Multi-resolution Tensor Learning for Large-Scale Spatial Data

High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In order to not "over-train" on coarse resolution models, we investigate an information-theoretic fine-graining criterion to decide when to transition into higher-resolution models. We provide both theoretical and empirical evidence for the advantages of this approach. When applied to two real-world large-scale spatial datasets for basketball player and animal behavior modeling, our approach demonstrate 3 key benefits: 1) it efficiently captures higher-order interactions (i.e., tensor latent factors), 2) it is orders of magnitude faster than fixed resolution learning and scales to very fine-grained spatial resolutions, and 3) it reliably yields accurate and interpretable models

Svestka's Research: Then and Now

Zdenek Svestka's research work influenced many fields of solar physics, especially in the area of flare research. In this article I take five of the areas that particularly interested him and assess them in a "then and now" style. His insights in each case were quite sound, although of course in the modern era we have learned things that he could not readily have envisioned. His own views about his research life have been published recently in this journal, to which he contributed so much, and his memoir contains much additional scientific and personal information (Svestka, 2010).Comment: Invited review for "Solar and Stellar Flares," a conference in honour of Prof. Zden\v{e}k \v{S}vestka, Prague, June 23-27, 2014. This is a contribution to a Topical Issue in Solar Physics, based on the presentations at this meeting (Editors Lyndsay Fletcher and Petr Heinzel

Radically enactive high cognition

I advance the Radically Enactive Cognition (REC) program by developing Hutto & Satne’s (2015) and Hutto & Myin’s (2017) idea that contentful cognition emerges through sociocultural activities, which require a contentless form of intentionality. Proponents of REC then face a functional challenge: what is the function of higher cognitive skills, given the empirical findings that engaging in higher-cognitive activities is not correlated with cognitive amelioration (Kornblith, 2012)? I answer that functional challenge by arguing that higher cognition is an adaptive tool of the social systems we are embedded in, therefore, it is not necessarily aimed at achieving better cognitive states. In order to do so, I suggest interpreting key insights from autopoietic enactivism through REC lenses

Sketch-based skeleton-driven 2D animation and motion capture.

This research is concerned with the development of a set of novel sketch-based skeleton-driven 2D animation techniques, which allow the user to produce realistic 2D character animation efficiently. The technique consists of three parts: sketch-based skeleton-driven 2D animation production, 2D motion capture and a cartoon animation filter. For 2D animation production, the traditional way is drawing the key-frames by experienced animators manually. It is a laborious and time-consuming process. With the proposed techniques, the user only inputs one image ofa character and sketches a skeleton for each subsequent key-frame. The system then deforms the character according to the sketches and produces animation automatically. To perform 2D shape deformation, a variable-length needle model is developed, which divides the deformation into two stages: skeleton driven deformation and nonlinear deformation in joint areas. This approach preserves the local geometric features and global area during animation. Compared with existing 2D shape deformation algorithms, it reduces the computation complexity while still yielding plausible deformation results. To capture the motion of a character from exiting 2D image sequences, a 2D motion capture technique is presented. Since this technique is skeleton-driven, the motion of a 2D character is captured by tracking the joint positions. Using both geometric and visual features, this problem can be solved by ptimization, which prevents self-occlusion and feature disappearance. After tracking, the motion data are retargeted to a new character using the deformation algorithm proposed in the first part. This facilitates the reuse of the characteristics of motion contained in existing moving images, making the process of cartoon generation easy for artists and novices alike. Subsequent to the 2D animation production and motion capture,"Cartoon Animation Filter" is implemented and applied. Following the animation principles, this filter processes two types of cartoon input: a single frame of a cartoon character and motion capture data from an image sequence. It adds anticipation and follow-through to the motion with related squash and stretch effect