Cross-dimensional Weighting for Aggregated Deep Convolutional Features

We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific non-parametric schemes for both spatial- and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects. We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks. We also provide an easy-to-use, open source implementation that reproduces our results.Comment: Accepted for publications at the 4th Workshop on Web-scale Vision and Social Media (VSM), ECCV 201

Hereditarily Indecomposable Banach algebras of diagonal operators

We provide a characterization of the Banach spaces $X$ with a Schauder basis $(e_n)_{n\in\mathbb{N}}$ which have the property that the dual space $X^*$ is naturally isomorphic to the space $\mathcal{L}_{diag}(X)$ of diagonal operators with respect to $(e_n)_{n\in\mathbb{N}}$ . We also construct a Hereditarily Indecomposable Banach space ${\mathfrak X}_D$ with a Schauder basis $(e_n)_{n\in\mathbb{N}}$ such that ${\mathfrak X}^*_D$ is isometric to $\mathcal{L}_{diag}({\mathfrak X}_D)$ with these Banach algebras being Hereditarily Indecomposable. Finally, we show that every $T\in \mathcal{L}_{diag}({\mathfrak X}_D)$ is of the form $T=\lambda I+K$, where $K$ is a compact operator.Comment: 35 pages, submitted for publication to Israel J. Mat

Strictly singular non-compact diagonal operators on HI spaces

We construct a Hereditarily Indecomposable Banach space \eqs_d with a Schauder basis \seq{e}{n} on which there exist strictly singular non-compact diagonal operators. Moreover, the space \mc{L}_{\diag}(\eqs_d) of diagonal operators with respect to the basis \seq{e}{n} contains an isomorphic copy of $\ell_{\infty}(\N)$

Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

Diffusion bonding effects on the adhesion of tungsten dust on tungsten surfaces

Abstract High temperature excursions have the potential to strongly enhance the room temperature adhesion of tokamak dust. Planar tungsten substrates containing adhered nearly monodisperse spherical tungsten dust have been exposed to linear plasmas and vacuum furnaces. Prolonged thermal treatments of varying peak temperature and constant duration were followed by room temperature adhesion measurements with the electrostatic detachment method. Adhesive forces have been observed to strongly depend on the thermal pre-history, greatly increasing above a threshold temperature. Adhesive forces have been measured up to an order of magnitude larger than those of untreated samples. This enhancement has been attributed to atomic diffusion that slowly eliminates the omnipresent nanometer-scale surface roughness, ultimately switching the dominant interaction from long-range weak van der Waals forces to short-range strong metallic bonding