Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.Comment: 10 pages in Conference on Computer Vision and Pattern Recognition (CVPR), 201

Quasi-Newton Methods: A New Direction

Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.Comment: ICML201

Nuclear translocation and signalling of L1-CAM in human carcinoma cells requires ADAM10 and presenilin/gamma-secretase activity

L1-CAM (L1 cell-adhesion molecule), or more simply L1, plays an important role in the progression of human carcinoma. Overexpression promotes tumour-cell invasion and motility, growth in nude mice and tumour metastasis. It is feasible that L1-dependent signalling contributes to these effects. However, little is known about its mechanism in tumour cells. We reported previously that L1 is cleaved by ADAM (a disintegrin and metalloprotease) and that the cytoplasmic part is essential for L1 function. Here we analysed more closely the role of proteolytic cleavage in L1-mediated nuclear signalling. Using OVMz carcinoma cells and L1-transfected cells as a model, we found that ADAM10-mediated cleavage of L1 proceeds in lipid raft and non-raft domains. The cleavage product, L1-32, is further processed by PS (presenilin)/gamma-secretase to release L1-ICD, an L1 intracellular domain of 28 kDa. Overexpression of dominantnegative PS1 or use of a specific gamma-secretase inhibitor leads to an accumulation of L1-32. Fluorescence and biochemical analysis revealed a nuclear localization for L1-ICD. Moreover, inhibition of ADAM10 and/or gamma-secretase blocks nuclear translocation of L1-ICD and L1-dependent gene regulation. Overexpression of recombinant L1-ICD mediates gene regulation in a similar manner to full-length L1. Our results establish for the first time that regulated proteolytic processing by ADAM10 and PS/gamma-secretase is essential for the nuclear signalling of L1 in human carcinoma cell lines. Key words: a disintegrin and metalloprotease 10 (ADAM10), L1 cell-adhesion molecule (L1-CAM), nuclear translocation, presenilin (PS)/gamma-secretase activity, raft, signalling

Tethered orbital refueling study

One of the major applications of the space station will be to act as a refueling depot for cryogenic-fueled space-based orbital transfer vehicles (OTV), Earth-storable fueled orbit maneuvering vehicles, and refurbishable satellite spacecraft using hydrazine. One alternative for fuel storage at the space station is a tethered orbital refueling facility (TORF), separated from the space station by a sufficient distance to induce a gravity gradient force that settles the stored fuels. The technical feasibility was examined with the primary focus on the refueling of LO2/LH2 orbital transfer vehicles. Also examined was the tethered facility on the space station. It was compared to a zero-gravity facility. A tethered refueling facility should be considered as a viable alternative to a zero-gravity facility if the zero-gravity fluid transfer technology, such as the propellant management device and no vent fill, proves to be difficult to develop with the required performance

Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets

In model predictive control, a high quality of control can only be achieved, if the model of the system reflects the real-world process as precisely as possible. Therefore, the controller should be capable of both handling a nonlinear system description and systematically incorporating uncertainties affecting the system. Since stochastic nonlinear model predictive control (SNMPC) problems in general cannot be solved in closed form, either the system model or the occurring densities have to be approximated. In this paper, we present an SNMPC framework, which approximates the densities and the reward function by their wavelet expansions. Due to the few requirements on the shape and family of the densities or reward function, the presented technique can be applied to a large class of SNMPC problems. For accelerating the optimization, we additionally present a novel thresholding technique, the so-called dynamic thresholding, which neglects coefficients that are insignificant, while at the same time guaranteeing that the optimal control input is still chosen. The capabilities of the proposed approach are demonstrated by simulations with a path planning scenario

Permutohedral Lattice CNNs

This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures

Nonlinear Bayesian Estimation with Compactly Supported Wavelets

Bayesian estimation for nonlinear systems is still a challenging problem, as in general the type of the true probability density changes and the complexity increases over time. Hence, approximations of the occurring equations and/or of the underlying probability density functions are inevitable. In this paper, we propose an approximation of the conditional densities by wavelet expansions. This kind of representation allows a sparse set of characterizing coefficients, especially for smooth or piecewise smooth density functions. Besides its good approximation properties, fast algorithms operating on sparse vectors are applicable and thus, a good trade-off between approximation quality and run-time can be achieved. Moreover, due to its highly generic nature, it can be applied to a large class of nonlinear systems with a high modeling accuracy. In particular, the noise acting upon the system can be modeled by an arbitrary probability distribution and can influence the system in any way

Boron-Carbohydrate Interactions

Boron-polyol interactions are of fundamental importance to human health [1], plant growth [2] and quorum sensing among certain bacteria [3]. Such diversity is perhaps not surprising when one considers boron is one of the ten most abundant elements in sea water and carbohydrates make up the planet’s most abundant class of biomass. Several boronic acids matrices are commercially available for the purification of glycoproteins by affinity chromatography [4], and boronic acids are also useful carbohydrate protecting groups.[5,6] Recently, complexes between boron and sugars have become a lynchpin for the development of synthetic carbohydrate receptors.[7] These complexes involve covalent interactions that are reversible in aqueous solution. This chapter reviews current understanding of these processes, provides a historical perspective on their discovery, identifies methods for studying these complexes and classifies these interactions by carbohydrate type. Such information is key to the design and synthesis of synthetic lectins, also termed “boronolectins” when containing boron [7].Office of the Snr Dep Vice Chancellor, Institute for GlycomicsFull Tex

Uncovering the local factors that helped shape the Brexit referendum

Why was support for Brexit so widely divergent across the UK? Drawing on a new study, José Javier Olivas Osuna, Max Kiefel and Kira Gartzou-Katsouyanni illustrate that while a variety of economic and cultural explanations for the result have been put forward, these processes were shaped at the local level. They find that citizens with similar socio-demographic profiles adopted very different attitudes toward Brexit depending on the local context in which they lived