Improving the Scalability of Multi-Agent Systems

There is an increasing demand for designers and developers to construct ever larger multi-agent systems. Such systems will be composed of hundreds or even thousands of autonomous agents. Moreover, in open and dynamic environments, the number of agents in the system at any one time will fluctuate significantly. To cope with these twin issues of scalability and variable numbers, we hypothesize that multi-agent systems need to be both /self-building/ (able to determine the most appropriate organizational structure for the system by themselves at run-time) and /adaptive/ (able to change this structure as their environment changes). To evaluate this hypothesis we have implemented such a multi-agent system and have applied it to the domain of automated trading. Preliminary results supporting the first part of this hypothesis are presented: adaption and self-organization do indeed make the system better able to cope with large numbers of agents

Peptoid-Based KLVFF Mimics: A Unique Approach to Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the leading form of dementia worldwide. AD patients experience a slow, gradual cognitive decline that includes loss of memory and behavioral stability as the disease progresses. Surprisingly, AD is the sixth leading cause of death in the United States and has had a profound impact on the U.S. economy. Though there are medications to help improve the quality of life of diagnosed patients for a period of time, there is still no cure for AD. AD is characterized by the build-up of amyloid plaques that develop from the aggregation of the amyloid beta protein (Aβ) in the body. Current treatment options for AD has been the development of compounds that can target and either inhibit or modulate the formation of Aβ aggregates. Several small molecules and peptides have been studied for their ability to interact and inhibit or modulate Aβ aggregation. However, despite promising results in-vitro, none of these compounds have led to a therapeutic treatment. In this study, we demonstrate five novel peptoid modulators of Aβ aggregation. These peptoids were the first to be designed from the hydrophobic core of Aβ. Results have indicated that inclusion of aromatic side chains, peptoid secondary structure, side chain placement, and inclusion of charged sequences have a profound impact on Aβ aggregation. Overall, this study provides insight to the potential of novel peptoids as a therapeutic option for AD

Effecting successful knowledge transfer: lessons from the UK Knowledge Transfer Partnership scheme

This article focuses on one case example to illustrate how the Knowledge Transfer Partnership (KTP) scheme has been used to benefit a small to medium sized enterprise (SME) in the UK. The KTP product is arguably the most used vehicle for effecting knowledge transfer between universities and local industries in the UK, and three distinct KTP projects (implemented in the one company) are discussed. The impact of these projects is then reviewed in terms of innovation initiatives, professional development, skills enhancement and organisational change. The change implications for the university are also discussed and sets of guidelines for KTP selling, project design and project supervision are suggeste

E-business and process change: two case studies (towards an assessment framework)

Purpose The purpose of this research paper is to explore the impacts of e-business technology adoption at process level in SMEs. Methodology/approach The paper reviews a range of literature and mainstream models relating to e-business impacts in SMEs and then focuses on two in-depth case studies. The cases draw their empirical material from the involvement of the author in e-business project management in these two companies.. Findings The two case studies show significant impacts of e-business technology at process level. They also illustrate that contrasting information systems strtaegies can be equally successful in embracing e-business process change. Originality/value The value of the case studies lies in their originality and the paper highlights the potential impacts and benefits of e-business at process level

Peptoids and methods for treating Alzheimer\u27s disease

Provided herein are peptoids capable of inhibiting or reversing amyloid .beta. (A.beta.) fibril or plaque production. The peptoids form a helical structure with three monomers per helical turn and have at least four monomers with a side-chain having an arylalkyl or aryl group. The peptoid may be achiral. Also provided are methods of using the peptoids to inhibit or reverse aggregation of A.beta. and methods of treating subjects with Alzheimer\u27s disease (AD) or slowing the progression of AD

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers

Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which is a notoriously hard problem. In this work, we present a large-scale analysis of common temporal rollout strategies, identifying the neglect of non-dominant spatial frequency information, often associated with high frequencies in PDE solutions, as the primary pitfall limiting stable, accurate rollout performance. Based on these insights, we draw inspiration from recent advances in diffusion models to introduce PDE-Refiner; a novel model class that enables more accurate modeling of all frequency components via a multistep refinement process. We validate PDE-Refiner on challenging benchmarks of complex fluid dynamics, demonstrating stable and accurate rollouts that consistently outperform state-of-the-art models, including neural, numerical, and hybrid neural-numerical architectures. We further demonstrate that PDE-Refiner greatly enhances data efficiency, since the denoising objective implicitly induces a novel form of spectral data augmentation. Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.Comment: Project website: https://phlippe.github.io/PDERefiner