A Sub-Picojoule per Bit Integrated Magneto-Optic Modulator on Silicon: Modeling and Experimental Demonstration

Integrated magneto-optic (MO) modulators are an attractive but not fully explored alternative to electro-optic (EO) modulators. They are current driven, structurally simple, and could potentially achieve high efficiency in cryogenic and room temperature environments where fJ bit−1 optical interfaces are needed. In this paper, the performance and energy efficiency of a novel MO modulator at room temperature are for the first time assessed. First, a model of the micro-ring-based modulator is implemented to investigate the design parameters and their influence on the performance. Then, a fabricated device is experimentally characterized to assess its performance in terms of bit rate and energy efficiency. The model shows efficient operation at 1.2 Gbps using a 16 mA drive current, consuming only 155 fJ bit−1. The experimental results show that the MO effect is suitable for modulation, achieving error-free operation above 16 mA with a power consumption of 258 fJ bit−1 at a transient limited data rate of 1.2 Gbps

An integrated magneto-optic modulator for cryogenic applications

Superconducting circuits can operate at higher energy efficiencies than their room-temperature counterparts and have the potential to enable large-scale control and readout of quantum computers. However, the required interface with room-temperature electronics creates difficulties in scaling up such cryogenic systems. One option is to use optical fibres as a medium in conjunction with fast optical modulators that can be efficiently driven by electrical signals at low temperatures. However, as superconducting circuits are current operated with low impedances, they interface poorly with conventional electro-optical modulators. Here we report an integrated current-driven modulator that is based on the magneto-optic effect and can operate at temperatures as low as 4 K. The device combines a magneto-optic garnet crystal with a silicon waveguide resonator and integrates an electromagnet to modulate the refractive index of the garnet. The modulator offers data rates of up to 2 Gbps with an energy consumption below 4 pJ per bit of transferred information, which could be reduced to less than 50 fJ per bit by replacing dissipative electrodes with superconductors and optimizing the geometric parameters

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic

Pseudomonas aeruginosa mutants defective in glucose uptake have pleiotropic phenotype and altered virulence in non-mammal infection models

Pseudomonas spp. are endowed with a complex pathway for glucose uptake that relies on multiple transporters. In this work we report the construction and characterization of Pseudomonas aeruginosa single and multiple mutants with unmarked deletions of genes encoding outer membrane (OM) and inner membrane (IM) proteins involved in glucose uptake. We found that a triple \u394gltKGF \u394gntP \u394kguT mutant lacking all known IM transporters (named GUN for Glucose Uptake Null) is unable to grow on glucose as unique carbon source. More than 500 genes controlling both metabolic functions and virulence traits show differential expression in GUN relative to the parental strain. Consistent with transcriptomic data, the GUN mutant displays a pleiotropic phenotype. Notably, the genome-wide transcriptional profile and most phenotypic traits differ between the GUN mutant and the wild type strain irrespective of the presence of glucose, suggesting that the investigated genes may have additional roles besides glucose transport. Finally, mutants carrying single or multiple deletions in the glucose uptake genes showed attenuated virulence relative to the wild type strain in Galleria mellonella, but not in Caenorhabditis elegans infection model, supporting the notion that metabolic functions may deeply impact P. aeruginosa adaptation to specific environments found inside the host

Multi-Chiplet System Architecture with Shared Uniform Access Memory Based on Board-Level Optical Interconnects

We propose a new multi-chiplet system architecture based on shared uniform memory access and on-board optical interconnects. System-level simulation results demonstrate that such systems offer improved execution times and energy efficiency over conventional computing architectures

Bandwidth Reconfigurable Optical Switching Architecture for CPU-GPU Computing Systems with Shared Memory

We propose a reconfigurable optical switching architecture for shared-memory CPU-GPU systems. System-level simulations show improved execution time and energy efficiency up to 34% and 25% respectively compared to a static point-to-point architecture for specific application sets.</jats:p

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic