Identifying ENSO Phase Impacts on Area Yield Insurance Rates: An Application of Non-Parametric Analysis

The paper reports results of non-parametric analysis of peanut, corn, and cotton yield distributions by the ElNino Southern Oscillation (ENSO) phases in the Southeastern U.S. For validation purposes, the historical yield data is complemented by a set of simulated peanut yields generated using daily weather data. The hypothesis, justified by the observed South-Eastern climate differences and research on ENSO cycles and planting dates, is that different climate conditions during ENSO cycles translate into different yield distributions and, therefore, insurance premiums (loss to coverage ratios). Kernel density estimates of historical county yield data show consistent patterns in the actuarially fair rate schedules grouped by ENSO phases and geographical areas. In particular, corn and cotton yield insurance premiums appear to be the most dependent on the ENSO phases and are the highest, regardless of coverage, during ElNino and the lowest during LaNina. Peanut premiums are higher during Neutral years and lowest during LaNina. The results appear to be robust to the transformations used to make the yield series stationary. While these dependencies do not necessarily correspond to the precipitation and solar radiation characteristics of the corresponding ENSO cycles in the Southeastern US, drawing direct analogies with yield variability is premature as many less documented factors, like the spacing of sunny and rainy days, may be just as important. The comparisons of the empirical and simulated peanut yield distributions show that they are similar in many ways and that the dissimilarities can be explained by known factors. These findings should be more relevant for the area yield insurance as opposed to the APH arrangements as the yield data used in designing contracts for the former reflects the systemic risk more influenced by climate than by the farm-level, basis risk factors accommodated in the APH plans.Risk and Uncertainty, Q140, C220, G220,

Dimension minimization of a quantum automaton

A new model of a Quantum Automaton (QA), working with qubits is proposed. The quantum states of the automaton can be pure or mixed and are represented by density operators. This is the appropriated approach to deal with measurements and dechorence. The linearity of a QA and of the partial trace super-operator, combined with the properties of invariant subspaces under unitary transformations, are used to minimize the dimension of the automaton and, consequently, the number of its working qubits. The results here developed are valid wether the state set of the QA is finite or not. There are two main results in this paper: 1) We show that the dimension reduction is possible whenever the unitary transformations, associated to each letter of the input alphabet, obey a set of conditions. 2) We develop an algorithm to find out the equivalent minimal QA and prove that its complexity is polynomial in its dimension and in the size of the input alphabet.Comment: 26 page

The non-self-adjointness of the radial momentum operator in n dimensions

The non self-adjointness of the radial momentum operator has been noted before by several authors, but the various proofs are incorrect. We give a rigorous proof that the $n$-dimensional radial momentum operator is not self- adjoint and has no self-adjoint extensions. The main idea of the proof is to show that this operator is unitarily equivalent to the momentum operator on $L^{2}[(0,\infty),dr]$ which is not self-adjoint and has no self-adjoint extensions.Comment: Some text and a reference adde

Decoherence and the rate of entropy production in chaotic quantum systems

We show that for an open quantum system which is classically chaotic (a quartic double well with harmonic driving coupled to a sea of harmonic oscillators) the rate of entropy production has, as a function of time, two relevant regimes: For short times it is proportional to the diffusion coefficient (fixed by the system--environment coupling strength). For longer times (but before equilibration) there is a regime where the entropy production rate is fixed by the Lyapunov exponent. The nature of the transition time between both regimes is investigated.Comment: Revtex, 4 pages, 3 figures include

Dipolar atomic spin ensembles in a double-well potential

We experimentally study the spin dynamics of mesoscopic ensembles of ultracold magnetic spin-3 atoms located in two separated wells of an optical dipole trap. We use a radio-frequency sweep to selectively flip the spin of the atoms in one of the wells, which produces two separated spin domains of opposite polarization. We observe that these engineered spin domains are metastable with respect to the long-range magnetic dipolar interactions between the two ensembles. The absence of inter-cloud dipolar spin-exchange processes reveals a classical behavior, in contrast to previous results with atoms loaded in an optical lattice. When we merge the two subsystems, we observe spin-exchange dynamics due to contact interactions which enable the first determination of the s-wave scattering length of 52Cr atoms in the S=0 molecular channel a_0=13.5^{+11}_{-10.5}a_B (where a_B is the Bohr radius).Comment: 9 pages, 7 figure

New bases for a general definition for the moving preferred basis

One of the challenges of the Environment-Induced Decoherence (EID) approach is to provide a simple general definition of the moving pointer basis or moving preferred basis. In this letter we prove that the study of the poles that produce the decaying modes in non-unitary evolution, could yield a general definition of the relaxation, the decoherence times, and the moving preferred basis. These probably are the most important concepts in the theory of decoherence, one of the most relevant chapters of theoretical (and also practical) quantum mechanics. As an example we solved the Omnes (or Lee-Friedrich) model using our theory.Comment: 6 page

Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases