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This master thesis aims at providing a basic understanding of the phenomenon of gravitational lensing of gravitational waves, as well as providing a concrete tool to analyse the data and identify it, namely a neural network. First, the basic concepts of the gravitational lensing effect are introduced. Then, the particular case of the lensing of gravitational waves is investigated mathematically and the results are discussed. Once this concept is understood, a neural network model designed to identify lensed gravitational waves is presented and its performance are discussed. Finally, the importance of detecting this effect is stressed and some improvements of the proposed model are suggested for future works.

Gravitational lensing --- Gravitational waves --- Convolutional neural network --- Physique, chimie, mathématiques & sciences de la terre > Aérospatiale, astronomie & astrophysique

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Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world and the most common cause of dementia. In recent times, accurate and early detection of AD plays a vital role in patient care and further treatment. Lately, studies on AD diagnosis has attached a great significanceto artificial-based diagnostic algorithms. During this master thesis we explore
how deep learning models can handle neuroimages in order to identify andpredict the evolution of the disease. Different from the traditional machine learning algorithms, deep learning does not require manually extracted features but instead utilizes 3D image processing models to learn features for the diagnosis and the prognosis of AD. The contribution of this work relies on a
more rigorous preprocessing phase involving skull-stripping and intensity normalization of the medical images. The hippocampus, a brain area critical for learning and memory, is especially affected at early stages of Alzheimer’s disease. In some parts of this work, It will be used as a region of interest for our algorithms that will consist in convolutional neural networks, the typical image classifier models, and vision transformers, a novel deep learning architecture.

Deep learning --- Machine learning --- Alzheimer's disease --- convolutional neural network (CNN) --- Vision transformer (ViT) --- classification --- region of interest --- ADNI --- Ingénierie, informatique & technologie > Sciences informatiques

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Deep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics.

History of engineering & technology --- deep learning --- RGB --- depth --- facial landmarking --- merging networks --- 3D geometry data --- 2D attribute maps --- fused CNN feature --- coarse-to-fine --- convolutional neural network (CNN) --- deep metric learning --- multi-task learning --- image classification --- age estimation --- generative adversarial network --- emotion classification --- facial key point detection --- facial images processing --- convolutional neural networks --- face liveness detection --- convolutional neural network --- thermal image --- external knowledge

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Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it).

Language & Linguistics --- EVALITA --- COVID-19 Infodemic --- linguistica computazionale --- Automatic Misogyny Identification --- Misogyny on Twitter Posts --- AlBERTo --- Convolutional Neural Network --- BERT Model --- Hate Speech Detection --- Multimodal Meme Detection --- MEME management --- Language Game ``La Ghigliottina'' --- Language Game ``La

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New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, the tracking of animal or plant wellbeing, and land-use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim of enhancing production efficiency and contributing to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision-making. The papers in this Special Issue evaluate agri-business innovation that can enhance farm-level decision-making.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- dairy cows --- computer vision --- behaviors --- monitoring --- management --- behavior --- birth --- observations --- sheep --- proximal --- sensing --- LiDAR --- photogrammetry --- grasslands --- pastures --- Adversarial-VAE --- tomato leaf disease identification --- image generation --- convolutional neural network --- potato management --- tuber formation stage --- precipitation patterns