s)ds - τ ∈Tij log(λ ij (τ ))   + |U \ P(i)| |N (i)|   j∈N (i) 1 0 λ ij (s)ds   V. EXPERIMENTS We performed various experiments to answer the following research questions. First, we evaluate the uncertainty of the latent positions on simulated data, and on real-world datasets. Next, we try to understand qualitatively how the parameters of the model affect the resulting latent positions. Finally, we try to understand to what extent the TGNE method allows Moreover, for a high value of τ , the node changing community will yield a higher variance around its associated embedding position FIGURE 2: Resulting latent positions on synthetic data generated from the Stochastic Block Model.
Datasets. In our experiments, we used a simulated dataset, as well as four real-world datasets, for which we provide a brief description below. The HighSchool dataset  [7]  is a contact network of student in a French preparatory class High School in Marseille. Their interactions were recorded using wearable devices over 9 days. The resulting embeddings are shown in Figure  3a. The MIT Reality Mining Dataset  [6]  is a dataset of face-to-face contacts between participants of an experiment ran by members of MIT media Lab. The data was collected over the course of around 9 months, between 2004 and 2005. The obtained embeddings for this dataset are shown in Figure  3b. The Workplace dataset is a dataset of face-to-face contacts between employees in a workplace  [9]. Their interactions were recorded on 11 days (2013/06/24 to the 2013/07/05).
C. TEMPORAL NETWORK RECONSTRUCTION Setup. We evaluate TGNE on the task of reconstructing missing interactions from a partially observed continuoustime temporal graph. To do that, we split the edges in the network into train, validation and test sets. Then for each split, and each interval I k , we predict whether each edge e = (i, j) interacts in the interval I k , i. e. whether there exists an interaction (i, j, t) in history, such that t ∈ I k. For each interval and each positive edge we sample a single negative edge, thus casting the problem into a binary classification  task of the node-pair/ interval triplets (i, j, k). For the High-School dataset and the UCI dataset we use 10 % of the edges as test edges, and the rest as train edges. For the Workplace dataset we use 30 percent of the edges as test edges, and the rest as train edges.
In order to discuss more precisely the regularizing effect of the prior for Network reconstruction, techniques such as tensor decomposition could have been explored, however, since TGNE is derived from Latent Space Models, we decided to stick with this class of models in this work. Results. The results are provided in Table  2. On the High School Dataset, it can be seen that while a binary Latent Space Distance Model could be used to predict the presence/absence of links, its resulting configuration of node embeddings overfits the training data, and thus does not perform well on the test data. In contrast, the embeddings obtained using TGNE perform worse on the training set, but much better on the test set. This showcases the benefits of the regularization term on the predictive abilities of the model.
This research was funded by the  ERC  under the  EU 's  7th Framework and H2020 Programmes  ( ERC Grant  Agreement no. 615517  and  963924 ),  Flemish Government  under the ' Onderzoeksprogramma Artificiële Intelligentie ( AI) Vlaanderen' programme , and the  FWO  (project no. G0F9816N ,  3G042220 ).
(a) Resulting latent positions on the High School Dataset, with τ = 1. 0, K = 15. Only the nodes that appear during each time frame are shown. The color corresponds to the class of the student, while the size is proportional to the uncertainty on their latent positions. Students from similar classes (e. g. Physics/Chemistry students (PC and PC*) cluster together more than with other students (e. g. Mathematics/Physics: MP*1 and MP* 2)). Some students from PSI* (Physics and Engineering Science) still seem to interact with the PC/PC*. (b) Resulting latent positions on the MIT Reality Mining Dataset, with K = 15, σ = 0. 1. The embedding positions allow distinguishing members of different departments (the people of the Sloan Business school form a clear separate community). Moreover, the professor (represented by a purple triangle) is in the center of the embedding space and over time moves around between the communities he mostly interacts with.