The recent success of Machine Learning encouraged research using artificial neural networks (NNs) in computer graphics. A good example is the bidirectional texture function (BTF), a data-driven representation of surface materials that can encapsulate complex behaviors that would otherwise be too expensive to calculate for real-time applications, such as self-shadowing and interreflections. We propose two changes to the state-of-the-art using neural networks for BTFs, specifically NeuMIP. These changes, suggested by recent work in neural scene representation and rendering, aim to improve baseline quality, memory footprint, and performance. We conduct an ablation study to evaluate the impact of each change. We test both synthetic and real data, and provide a working implementation within the Mitsuba 2 rendering framework. Our results show that our method outperforms the baseline in all these metrics and that neural BTF is part of the broader field of neural scene representation. Project website: https://traverse-research.github.io/NeuBTF/.
One of the key challenges in the rapid technological advance of Virtual Reality (VR) and Mixed Reality (MR) concerns the design of collaborative experiences. VR systems do not readily support team collaboration because they tend to focus on individual experiences and do not easily facilitate naturalistic collaboration. MR environments provide solutions for collaborative experiences, but establishing smooth communication between hardware components and software modules faces a major hurdle. This paper presents the background to and main challenges of an ongoing project on collaboration in an MR lab, aiming to design a serious 'team collaboration' game. To this end, we utilized a common game engine to engineer a cost-effective solution that would make the game playable in a configuration operated by WorldViz and Volfoni equipment. Evaluation of various solutions in the development process found a Unity 3D Cluster Rendering Beta solution to be the most cost-effective and successful.
The objective was to create a video clip of 60-90 seconds in which Mai was presented during the opening of MindLabs building.Mai had to convey MindLabs message: "Helping to address societal challenges using human-centered AI and technology and connecting researchers, developers, students, entrepreneurs, social institutions, and governments for this purpose."
The Water Framework Directive imposes challenges regarding the environmental risk of plastic pollution. The quantification, qualification, monitoring, and risk assessment of nanoplastics and small microplastic (<20 µm) is crucial. Environmental nano- and micro-plastics (NMPs) are highly diverse, accounting for this diversity poses a big challenge in developing a comprehensive understanding of NMPs detection, quantification, fate, and risks. Two major issues currently limit progress within this field: (a) validation and broadening the current analytical tools (b) uncertainty with respect to NMPs occurrence and behaviour at small scales (< 20 micron). Tracking NMPs in environmental systems is currently limited to micron size plastics due to the size detection limit of the available analytical techniques. There are currently no methods that can detect nanoplastics in real environmental systems. A major bottleneck is the incompatibility between commercially available NMPs and those generated from plastic fragments degradation in the environment. To track nanoplastics in environmental and biological systems, some research groups synthesized metal-doped nanoplastics, often limited to one polymer type and using high concentrations of surfactants, rendering these synthesized nanoplastics to not be representative of nanoplatics found in real environment. NanoManu proposes using Electrohydrodynamic Atomization to generate metal doped NMPs of different polymers types, sizes, and shapes, which will be representative of the real environmental nanoplastics. The synthesized nanoplastics will be used as model particles in environmental studies. The synthesized nanoplastics will be characterized and tested using different analytical methods, e.g., SEM-EDX, TEX, GCpyrMS, FFF, µFTIR and SP-ICP-MS. NanoManu is a first and critical step towards generating a comprehensive state-of-the-art analytical and environmental knowledge on the environmental fate and risks of nanoplastics. This knowledge impacts current risk assessment tools, efficient interventions to limit emissions and adequate regulations related to NMPs.
Dit projectvoorstel is gericht op de ontwikkeling van nieuwe moleculen om zelf, thuis infectieziekten te diagnosticeren. Om de diagnose van infectieziektes te bevorderen, met name in afgelegen gebieden, is de innovatieve strategie van point-of-care (POC), een snelle, accurate en sensitieve diagnostische test die door een patiënt zelf kan worden uitgevoerd, uitermate geschikt. Een simpel en klein toestel dat enzymatische activiteit uit microben kan meten is in ontwikkeling bij Enzyre B.V. Dit voorstel gaat over de ontwikkeling van nieuwe lichtgevende moleculen die de detectie van infectieziektes kunnen aantonen door middel van het Enzyre platform. Hiervoor wordt een nieuwe chemisch aanpak om dit soort lichtgevende moleculen te maken ontwikkeld. Dit is relevant voor de preventie en het monitoren controle van potentiële pandemieën zoals bijvoorbeeld de recente uitbraak van SARS-Cov-2, maar ook MERS, SARS, HIV, Ebola en meerdere influenza pandemieën uit het verleden
