67 research outputs found
Development of longitudinal handling qualities criteria for large advanced supersonic aircraft
A piloted simulation study was conducted with the aim of advancing the development of longitudinal handling qualities criteria for large supersonic cruise aircraft. The areas of study investigated included high-speed cruise maneuvering, and stall-recovery control power. Comparisons were made with existing criteria and, for the cruise condition, a time response criterion was developed which correlated well with pilot ratings and comments. For low-speed stall recovery a new criterion was developed in terms of nose-down angular acceleration capability
Development of longitudinal handling qualities criteria for large advanced supersonic aircraft
Longitudinal handling qualities criteria in terms of airplane response characteristics were developed. The criteria cover high speed cruise maneuvering, landing approach, and stall recovery. Data substantiating the study results are reported
Generalized Species Sampling Priors with Latent Beta reinforcements
Many popular Bayesian nonparametric priors can be characterized in terms of
exchangeable species sampling sequences. However, in some applications,
exchangeability may not be appropriate. We introduce a {novel and
probabilistically coherent family of non-exchangeable species sampling
sequences characterized by a tractable predictive probability function with
weights driven by a sequence of independent Beta random variables. We compare
their theoretical clustering properties with those of the Dirichlet Process and
the two parameters Poisson-Dirichlet process. The proposed construction
provides a complete characterization of the joint process, differently from
existing work. We then propose the use of such process as prior distribution in
a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte
Carlo sampler for posterior inference. We evaluate the performance of the prior
and the robustness of the resulting inference in a simulation study, providing
a comparison with popular Dirichlet Processes mixtures and Hidden Markov
Models. Finally, we develop an application to the detection of chromosomal
aberrations in breast cancer by leveraging array CGH data.Comment: For correspondence purposes, Edoardo M. Airoldi's email is
[email protected]; Federico Bassetti's email is
[email protected]; Michele Guindani's email is
[email protected] ; Fabrizo Leisen's email is
[email protected]. To appear in the Journal of the American
Statistical Associatio
On the equivalence of game and denotational semantics for the probabilistic mu-calculus
The probabilistic (or quantitative) modal mu-calculus is a fixed-point logic
de- signed for expressing properties of probabilistic labeled transition
systems (PLTS). Two semantics have been studied for this logic, both assigning
to every process state a value in the interval [0,1] representing the
probability that the property expressed by the formula holds at the state. One
semantics is denotational and the other is a game semantics, specified in terms
of two-player stochastic games. The two semantics have been proved to coincide
on all finite PLTS's, but the equivalence of the two semantics on arbitrary
models has been open in literature. In this paper we prove that the equivalence
indeed holds for arbitrary infinite models, and thus our result strengthens the
fruitful connection between denotational and game semantics. Our proof adapts
the unraveling or unfolding method, a general proof technique for proving
result of parity games by induction on their complexity
The Complexity of Nash Equilibria in Stochastic Multiplayer Games
We analyse the computational complexity of finding Nash equilibria in
turn-based stochastic multiplayer games with omega-regular objectives. We show
that restricting the search space to equilibria whose payoffs fall into a
certain interval may lead to undecidability. In particular, we prove that the
following problem is undecidable: Given a game G, does there exist a Nash
equilibrium of G where Player 0 wins with probability 1? Moreover, this problem
remains undecidable when restricted to pure strategies or (pure) strategies
with finite memory. One way to obtain a decidable variant of the problem is to
restrict the strategies to be positional or stationary. For the complexity of
these two problems, we obtain a common lower bound of NP and upper bounds of NP
and PSPACE respectively. Finally, we single out a special case of the general
problem that, in many cases, admits an efficient solution. In particular, we
prove that deciding the existence of an equilibrium in which each player either
wins or loses with probability 1 can be done in polynomial time for games where
the objective of each player is given by a parity condition with a bounded
number of priorities
A Structured Model of Video Reproduces Primary Visual Cortical Organisation
The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition
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