This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate neurons. Employing numerical simulations, we explore various neuron configurations, both linear (NAND, NOR) and nonlinear (XNOR), to assess their effectiveness in image classification tasks. We demonstrate that structural nonlinearity, derived from the network's layout, plays a crucial role in facilitating complex computational tasks, effectively reducing the reliance on the inherent nonlinearity of individual neurons. Our findings suggest that the network's configuration and the interaction among its elements can emulate the benefits of nonlinearity, thus potentially simplifying the design and manufacturing of neuromorphic systems and enhancing their scalability. This shift in focus from individual neuron properties to network architecture could lead to significant advancements in the efficiency and applicability of neuromorphic computing.

Peat, a crucial component in whisky production, imparts distinctive and irreplaceable flavours to the final product. However, the extraction of peat disrupts ancient ecosystems and releases significant amounts of carbon, contributing to climate change. This paper aims to address this issue by conducting a feasibility study on enhancing peat use efficiency in whisky manufacturing through non-destructive analysis using hyperspectral imaging. Results show that shot-wave infrared (SWIR) data is more effective for analyzing peat samples and predicting total phenol levels, with accuracies up to 99.81%.

We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through non-resonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system's performance was evaluated using the MNIST dataset for handwritten digit recognition, showcasing the potential to outperform existing polaritonic neuromorphic systems, as demonstrated by its impressive predicted classification accuracy of up to 97.5%.

Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates. Preliminary validations of such devices, including transistors, have shown encouraging results even under ambient conditions. A significant challenge still remains for large scale application however: the lack of a robust solver that can be used to simulate complex nonlinear systems which require an extended period of time to stabilize. Addressing this need, we propose the application of a machine-learning-based Fourier Neural Operator approach to find the solution to the Gross-Pitaevskii equations coupled with extra exciton rate equations. This work marks the first direct application of Neural Operators to an exciton-polariton condensate system. Our findings show that the proposed method can predict final-state solutions to a high degree of accuracy almost 1000 times faster than CUDA-based GPU solvers. Moreover, this paves the way for potential all-optical chip design workflows by integrating experimental data.

Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today (April 8 2021) and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."

Algorithms similar to those used by Netflix, Amazon and Facebook have shown the ability to decipher the'biological language' of cancer, Alzheimer's and other neurodegenerative diseases. Researchers trained a large-scale language model with a recommendation AI to look at what happens when something goes wrong with proteins that leads to the development of a disease. The work, conducted by St. John's College and the University of Cambridge, programed the algorithm to learn the language of shapeshifting droplets of proteins found in cells in order to understand their function and malfunction. By learning these protein droplets' language, the team can then'correct the grammatical mistakes inside cells that cause disease.'' Professor Tuomas Knowles, a Fellow at St John's College, said: 'Any defects connected with these protein droplets can lead to diseases such as cancer. 'This is why bringing natural language processing technology into research into the molecular origins of protein malfunction is vital if we want to be able to correct the grammatical mistakes inside cells that cause disease.' Machine learning technology has made waves in the tech industry – Netflix uses it to recommend series, Facebook's suggest someone to friend and Amazon's Alexa has an algorithm to recognize people based on their voice.

Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."

Atomic interactions in a Bose-Einstein condensate (BEC) can lead to complex collective behavior. Experimentally, these interactions are often tuned by varying an external magnetic field. The collisions between atoms exposed to the modulated field sent the atoms flying out of the condensate in jets of seemingly random directions. A pattern-recognition technique revealed that certain directions were associated with particularly large numbers of scattered atoms. The pattern of the scattering maxima could be attributed to secondary collisions.