It has been long recognized that a key obstacle to achieving human-level object recognition performance is the problem of invariance [10]. The human visual system excels at factoring out the image transformations that distort object appearance under natural conditions. Models with a cortex-inspired architecture such as HMAX [9, 13] as well as nonbiological convolutional neural networks [5] are ...

The relationship between Koopman mode decomposition, resolvent mode decomposition, and exact invariant solutions of the Navier-Stokes equations is clarified. The correspondence rests upon the invariance of the system operators under symmetry operations such as spatial translation. The usual interpretation of the Koopman operator is generalized to permit combinations of such operations, in addit...

Judgment aggregation problems are language dependent in that they may be framed in different yet equivalent ways. We formalize this dependence via the notion of translation invariance, adopted from the philosophy of science, and we argue for its normative desirability. We show that in the canonical judgment aggregation model, no reasonable judgment aggregation functions are translation invarian...

Object recognition is one of the most important tasks of the visual cortex. Even though it has been closely studied in the field of computer vision and neuroscience, the underlying processes in the visual cortex are not completely understood. A model that lately has gained attention is the HMAX model, which describes a feedforward hierarchical structure. This model shows a degree of scale and t...

This paper presents a fusion of the active appearance model (AAM) and the Riemannian elasticity framework which yields a non-linear shape model and a linear texture model – the active elastic appearance model (EAM). The non-linear elasticity shape model is more flexible than the usual linear subspace model, and it is therefore able to capture more complex shape variations. Local rotation and tr...

Abstract: We use the Ward identities corresponding to general linear transformations, and derive relations between transport coefficients of (2 + 1)-dimensional systems. Our analysis includes relativistic and Galilean invariant systems, as well as systems without boost invariance such as Lifshitz theories. We consider translation invariant, as well as broken translation invariant cases, and inc...

Neural network (connectionist) models are designed to encode image features and provide the building blocks for object and shape recognition. These models generally call for: a) initial diffuse connections from one neuron population to another, and b) training to bring about a functional change in those connections so that one or more high-tier neurons will selectively respond to a specific sha...

We study the approximation of finite-dimensional rate-independent quasistatic systems, via a vanishing-inertia asymptotic analysis dynamic evolutions. prove uniform convergence solutions to one, employing variational concept energetic solution. Motivated by applications in soft locomotion, we allow time-dependence dissipation potential, and translation invariance potential energy.

The literature on measure translation tends to hold, overtly or covertly, a questionable assumption about the possibility of exact and almost completely ignores issues within language variation. Equally, psychometric methods used assess cross-cultural validity after focus large sample tests cross-sectional measurement invariance. Such invariance is often not found dubious pertinence change/outc...