The Underlying Term Is Democracy: An Interview With Julian Stallabrass

In Art Incorporated, you seek to debunk the myth of the artworld as autonomous of the market forces of global capitalism. Instead, you argue, works of art have become yet another commodity. However, one could say that works of art have always been commodities as well as objects of aesthetic appreciation. What makes the problem pertinent now, in the age of artists like Takashi Murakami, Jeff Koons and Damien Hirst

Time Evolution and Deterministic Optimisation of Correlator Product States

We study a restricted class of correlator product states (CPS) for a spin-half chain in which each spin is contained in just two overlapping plaquettes. This class is also a restriction upon matrix product states (MPS) with local dimension $2^n$ ($n$ being the size of the overlapping regions of plaquettes) equal to the bond dimension. We investigate the trade-off between gains in efficiency due to this restriction against losses in fidelity. The time-dependent variational principle formulated for these states is numerically very stable. Moreover, it shows significant gains in efficiency compared to the naively related matrix product states - the evolution or optimisation scales as $2^{3n}$ for the correlator product states versus $2^{4n}$ for the unrestricted matrix product state. However, much of this advantage is offset by a significant reduction in fidelity. Correlator product states break the local Hilbert space symmetry by the explicit selection of a local basis. We investigate this dependence in detail and formulate the broad principles under which correlator product states may be a useful tool. In particular, we find that scaling with overlap/bond order may be more stable with correlator product states allowing a more efficient extraction of critical exponents - we present an example in which the use of correlator product states is several orders of magnitude quicker than matrix product states.Comment: 19 pages, 14 figure

Tailoring ink-substrate interactions via thin polymeric layers for high-resolution printing

The surface properties of a substrate are among the most important parameters in the printing technology of functional materials, determining not only the printing resolution but also the stability of the printed features. This paper addresses the wetting difficulties encountered during inkjet printing on homogeneous substrates as a result of improper surface properties. We show that the wetting of a substrate and, consequently, the quality of the printed pattern, can be mediated through the deposition of polymeric layers that are a few nanometers thick. The chemical nature of the polymers determines the surface energy and polarity of the thin layer. Some applications, however, require a rigorous adjustment of the surface properties. We propose a simple and precise method of surface-energy tailoring based on the thermal decomposition of poly(methyl methacrylate) (PMMA) layers. A smooth transition in the wetting occurs when the thickness of the PMMA layer approaches zero, probably due to percolating the underlying surface of the substrate, which enables the inkjet printing of complex structures with a high resolution. In particular, the wetting of three substrate-ink systems was successfully adjusted using the thin polymeric layer: (i) a tantalum-oxide-based ink on indium-tin-oxide-coated glass, (ii) a ferroelectric lead zirconate titanate ink on a platinized silicon substrate, and (iii) a silver nanoparticle ink on an alumina substrate

Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz

This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for the fully connected layers in a convolutional neural network and test this implementation on the CIFAR-10 and CIFAR-100 datasets. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA layers with 14000 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers, scaling like O(N).Comment: 8 pages, 2 figure