50,553 research outputs found
Predictable migration and communication in the Quest-V multikernal
Quest-V is a system we have been developing from the ground up, with objectives focusing on safety, predictability and efficiency. It is designed to work on emerging multicore processors with hardware virtualization support. Quest-V is implemented as a ``distributed system on a chip'' and comprises multiple sandbox kernels. Sandbox kernels are isolated from one another in separate regions of physical memory, having access to a subset of processing cores and I/O devices. This partitioning prevents system failures in one sandbox affecting the operation of other sandboxes. Shared memory channels managed by system monitors enable inter-sandbox communication.
The distributed nature of Quest-V means each sandbox has a separate physical clock, with all event timings being managed by per-core local timers. Each sandbox is responsible for its own scheduling and I/O management, without requiring intervention of a hypervisor. In this paper, we formulate bounds on inter-sandbox communication in the absence of a global scheduler or global system clock. We also describe how address space migration between sandboxes can be guaranteed without violating service constraints. Experimental results on a working system show the conditions under which Quest-V performs real-time communication and migration.National Science Foundation (1117025
Recipe for inferring sub-surface solar magnetism via local mode-coupling using Slepian basis functions
Direct seismic imaging of sub-surface flow, sound-speed and magnetic field is
crucial for predicting flux tube emergence on the solar surface, an important
ingredient for space weather. The sensitivity of helioseismic mode-amplitude
cross-correlation to - and -mode oscillations enable formal inversion of
such sub-photospheric perturbations. It is well-known that such problems are
written in the form of an integral equation that connects the perturbations to
the observations via ``sensitivity kernels". While the sensitivity kernels for
flow and sound-speed have been known for decades and have been used
extensively, formulating kernels for general magnetic perturbations had been
elusive. A recent study proposed sensitivity kernels for Lorentz-stresses
corresponding to global magnetic fields of general geometry. The present study
is devoted to proposing kernels for inferring Lorentz-stresses as well as the
solenoidal magnetic field in a local patch on the Sun via Cartesian
mode-coupling. Moreover, for the first time in solar physics, Slepian functions
are employed to parameterize perturbations in the horizontal dimension. This is
shown to increase the number of data constraints in the inverse problem,
implying an increase in the precision of inferred parameters. This paves the
path to reliably imaging sub-surface solar magnetic features in, e.g.,
supergranules, sunspots and (emerging) active regions.Comment: 18 pages, 5 figures; Accepted for publication in the Astrophysical
Journa
Limits of Preprocessing
We present a first theoretical analysis of the power of polynomial-time
preprocessing for important combinatorial problems from various areas in AI. We
consider problems from Constraint Satisfaction, Global Constraints,
Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a
complexity theoretic assumption, none of the considered problems can be reduced
by polynomial-time preprocessing to a problem kernel whose size is polynomial
in a structural problem parameter of the input, such as induced width or
backdoor size. Our results provide a firm theoretical boundary for the
performance of polynomial-time preprocessing algorithms for the considered
problems.Comment: This is a slightly longer version of a paper that appeared in the
proceedings of AAAI 201
Nested Markov Properties for Acyclic Directed Mixed Graphs
Directed acyclic graph (DAG) models may be characterized in at least four
different ways: via a factorization, the d-separation criterion, the
moralization criterion, and the local Markov property. As pointed out by Robins
(1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of
DAG models also imply equality constraints that are not conditional
independences. The well-known `Verma constraint' is an example. Constraints of
this type were used for testing edges (Shpitser et al., 2009), and an efficient
marginalization scheme via variable elimination (Shpitser et al., 2011).
We show that equality constraints like the `Verma constraint' can be viewed
as conditional independences in kernel objects obtained from joint
distributions via a fixing operation that generalizes conditioning and
marginalization. We use these constraints to define, via Markov properties and
a factorization, a graphical model associated with acyclic directed mixed
graphs (ADMGs). We show that marginal distributions of DAG models lie in this
model, prove that a characterization of these constraints given in (Tian and
Pearl, 2002b) gives an alternative definition of the model, and finally show
that the fixing operation we used to define the model can be used to give a
particularly simple characterization of identifiable causal effects in hidden
variable graphical causal models.Comment: 67 pages (not including appendix and references), 8 figure
An oil painters recognition method based on cluster multiple kernel learning algorithm
A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly
Using seismic inversions to obtain an internal mixing processes indicator for main-sequence solar-like stars
Determining accurate and precise stellar ages is a major problem in
astrophysics. These determinations are either obtained through empirical
relations or model-dependent approaches. Currently, seismic modelling is one of
the best ways of providing accurate ages. However, current methods are affected
by simplifying assumptions concerning mixing processes. In this context,
providing new structural indicators which are less model-dependent and more
sensitive to such processes is crucial. We build a new indicator for core
conditions on the main sequence, which should be more sensitive to structural
differences and applicable to older stars than the indicator t presented in a
previous paper. We also wish to analyse the importance of the number and type
of modes for the inversion, as well as the impact of various constraints and
levels of accuracy in the forward modelling process that is used to obtain
reference models for the inversion. First, we present a method to obtain new
structural kernels and use them to build an indicator of central conditions in
stars and test it for various effects including atomic diffusion, various
initial helium abundances and metallicities, following the seismic inversion
method presented in our previous paper. We then study its accuracy for 7
different pulsation spectra including those of 16CygA and 16CygB and analyse
its dependence on the reference model by using different constraints and levels
of accuracy for its selection We observe that the inversion of the new
indicator using the SOLA method provides a good diagnostic for additional
mixing processes in central regions of stars. Its sensitivity allows us to test
for diffusive processes and chemical composition mismatch. We also observe that
octupole modes can improve the accuracy of the results, as well as modes of low
radial order.Comment: Accepted for publication in Astronomy and Astrophysic
Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning
We present a first theoretical analysis of the power of polynomial-time
preprocessing for important combinatorial problems from various areas in AI. We
consider problems from Constraint Satisfaction, Global Constraints,
Satisfiability, Nonmonotonic and Bayesian Reasoning under structural
restrictions. All these problems involve two tasks: (i) identifying the
structure in the input as required by the restriction, and (ii) using the
identified structure to solve the reasoning task efficiently. We show that for
most of the considered problems, task (i) admits a polynomial-time
preprocessing to a problem kernel whose size is polynomial in a structural
problem parameter of the input, in contrast to task (ii) which does not admit
such a reduction to a problem kernel of polynomial size, subject to a
complexity theoretic assumption. As a notable exception we show that the
consistency problem for the AtMost-NValue constraint admits a polynomial kernel
consisting of a quadratic number of variables and domain values. Our results
provide a firm worst-case guarantees and theoretical boundaries for the
performance of polynomial-time preprocessing algorithms for the considered
problems.Comment: arXiv admin note: substantial text overlap with arXiv:1104.2541,
arXiv:1104.556
Probability density estimation with tunable kernels using orthogonal forward regression
A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately
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