SensX: About Sensing and Assessment of Complex Human Motion

The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017, Calabria Italy 6 pages, 5 figure

Анализ локализации корней интервального полинома в заданном секторе

Анализируется отображение параметрического многогранника полинома в сектор Г[m] корневой плоскости, определяемый числом m интервальных коэффициентов. Находятся (2m-2) вершин многогранника, отображение которых в сектор Г[m] гарантирует локализацию в нем всех корней интервального полинома. Формулируются критерии локализации корней в заданном секторе Г при различных соотношениях его угла с углом сектора Г[m]

Optimizing Geometry Compression using Quantum Annealing

The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications. We propose a quantum-enabled lossy 3d point cloud compression pipeline based on the constructive solid geometry (CSG) model representation. Key parts of the pipeline are mapped to NP-complete problems for which an efficient Ising formulation suitable for the execution on a Quantum Annealer exists. We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks of the proposed compression pipeline. Additionally, we discuss the properties of the overall pipeline regarding result optimality and described Ising formulations.Comment: 6 pages, 3 figure

Open Innovation: die T-Systems-LMU München Kooperation „Mobile Business Applications“

Immer mehr Unternehmen öffnen ihren Innovationsprozess und binden aktiv externe Mitarbeiter, Partner, Lieferanten, Kunden und Forschungseinrichtungen in diesen Entwicklungsprozess mit ein. Somit können sowohl externe als auch interne Wege genutzt werden, um neue Produkte und Lösungen zu generieren. Diese Öffnung des Innovationsprozesses bezeichnet man als Open Innovation. Die Methode des offenen Innovationsprozesses eröffnet Unternehmen die Chance, durch Kommunikation mit Unternehmensexternen zu gänzlich neuen Erkenntnissen zu gelangen. Diesen Weg geht das T-Systems Innovation Center in München gemeinsam mit der Ludwig-Maximilians-Universität München. Seit nunmehr gut vier Jahren ist der Lehrstuhl für Mobile und Verteilte Systeme wissenschaftlicher Partner des Innovation Centers. Die Kooperation ist für beide Seiten ein Gewinn: Die entstehenden Rapid Prototyping Demonstratoren zeigen den Entwicklern und dem Management unmittelbar die Möglichkeiten und Funktionalitäten neuer Lösungen und ermöglichen es, in einem frühen Entwicklungsstadium durch Live-Demonstrationen das der Feedback der Kunden in die weitere Entwicklung einzubauen. Auf der anderen Seite werden die Studenten durch die betreuenden Wissenschaftler der Universität anhand von realen Forschungs- und Entwicklungsprojekten ausgebildet. Durch die gleichzeitige Betreuung der Studenten durch Innovations-Manager der Industrie bekommen diese in einer sehr frühen Phase ihrer Ausbildung ein Gefühl dafür, was sich am Markt aufgrund realer Kundenbedürfnisse durchsetzen wird

Approximate Approximation on a Quantum Annealer

Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical devices that aim at this class of problems by exploiting quantum mechanical properties of nature. However, they compete with efficient heuristics and probabilistic or randomised algorithms on classical machines that allow for finding approximate solutions to large NP-complete problems. While first implementations of QA have become commercially available, their practical benefits are far from fully explored. To the best of our knowledge, approximation techniques have not yet received substantial attention. In this paper, we explore how problems' approximate versions of varying degree can be systematically constructed for quantum annealer programs, and how this influences result quality or the handling of larger problem instances on given set of qubits. We illustrate various approximation techniques on both, simulations and real QA hardware, on different seminal problems, and interpret the results to contribute towards a better understanding of the real-world power and limitations of current-state and future quantum computing.Comment: Proceedings of the 17th ACM International Conference on Computing Frontiers (CF 2020

Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to open-loop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performance-memory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.Comment: Presented at AAAI 201