126 research outputs found
Be Relevant, Careful, and Appropriate: Scary Advice on the Use of Humor to the Novice Public Speaker
Most contemporary public speaking texts contain some reference to the effective use of humor by public speakers. This advice tends to reflect common assumptions on the role of humor in public speaking and the ability of the novice speaker to incorporate humor in a speech. A review of 27 contemporary texts explores the trend in humor instruction and offers 11 categories which summarize the treatment of humor: (1) theories of humor, (2) rationale for the use of humor, (3) guidelines for the use of humor, (4) sources of humor, (5) humor as a factor of attention, (6) specific humorous techniques to employ in a speech, (7) injunctions on the use of humor, (8) who should use humor, (9) the use of self-deprecating humor, (10) how to deliver the humor, (11) humorous speaking
Maritime expressions:a corpus based exploration of maritime metaphors
This study uses a purpose-built corpus to explore the linguistic legacy of Britain’s maritime history found in the form of hundreds of specialised ‘Maritime Expressions’ (MEs), such as TAKEN ABACK, ANCHOR and ALOOF, that permeate modern English. Selecting just those expressions commencing with ’A’, it analyses 61 MEs in detail and describes the processes by which these technical expressions, from a highly specialised occupational discourse community, have made their way into modern English. The Maritime Text Corpus (MTC) comprises 8.8 million words, encompassing a range of text types and registers, selected to provide a cross-section of ‘maritime’ writing. It is analysed using WordSmith analytical software (Scott, 2010), with the 100 million-word British National Corpus (BNC) as a reference corpus. Using the MTC, a list of keywords of specific salience within the maritime discourse has been compiled and, using frequency data, concordances and collocations, these MEs are described in detail and their use and form in the MTC and the BNC is compared. The study examines the transformation from ME to figurative use in the general discourse, in terms of form and metaphoricity. MEs are classified according to their metaphorical strength and their transference from maritime usage into new registers and domains such as those of business, politics, sports and reportage etc. A revised model of metaphoricity is developed and a new category of figurative expression, the ‘resonator’, is proposed. Additionally, developing the work of Lakov and Johnson, Kovesces and others on Conceptual Metaphor Theory (CMT), a number of Maritime Conceptual Metaphors are identified and their cultural significance is discussed
The power of random measurements: measuring Tr(\rho^n) on single copies of \rho
While it is known that Tr(\rho^n) can be measured directly (i.e., without
first reconstructing the density matrix) by performing joint measurements on n
copies of the same state rho, it is shown here that random measurements on
single copies suffice, too. Averaging over the random measurements directly
yields estimates of Tr(\rho^n), even when it is not known what measurements
were actually performed (so that one cannot reconstruct \rho)
Homological perturbation theory for nonperturbative integrals
We use the homological perturbation lemma to produce explicit formulas
computing the class in the twisted de Rham complex represented by an arbitrary
polynomial. This is a non-asymptotic version of the method of Feynman diagrams.
In particular, we explain that phenomena usually thought of as particular to
asymptotic integrals in fact also occur exactly: integrals of the type
appearing in quantum field theory can be reduced in a totally algebraic fashion
to integrals over an Euler--Lagrange locus, provided this locus is understood
in the scheme-theoretic sense, so that imaginary critical points and
multiplicities of degenerate critical points contribute.Comment: 22 pages. Minor revisions from previous versio
A perturbative approach to the reconstruction of the eigenvalue spectrum of a normal covariance matrix from a spherically truncated counterpart
In this paper we propose a perturbative method for the reconstruction of the
covariance matrix of a multinormal distribution, under the assumption that the
only available information amounts to the covariance matrix of a spherically
truncated counterpart of the same distribution. We expand the relevant
equations up to the fourth perturbative order and discuss the analytic
properties of the first few perturbative terms. We finally compare the proposed
approach with an exact iterative algorithm (presented in Palombi et al. (2017))
in the hypothesis that the spherically truncated covariance matrix is estimated
from samples of various sizes.Comment: 39 pages, 7 figures. v2: version accepted for publication in J. Comp.
Appl. Mat
Modelling network travel time reliability under stochastic demand
A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed
Applications of sensitivity analysis for probit stochastic network equilibrium
Network equilibrium models are widely used by traffic practitioners to aid them in making decisions concerning the operation and management of traffic networks. The common practice is to test a prescribed range of hypothetical changes or policy measures through adjustments to the input data, namely the trip demands, the arc performance (travel time) functions, and policy variables such as tolls or signal timings. Relatively little use is, however, made of the full implicit relationship between model inputs and outputs inherent in these models. By exploiting the representation of such models as an equivalent optimisation problem, classical results on the sensitivity analysis of non-linear programs may be applied, to produce linear relationships between input data perturbations and model outputs. We specifically focus on recent results relating to the probit Stochastic User Equilibrium (PSUE) model, which has the advantage of greater behavioural realism and flexibility relative to the conventional Wardrop user equilibrium and logit SUE models. The paper goes on to explore four applications of these sensitivity expressions in gaining insight into the operation of road traffic networks. These applications are namely: identification of sensitive, ‘critical’ parameters; computation of approximate, re-equilibrated solutions following a change (post-optimisation); robustness analysis of model forecasts to input data errors, in the form of confidence interval estimation; and the solution of problems of the bi-level, optimal network design variety. Finally, numerical experiments applying these methods are reported
Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields
Spatially referenced data often have autocovariance functions with elliptical
isolevel contours, a property known as geometric anisotropy. The anisotropy
parameters include the tilt of the ellipse (orientation angle) with respect to
a reference axis and the aspect ratio of the principal correlation lengths.
Since these parameters are unknown a priori, sample estimates are needed to
define suitable spatial models for the interpolation of incomplete data. The
distribution of the anisotropy statistics is determined by a non-Gaussian
sampling joint probability density. By means of analytical calculations, we
derive an explicit expression for the joint probability density function of the
anisotropy statistics for Gaussian, stationary and differentiable random
fields. Based on this expression, we obtain an approximate joint density which
we use to formulate a statistical test for isotropy. The approximate joint
density is independent of the autocovariance function and provides conservative
probability and confidence regions for the anisotropy parameters. We validate
the theoretical analysis by means of simulations using synthetic data, and we
illustrate the detection of anisotropy changes with a case study involving
background radiation exposure data. The approximate joint density provides (i)
a stand-alone approximate estimate of the anisotropy statistics distribution
(ii) informed initial values for maximum likelihood estimation, and (iii) a
useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure
Learning from Complex Systems: On the Roles of Entropy and Fisher Information in Pairwise Isotropic Gaussian Markov Random Fields
Markov Random Field models are powerful tools for the study of complex
systems. However, little is known about how the interactions between the
elements of such systems are encoded, especially from an information-theoretic
perspective. In this paper, our goal is to enlight the connection between
Fisher information, Shannon entropy, information geometry and the behavior of
complex systems modeled by isotropic pairwise Gaussian Markov random fields. We
propose analytical expressions to compute local and global versions of these
measures using Besag's pseudo-likelihood function, characterizing the system's
behavior through its \emph{Fisher curve}, a parametric trajectory accross the
information space that provides a geometric representation for the study of
complex systems. Computational experiments show how the proposed tools can be
useful in extrating relevant information from complex patterns. The obtained
results quantify and support our main conclusion, which is: in terms of
information, moving towards higher entropy states (A --> B) is different from
moving towards lower entropy states (B --> A), since the \emph{Fisher curves}
are not the same given a natural orientation (the direction of time).Comment: 46 pages, 16 Figure
Spectral estimation on a sphere in geophysics and cosmology
We address the problem of estimating the spherical-harmonic power spectrum of
a statistically isotropic scalar signal from noise-contaminated data on a
region of the unit sphere. Three different methods of spectral estimation are
considered: (i) the spherical analogue of the one-dimensional (1-D)
periodogram, (ii) the maximum likelihood method, and (iii) a spherical analogue
of the 1-D multitaper method. The periodogram exhibits strong spectral leakage,
especially for small regions of area , and is generally unsuitable
for spherical spectral analysis applications, just as it is in 1-D. The maximum
likelihood method is particularly useful in the case of nearly-whole-sphere
coverage, , and has been widely used in cosmology to estimate
the spectrum of the cosmic microwave background radiation from spacecraft
observations. The spherical multitaper method affords easy control over the
fundamental trade-off between spectral resolution and variance, and is easily
implemented regardless of the region size, requiring neither non-linear
iteration nor large-scale matrix inversion. As a result, the method is ideally
suited for most applications in geophysics, geodesy or planetary science, where
the objective is to obtain a spatially localized estimate of the spectrum of a
signal from noisy data within a pre-selected and typically small region.Comment: Submitted to the Geophysical Journal Internationa
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