Substitutional reality:using the physical environment to design virtual reality experiences

Experiencing Virtual Reality in domestic and other uncontrolled settings is challenging due to the presence of physical objects and furniture that are not usually defined in the Virtual Environment. To address this challenge, we explore the concept of Substitutional Reality in the context of Virtual Reality: a class of Virtual Environments where every physical object surrounding a user is paired, with some degree of discrepancy, to a virtual counterpart. We present a model of potential substitutions and validate it in two user studies. In the first study we investigated factors that affect participants' suspension of disbelief and ease of use. We systematically altered the virtual representation of a physical object and recorded responses from 20 participants. The second study investigated users' levels of engagement as the physical proxy for a virtual object varied. From the results, we derive a set of guidelines for the design of future Substitutional Reality experiences

Pairing in the Hubbard model: the Cu_{5}O_{4} Cluster versus the Cu-O plane

We study the Cu_{5}O_{4} cluster by exact diagonalization of a three-band Hubbard model and show that bound electron or hole pairs are obtained at appropriate fillings, and produce superconducting flux quantisation. The results extend earlier cluster studies and illustrate a canonical transformation approach to pairing that we have developed recently for the full plane. The quasiparticles that in the many-body problem behave like Cooper pairs are W=0 pairs, that is, two-hole eigenstates of the Hubbard Hamiltonian with vanishing on-site repulsion. The cluster allows W=0 pairs of d symmetry, due to a spin fluctuation, and s symmetry, due to a charge fluctuation. Flux quantisation is shown to be a manifestation of symmetry properties that hold for clusters of arbitrary size.Comment: 13 pages, 3 figures, a few intermediate steps added for clarit

Actuar es creer

Se analiza al actor dramático desde un punto de vista epistemológi - co, toda vez que el arte del teatro puede interpretarse como un estudio sobre el conocimiento del hombre a partir de él mismo. Observar la actuación dramática como objeto de estudio implica reconocer la paradoja constante de la creación ficticia de una realidad simultánea a la cotidiana. Así, la creencia del histrión debe ser una disposición a actuar como si la situación planteada fuera verdade - ra, y determinar una postura general de vida que permita hacer de la actuación una profesión capaz de mostrar la complejidad del ser humano en un escenario.Se analiza al actor dramático desde un punto de vista epistemológico, toda vez que el arte del teatro puede interpretarse como un estudio sobre el conocimiento del hombre a partir de él mismo. Observar la actuación dramática como objeto de estudio implica reconocer la paradoja constante de la creación ficticia de una realidad simultánea a la cotidiana. Así, la creencia del histrión debe ser una disposición a actuar como si la situación planteada fuera verdadera, y determinar una postura general de vida que permita hacer de la actuación una profesión capaz de mostrar la complejidad del ser humano en un escenario

PathologyGAN: Learning deep representations of cancer tissue

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H&E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio