The interactive, digital future with its seductive vision of Internet-of-Things connected sensors, actuators and displays comes at a high cost in terms of both energy demands and the clutter it brings to the physical world. But what if such devices were made of materials that enabled them to self-power their interactive features? And, what if those materials were directly used to build aesthetically pleasing environments and objects that met practical physical needs as well as digital ones? In this paper we introduce PV-Tiles ? a novel material that closely couples photovoltaic energy harvesting and light sensing materials with digital interface components. We consider potential contexts, use-cases and light gestures surfaced through co-creation workshops; and, present initial technological designs and prototypes. The work opens a new set of opportunities and collaborations between HCI and material science, stimulating technical and design pointers to accommodate and exploit the material's properties.

The viewshed of a point v on a grid terrain T, viewshedT (v), is the set of grid points in T that are visible from v. We describe a novel algorithm for computing viewshedT (v) using a multiresolution approach: Given a parameter k > 1 that represents the block size, we create a grid T′ which is a lower-resolution version of T, such that each point in T corresponds to a block of [EQUATION] points in T; T′ has size Θ(n/k), where n is the size of the original grid. The key of our approach is using T′ to speed up the computation of viewshedT(v) while not introducing approximation. We compute viewshedT(v) in two steps: First we compute the viewshed of v on T'′, while maintaining the invariant that any block in T′ that is labeled as invisible may not contain any visible points. Thus, the first step's role is to use T′ to filter out blocks in T′ that are guaranteed to be invisible. The second step considers the blocks that were labeled as visible in T′ and computes the visibility of their points with full accuracy using the data in T. Overall the algorithm runs in O[EQUATION], where l is the total size of visible blocks in T′. When k > 1 and l = o(n), the running time of our algorithm improves on the previous best bound of O(n lg n) [9, 15]. We describe a suite of experimental results showing the performance of our algorithm in practice and a speedup of more than an order of magnitude compared to previous algorithms.

Most discussion hitherto of big data in the humanities has assumed that it is characterized by its heterogeneous nature. This paper examines the extent to which bibliographic records generated by libraries represent a more homogenous form of humanities big data, more closely related to the observational big data generated by scientific data. It is suggested from an examination of the British Library catalogue that, while superficially bibliographic records appear to be created according to consistent standards and form a more homogenous dataset, close examination reveals that bibliographical records often go through a marked process of historical development. However, the critical methods require to disaggregate such data are perhaps analogous to those used in some scientific disciplines.