129,202 research outputs found
STiC -- A multi-atom non-LTE PRD inversion code for full-Stokes solar observations
The inference of the underlying state of the plasma in the solar chromosphere
remains extremely challenging because of the nonlocal character of the observed
radiation and plasma conditions in this layer. Inversion methods allow us to
derive a model atmosphere that can reproduce the observed spectra by
undertaking several physical assumptions.
The most advanced approaches involve a depth-stratified model atmosphere
described by temperature, line-of-sight velocity, turbulent velocity, the three
components of the magnetic field vector, and gas and electron pressure. The
parameters of the radiative transfer equation are computed from a solid ground
of physical principles. To apply these techniques to spectral lines that sample
the chromosphere, NLTE effects must be included in the calculations.
We developed a new inversion code STiC to study spectral lines that sample
the upper chromosphere. The code is based the RH synthetis code, which we
modified to make the inversions faster and more stable. For the first time,
STiC facilitates the processing of lines from multiple atoms in non-LTE, also
including partial redistribution effects. Furthermore, we include a
regularization strategy that allows for model atmospheres with a complex
stratification, without introducing artifacts in the reconstructed physical
parameters, which are usually manifested in the form of oscillatory behavior.
This approach takes steps toward a node-less inversion, in which the value of
the physical parameters at each grid point can be considered a free parameter.
In this paper we discuss the implementation of the aforementioned techniques,
the description of the model atmosphere, and the optimizations that we applied
to the code. We carry out some numerical experiments to show the performance of
the code and the regularization techniques that we implemented. We made STiC
publicly available to the community.Comment: Accepted for publication in Astronomy & Astrophysic
Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff
In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy
Fluctuations and the Effective Moduli of an Isotropic, Random Aggregate of Identical, Frictionless Spheres
We consider a random aggregate of identical frictionless elastic spheres that
has first been subjected to an isotropic compression and then sheared. We
assume that the average strain provides a good description of how stress is
built up in the initial isotropic compression. However, when calculating the
increment in the displacement between a typical pair of contaction particles
due to the shearing, we employ force equilibrium for the particles of the pair,
assuming that the average strain provides a good approximation for their
interactions with their neighbors. The incorporation of these additional
degrees of freedom in the displacement of a typical pair relaxes the system,
leading to a decrease in the effective moduli of the aggregate. The
introduction of simple models for the statistics of the ordinary and
conditional averages contributes an additional decrease in moduli. The
resulting value of the shear modulus is in far better agreement with that
measured in numerical simulations
Market-based Recommendation: Agents that Compete for Consumer Attention
The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains
Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm laws
Self-normalized processes arise naturally in statistical applications.
Being unit free, they are not affected by scale changes. Moreover,
self-normalization often eliminates or weakens moment assumptions. In this
paper we present several exponential and moment inequalities, particularly
those related to laws of the iterated logarithm, for self-normalized random
variables including martingales. Tail probability bounds are also derived. For
random variables B_t>0 and A_t, let Y_t(\lambda)=\exp{\lambda A_t-\lambda
^2B_t^2/2}. We develop inequalities for the moments of A_t/B_{t} or sup_{t\geq
0}A_t/{B_t(\log \log B_{t})^{1/2}} and variants thereof, when EY_t(\lambda
)\leq 1 or when Y_t(\lambda) is a supermartingale, for all \lambda belonging to
some interval. Our results are valid for a wide class of random processes
including continuous martingales with A_t=M_t and B_t=\sqrt _t, and sums of
conditionally symmetric variables d_i with A_t=\sum_{i=1}^td_i and
B_t=\sqrt\sum_{i=1}^td_i^2. A sharp maximal inequality for conditionally
symmetric random variables and for continuous local martingales with values in
R^m, m\ge 1, is also established. Another development in this paper is a
bounded law of the iterated logarithm for general adapted sequences that are
centered at certain truncated conditional expectations and self-normalized by
the square root of the sum of squares. The key ingredient in this development
is a new exponential supermartingale involving \sum_{i=1}^td_i and
\sum_{i=1}^td_i^2.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Probability
(http://www.imstat.org/aop/) at http://dx.doi.org/10.1214/00911790400000039
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