Multispectral Optoacoustic Tomography (MSOT) brings a revolution to bio-optical imaging. Being insensitive to photon scattering, MSOT dramatically improves upon conventional bio-optic barriers by enabling (1) three-dimensional high-resolution optical imaging deep inside tissues (several millimetres to centimetres), by (2) high-scalability, ranging from optical-resolution microscopy to acoustic-resolution optical mesoscopy and macroscopy and by (3) novel label-free anatomical, physiological and molecular contrast at the tissue and single-cell-level, based on spectrally-resolved optical absorption. MSOT, originally supported by an ERC Advanced Award (2008) (TUM: Prof. Ntziachristos), is already commercialized by iThera Medical for macroscopy with systems sold around the world for small animal imaging. In parallel, ERC MSOT funding developed a mesoscopic implementation, termed raster-scan optoacoustic mesoscopy (RSOM), which has demonstrated innovative imaging capacity at 1-5mm depths. Driven by leading dermatologists (TUM: Prof. Biedermann; SUR: Prof. Costanzo) and market leader SMEs in optoacoustic and ultrasound technology (iThera, Rayfos, Sonaxis), INNODERM will design and prototype a handheld, portable, scalable, label-free RSOM device for point-of care dermatology applications, based on recommendations developed under an ERC proof of concept grant (2013) on MSOT. INNODERM brings together key photonic & ultrasound technologies and will validate the technical and economic viability of RSOM in dermatology suites for fast diagnosis and skin disease monitoring. RSOM can go beyond the abilities of current optical or optoacoustic devices and offer a paradigm shift in dermatology imaging, substantiating successful business cases.

Policymakers and public health experts unanimously recognise the disproportionate impacts of COVID-19 on vulnerable persons: even in countries with well-developed responses, the outbreak and its repercussions imperil the basic well-being of social groups whose livelihoods are already precarious, while the uneven distribution of suffering threatens to aggravate inequality and division. One complicating factor here is the intersectional nature of health and socioeconomic vulnerabilities. Another is the complexity of risk in contemporary socioecological systems. The COVINFORM project will draw upon intersectionality theory and complex systems analysis in an interdisciplinary critique of COVID-19 responses on the levels of government, public health, community, and information and communications. The project will conduct research on three levels: 1) on an EU27 MS plus UK level, quantitative secondary data will be analysed and models will be developed; 2) Within 15 target countries, documentary sources on the national level and in at least one local community per country will be analysed; 3) in 10 target communities, primary empirical research will be conducted, utilising both classical and innovative quantitative and qualitative methods (e.g. visual ethnography, participatory ethnography, and automated analysis of short video testimonials). Promising practices will be evaluated in target communities through case studies spanning diverse disciplines (social epidemiology, the economics of unpaid labour, the sociology of migration, etc.) and vulnerable populations (COVID-19 patients, precarious families, migrating health care workers, etc.). The project will culminate in the development of an online portal and visual toolkit for stakeholders in government, public health, and civil society integrating data streams, indices and indicators, maps, models, primary research and case study findings, empirically grounded policy guidance, and creative assessment tools.

The BIOTRAFO project will analyze the effect of temperature on the designs of power transformers that use biodegradable esters as coolant, the environmental and fire performance of these liquids will be also evaluated. These machines are very common in our power distribution systems. Since electricity is generated until it reaches households, it passes through an average of four transformers. Currently the liquid used in most of these machines is a petroleum derivative, since its good performance is well known. However, the environmental awareness of many companies is demanding new transformers that are cooled by esters of natural origin. In this framework, BIOTRAFO proposes a study that allows to know the temperature in the windings of the transformer when using biodegradable liquids, which by their nature are more viscous. This temperature is a critical factor for the useful life of the transformer, due to the aging of dielectric solid materials. The aging of these materials when immersed in these liquids will also be analyzed. Not only the question will be observed from a theoretical perspective, industrial platforms will also be used to test the generated models. The results of the research will be disseminated among specialized and non-specialized audiences, considering the commercial exploitation of the results obtained. The project will also carry out tasks of knowledge transfer generated for this purpose, the training aspects of the research personnel involved in the project will be taken care of.Finally, the management and coordination of the project will be efficiently organized. To carry out this task, a consortium of thirteen partners has been formed, six of which do not belong to the EU. All of them have knowledge and proven experience in the field of study. Ten of the partners belong to the academic sector and three are companies. Two are manufacturers of power transformers, while the other performs diagnostic work on transformers that are in operation

Efficient hardware for combinatorial optimization and machine learning impacts science, engineering, and society. With new computational models, photonics tackle problems intractable with conventional computing systems. However, existing devices only scale up to thousands of spins and operate at the second timescale. I demonstrate photonic machines for ultrafast parallel processing of millions of spins with microsecond timescale. The strategy is minimizing a class of functions, the Ising Hamiltonian, by a new computational system that uses a high-dimensional feature space and speeds up optimization by orders of magnitude by ultrafast nonlinear optical processes. I build digital, optoelectronics, and all-optical classical and quantum devices and benchmark with real-world, large-scale problems. By spatial modulation technology and a cheap, simple, and scalable design, light propagation is recurrently trained towards the ground state of a programmable Ising Hamiltonian. Starting from my proof-of-concept, first, I aim at the energetically efficient computing of large-scale Hamiltonians. Second, I include self-optimizing all-optical nonlinear ultrafast phase-locking processes. Third, I demonstrate record combinatorial optimization by letting the spins evolve in a high dimensional space to guarantee high success probability. HYPERSPIM leverages the interplay of classical and quantum dynamics through the onset of entanglement and squeezing. The unprecedented scale and versatility allow the first quantum optimization tests for real-world complex computational tasks. HYPERSPIM achieves the fastest and biggest optical computing device operating in classical and quantum regimes in an interdisciplinary route towards new photonic artificial intelligence, large-scale all-optical computing, and fundamental science.