Deep generation of 3D articulated models and animations from 2D stick figures

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches. (C) 2022 The Author(s). Published by Elsevier Ltd

Data augmentation for dementia detection in spoken language

Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive. Recent deep-learning techniques can offer a faster diagnosis and have shown promis ing results. However, they require large amounts of labelled data which is not easily available for the task of dementia detection. One effective solution to sparse data problems is data augmenta tion, though the exact methods need to be selected carefully. To date, there has been no empirical study of data augmentation on Alzheimer's disease (AD) datasets for NLP and speech process ing. In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains. We use a transformer-based model for both domains, and SVM and Random Forest models for the text and audio domains, respectively. We generate additional samples using traditional as well as deep learning based methods and show that data augmentation improves performance for both the text- and audio-based models and that such results are compara ble to state-of-the-art results on the popular ADReSS set, with carefully crafted architectures and features

Data Augmentation for Dementia Detection in Spoken Language

Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive. Recent deep-learning techniques can offer a faster diagnosis and have shown promising results. However, they require large amounts of labelled data which is not easily available for the task of dementia detection. One effective solution to sparse data problems is data augmentation, though the exact methods need to be selected carefully. To date, there has been no empirical study of data augmentation on Alzheimer's disease (AD) datasets for NLP and speech processing. In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains. We use a transformer-based model for both domains, and SVM and Random Forest models for the text and audio domains, respectively. We generate additional samples using traditional as well as deep learning based methods and show that data augmentation improves performance for both the text- and audio-based models and that such results are comparable to state-of-the-art results on the popular ADReSS set, with carefully crafted architectures and features.Comment: Accepted to INTERSPEECH 202

Ecology & computer audition: applications of audio technology to monitor organisms and environment

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition – a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence – is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states. We distinguish between applications related to organisms, such as species richness analysis and plant health monitoring, and applications related to the environment, such as melting ice monitoring or wildfire detection. This work positions computer audition in relation to alternative approaches by discussing methodological strengths and limitations, as well as ethical aspects. We conclude with an urgent call to action to the research community for a greater involvement of audio intelligence methodology in future ecosystem monitoring approaches

Sketch-based interaction and modeling: where do we stand?

Sketching is a natural and intuitive communication tool used for expressing concepts or ideas which are difficult to communicate through text or speech alone. Sketching is therefore used for a variety of purposes, from the expression of ideas on two-dimensional (2D) physical media, to object creation, manipulation, or deformation in three-dimensional (3D) immersive environments. This variety in sketching activities brings about a range of technologies which, while having similar scope, namely that of recording and interpreting the sketch gesture to effect some interaction, adopt different interpretation approaches according to the environment in which the sketch is drawn. In fields such as product design, sketches are drawn at various stages of the design process, and therefore, designers would benefit from sketch interpretation technologies which support these differing interactions. However, research typically focuses on one aspect of sketch interpretation and modeling such that literature on available technologies is fragmented and dispersed. In this paper, we bring together the relevant literature describing technologies which can support the product design industry, namely technologies which support the interpretation of sketches drawn on 2D media, sketch-based search interactions, as well as sketch gestures drawn in 3D media. This paper, therefore, gives a holistic view of the algorithmic support that can be provided in the design process. In so doing, we highlight the research gaps and future research directions required to provide full sketch-based interaction support

PRODUCTION AND DEVELOPMENT OF MONOCLINIC YTTRIUM TANTALATE (M '-YTaO4) X-RAY PHOSPHOR VIA SOL-GEL TECHNIQUE

International Conference on Production Research - Regional Conference Africa, Europe and the Middle East (ICPR-AEM) / 3rd International Conference on Quality and Innovation in Engineering and Management (QIEM) -- JUL 01-05, 2014 -- Cluj Napoca, ROMANIAWOS: 000346410700085Monoclinic yttrium tantalate (M'-YTaO4) are efficient X-ray phosphor used in X-ray medical imaging, in which these phosphor are used in films/screen cassettes, and also in electronic detector systems such as computed radiography, computed tomography and fluoroscopy. Performances of these phosphor are related to composition, crystalline structure, surface properties and luminescence properties of films. In this study, M'-YTaO4 films were synthesized by five steps sol-gel spin coating route on single crystal silicon substrate. And then these films were dried at 120 degrees C and were sintered at 1200 degrees C for 4 hours and slowly cooled to room temperature. The obtained films were characterized by means of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM) and differential thermal analysis (DTA). After sintering, monoclinic M'-YTaO4 phase was obtained.IFPR, Tech Univ Cluj NapocaCenter for Production; Applications of Electronic Materials (EMUM); TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [113S069]We would like to thank Center for Production and Applications of Electronic Materials (EMUM) and TUBITAK (For 113S069 No. project) for characterization studies, supplying of materials used in our study and to support financial

Genomic, transcriptomic and physiological analyses of silver‐resistant Saccharomyces cerevisiae obtained by evolutionary engineering

International audienceSilver is a non-essential metal used in medical applications as an antimicrobial agent, but it is also toxic for biological systems. To investigate the molecular basis of silver resistance in yeast, we employed evolutionary engineering using successive batch cultures at gradually increased silver stress levels up to 0.25-mM AgNO(3)in 29 populations and obtained highly silver-resistant and genetically stableSaccharomyces cerevisiaestrains. Cross-resistance analysis results indicated that the silver-resistant mutants also gained resistance against copper and oxidative stress. Growth physiological analysis results revealed that the highly silver-resistant evolved strain 2E was not significantly inhibited by silver stress, unlike the reference strain. Genomic and transcriptomic analysis results revealed that there were mutations and/or significant changes in the expression levels of the genes involved in cell wall integrity, cellular respiration, oxidative metabolism, copper homeostasis, endocytosis and vesicular transport activities. Particularly the missense mutation in theRLM1gene encoding a transcription factor involved in the maintenance of cell wall integrity and with 707 potential gene targets might have a key role in the high silver resistance of 2E, along with its improved cell wall integrity, as confirmed by the lyticase sensitivity assay results. In conclusion, the comparative physiological, transcriptomic and genomic analysis results of the silver-resistantS. cerevisiaestrain revealed potential key factors that will help understand the complex molecular mechanisms of silver resistance in yeast

A summary of the ComParE COVID-19 challenges

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals’ respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).</p

A summary of the ComParE COVID-19 challenges.

Peer reviewed: TrueThe COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals' respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS)