dataesr/scientific-paragraphs-categorization huggingface dataset:
The infrared data (Infrared Spectroscopy) indicated that interaction between PoANIS and HEC is weak, since no evidence of HEC vibrations was seen in the spectrum of PoANIS-HEC. We suggest that the presence of HEC inside and outside of the micelles during synthesis pushes the polymeric particles to aggregate towards the colloid's outer walls. XRD traces of the various deprotonated samples prepared are shown in Figure  4 , and the peaks are listed in Table  4. The positions and widths of these peaks were used to calculate the interplanar spacing (d) and crystalline domain width (L) (equivalent to the diameter for spherical particles) using the Bragg and Scherrer equations respectively: Previous studies have shown that PANI emeraldine base (EB; formed by deprotonating PANI-HCl) is an almost amorphous compound while PANI-HCl displays some crystallinity.
1 ). Consequently, the flow direction in the media channels was in the -y-direction. Computational analysis for oxygen concentration control. The flow field in the media and gas channels and the oxygen condition generated in the device were analyzed by 3D numerical simulation using commercial multiphysics software (COMSOL Multiphysics ver. 5. 5, COMSOL AB, Sweden)  27. In the flow analysis, the Navier-Stokes equations and the equation of continuity were solved as the governing equations. The physical properties of the cell culture medium, gas mixture, hydrogel, PDMS, and PC film are summarized in Table  2. The flowrates or the average flow speed of the cell culture medium and gas mixture were applied with reference to the Péclet number Pe m and Pe g , respectively. Here, Pe is a nondimensional value representing the ratio between convection and diffusion: Table  1.
18. In the conditions with flow exposure, fluid shear stress was applied to the ECs by circulating the cell culture medium in the media channel at Q m = 100 µl/min. Here, the actual viscosity and density of the cell culture medium at 37 °C were measured by using a vibration-type viscometer (VM-10A; SEKONIC, Japan) and an electronic balance (ASP413; AS ONE, Japan); the measured values were 0. 757 × 10 -3 Pa s and 0. 979 × 10 3 kg/m 3 , respectively. Hence, the cell culture medium generated a fluid shear stress τ of 0. 72 Pa at the center of the bottom of the media channel as shown later with computational results, which was in a physiological range  1. Phase-contrast microscopic images of the EC monolayer were obtained every 10 min for 6 h after the start of each experiment. The migration speed of the cells was measured by PIV with time-lapse microscopic phase-contrast images obtained every 30 min using JPIV open-source software  46.
6 µm intervals in the vertical (z-axial) direction using a confocal laser scanning microscope (LSM800; Carl Zeiss Microscopy, Germany), and the maximum intensity of the images was projected onto the xy-plane. The projected images were then analyzed using open-source software (ImageJ, National Institutes of Health, USA) to extract the outer and inner outlines of VE-cadherin surrounding each cell. The cell shape was approximated to be ellipsoidal based on the outer outline of VE-cadherin. Then, the orientation angle formed by the major axis and the flow direction (the -y-direction), and the aspect ratio between the lengths of the major and minor axes, were quantified. Furthermore, to evaluate cell-cell junctions, the area A cad of VE-cadherin of each cell was obtained as the difference of the areas A out and A in surrounded by the extracted outer and the inner outlines, and the ratio of the VE-cadherin area to the total cell area, A * cad (= A cad /A out ), was calculated  47.
In this paper, the authors present an analysis of a graphene Pythagoras Tree Fractal (GPTF) antenna with a thin SiO2 substrate for THz regime. The GPTF antenna is designed using a fractal geometry approach, which provides multiple resonant frequencies and enhances the overall radiation efficiency. The thin SiO2 substrate is used to reduce the substrate losses and improve the radiation performance of the antenna. The authors use the Finite-Difference Time-Domain (FDTD) software to simulate the performance of the proposed antenna. The results show that the proposed antenna exhibits high gain, low return loss, and wide bandwidth, making it a promising candidate for THz applications.
The Numerical results are given by using a full wave electromagnetic simulator based on FDTD method. In the field of electromagnetics, numerical simulations play a crucial role in designing and optimizing various electromagnetic devices and systems. The FDTD method is one of the most widely used numerical techniques to solve electromagnetic problems. The FDTD method discretizes both time and space domains and solves Maxwell's equations numerically. The CST Studio Suite is a widely used electromagnetic simulation software that utilizes the FDTD method to solve complex electromagnetic problems. It is a full-wave simulation software that provides accurate and efficient solutions for various electromagnetic applications. CST offers a user-friendly interface and powerful visualization tools that enable engineers and researchers to design, optimize and analyze a wide range of electromagnetic systems, such as antennas, microwave circuits, and electromagnetic compatibility.
Morphological characterization Cross-sectional Scanning Electron Microscopy (SEM) images were recorded on HITACHI S5500 microscope with a voltage of 3 kV. Atomic Force Microscopy (AFM) topography images (5 μm) 2 , (512 px)  2  were recorded in intermittent contact using a Bruker Dimension Fastscan. Silicon tips (FASTSCAN-A; scan rate 27. 7 μm). Data analysis (second-order flattening) was performed using the Nanoscope Analysis software.
X-ray photoelectron spectroscopy X-ray Photoelectron Spectroscopy (XPS) measurements were performed using a VersaProbe II (ULVAC-PHI) spectrometer equipped with a monochromatized micro-focused (beam size 100 µm 2 ) X-ray source (Al Kα 1486. 6 eV). More details on the experimental conditions are available in the ESI. † The data analysis was performed with Multipak software. All spectra were calibrated relatively to the contribution (C-C, C-H) of the C 1s at 284. 8 eV. The peaks were fitted by a pseudo-Voigt function (sum of Gaussian and Lorentzien curves with of ratio fixed at 80/20%).
Detailed description of the XPS data can be found in ESI. † For ZnO, the O 1s region can be decomposed using two contributions: a first one centered at 530. 1 eV (FWHM of 1. 2 eV) which can be attributed to the O 2-(O(II) in the hexagonal lattice of ZnO Wurzite) and thus to the Zn-O bond. 4] [45] [46]  The presence of hydroxide has already been observed in the literature and it has been shown that by decreasing the temperature during ALD deposition, this contribution increases significantly. 18, 47 Previous works have also shown that this Zn-OH contribution does not vanish after a long time sputtering which indicates the presence of this electronic state also in the depth of the ZnO film. 42 This result is in good agreement with ToF-SIMS results where a signal from ZnOH -ions vanished just in the silicon substrate region.
Conclusions In this work, an improved process for ZIF-8 thin film realization is described, based on repeated exposure to ligand and water of a ZnO sacrificial layer. This approach allows the control of the thickness of the film up to 230 nm. Molecular composition of the film was probed in depth by XPS and TOF-SIMS, highlighting the presence of Zn-OH groups in each region of the stack (ZIF-8 and ZnO) and a significant enrichment at the interface. Experimental data and molecular dynamics simulations suggested that the Zn-OH defects in the ZIF-8 layer are in fact mandatory to allow diffusion of the ligand to the growing interface. These elements tend to explain the efficiency of our cycling process. At the same time, this insight suggests a limit to growth which is consubstantial with the method proposed: upon growth, the top layer of the growing ZIF film, that is the one closer to the ultimate surface, is cured from its initial defects present closer to the ZnO/ZIF-8 interphase.
Acknowledgements The authors thank  T. Julien  and  G. Castellan  for ALD depositions,  B. Assie  for the technical support on the conversion reactor,  C. Licitra  for the support on ellipsometry measurements,  N. Rochat  for her help on FTIR experiments and  P. Gergaud  for XRD measurements. This work was initiated in the framework of the 'nano-chemistry platform' partnership between the C2P2 research unit, initial address of authors VP et EAQ, and the  "Direction de la Recherche Technologique" (DRT)  of  CEA. The  C2P2 unit (UMR 5265 ) has since merged in the CP2M research unit ( UMR 5128 ), both under CNRS,  CPE Lyon and Université Claude Bernard Lyon 1 tutelage. Part of this work, carried out on the Platform for Nanocharacterization (PFNC), was supported by the ' Recherches Technologiques de Base' program.
Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new Autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.
Introduction The exponential growth of digital information has led to the widespread adoption of machine learning solutions. While ML can assist in decision-making and data analysis, human expertise is often required  [1, 2]. Human interventions are required primarily as the domain experts due to the fact that they can provide unique characteristics of the domain. It may drastically affect the performance of the algorithms. Later on, expert data-scientists are needed due to the large number of algorithms and hyperparameters configurations which otherwise make brute force infeasible search  [2] [3] [4]. In such situations, algorithm selection is one of the main challenges in applying ML to a new problem. It denotes the identification of algorithm(s) (or algorithm families) that are likely to perform better on a given combination of datasets, tasks, and evaluation measures  [5].
Algorithm selection and configuration (hyperparameters tuning) is a difficult process for the novice users; since the performance of an algorithm is basically a "black box" affected by multiple characteristics of the dataset. It includes instances distribution, the number of features and their composition, and the number of classes, etc  [6]. The process of discovering appropriate algorithm and its optimal hyperparameters configurations can be automated in order to prevent the inherent complexities. It may also help to accelerate the testing of multiple configurations  [6, 7]. Automated machine learning (AutoML) refers to decision support systems that attempt to automate all or part of the machine learning pipeline. It has been highlighted in the recent research studies  [2, [8] [9] [10]  that the AutoML techniques to automate the algorithm selection and configuration process (notably Auto-sklearn  [5] , Auto-Weka  [11] , and TPOT  [12] ) tend to be time consuming and burdensome for computational resources.
It is due to the fact that they need to execute, multiple times, each candidate algorithm and configuration on the data. This fact is further emphasized for the large datasets, where even a few execution cycles may take several hours, hence making them impractical in real world scenarios  [6]. An alternative approach for addressing the algorithm selection problem is meta-learning  [2, 9, [13] [14] [15]. Among others, one of the aims of meta-learning is to assist the identification of the most appropriate learning algorithm (s) for a given problem by mapping datasets's characteristics to the predicted data mining performance (e. g. , execution time, predictive performances, etc. ). To better serve the purpose, meta-learning systems use a set of data characteristics, called meta-features, to represent and characterize data mining tasks.
The meat-learning systems then identify and analyse the correlations between these attributes and the performance of learning algorithms  [16]. The proper identification of data properties is essential to map tasks to learning mechanisms. Instead of executing all learning algorithms to obtain the optimal one, meta-learning is performed on the meta-data characterizing the data mining tasks to identify the optimal or near optimal learning algorithm for the given task. As a data-driven approach, the effectiveness of meta-learning is largely dependent on the description of tasks (i. e. , meta-features). In the current context, meta-learning requires meta-features that represent the primary learning tasks or datasets to transfer knowledge across them. We observe, in the available literature that several approaches in meta-learning use families of meta-features as input to quantify task similarity.
It is common to compute tasks similarity as the Euclidean distance between two meta-features vectors. While these approaches have shown to be effective in simple scenarios, they exhibit clear limitations  [17]. The foremost non-trivial task among the exhibited limitations is the identification and selection of relevant meta-features. Several research questions can emerge to better address these limitations such that What criteria should we invoke to include or discard a family of meta-features? For instance, statistical meta-features are not always intuitive and lack expressiveness. In  [18] , the authors have shown how different datasets may share identical statistical properties but noticeably they have different data distribution. Ultimately, the selection of meta-features is an ad hoc process based on domain knowledge.
It is highly desirable to develop the more predictive meta-features and select the more informative ones in order to improve the effectiveness of meta-learning  [16, 19, 20]. We believe that traditional meta-features are not always capable of capturing crucial characteristics of a given task, even though some of them are very task specific  [21]. This can be attributed to the fact that they only model the general characteristics of the dataset (e. g. number of instances/features, imbalance, etc. ). Learning relevant meta-features can be useful to better identify the hidden relationships across tasks, to necessarily build the accurate meta-models. A different approach that has achieved popularity in recent years invokes Deep Neural Networks (DNNs). The strength behind DNNs is their capacity to learn data characteristics from the diverse and large amount of data  [22].
DNNs have had a strong impact in application areas such as image understanding and speech recognition  [21, 23]. However, their use in meta-learning is still incipient and requires further investigation. The development of deep learning for features generation has been largely studied in the literature. It represents different datasets and tasks as embedding generated by trained deep networks. In  [24] , the authors solve different automatic speech recognition tasks through a two-step learning process. In the first step, the algorithm perform classification with DNNs, which is followed by the extraction of intrinsic features from the DNN output. In the second step, extracted features are used to improve model predictions. Our hypothesis is that DNNs provide the means to extract intrinsic meta-features from data.
In particular, autoencoder is a type of artificial neural networks offering good results due to their architecture and operations  [25] [26] [27] [28]. In this paper, we propose an instance-based algorithm, that learns latent meta-features from families of traditional ones. Its objective is to obtain meaningful and more informational meta-features. Specifically, the present work introduces AeKNN, a kNN-based algorithm with built-in latent features extraction strategy. AeKNN projects the training patterns into a lower-dimensional space, with the help of an Autoencoder (Ae). The goal is to produce new meta-features of higher quality from the initial data characteristics. In short, AeKNN combines two reference methods, k-Nearest Neighbors (kNN) and autoencoder, in order to take advantages of autoenconder in learning higher-level features.
Thus, it supports kNN in performing pipelines recommendation in meta-learning paradigm. The main contribution of this paper is the design of a novel meta-model, called as AeKNN, which combines an efficient latent features extraction mechanism (autoencoder) with a popular classification model (kNN). For the experimentation purposes, a collection of 400 real world problems and 8 ML algorithms has been used to assess the competitiveness of the proposed meta-model. It accumulate a knowledge base of more than 4 million evaluated pipelines. The rest of the paper is organized as follows: In "Theoretical background and related works" section 2, a brief review of the closely related works is introduced, including meta-learning for algorithm selection and data characterization techniques. In "Proposed AekNN based data characterization approach" section, the proposed AeKNN meta-model is described.
Theoretical background and related works Meta-learning involves two basic aspects: the characterization of the learning problems (datasets), and the identification of the correlation between the optimal learning algorithm (s) and the problems characteristics. The first aspect relates to the techniques for characterizing datasets with meta-features, which constitutes the meta-data for meta-learning, whilst the second one is the learning stage at meta-level, which develops meta-models for the selection of appropriate algorithms and related hyperparameters configuration in respect of previously unseen datasets.
Meta-learning for algorithm selection One of the main challenges in applying ML to new problems is the algorithm selection and related hyperparameters tuning, with the need to take the dataset characteristics (meta-features), type of task (classification, regression, clustering), and evaluation measure (e. g. predictive accuracy, precision, Recall, F1 score) into account. Let us consider the following contextual information to better understand the algorithm selection problems. The ASP can be formally defined as follows : given a set of learning algorithms space A = {A (1) ,. , A (i) } , a dataset D divided into disjoint training D train , and valida- tion D validation sets, a task T , and an evaluation measure M , the goal of ASP is to identify the algorithm (s) A (i) * where A (i) * ∈ A and A (i) * is a tuned version of A (i) that minimizes or maximizes the M on D  [29].
In particular, the general statement of the algorithms selection and optimization problem is defined as : where L(A (i) , D train , D validation ) is the loss function (e. g. error rate, false positives, etc). While it is usually necessary to rely on the human expertise (data scientists) to tackle the ASP challenges and difficulties, there has been increased interest in recent years in AutoML solutions. These solutions are proposed as decision support systems to find suitable ML pipelines for a given dataset. Existing AutoML solutions for algorithm selection are typically based on Bayesian optimization  [30] , deep reinforcement learning  [31]  evolutionary algorithms  [12, 12] , and budget-based evaluation  [32]. While all of the above-mentioned solutions are effective, they are computationally expensive (both in terms of resources and runtime), because they require an iterative search of different models and configurations.
In addition, when given a new dataset, most of the above solutions have to start the search for an optimal pipeline "from scratch" and evaluate each configuration on the given dataset before the recommendation. This limitation is particularly problematic when dealing with large datasets resulting in long processing time. Meta-learning or learn to learn is an alternative approach for dealing with the ASP. Meta-learning paradigm aims to learn a mapping from the behavior of learning algorithms to the datasets characteristics (meta-features) that contribute to the improved (1) A (i) * ∈ argmin A∈A L(A (i) , D train , D validation ) performance of one algorithm configuration over others  [33]. This knowledge can be then used to better identify high-performance algorithms in order to solve the tasks on previously unseen datasets  [34, 35].
As described in  [36]  and illustrated in Fig. 1 , meta-learning frameworks consist of a collection of learning algorithms and datasets. First, the framework extracts meta-features that capture (or aspire to capture) the "essence" of a given dataset. Each learning algorithm is then applied with different related hyperparameters configurations on each dataset, and estimates the performance according to a performance measure. The extracted meta-features and the performance of the evaluated learning algorithms and configurations refer as meta-data. Then, a learning algorithm (meta-model) is trained on the meta-data to match the values of the meta-features with the most suitable algorithm (s) for each dataset. Finally, for a new dataset, the meta-learning system extracts the meta-features and uses the meta-model to recommend algorithm (s) for that dataset.
Meta-data involves the extraction of significant and meaningful meta-features on the datasets or the used models  [30, 34, 37, 38]. The majority of meta-features can be divided into five families, as shown in the next section. Over the years, several studies have explored the application of meta-learning to various tasks such as algorithms recommendations  [7] [8] [9] , transfer learning  [39] , and assemble methods  [40]. Many state-of-the-art AutoML systems use meta-learning as a way of improving their accuracy and running time  [30, 41]  and multiple studies describe ways to induces models from meta-knowledge as decision support systems for the ASP  [6, [11] [12] [13] 37]. Some ML algorithms are often used to induce a meta-model for recommending algorithms in meta-learning. These algorithms can be an adapted version of the k-Nearest Neighbors algorithm  [42].
Data characterization The major task, to characterize the datasets for meta-learning, is to capture the information about learning complexity on some given dataset and identify structural similarities or differences among datasets  [16]. The most early attempts to characterize datasets in order to predict the performance of classification algorithms were made by Rendell et al. [43]. We observe in the literature that broadly two main strategies are proposed subject to characterize a dataset for suggesting which algorithm is more appropriate for a specific task or dataset. Among them are the methodologies using statistical measures and a set of simplified learners. The former attempt to describe the properties of datasets using statistical and informational measures. In the later, a dataset is characterized using the training performance (e.