Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

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...     

Scavenging of reactive oxygen and nitrogen species with nanomaterials

Reactive oxygen and nitrogen species (RONS) are essential for normal physiological processes and play important roles in cell signaling, immunity, and tissue homeostasis. However, excess radical species are implicated in the development and augmented pathogenesis of various diseases. Several antioxidants may restore the chemical balance, but their use is limited by disappointing results of clinical trials. Nanoparticles are an attractive therapeutic alternative because they can change the biodistribution profile of antioxidants, and possess intrinsic ability to scavenge RONS. Herein, we review the types of RONS, how they are implicated in several diseases, and the types of nanoparticles with inherent antioxidant capability, their mechanisms of action, and their biological applications.     

Ultralow Expansion Glass as Material for Advanced Micromechanical Systems

Ultralow expansion (ULE) glasses are of special interest for temperature stabilized systems for example in precision metrology. Nowadays, ULE materials are mainly used in macroscopic and less in micromechanical systems. Reasons for this are a lack of technologies for parallel fabricating high-quality released microstructures with a high accuracy. As a result, there is a high demand in transferring these materials into miniaturized application examples, realistic system modeling, and the investigation of microscopic material properties. Herein, a technological base for fabricating released micromechanical structures and systems with a structure height above 100 μm in ULE 7972 glass is established. Herein, the main fabrication parameters that are important for the system design and contribute thus to the introduction of titanium silicate as material for glass-based micromechanical systems are discussed. To study the mechanical properties in combination with respective simulation models, microcantilevers are used as basic mechanical elements to evaluate technological parameters and other impact factors. The implemented models allow to predict the micromechanical system properties with a deviation of only ±5% and can thus effectively support the micromechanical system design in an early stage of development.     

Using Selective Electron Beam Melting to Enhance the High-Temperature Strength and Creep Resistance of NiAl–28Cr–6Mo In Situ Composites

By increasing the density of interfaces in NiAl–CrMo in situ composites, the mechanical properties can be significantly improved compared to conventionally cast material. The refined microstructure is achieved by manufacturing through electron beam powder bed fusion (PBF-EB). By varying the process parameters, an equiaxed or columnar cell morphology can be obtained, exhibiting a plate-like or an interconnected network of the (Cr,Mo) reinforcement phase which is embedded in a NiAl matrix. The microstructure of the different cell morphologies is investigated in detail using scanning electron microscope, transmission electron microscopy, and atom probe tomography. For both morphologies, the mechanical properties at elevated temperatures are analyzed by compression and creep experiments parallel and perpendicular to the building direction. In comparison to cast NiAl and NiAl–(Cr, Mo), the yield strength of the PBF-EB fabricated specimens is significantly improved at temperatures up to 1,027 °C. While the columnar morphology exhibits the best improved mechanical properties at high temperatures, the equiaxial morphology shows nearly ideal isotropic mechanical behavior, which is a substantial advantage over directionally solidified material.     

Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships

The majority of approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive lightweight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. We also show the viability and the benefit of exploiting both qualitative and quantitative temporal relationships like the duration of the activities and their temporal order. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities’ start and end times. We evaluate the approach on an established dataset where it outperforms state-of-the-art algorithms for activity recognition.     

Architecture analysis of enterprise systems modifiability: a metamodel for software change cost estimation

Enterprise architecture models can be used in order to increase the general understanding of enterprise systems and specifically to perform various kinds of analysis. The present paper proposes a metamodel for enterprise systems modifiability analysis, i.e. assessing the cost of making changes to enterprise-wide systems. The enterprise architecture metamodel is formalized using probabilistic relational models, which enables the combination of regular entity-relationship modeling aspects with means to perform enterprise architecture analysis. The content of the presented metamodel is validated based on survey and workshop data and its estimation capability is tested with data from 21 software change projects. To illustrate the applicability of the metamodel an instantiated architectural model based on a software change project conducted at a large Nordic transportation company is detailed.     

IRIS: illustrative rendering for integral surfaces

Integral surfaces are ideal tools to illustrate vector fields and fluid flow structures. However, these surfaces can be visually complex and exhibit difficult geometric properties, owing to strong stretching, shearing and folding of the flow from which they are derived. Many techniques for non-photorealistic rendering have been presented previously. It is, however, unclear how these techniques can be applied to integral surfaces. In this paper, we examine how transparency and texturing techniques can be used with integral surfaces to convey both shape and directional information. We present a rendering pipeline that combines these techniques aimed at faithfully and accurately representing integral surfaces while improving visualization insight. The presented pipeline is implemented directly on the GPU, providing real-time interaction for all rendering modes, and does not require expensive preprocessing of integral surfaces after computation.     

Invariant object recognition and pose estimation with slow feature analysis

Primates are very good at recognizing objects independent of viewing angle or retinal position, and they outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.     

A short numerical study on the optimization methods influence on topology optimization

Structural and Multidisciplinary Optimization - Structural topology optimization problems are commonly defined using continuous design variables combined with material interpolation schemes. One of...