Dynamics of condensation in the totally asymmetric inclusion process

We study the dynamics of condensation of the inclusion process on a one-dimensional periodic lattice in the thermodynamic limit, generalising recent results on finite lattices for symmetric dynamics. Our main focus is on totally asymmetric dynamics which have not been studied before, and which we also compare to exact solutions for symmetric systems. We identify all relevant dynamical regimes and corresponding time scales as a function of the system size, including a coarsening regime where clusters move on the lattice and exchange particles, leading to a growing average cluster size. Suitable observables exhibit a power law scaling in this regime before they saturate to stationarity following an exponential decay depending on the system size. Our results are based on heuristic derivations and exact computations for symmetric systems, and are supported by detailed simulation data.Comment: 23 pages, 6 figures, updated references and introductio

Dynamics of condensation in stochastic particle systems

Condensation is a special class of phase transition which has been observed throughout the natural and social sciences. The understanding of the critical behaviour of such systems is a very active area of current research, in particular a mathematical description of the formation and time evolution of the condensate. In this thesis we study these phenomena in several models. In particular we focus on the recently introduced inclusion process, and we compare it with related classical mass transport models such as zero range processes. We first give a brief review of relevant definitions and properties of interacting particle systems, in particular recent literatures on the condensation and stationary behaviour of a large class of interacting particle systems with stationary product measures, which forms the theoretical basis of this thesis. The second part of this thesis is on the dynamics of condensation in the inclusion process on a one-dimensional periodic lattice in the thermodynamic limit. This generalises recent results which were limited to finite lattices and symmetric dynamics. Our main focus is firstly on totally asymmetric dynamics which have not been studied before, which we compare to exact solutions for symmetric systems. We identify all the relevant dynamical regimes and corresponding time scales as a function of the system size, including a coarsening regime where clusters move on the lattice and exchange particles, leading to a growing average cluster size. After establishing the general approach to study dynamics of condensation in totally asymmetric processes, we extend the results to more general partially asymmetric cases as well as higher dimensional cases. In the third part of this thesis we derive some preliminary exact results on symmetric systems through duality, which recovers heuristic results in previous chapter and allows us to treat coarsening in the infinite lattice directly

DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework that leverages deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems. Our code is publicly available at https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202

Research on the Development Path of Rural Industry Revitalization by Non-Legacy of Folklore: Taking Chaoshan Meeting of p county in Linfen City as an Example

with the development of digital economy, the protection and inheritance of intangible cultural heritage has become an important issue to be solved. As an important part of Chinese traditional culture, non-heritage of folk-custom carries rich historical and cultural connotation and national spirit. However, with the changes of the times and the widening gap between urban and rural development, many non-heritage projects of folk-custom are facing the crisis of loss. As a folk celebration with profound historical and cultural background, Chaoshan meeting in p county of Linfen City, Shanxi province carries rich local cultural connotation. This paper aims to analyze the present situation of Chaoshan society in Linfen city, Shanxi Province, and discuss the development dilemma of micro-, middle-and macro-levels, and put forward a targeted development path to promote the revitalization of rural industries and inheritance of non-heritage protection work in-depth development

A possible pathway for rapid growth of sulfate during haze days in China

Rapid industrialization and urbanization have caused frequent occurrence of haze in China during wintertime in recent years. The sulfate aerosol is one of the most important components of fine particles (PM[subscript 2. 5]) in the atmosphere, contributing significantly to the haze formation. However, the heterogeneous formation mechanism of sulfate remains poorly characterized. The relationships of the observed sulfate with PM[subscript 2. 5], iron, and relative humidity in Xi'an, China have been employed to evaluate the mechanism and to develop a parameterization of the sulfate heterogeneous formation involving aerosol water for incorporation into atmospheric chemical transport models. Model simulations with the proposed parameterization can successfully reproduce the observed sulfate rapid growth and diurnal variations in Xi'an and Beijing, China. Reasonable representation of sulfate heterogeneous formation in chemical transport models considerably improves the PM2. 5 simulations, providing the underlying basis for better understanding the haze formation and supporting the design and implementation of emission control strategies

Transcriptomic analysis reveals transcription factors involved in vascular bundle development and tissue maturation in ginger rhizomes (Zingiber officinale Roscoe)

Ginger (Zingiber officinale Roscoe) is an important vegetable with medicinal value. Rhizome development determines ginger yield and quality. However, little information is available about the molecular features underlying rhizome expansion and maturation. In this study, we investigated anatomy characteristics, lignin accumulation and transcriptome profiles during rhizome development. In young rhizomes, the vascular bundle (VB) was generated with only vessels in it, whereas in matured rhizomes, three to five layers of fibre bundle in the xylem were formed, resulting in VB enlargement. It indicates VB development favouring rhizome swelling. With rhizome matured, the lignin content was remarkably elevated, thus facilitating tissue lignification. To explore the regulators for rhizome development, nine libraries including ginger young rhizomes (GYR), growing rhizomes (GGR), and matured rhizomes (GMR) were established for RNA-Seq, a total of 1264 transcription factors (TFs) were identified. Among them, 35, 116, and 14 differentially expressed TFs were obtained between GYR and GGR, GYR and GMR, and GGR and GMR, respectively. These TFs were further divided into three categories. Among them, three ZobHLHs (homologs of Arabidopsis LHW and AtbHLH096) as well as one DIVARICATA homolog in ginger might play crucial roles in controlling VB development. Four ZoWRKYs and two ZoNACs might be potential regulators associated with rhizome maturation. Three ZoAP2/ERFs and one ZoARF might participate in rhizome development via hormone signalling. This result provides a molecular basis for rhizome expansion and maturation in ginger

NEOLAF, an LLM-powered neural-symbolic cognitive architecture

This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligent agents than both the pure connectionist and pure symbolic approaches due to its explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and self-improvement. The paper further presents a compelling experiment where a NEOLAF agent, built as a problem-solving agent, is fed with complex math problems from the open-source MATH dataset. The results demonstrate NEOLAF's superior learning capability and its potential to revolutionize the field of cognitive architectures and self-improving adaptive instructional systems