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https://www.selleckchem.com/products/polyinosinic-acid-polycytidylic-acid.html Word embeddings based on a conditional model are commonly used in Natural Language Processing (NLP) tasks to embed the words of a dictionary in a low dimensional linear space. Their computation is based on the maximization of the likelihood of a conditional probability distribution for each word of the dictionary. These distributions form a Riemannian statistical manifold, where word embeddings can be interpreted as vectors in the tangent space of a specific reference measure on the manifold. A novel family of word embeddings, called α-embeddings have been recently introduced as deriving from the geometrical deformation of the simplex of probabilities through a parameter α, using notions from Information Geometry. After introducing the α-embeddings, we show how the deformation of the simplex, controlled by α, provides an extra handle to increase the performances of several intrinsic and extrinsic tasks in NLP. We test the α-embeddings on different tasks with models of increasing complexity, showing that the advantages associated with the use of α-embeddings are present also for models with a large number of parameters. Finally, we show that tuning α allows for higher performances compared to the use of larger models in which additionally a transformation of the embeddings is learned during training, as experimentally verified in attention models.This study investigated the effect of decaffeinated green tea extract (dGTE), with or without antioxidant nutrients, on fat oxidation, body composition and cardio-metabolic health measures in overweight individuals engaged in regular exercise. Twenty-seven participants (20 females, 7 males; body mass 77.5 ± 10.5 kg; body mass index 27.4 ± 3.0 kg·m2; peak oxygen uptake (O2peak) 30.2 ± 5.8 mL·kg-1·min-1) were randomly assigned, in a double-blinded manner, either dGTE (400 mg·d-1 (-)-epigallocatechin-3-gallate (EGCG), n = 9); a novel dGTE+ (400 mg·d-

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