Unipotent group actions on affine varieties

Algebraic actions of unipotent groups $U$ actions on affine $k-$varieties $X$ ($k$ an algebraically closed field of characteristic 0) for which the algebraic quotient $X//U$ has small dimension are considered$.$ In case $X$ is factorial, $O(X)^{\ast}=k^{\ast},$ and $X//U$ is one-dimensional, it is shown that $O(X)^{U}$=$k[f]$, and if some point in $X$ has trivial isotropy, then $X$ is $U$ equivariantly isomorphic to $U\times A^{1}(k).$ The main results are given distinct geometric and algebraic proofs. Links to the Abhyankar-Sathaye conjecture and a new equivalent formulation of the Sathaye conjecture are made.Comment: 10 pages. This submission comes out of an older submission ("A commuting derivations theorem on UFDs") and contains part of i

Designing for multi-user interaction in the home environment: Implementing social translucence

© 2016 ACM. Interfaces of interactive systems for domestic use are usually designed for individual interactions although these interactions influence multiple users. In order to prevent conflicts and unforeseen influences on others we propose to leverage the human ability to take each other into consideration in the interaction. A promising approach for this is found in the social translucence framework, which was originally described by Erickson & Kellogg. In this paper, we investigate how to design multi-user interfaces for domestic interactive systems through two design cases where we focus on the implementation of social translucence constructs (visibility, awareness, and accountability) in the interaction. We use the resulting designs to extract design considerations: interfaces should not prescribe behavior, need to offer sufficient interaction alternatives, and previous settings need to be retrievable. We also identify four steps that can be integrated in any design process to help designers in creating interfaces that support multi-user interaction through social translucence

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications

Abstract not provide

A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information

We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level “saliency map” representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions

Beknopte documentatie Landbouwdatabank Voedingsmiddelen RIKILT

In deze beknopte documentatie van de Landbouwdatabank Voedingsmiddelen RIKILT worden de struktuur en de toepassingsmogelijkheden van de databank beschreven. De mogelijkheden van een informatiesysteem hangen direkt samen met de struktuur. Naast een beschrijving van de struktuur van de databank worden voorbeelden gegeven van de in dit stadium voorgeprogrammeerde overzichten. Voorts wordt aangegeven welke kriteria zijn vastgesteld voor opname van gegevens in de databank en wordt een voorbeeld gegeven van een invoer formulier