Harmonic Networks: Deep Translation and Rotation Equivariance

Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and low computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges

Interpretable transformations with Encoder-Decoder Networks

Deep feature spaces have the capacity to encode complex transformations of their input data. However, understanding the relative feature-space relationship between two transformed encoded images is difficult. For instance, what is the relative feature space relationship between two rotated images? What is decoded when we interpolate in feature space? Ideally, we want to disentangle confounding factors, such as pose, appearance, and illumination, from object identity. Disentangling these is difficult because they interact in very nonlinear ways. We propose a simple method to construct a deep feature space, with explicitly disentangled representations of several known transformations. A person or algorithm can then manipulate the disentangled representation, for example, to re-render an image with explicit control over parameterized degrees of freedom. The feature space is constructed using a transforming encoder-decoder network with a custom feature transform layer, acting on the hidden representations. We demonstrate the advantages of explicit disentangling on a variety of datasets and transformations, and as an aid for traditional tasks, such as classification

Tactual perception: a review of experimental variables and procedures

This paper reviews literature on tactual perception. Throughout this review we will highlight some of the most relevant variables in touch literature: interaction between touch and other senses; type of stimuli, from abstract stimuli such as vibrations, to two- and three-dimensional stimuli, also considering concrete stimuli such as the relation between familiar and unfamiliar stimuli or the haptic perception of faces; type of participants, separating studies with blind participants, studies with children and adults, and an analysis of sex differences in performance; and finally, type of tactile exploration, considering conditions of active and passive touch, the relevance of movement in touch and the relation between exploration and time. This review intends to present an organised overview of the main variables in touch experiments, attending to the main findings described in literature, to guide the design of future works on tactual perception and memory.This work was funded by the Portuguese “Foundation for Science and Technology” through PhD scholarship SFRH/BD/35918/2007

Role of Ox-PAPCs in the Differentiation of Mesenchymal Stem Cells (MSCs) and Runx2 and PPARγ2 Expression in MSCs-Like of Osteoporotic Patients

BACKGROUND: Mesenchymal stem cells (MSCs) can differentiate into osteoblasts and adipocytes and conditions causing bone loss may induce a switch from the osteoblast to adipocyte lineage. In addition, the expression of Runx2 and the PPARγ2 transcription factor genes is essential for cellular commitment to an osteogenic and adipogenic differentiation, respectively. Modified lipoproteins derived from the oxidation of arachidonate-containing phospholipids (ox-PAPCs: POVPC, PGPC and PEIPC) are considered important factors in atherogenesis. METHODOLOGY: We investigated the effect of ox-PAPCs on osteogenesis and adipogenesis in human mesenchymal stem cells (hMSCs). In particular, we analyzed the transcription factor Runx2 and the PPARγ2 gene expression during osteogenic and adipogenic differentiation in absence and in presence of ox-PAPCs. We also analyzed gene expression level in a panel of osteoblastic and adipogenic differentiation markers. In addition, as circulating blood cells can be used as a "sentinel" that responds to changes in the macro- or micro-environment, we analyzed the Runx2 and the PPARγ2 gene expression in MSCs-like and ox-PAPC levels in serum of osteoporotic patients (OPs). Finally, we examined the effects of sera obtained from OPs in hMSCs comparing the results with age-matched normal donors (NDs). PRINCIPAL FINDINGS: Quantitative RT-PCR demonstrated that ox-PAPCs enhanced PPARγ2 and adipogenic gene expression and reduced Runx2 and osteoblast differentiation marker gene expression in differentiating hMSCs. In OPs, ox-PAPC levels and PPARγ2 expression were higher than in NDs, whereas Runx2 was lower than in ND circulant MSCs-like. CONCLUSIONS: Ox-PAPCs affect the osteogenic differentiation by promoting adipogenic differentiation and this effect may appear involved in bone loss in OPs