4,750 research outputs found
Cosmological Relativity: A General-Relativistic Theory for the Accelerating Expanding Universe
Recent observations of distant supernovae imply, in defiance of expectations,
that the universe growth is accelerating, contrary to what has always been
assumed that the expansion is slowing down due to gravity. In this paper a
general-relativistic cosmological theory that gives a direct relationship
between distances and redshifts in an expanding universe is presented. The
theory is actually a generalization of Hubble's law taking gravity into account
by means of Einstein's theory of general relativity. The theory predicts that
the universe can have three phases of expansion, decelerating, constant and
accelerating, but it is shown that at present the first two cases are excluded,
although in the past it had experienced them. Our theory shows that the
universe now is definitely in the stage of accelerating expansion, confirming
the recent experimental results
Local Strategy Improvement for Parity Game Solving
The problem of solving a parity game is at the core of many problems in model
checking, satisfiability checking and program synthesis. Some of the best
algorithms for solving parity game are strategy improvement algorithms. These
are global in nature since they require the entire parity game to be present at
the beginning. This is a distinct disadvantage because in many applications one
only needs to know which winning region a particular node belongs to, and a
witnessing winning strategy may cover only a fractional part of the entire game
graph.
We present a local strategy improvement algorithm which explores the game
graph on-the-fly whilst performing the improvement steps. We also compare it
empirically with existing global strategy improvement algorithms and the
currently only other local algorithm for solving parity games. It turns out
that local strategy improvement can outperform these others by several orders
of magnitude
An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms
This paper presents a new exponential lower bound for the two most popular
deterministic variants of the strategy improvement algorithms for solving
parity, mean payoff, discounted payoff and simple stochastic games. The first
variant improves every node in each step maximizing the current valuation
locally, whereas the second variant computes the globally optimal improvement
in each step. We outline families of games on which both variants require
exponentially many strategy iterations
Multidimensional perfect fluid cosmology with stable compactified internal dimensions
Multidimensional cosmological models in the presence of a bare cosmological
constant and a perfect fluid are investigated under dimensional reduction to
4-dimensional effective models. Stable compactification of the internal spaces
is achieved for a special class of perfect fluids. The external space behaves
in accordance with the standard Friedmann model. Necessary restrictions on the
parameters of the models are found to ensure dynamical behavior of the external
(our) universe in agreement with observations.Comment: 11 pages, Latex2e, uses IOP packages, submitted to Class.Quant.Gra
Combinatorial simplex algorithms can solve mean payoff games
A combinatorial simplex algorithm is an instance of the simplex method in
which the pivoting depends on combinatorial data only. We show that any
algorithm of this kind admits a tropical analogue which can be used to solve
mean payoff games. Moreover, any combinatorial simplex algorithm with a
strongly polynomial complexity (the existence of such an algorithm is open)
would provide in this way a strongly polynomial algorithm solving mean payoff
games. Mean payoff games are known to be in NP and co-NP; whether they can be
solved in polynomial time is an open problem. Our algorithm relies on a
tropical implementation of the simplex method over a real closed field of Hahn
series. One of the key ingredients is a new scheme for symbolic perturbation
which allows us to lift an arbitrary mean payoff game instance into a
non-degenerate linear program over Hahn series.Comment: v1: 15 pages, 3 figures; v2: improved presentation, introduction
expanded, 18 pages, 3 figure
Stress variations near surfaces in diamond-like amorphous carbon
Using Monte Carlo simulations within the empirical potential approach, we
examine the effect produced by the surface environment on the atomic level
stresses in tetrahedral amorphous carbon. Both the distribution of stresses and
the distributions of sp^2 and sp^3 atoms as a function of depth from the
surface are highly inhomogeneous. They show the same close relationship between
local stress and bonding hybridization found previously in the bulk of the
material. Compressive local stress favors the formation of sp^3 sites, while
tensile stress favors the formation of sp^2 sites.Comment: 7pages, 4 figure
PCN210 G-Ba Assessments of Oncology Trials: Is Increased Overall Survival a “Must Have”?
Symmetric Strategy Improvement
Symmetry is inherent in the definition of most of the two-player zero-sum
games, including parity, mean-payoff, and discounted-payoff games. It is
therefore quite surprising that no symmetric analysis techniques for these
games exist. We develop a novel symmetric strategy improvement algorithm where,
in each iteration, the strategies of both players are improved simultaneously.
We show that symmetric strategy improvement defies Friedmann's traps, which
shook the belief in the potential of classic strategy improvement to be
polynomial
Report of Virtual Conference. BASICS Phase I - Achievements and Learnings Meeting. May 18-19, 2020
The Building an Economically Sustainable Integrated Cassava Seed System in Nigeria (BASICS) project began in 2016 and formally ends on 30 June 2020. The project has made progress in demonstrating that commercially viable production and sale of breeder, foundation and certified seed is possible. Furthermore, the project has established a strong basis for building a sustainable seed system by developing building blocks across the seed value chain. This meeting had the following objectives: 1. To identify the achievements and lessons learned in each of the project components; 2. To identify the shortcomings in each component (what would I do differently, knowing what I know now?), remaining challenges and ideas to overcome them; 3. To assess and discuss the challenges and progress made in integrating the components into an integrated seed system and identify ways integration can be improved; 4. To assess and discuss the commercial sustainability of the seed system and identify options to promote its sustainability and further scaling; and 5. To make plans for the publication of the findings and lessons learned during BASICS-
Professional or amateur? The phonological output buffer as a working memory operator
The Phonological Output Buffer (POB) is thought to be the stage in language production where phonemes are held in working memory and assembled into words. The neural implementation of the POB remains unclear despite a wealth of phenomenological data. Individuals with POB impairment make phonological errors when they produce words and non-words, including phoneme omissions, insertions, transpositions, substitutions and perseverations. Errors can apply to different kinds and sizes of units, such as phonemes, number words, morphological affixes, and function words, and evidence from POB impairments suggests that units tend to substituted with units of the same kind-e.g., numbers with numbers and whole morphological affixes with other affixes. This suggests that different units are processed and stored in the POB in the same stage, but perhaps separately in different mini-stores. Further, similar impairments can affect the buffer used to produce Sign Language, which raises the question of whether it is instantiated in a distinct device with the same design. However, what appear as separate buffers may be distinct regions in the activity space of a single extended POB network, connected with a lexicon network. The self-consistency of this idea can be assessed by studying an autoassociative Potts network, as a model of memory storage distributed over several cortical areas, and testing whether the network can represent both units of word and signs, reflecting the types and patterns of errors made by individuals with POB impairment
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