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

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

Abstract not provide

Pattern recognition, attention, and information bottlenecks in the primate visual system

In its evolution, the primate visual system has developed impressive capabilities for recognizing complex patterns in natural images. This process involves many stages of analysis and a variety of information processing strategies. This paper concentrates on the importance of 'information bottlenecks,' which restrict the amount of information that can be handled at different stages of analysis. These steps are crucial for reducing the overwhelming computational complexity associated with recognizing countless objects from arbitrary viewing angles, distances, and perspectives. The process of directed visual attention is an especially important information bottleneck because of its flexibility in determining how information is routed to high-level pattern recognition centers

Ground reaction forces during walking with different load and slope combinations in rats

BACKGROUND: Treadmill animal models are commonly used to study effects of exercise on bone. Since mechanical loading induces bone strain, resulting in bone formation, exercise that induces higher strains is likely to cause more bone formation. Our aim was to investigate the effect of slope and additional load on limb bone strain. METHODS: Horizontal and vertical ground reaction forces on left fore-limb (FL) and hind-limb (HL) of twenty 23-week old female Wistar rats (weight 279 ± 26 g) were measured for six combinations of SLOPE (-10°, 0°, +10°) and LOAD (0 to 23% of body mass). Peak force (Fmax), rate of force rise (RC), stance time (Tstance) and impulse (Fint) on FLs and HLs were analyzed. RESULTS: For the FL, peak ground reaction forces and rate of force rise were highest when walking downward -10° with load (Fmax = 2.09±0.05 N, FLRC = 34±2 N/s) For the HL, ground reaction forces and rate of force rise were highest when walking upward +10°, without load (Fmax = 2.20±0.05 N, HLRC = 34±1 N/s). Load increased stance time. Without additional load, estimates for the highest FL loading (slope is -10°) were larger than for the highest HL loading (slope is +10°) relative to level walking. CONCLUSIONS: Thus, walking downward has a higher impact on FL bones, while walking upward is a more optimal HL exercise. Additional load may have a small effect on FL loading

A Conceptual Cortical Surface Atlas

Volumetric, slice-based, 3-D atlases are invaluable tools for understanding complex cortical convolutions. We present a simple scheme to convert a slice-based atlas to a conceptual surface atlas that is easier to visualize and understand. The key idea is to unfold each slice into a one-dimensional vector, and concatenate a succession of these vectors – while maintaining as much spatial contiguity as possible – into a 2-D matrix. We illustrate our methodology using a coronal slice-based atlas of the Rhesus Monkey cortex. The conceptual surface-based atlases provide a useful complement to slice-based atlases for the purposes of indexing and browsing

Principal Component Regression predicts functional responses across individuals

International audienceInter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes

Threat of an influenza pandemic: family physicians in the front line

<p>Abstract</p> <p>Background</p> <p>The chance of an influenza pandemic is real and clinicians should keep themselves informed about the rationale and science behind preventive and therapeutic principles relating to an (impending) influenza pandemic.</p> <p>Discussion</p> <p>Vaccination is considered the best prevention in case of a pandemic threat and first choice to contain the impact of a pandemic. Pending the availability of an effective pandemic vaccine, antivirals are likely the only effective agents for prevention and treatment. When an influenza pandemic is impending, all interventions aim to prevent people becoming infected and to suppress replication and transmission of the virus as much as possible. Antivirals will be prescribed to patients with laboratory confirmed pre-pandemic influenza as well as to their contacts (post-exposure prophylaxis) which may delay development of or even prevent a pandemic. During a manifest influenza pandemic, however, there is large-scale spreading of the influenza virus. Therefore, preventive use of antivirals is less efficient to prevent transmission. Delaying the pandemic is then important in order to prevent exhausting public health resources and disruption of society. Thus, during a manifest pandemic everyone with influenza symptoms should receive antivirals as quickly as possible, regardless of virological confirmation. To ensure optimal effectiveness of antivirals and to minimize development of drug resistant viral strains, the use of antivirals for annual influenza should be restrictive. The crucial position of family physicians during an (impending) influenza pandemic necessitates the development of primary health care guidelines on this topic for all countries.</p> <p>Summary</p> <p>Family physicians will play a key role in assessing and treating victims of a new influenza virus, and in reassuring the worried well. We outline various possible interventions in the event of an impending and a manifest influenza pandemic, such as non-medial measures, prescription of antivirals, and vaccination, and emphasize the need for pandemic influenza preparedness.</p

Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We do so by estimating the sample mean and covariance in the latent space of the 2D model over the slice direction. This combined model lets us sample new coherent stacks of latent variables to decode into slices of a volume. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy. We demonstrate that our proposed model is competitive in generating high quality volumes at high resolutions according to both traditional metrics and our proposed evaluation.Comment: accepted for publication at MICCAI 2020. Code available https://github.com/voanna/slices-to-3d-brain-vae

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding