Immunotherapy has emerged as a prominent approach in melanoma treatment, however a substantial number of patients do not respond effectively. This highlights the critical need to accurately predict immunotherapy responses for designing personalised treatment strategies. Current practices rely predominantly on clinical data and the expertise of oncologists; however, a deeper understanding of molecular interactions through tissue-based biomarkers offers a promising avenue for advancement. Whole-slide multiplex immunofluorescence (mIF) images enable detailed analysis of cells/tissues in their microenvironment, deepening insights into disease mechanisms. However, numerous channels, an extensive image size, and spatially dispersed information of mIF images pose analytical challenges, requiring advanced techniques to effectively learn these intricate features for optimal performance. In this study, we introduce a novel deep-learning framework, Channel Optimisation with Multi-Instance Learning (COMIL), specifically designed to classify whole-slide mIF images for predicting immunotherapy response in melanoma patients. The study demonstrates that a feature extraction method that models inter-channel relationships and captures complex interdependencies among multiple channels of mIF images enhances classification performance. Additionally, incorporating this method within an MIL framework, optimised at both the slide and instance levels, further improves the classification performance of whole-slide mIF images. Evaluated on mIF images from the Melanoma Institute Australia showed that COMIL outperformed the baseline methods, underscoring its potential in the prediction of the immunotherapy response.  

The development of assisting techniques in shared lateral driving control is a growing field. However, the acceptance and use of these systems requires their adaptation and individualization to the unique preferences and driving styles of individual users. In this work, we present the Individual Path Pattern Targeting (IPPT) algorithm for shared control, a novel approach to detect path following tendencies of drivers, who share the driving task with an intervening assistance system in lateral control. The algorithm provides a basis to adapt the objective target of the system to the path tendencies of the driver. The algorithm is applied to real driving data of a shared control scenario with a Lane-Keeping Assist System. The data of three drivers who differ in their attitude towards assisting techniques, is analyzed. The resulting adapted reference paths for each driver align with their self-reported preferences, though further research with a larger sample size is necessary to validate these findings.

Experimental results from a 65nm CMOS commercial technology SRAM test chip reveal a linear correlation between a new electrical parameter-the word-line voltage margin (VWLVM) and the measured circuit alpha-SER. Additional experiments show that no other memory cell electrical robustness-related parameters exhibit such correlation. The technique proposed is based on correlating the V WLVM to the SER measured on a small number of circuit samples to determine the correlation parameters. Then, the remaining non-irradiated circuits SER is determined from electrical measurements (VWLVM) without the need of additional radiation experiments. This method represents a significant improvement in time and cost, while simplifying the SER-determination methods since most of the circuits do not require irradiation. The technique involves a minor memory design modification that does not degrade circuit performance, while circuit area increase is negligible.