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The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiN x ) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.

Fuel cell performance is determined by the complex interplay of mass transport, energy transfer and electrochemical processes. The convolution of these processes leads to spatial heterogeneity in the way that fuel cells perform, particularly due to reactant consumption, water management and the design of fluid-flow plates. It is therefore unlikely that any bulk measurement made on a fuel cell will accurately represent performance at all parts of the cell. The ability to make spatially resolved measurements in a fuel cell provides one of the most useful ways in which to monitor and optimise performance. This Minireview explores a range of in situ techniques being used to study fuel cells and describes the use of novel experimental techniques that the authors have used to develop an 'experimental functional map' of fuel cell performance. These techniques include the mapping of current density, electrochemical impedance, electrolyte conductivity, contact resistance and CO poisoning distribution within working PEFCs, as well as mapping the flow of reactant in gas channels using laser Doppler anemometry (LDA). For the high-temperature solid oxide fuel cell (SOFC), temperature mapping, reference electrode placement and the use of Raman spectroscopy are described along with methods to map the microstructural features of electrodes. The combination of these techniques, applied across a range of fuel cell operating conditions, allows a unique picture of the internal workings of fuel cells to be obtained and have been used to validate both numerical and analytical models.