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In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.
As the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our msystem. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users’ health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users.
One-step preparation of bilayered films from kraft lignin and cellulose acetate to mimic tree bark
(2020)
This contribution presents the development of a dry-cast method for the one-step preparation of bio-based films from wood polymers that mimic the bilayered structure of tree bark, the natural protective layer of the tree. In a simplified view, natural bark can be considered as the superposition of an external homogeneous and non-porous layer (outer bark) and a porous substructure layer (inner bark). This work is a first step for the future development of bio-based biomimetic wood coatings. The film had a bark-like appearance and its total density, bulk density and porosity were similar to values measured in natural bark. Furthermore, the structural characteristics of the studied film, namely specific surface area (BET) and pore size distribution, as well as the performance of the water adsorption ability were investigated and discussed.
High-quality rendering of spatial sound fields in real-time is becoming increasingly important with the steadily growing interest in virtual and augmented reality technologies. Typically, a spherical microphone array (SMA) is used to capture a spatial sound field. The captured sound field can be reproduced over headphones in real-time using binaural rendering, virtually placing a single listener in the sound field. Common methods for binaural rendering first spatially encode the sound field by transforming it to the spherical harmonics domain and then decode the sound field binaurally by combining it with head-related transfer functions (HRTFs). However, these rendering methods are computationally demanding, especially for high-order SMAs, and require implementing quite sophisticated real-time signal processing. This paper presents a computationally more efficient method for real-time binaural rendering of SMA signals by linear filtering. The proposed method allows representing any common rendering chain as a set of precomputed finite impulse response filters, which are then applied to the SMA signals in real-time using fast convolution to produce the binaural signals. Results of the technical evaluation show that the presented approach is equivalent to conventional rendering methods while being computationally less demanding and easier to implement using any real-time convolution system. However, the lower computational complexity goes along with lower flexibility. On the one hand, encoding and decoding are no longer decoupled, and on the other hand, sound field transformations in the SH domain can no longer be performed. Consequently, in the proposed method, a filter set must be precomputed and stored for each possible head orientation of the listener, leading to higher memory requirements than the conventional methods. As such, the approach is particularly well suited for efficient real-time binaural rendering of SMA signals in a fixed setup where usually a limited range of head orientations is sufficient, such as live concert streaming or VR teleconferencing.
3d printing is capable of providing dose individualization for pediatric medicines and translating the precision medicine approach into practical application. In pediatrics, dose individualization and preparation of small dosage forms is a requirement for successful therapy, which is frequently not possible due to the lack of suitable dosage forms. For precision medicine, individual characteristics of patients are considered for the selection of the best possible API in the most suitable dose with the most effective release profile to improve therapeutic outcome. 3d printing is inherently suitable for manufacturing of individualized medicines with varying dosages, sizes, release profiles and drug combinations in small batch sizes, which cannot be manufactured with traditional technologies. However, understanding of critical quality attributes and process parameters still needs to be significantly improved for this new technology. To ensure health and safety of patients, cleaning and process validation needs to be established. Additionally, adequate analytical methods for the in-process control of intermediates, regarding their printability as well as control of the final 3d printed tablets considering any risk of this new technology will be required. The PolyPrint consortium is actively working on developing novel polymers for fused deposition modeling (FDM) 3d printing, filament formulation and manufacturing development as well as optimization of the printing process, and the design of a GMP-capable FDM 3d printer. In this manuscript, the consortium shares its views on quality aspects and measures for 3d printing from drug-loaded filaments, including formulation development, the printing process, and the printed dosage forms. Additionally, engineering approaches for quality assurance during the printing process and for the final dosage form will be presented together with considerations for a GMP-capable printer design.
We study p-adic L-functions Lp(s, 휒) for Dirichlet characters 휒. We show that Lp(s, 휒) has a Dirichlet series expansion for each regularization parameter c that is prime to p and the conductor of 휒. The expansion is proved by transforming a known formula for p-adic L-functions and by controlling the limiting behavior. A fnite number of Euler factors can be factored of in a natural manner from the p-adic Dirichlet series. We also provide an alternative proof of the expansion using p-adic measures and give an explicit formula for the values of the regularized Bernoulli distribution. The result is particularly simple for c = 2, where we obtain a Dirichlet series expansion that is similar to the complex case.
The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.
This paper introduces a Business Cycle Indicator to compile a transparent and reliable chronology of past business cycle turning points for Germany. The Indicator is derived applying the statistical method of Principal Component Analysis, based on information from 20 economic time series. In this way, the Business Cycle Indicator grasps the development of the broader economic activity and has several advantages over a business cycle assessment based on quarterly series of Gross Domestic Product.
In Latin America and the Caribbean, river restoration projects are increasing, but many lack strategic planning and monitoring. We tested the applicability of a rapid visual social–ecological stream assessment method for restoration planning, complemented by a citizen survey on perceptions and uses of blue and green infrastructure. We applied the method at three urban streams in Jarabacoa (Dominican Republic) to identify and prioritize preferred areas for nature-based solutions. The method provides spatially explicit information for strategic river restoration planning, and its efficiency makes it suitable for use in data-poor contexts. It identifies well-preserved, moderately altered, and critically impaired areas regarding their hydromorphological and socio-cultural conditions, as well as demands on green and blue infrastructure. The transferability of the method can be improved by defining reference states for assessing the hydromorphology of tropical rivers, refining socio-cultural parameters to better address river services and widespread urban challenges, and balancing trade-offs between ecological and social restoration goals.
Kompetenzen auf dem Gebiet der Datenbanken gehören zum Pflichtbereich der Informatik. Das Angebot an Lehrbüchern, Vorlesungsformaten und Tools lässt sich jedoch für Lehrende oft nur eingeschränkt in die eigene Lehre integrieren. In diesem Aufsatz schildern wir unsere Erfahrungen in der Nutzung (frei) verfügbarer und der Entwicklung eigener digitaler Inhalte für grundlegende Datenbankveranstaltungen. Die Präferenzen der Studierenden werden mittels Nutzungsanalysen und Befragungen ermittelt. Wir stellen die Anforderungen auf, wie die nicht selten aufwendig herzustellenden digitalen Materialien von Lehrenden in ihre Lehr- und Lernumgebungen integriert werden können. Als konstruktive Antwort auf diese Herausforderung wird das Konzept EILD zur Entwicklung von Inhalten für die Lehre im Fach Datenbanken vorgestellt. Die Inhalte sollen in vielfältigen Lernszenarien eingesetzt werden können und mit einer Creative Commons (CC) Lizenzierung als OER (open educational resources) frei zur Verfügung stehen.