Proteomics in aortic aneurysm – What have we learnt so far?

Aortic aneurysm is a deceptively indolent disease that can cause severe complications such as aortic rupture and dissection. In the normal aorta, vascular smooth muscle cells within the medial layer produce and sustain the extracellular matrix (ECM) that provides structural support but also retains soluble growth factors and regulates their distribution. Although the ECM is an obvious target to identify molecular processes leading to structural failure within the vessel wall, an in-depth proteomics analysis of this important sub-proteome has not been performed. Most proteomics analyses of the vasculature to date used homogenized tissue devoid of spatial information. In such homogenates, quantitative proteomics comparisons are hampered by the heterogeneity of clinical samples (i.e. cellular composition) and the dynamic range limitations stemming from highly abundant cellular proteins. An unbiased proteomics discovery approach targeting the ECM instead of the cellular proteome may decipher the complex, multivalent signals that are presented to cells during aortic remodelling. A better understanding of the ECM in healthy and diseased vessels will provide important pathogenic insights and has potential to reveal novel biomarkers.     

Processing and interactive editing of huge point clouds from 3D scanners

This paper describes a new out-of-core multi-resolution data structure for real-time visualization, interactive editing and externally efficient processing of large point clouds. We describe an editing system that makes use of the novel data structure to provide interactive editing and preprocessing tools for large scanner data sets. Using the new data structure, we provide a complete tool chain for 3D scanner data processing, from data preprocessing and filtering to manual touch-up and real-time visualization. In particular, we describe an out-of-core outlier removal and bilateral geometry filtering algorithm, a toolset for interactive selection, painting, transformation, and filtering of huge out-of-core point-cloud data sets and a real-time rendering algorithm, which all use the same data structure as storage backend. The interactive tools work in real-time for small model modifications. For large scale editing operations, we employ a two-resolution approach where editing is planned in real-time and executed in an externally efficient offline computation afterwards. We evaluate our implementation on example data sets of sizes up to 63 GB, demonstrating that the proposed technique can be used effectively in real-world applications.     

Harnessing the strengths of anytime algorithms for constant data streams

Anytime algorithms have been proposed for many different applications, e.g., in data mining. Their strengths are the ability to first provide a result after a very short initialization and second to improve their result with additional time. Therefore, anytime algorithms have so far been used when the available processing time varies, e.g., on varying data streams. In this paper we propose to employ anytime algorithms on constant data streams, i.e., for tasks with constant time allowance. We introduce two approaches that harness the strengths of anytime algorithms on constant data streams and thereby improve the over all quality of the result with respect to the corresponding budget algorithm. We derive formulas for the expected performance gain and demonstrate the effectiveness of our novel approaches using existing anytime algorithms on benchmark data sets.     

Designing Personalized Garments with Body Movement

The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill-fitting clothes, high return rates and overproduction. Recent research efforts in both industry and academia, therefore, focus on virtual try-on and on-demand fabrication of individually fitting garments. We propose an interactive design tool for creating custom-fit garments based on 3D body scans of the intended wearer. Our method explicitly incorporates transitions between various body poses to ensure a better fit and freedom of movement. The core of our method focuses on tools to create a 3D garment shape directly on an avatar without an underlying sewing pattern, and on the adjustment of that garment's rest shape while interpolating and moving through the different input poses. We alternate between cloth simulation and rest shape adjustment based on stretch to achieve the final shape of the garment. At any step in the real-time process, we allow for interactive changes to the garment. Once the garment shape is finalized for production, established techniques can be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern.     

Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer

Journal of Intelligent Manufacturing - Deep learning-based predictive quality enables manufacturing companies to make data-driven predictions of the quality of a produced product based on process...     

AlphaPilot: autonomous drone racing

Autonomous Robots - This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The...     

Quantum computing implementations with neutral particles

We review quantum information processing with cold neutral particles, that is, atoms or polar molecules. First, we analyze the best suited degrees of freedom of these particles for storing quantum information, and then we discuss both single- and two-qubit gate implementations. We focus our discussion mainly on collisional quantum gates, which are best suited for atom-chip-like devices, as well as on gate proposals conceived for optical lattices. Additionally, we analyze schemes both for cold atoms confined in optical cavities and hybrid approaches to entanglement generation, and we show how optimal control theory might be a powerful tool to enhance the speed up of the gate operations as well as to achieve high fidelities required for fault tolerant quantum computation.     

Monte Carlo techniques for phrase-based translation

Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined by a translation model. We define a novel Gibbs sampler for sampling translations given a source sentence and show that it effectively explores this posterior distribution. In doing so we overcome the limitations of heuristic beam search and obtain theoretically sound solutions to inference problems such as finding the maximum probability translation and minimum risk training and decoding.