Predicting the immunomodulatory activity of probiotic lactic acid bacteria using supervised machine learning in a Cornu aspersum snail model.

In the process of screening for probiotic strains, there are no clearly established bacterial phenotypic markers which could be used for the prediction of their in vivo mechanism of action. In this work, we demonstrate for the first time that Machine Learning (ML) methods can be used for accurately predicting the in vivo immunomodulatory activity of probiotic strains based on their cell surface phenotypic features using a snail host-microbe interaction model. A broad range of snail gut presumptive probiotics, including 240 new lactic acid bacterial strains (Lactobacillus, Leuconostoc, Lactococcus, and Enterococcus), were isolated and characterized based on their capacity to withstand snails' gastrointestinal defense barriers, such as the pedal mucus, gastric mucus, gastric juices, and acidic pH, in association with their cell surface hydrophobicity, autoaggregation, and biofilm formation ability.

Modeling limit order trading with a continuous action policy for deep reinforcement learning.

Limit Orders allow buyers and sellers to set a "limit price" they are willing to accept in a trade. On the other hand, market orders allow for immediate execution at any price. Thus, market orders are susceptible to slippage, which is the additional cost incurred due to the unfavorable execution of a trade order.

Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision.

Analog photonic computing comprises a promising candidate for accelerating the linear operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low footprint and low power consumption computing capabilities. However, the confined photonic hardware size, along with the limited bit precision of high-speed electro-optical components, impose stringent requirements towards surpassing the performance levels of current digital processors. Herein, we propose and experimentally demonstrate a speed-optimized dynamic precision neural network (NN) inference via tiled matrix multiplication (TMM) on a low-radix silicon photonic processor.

Quantization-aware training for low precision photonic neural networks.

Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards this is photonic neuromorphic architectures, which can achieve femtojoule per MAC efficiencies. Despite the benefits that arise from the use of neuromorphic architectures, a significant bottleneck is the use of expensive high-speed and precision analog-to-digital (ADCs) and digital-to-analog conversion modules (DACs) required to transfer the electrical signals, originating from the various Artificial Neural Networks (ANNs) operations (inputs, weights, etc.