Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Im-portantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive Vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-CellI 00k, a bioimage dataset with a rich set of 79 experi-ments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per exper-iment. Our architecture, trained in a self-supervised man-ner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with differ-ent microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses.

We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized for retrieval (using contrastive learning) and reconstruction (using a diffusion prior). MindEye can map fMRI brain activity to any high dimensional multimodal latent space, like CLIP image space, enabling image reconstruction using generative models that accept embeddings from this latent space. We comprehensively compare our approach with other existing methods, using both qualitative side-by-side comparisons and quantitative evaluations, and show that MindEye achieves state-of-the-art performance in both reconstruction and retrieval tasks. In particular, MindEye can retrieve the exact original image even among highly similar candidates indicating that its brain embeddings retain fine-grained image-specific information. This allows us to accurately retrieve images even from large-scale databases like LAION-5B. We demonstrate through ablations that MindEye's performance improvements over previous methods result from specialized submodules for retrieval and reconstruction, improved training techniques, and training models with orders of magnitude more parameters. Furthermore, we show that MindEye can better preserve low-level image features in the reconstructions by using img2img, with outputs from a separate autoencoder. All code is available on GitHub.

Although many physical limitations have been reached in modern micro-lithography, printed critical dimensions continue to shrink according to the International Technology Roadmap for Semiconductors (ITRS) [1]. To meet the demands imposed by this guideline, the traditional separation between design and manufacturing communities is being bridged. Many EDA tools package manufacturing data for delivery into established simulation engines for design verification. However, none of them provide practical implementations of design optimizations at an early stage in the design flow.This paper presents an automated layout modification flow for metal layers with the goal of enhancing manufacturability. It can easily be deployed in a current custom design flow in a way that is visible to designers. The result of this scheme is improvements to process windows and yield, while minimizing circuit performance detractors. The flow is verified through analyses of both the impact on circuit performance and the benefit to manufacturability. It has been implemented in a state-of-the-art 65 nm chip design. Both silicon yield and electrical performance data are currently being collected and analyzed.