Understanding the meaning of words in context is fundamental for reading comprehension. The perceived difficulty, hereafter referred to as complexity, of a target word within a given text varies widely among readers. With an increased demand for distance learning and educational technologies[107], research into automatically predicting which words are likely to cause comprehension problems is becoming a popular area of research [115, 147, 185]. Systems have been created to identify complex words that are difficult to acquire, reproduce, or understand for children [79], second-language learners [89], people suffering from a reading disability, such as dyslexia [131] or aphasia [35, 53], or more generally, individuals with low literacy [59, 175]. In Computational Linguistics and Natural Language Processing (NLP), the task of automatically recognizing complex words is most often achieved by training machine learning (ML) models. These ML models assign a complexity value to each target word within an inputted extract, sentence, or text that allows for the identification of complex words. This information can then be used to improve downstream lexical and text simplification systems that provide simpler alternatives to aid reading comprehension. Take the extract shown in Table 1 for example.

Similar to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult due to privacy concerns. As a result, conditions like Rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods for computer-aided diagnosis. In recent years, Generative Adversarial Networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. In this study, for the first time, a small dataset of Rosacea with 300 full-face images is utilized to further investigate the possibility of generating synthetic data. The preliminary experiments show how fine-tuning the model and varying experimental settings significantly affect the fidelity of the Rosacea features. It is demonstrated that $R_1$ Regularization strength helps achieve high-fidelity details. Additionally, this study presents qualitative evaluations of synthetic/generated faces by expert dermatologists and non-specialist participants. The quantitative evaluation is presented using a few validation metric(s). Furthermore a number of limitations and future directions are discussed. Code and generated dataset are available at: \url{https://github.com/thinkercache/stylegan2-ada-pytorch}

This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images. Although neural networks have been applied to solve this problem, most machine learning-based approaches assume that the training and test data follow the same distribution, which is not always true in real-world scenarios. To address this limitation, we introduce SHIFT15M, a dataset that can be used to evaluate set-to-set matching models when the distribution of data changes between training and testing. We conduct benchmark experiments that demonstrate the performance drop of naive methods due to distribution shift. Additionally, we provide software to handle the SHIFT15M dataset in a simple manner, with the URL for the software to be made available after publication of this manuscript. We believe proposed SHIFT15M dataset provide a valuable resource for evaluating set-to-set matching models under the distribution shift.

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.

Tried-and-true flapping wing robot simulation is essential in developing flapping wing mechanisms and algorithms. This paper presents a novel application-oriented flapping wing platform, highly compatible with various mechanical designs and adaptable to different robotic tasks. First, the blade element theory and the quasi-steady model are put forward to compute the flapping wing aerodynamics based on wing kinematics. Translational lift, translational drag, rotational lift, and added mass force are all considered in the computation. Then we use the proposed simulation platform to investigate the passive wing rotation and the wing-tail interaction phenomena of a particular flapping-wing robot. With the help of the simulation tool and a novel statistic based on dynamic differences from the averaged system, several behaviors display their essence by investigating the flapping wing robot dynamic characteristics. After that, the attitude tracking control problem and the positional trajectory tracking problem are both overcome by robust control techniques. Further comparison simulations reveal that the proposed control algorithms compared with other existing ones show apparent superiority. What is more, with the same control algorithm and parameters tuned in simulation, we conduct real flight experiments on a self-made flapping wing robot, and obtain similar results from the proposed simulation platform. In contrast to existing simulation tools, the proposed one is compatible with most existing flapping wing robots, and can inherently drill into each subtle behavior in corresponding applications by observing aerodynamic forces and torques on each blade element.

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

An artificial-intelligence model trialled in Chile's Atacama Desert could one day detect signs of life on other planets. Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet.

Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet. The results were reported on 6 March in Nature Astronomy1.

With over 144,370,000 square miles of surface terrain, Mars has a lot of places where signs of potential life could hide. Factor in the ultra-valuable time of current and future rovers, and it makes it even more challenging to scour for evidence of potential ancient microbes and organisms in an efficient way. To even the playing field a bit, SETI is turning again to artificial intelligence and machine learning in an effort to calculate the most likely and promising places for rovers--and, perhaps one day, astronauts--to look for clues of life. And as first detailed on Monday in Nature Astronomy, the team's new AI machine learning modeling is already showing potential to speed up humanity's search for alien life. To build their AI, the interdisciplinary project led by SETI Institute Senior Research Scientist Kim Warren-Rhodes trained a program on datasets drawn from a region called Salar de Pajonales.

Neologisms are certain uses, expressions, and words that did not traditionally exist in a language, but are incorporated into it due to the need of speakers to adapt to a new reality [1]. That is, neologisms are those new words and expressions that speakers incorporate into a language, as new things and new ways of doing to name arise. They are the exact opposite of archaisms. The appearance of neologisms is a common and ordinary process in all languages, forced as they are to adapt and update or die. However, a word can be considered a neologism only for a certain time, since once it has been incorporated and normalized as part of the language, it simply ceases to be a novelty. The simplest way to classify neologisms would be from the method used to create them, thus we have: 1. morphological neologisms: they are built using words that already exist in the language, through the processes of composition or derivation. For example, the word "aircraft" was once a neologism, made up of the prefix "air" and the suffix "craft". This also happens with "teleoperators" or with "biosecurity".