Critical Transitions In a Model of a Genetic Regulatory System

We consider a model for substrate-depletion oscillations in genetic systems, based on a stochastic differential equation with a slowly evolving external signal. We show the existence of critical transitions in the system. We apply two methods to numerically test the synthetic time series generated by the system for early indicators of critical transitions: a detrended fluctuation analysis method, and a novel method based on topological data analysis (persistence diagrams).Comment: 19 pages, 8 figure

Non-global parameter estimation using local ensemble Kalman filtering

We study parameter estimation for non-global parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, we present a methodology whereby spatially-varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as our numerical testbed, we show that this parameter estimation methodology accurately estimates parameters which vary in both space and time, as well as parameters representing physics absent from the model

Design of a Particle Beam Satellite System for Lunar Prospecting

One potential use for neutral particle beam (NPB) technology is as an active orbital probe to investigate the composition of selected locations on the lunar surface. Because the beam is narrow and can be precisely directed, the NPB probe offers possibilities for high resolution experiments that cannot be accomplished using passive techniques. Rather, the combination of both passive and active techniques can be used to provide both full-coverage mapping (passively) at low resolution (tens of kilometers) and high-resolution information for discrete locations of special interest. A preliminary study of NPB applicability for this dual-use application was recently conducted. The study was completed in Feb. 1993. A novel feature was the consideration of the use of a Russian launch vehicle (e.g., the Proton). The use of other Russian space hardware and capabilities was also encouraged. This paper describes the lunar prospector system design. Other researchers discuss the issues and opportunities involving lunar scientific experimentation using an NPB. The NPB lunar prospector utilizes a modified design of the Far Field Optics Experiment (FOX). Like the Earth-orbiting FOX, the core capability of the NPB lunar prospector will be a pulsed RF LINAC that produces a 5-MeV proton beam that is projected to the target with a 30-micro-r beam divergence and a 10-micro-r beam-pointing accuracy. Upon striking the lunar surface, the proton beam will excite characteristic radiation (e.g., X-rays) that can be sensed by one or more detectors on the NPB platform or on a separate detector satellite

Autumn Fancies : Etude

https://digitalcommons.library.umaine.edu/mmb-ps/2836/thumbnail.jp

THE POTENTIAL OF DAIRY FUTURES CONTRACTS AS RISK MANAGEMENT TOOLS

We examine the young dairy futures market as a risk management tool. Using New York Board of Trade (NYBOT) data, we find that the BFP futures market is efficient and may potentially be a useful hedging tool. However, we also find that competition from Chicago Mercantile Exchange (CME) contracts has significant detrimental effects on the NYBOT dairy futures contracts. As a result NYBOT dairy futures contracts are likely to dry up.Financial Economics, Livestock Production/Industries,

Using machine learning to predict catastrophes in dynamical systems

Nonlinear dynamical systems, which include models of the Earth\u27s climate, financial markets and complex ecosystems, often undergo abrupt transitions that lead to radically different behavior. The ability to predict such qualitative and potentially disruptive changes is an important problem with far-reaching implications. Even with robust mathematical models, predicting such critical transitions prior to their occurrence is extremely difficult. In this work, we propose a machine learning method to study the parameter space of a complex system, where the dynamics is coarsely characterized using topological invariants. We show that by using a nearest neighbor algorithm to sample the parameter space in a specific manner, we are able to predict with high accuracy the locations of critical transitions in parameter space. (C) 2011 Elsevier B.V. All rights reserved

Zeno-effect Computation: Opportunities and Challenges

Adiabatic quantum computing has demonstrated how quantum Zeno can be used to construct quantum optimisers. However, much less work has been done to understand how more general Zeno effects could be used in a similar setting. We use a construction based on three state systems rather than directly in qubits, so that a qubit can remain after projecting out one of the states. We find that our model of computing is able to recover the dynamics of a transverse field Ising model, several generalisations are possible, but our methods allow for constraints to be implemented non-perturbatively and does not need tunable couplers, unlike simple transverse field implementations. We further discuss how to implement the protocol physically using methods building on STIRAP protocols for state transfer. We find a substantial challenge, that settings defined exclusively by measurement or dissipative Zeno effects do not allow for frustration, and in these settings pathological spectral features arise leading to unfavorable runtime scaling. We discuss methods to overcome this challenge for example including gain as well as loss as is often done in optical Ising machines