Automatic Implementation of Neural Networks through Reaction Networks -- Part I: Circuit Design and Convergence Analysis

Information processing relying on biochemical interactions in the cellular environment is essential for biological organisms. The implementation of molecular computational systems holds significant interest and potential in the fields of synthetic biology and molecular computation. This two-part article aims to introduce a programmable biochemical reaction network (BCRN) system endowed with mass action kinetics that realizes the fully connected neural network (FCNN) and has the potential to act automatically in vivo. In part I, the feedforward propagation computation, the backpropagation component, and all bridging processes of FCNN are ingeniously designed as specific BCRN modules based on their dynamics. This approach addresses a design gap in the biochemical assignment module and judgment termination module and provides a novel precise and robust realization of bi-molecular reactions for the learning process. Through equilibrium approaching, we demonstrate that the designed BCRN system achieves FCNN functionality with exponential convergence to target computational results, thereby enhancing the theoretical support for such work. Finally, the performance of this construction is further evaluated on two typical logic classification problems

Coalescence modeling and experimental validation of sintering of thermoplastic polyamide fibers

In order to study the coalescence mechanisms of thermoplastic polymer powders, a 2D mathematical model has been established based on Frenkel, Eshelby and Pokluda’s model. Sintering experiments have been carried out by using two polyamide fibers that can be considered as infinite cylinders with its length much larger than the diameter. 2D mathematical model has been validated through comparison with results of sintering experiments as well as Constrained Natural Element Method (C-NEM) coalescence simulation. This consistence shows that the proposed coalescence model and experimental results can provide a reference for the numerical simulation of sintering process

Automatic Implementation of Neural Networks through Reaction Networks--Part II: Error Analysis

This paired article aims to develop an automated and programmable biochemical fully connected neural network (BFCNN) with solid theoretical support. In Part I, a concrete design for BFCNN is presented, along with the validation of the effectiveness and exponential convergence of computational modules. In this article, we establish the framework for specifying the realization errors by monitoring the errors generated from approaching equilibrium points in individual modules, as well as their vertical propagation from upstream modules and horizontal accumulation from previous iterations. Ultimately, we derive the general error upper bound formula for any iteration and illustrate its exponential convergence order with respect to the phase length of the utilized chemical oscillator. The numerical experiments, based on the classification examples, reveal the tendency of total errors related to both the phase length and iteration number

Enabling urban-scale energy modelling: a new spatial approach

Urban-scale energy modelling provides an ideal tool for studying non-domestic energy consumption and emissions reduction at the community level. In principle, an approach based on the characteristics of individual commercial premises and buildings is attractive, but it poses a number of challenges, the most immediate of which is deciding precisely what to model. For a range of reasons connected with their self-contained nature, individual non-domestic buildings would ideally be selected. However, the main information sources available - digital mapping and business taxation data - are not based on 'buildings' and do not use the concept, thus making an automated approach problematic. At the same time, manual identification of the distinct buildings in a city is not a practical proposition because of the numbers involved. The digital mapping and business taxation data are brought together in the Local Land and Property Gazetteer (LLPG). An analysis of the relationships between the relevant elements, namely building polygons and premises attracting business taxation, allowed a unit to be defined that matches the definition of a 'building' in most circumstances and can be applied without the need for human intervention. This novel approach provides a firmer basis for modelling non-domestic building energy at the urban scale

Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?

The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.Comment: 43 pages, 20 figure

Interference of Cooper quartet Andreev bound states in a multi-terminal graphene-based Josephson junction

In a Josephson junction (JJ), Cooper pairs are transported via Andreev bound states (ABSs) between superconductors. The ABSs in the weak link of multi-terminal (MT) JJs can coherently hybridize two Cooper pairs among different superconducting electrodes, resulting in the Cooper quartet (CQ) involving four fermions entanglement. The energy spectrum of these CQ-ABS can be controlled by biasing MT-JJs due to the AC Josephson effect. Here, using gate tunable four-terminal graphene JJs complemented with a flux loop, we construct CQs with a tunable spectrum. The critical quartet supercurrent exhibits magneto-oscillation associated with a charge of 4e; thereby presenting the evidence for interference between entangled CQ-ABS. At a finite bias voltage, we find the DC quartet supercurrent shows non-monotonic bias dependent behavior, attributed to Landau-Zener transitions between different Floquet bands. Our experimental demonstration of coherent non-equilibrium CQ-ABS sets a path for design of artificial topological materials based on MT-JJs