8,473 research outputs found
From bridewealth to dowry? A Bayesian estimation of ancestral states of marriage transfers in Indo-European groups
Significant amounts of wealth have been exchanged as part of marriage settlements throughout history. Although various models have been proposed for interpreting these practices, their development over time has not been investigated systematically. In this paper we use a Bayesian MCMC phylogenetic comparative approach to reconstruct the evolution of two forms of wealth transfers at marriage, dowry and bridewealth, for 51 Indo-European cultural groups. Results indicate that dowry is more likely to have been the ancestral practice, and that a minimum of four changes to bridewealth is necessary to explain the observed distribution of the two states across the cultural groups
A Levinson-Galerkin algorithm for regularized trigonometric approximation
Trigonometric polynomials are widely used for the approximation of a smooth
function from a set of nonuniformly spaced samples
. If the samples are perturbed by noise, controlling
the smoothness of the trigonometric approximation becomes an essential issue to
avoid overfitting and underfitting of the data. Using the polynomial degree as
regularization parameter we derive a multi-level algorithm that iteratively
adapts to the least squares solution of optimal smoothness. The proposed
algorithm computes the solution in at most operations (
being the polynomial degree of the approximation) by solving a family of nested
Toeplitz systems. It is shown how the presented method can be extended to
multivariate trigonometric approximation. We demonstrate the performance of the
algorithm by applying it in echocardiography to the recovery of the boundary of
the Left Ventricle
A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter
We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm combines the Durbin-Levinson and Viterbi procedures. A Monte Carlo experiment reveals that the finite sample performance of the proposed algorithm for a simple mixture model of Markov-switching mean and ARFIMA(1, d, 1) process is satisfactory. We apply the Markov-switching-ARFIMA models to the U.S. real interest rates, the Nile river level, and the U.S. unemployment rates, respectively. The results are all highly consistent with the conjectures made or empirical results found in the literature. Particularly, we confirm the conjecture in Beran and Terrin (1996) that the observations 1 to about 100 of the Nile river data seem to be more independent than the subsequent observations, and the value of differencing parameter is lower for the first 100 observations than for the subsequent data.Markov chain; ARFIMA process; Viterbi algorithm; Long memory.
Forecasting and Evaluating Network Growth
This research assesses the implications of existing trends on future network investment, comparing alternative scenarios concerning budgets and investment rules across a variety of performance measures. The main scenarios compare 'stated decision rules';, processes encoded in flowcharts and weights developed from official documents or by discussion with agency staff, with 'revealed decision rules', weights estimated statistically based on observed historical behavior. This research specifies the processes necessary to run the network forecasting models with various decision rules. Results for different scenarios are presented including adding additional constraints for the transportation network expansion and calibration process details. We find that alternative decision rules make only small differences in overall system performance, though they direct investments to very different locations. However, changes in total budget can make a significant difference to system-wide performance.
R-Hadron and long lived particle searches at the LHC
If long lived charged particles exist, and produced at the LHC, they may
travel with velocity significantly slower than the speed of light. This unique
signature was not considered during the design of the LHC experiments, ATLAS
and CMS. As a result, hardware and trigger capabilities need to be evaluated.
Model independent approaches for finding long lived particles with the LHC
experiments are introduced. They are tested using two bench marks, one in GMSB
and one in Split SUSY. The focus is on hardware and trigger issues, as well as
reconstruction methods developed by ATLAS and CMS. Both experiments suggest
time of flight (TOF) based methods. However, the implementation is different.
In ATLAS a first beta estimation is done already at the trigger level. CMS also
uses dE/dx to estimate beta.Comment: Submitted for the SUSY07 proceedings, 4 pages, LaTeX, 7 eps figure
Learning detectors quickly using structured covariance matrices
Computer vision is increasingly becoming interested in the rapid estimation
of object detectors. Canonical hard negative mining strategies are slow as they
require multiple passes of the large negative training set. Recent work has
demonstrated that if the distribution of negative examples is assumed to be
stationary, then Linear Discriminant Analysis (LDA) can learn comparable
detectors without ever revisiting the negative set. Even with this insight,
however, the time to learn a single object detector can still be on the order
of tens of seconds on a modern desktop computer. This paper proposes to
leverage the resulting structured covariance matrix to obtain detectors with
identical performance in orders of magnitude less time and memory. We elucidate
an important connection to the correlation filter literature, demonstrating
that these can also be trained without ever revisiting the negative set
Classification of music genres using sparse representations in overcomplete dictionaries
This paper presents a simple, but efficient and robust, method for music genre classification that utilizes sparse representations in overcomplete dictionaries. The training step involves creating dictionaries, using the K-SVD algorithm, in which data corresponding to a particular music genre has a sparse representation. In the classification step, the Orthogonal Matching Pursuit (OMP) algorithm is used to separate feature vectors that consist only of Linear Predictive Coding (LPC) coefficients. The paper analyses in detail a popular case study from the literature, the ISMIR 2004 database. Using the presented method, the correct classification percentage of the 6 music genres is 85.59, result that is comparable with the best results published so far
The design of a digital voice data compression technique for orbiter voice channels
Voice bandwidth compression techniques were investigated to anticipate link margin difficulties in the shuttle S-band communication system. It was felt that by reducing the data rate on each voice channel from the baseline 24 (or 32) Kbps to 8 Kbps, additional margin could be obtained. The feasibility of such an alternate voice transmission system was studied. Several factors of prime importance that were addressed are: (1) achieving high quality voice at 8 Kbps; (2) performance in the presence of the anticipated shuttle cabin environmental noise; (3) performance in the presence of the anticipated channel error statistics; and (4) minimal increase in size, weight, and power over the current baseline voice processor
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