1,721,067 research outputs found
Points. Lack thereof
No full textI will discuss some aspects of the concept of “point” in quantum gravity, using mainly the tool of noncommutative geometry. I will argue that at Planck’s distances the very concept of point may lose its meaning. I will then show how, using the spectral action and a high momenta expansion, the connections between points, as probed by boson propagators, vanish. This discussion follows closely (Kurkov, Lizzi and Vassilevich, 2014)
Quantum Spacetime, Noncommutative Geometry and Observers
Full text linkI discuss some issues related to the noncommutative spaces κ and its angular variant ρ-Minkowski with particular emphasis on the role of observers
κ -Poincaré comodules, braided tensor products, and noncommutative quantum field theory
Full text linkWe discuss the obstruction to the construction of a multiparticle field theory on a κ-Minkowski noncommutative spacetime: the existence of multilocal functions which respect the deformed symmetries of the problem. This construction is only possible for a lightlike version of the commutation relations, if one requires invariance of the tensor product algebra under the coaction of the κ-Poincaré group. This necessitates a braided tensor product. We study the representations of this product, and prove that κ-Poincaré-invariant N-point functions belong to an Abelian subalgebra, and are therefore commutative. We use this construction to define the 2-point Whightman and Pauli-Jordan functions, which turn out to be identical to the undeformed ones. We finally outline how to construct a free scalar κ-Poincaré-invariant quantum field theory, and identify some open problems
Explainability of a CNN for breast density assessment
No full textDeep neural network explainability is a critical issue in Artificial Intelligence (AI). This work aims to develop a method to explain a deep residual Convolutional Neural Network able to automatically classify mammograms into breast density classes. Breast density, a risk factor for breast cancer, is defined as the amount of fibroglandular tissue compared to fat tissue visible on a mammogram. We studied the explainability of the classifier to understand the reasons behind its predictions, in fact with a deep multi-layer structure, it acts like a black-box. As there is no well-established method, we explored different possible analyses and visualization techniques. The main obtained results were the achievement of a performance improvement in terms of accuracy and a contribution to assess trust in the model. This is fundamental for a potential application in clinical practice
Geometry of the gauge algebra in non-commutative yang-mills theory
No full textA detailed description of the infinite-dimensional Lie algebra of ⋆-gauge transformations in non-commutative Yang-Mills theory is presented. Various descriptions of this algebra are given in terms of inner automorphisms of the underlying deformed algebra of functions on spacetime, of deformed symplectic diffeomorphisms, of the infinite unitary Lie algebra u(∞), and of the C∗-algebra of compact operators on a quantum mechanical Hilbert space. The spacetime and string interpretations are also elucidated
Deep-Learning Based Analyses of Mammograms to Improve the Estimation of Breast Cancer Risk
Full text linkBreast cancer is the most commonly diagnosed cancer among women worldwide. Survival rates strongly depend on early diagnosis, and for this reason mammographic screening is performed in developed countries. New artificial intelligence-based techniques have the potential to include and quantify fibroglandular (or dense) parenchyma in breast cancer risk models
A deep convolutional neural network for breast density assessment: Optimization and explainability.
No full textThis work aims to develop a method for deep neural network explainability. It is the ability to explain the algorithm behaviour and its predictions when it has a deep multi-layer nonlinear structure. This is a critical issue in Artificial Intelligence. An already developed deep Residual Convolutional Neural Network is able to automatically classify mammograms into breast density classes. The explainability of the network has been studied through various analyses and visualization techniques, assessing trust in the model, which is fundamental for its potential application in clinical practice, and also achieving a performance improvement in terms of accuracy
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