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$renewcommand{Re}{mathbb{R}}$Given a set $P$ of $n$ points in $Re^d$, consider the problem of computing $k$ subsets of $P$ that form clusters that are well-separated from each other, and each of them is large (cardinality wise). We provide tight upper and lower bounds, and corresponding algorithms, on the quality of separation, and the size of the clusters that can be computed, as a function of $n,d,k,s$, and $Phi$, where $s$ is the desired separation, and $Phi$ is the spread of the point set $P$.
We present new broad-band optical images of some merging Seyfert galaxies that were earlier considered to be non-interacting objects. On our deep images obtained at the Russian 6-m telescope we have detected elongated tidal envelopes belonging to sat
We simulate the formation of a large X-ray cluster using a fully 3D hydrodynamical code coupled to a Particle-Mesh scheme which models the dark matter component. We focus on a possible decoupling between electrons and ions temperatures. We then solve
Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding space than representations of images from different classes. Much work
For each finite classical group $G$, we classify the subgroups of $G$ which act transitively on a $G$-invariant set of subspaces of the natural module, where the subspaces are either totally isotropic or nondegenerate. Our proof uses the classificati
Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to reason, leading them to perfo