757 research outputs found
Abundance and American democracy: a test of dire predictions
The American political system was severely tested in the 1970s and it is not yet obvious that the system's response to those tests was adequate. Some scholars have argued that the confusion we witnessed in energy, environmental and economic policies was symptomatic of even worse situations to come. Their consensus is that our style of democratic politics is incapable of dealing with the problems we increasingly face. Consequently, they predict that democracy's days are numbered. Furthermore, many Americans sense that the "joy ride" may be over, and that our economy may be hard pressed to maintain standards, much less continue its historic growth. One poll showed a 34 percent increase, since 1977, in respondents who believe, "The United States is in deep and serious trouble," and a well known economist, employing the terminology of game theory, has suggested that ours has become a "Zero-Sum society.
Irredundant Triangular Decomposition
Triangular decomposition is a classic, widely used and well-developed way to
represent algebraic varieties with many applications. In particular, there
exist sharp degree bounds for a single triangular set in terms of intrinsic
data of the variety it represents, and powerful randomized algorithms for
computing triangular decompositions using Hensel lifting in the
zero-dimensional case and for irreducible varieties. However, in the general
case, most of the algorithms computing triangular decompositions produce
embedded components, which makes it impossible to directly apply the intrinsic
degree bounds. This, in turn, is an obstacle for efficiently applying Hensel
lifting due to the higher degrees of the output polynomials and the lower
probability of success. In this paper, we give an algorithm to compute an
irredundant triangular decomposition of an arbitrary algebraic set defined
by a set of polynomials in C[x_1, x_2, ..., x_n]. Using this irredundant
triangular decomposition, we were able to give intrinsic degree bounds for the
polynomials appearing in the triangular sets and apply Hensel lifting
techniques. Our decomposition algorithm is randomized, and we analyze the
probability of success
Permutable entire functions and multiply connected wandering domains
Let f and g be permutable transcendental entire functions. We use a recent analysis of the dynamical behaviour in multiply connected wandering domains to make progress on the long standing conjecture that the Julia sets of f and g are equal; in particular, we show that J(f)=J(g) provided that neither f nor g has a simply connected wandering domain in the fast escaping set
Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning
We study the problem of learning good heuristic functions for classical
planning tasks with neural networks based on samples that are states with their
cost-to-goal estimates. It is well known that the learned model quality depends
on the training data quality. Our main goal is to understand better the
influence of sample generation strategies on the performance of a greedy
best-first heuristic search guided by a learned heuristic function. In a set of
controlled experiments, we find that two main factors determine the quality of
the learned heuristic: the regions of the state space included in the samples
and the quality of the cost-to-goal estimates. Also, these two factors are
interdependent: having perfect estimates of cost-to-goal is insufficient if an
unrepresentative part of the state space is included in the sample set.
Additionally, we study the effects of restricting samples to only include
states that could be evaluated when solving a given task and the effects of
adding samples with high-value estimates. Based on our findings, we propose
practical strategies to improve the quality of learned heuristics: three
strategies that aim to generate more representative states and two strategies
that improve the cost-to-goal estimates. Our resulting neural network heuristic
has higher coverage than a basic satisficing heuristic. Also, compared to a
baseline learned heuristic, our best neural network heuristic almost doubles
the mean coverage and can increase it for some domains by more than six times.Comment: 27 page
Bose-Einstein Condensation in a CO_2-laser Optical Dipole Trap
We report on the achieving of Bose-Einstein condensation of a dilute atomic
gas based on trapping atoms in tightly confining CO_2-laser dipole potentials.
Quantum degeneracy of rubidium atoms is reached by direct evaporative cooling
in both crossed and single beam trapping geometries. At the heart of these
all-optical condensation experiments is the ability to obtain high initial
atomic densities in quasistatic dipole traps by laser cooling techniques.
Finally, we demonstrate the formation of a condensate in a field insensitive
m_F=0 spin projection only. This suppresses fluctuations of the chemical
potential from stray magnetic fields.Comment: 8 pages, 5 figure
Quartz Cherenkov Counters for Fast Timing: QUARTIC
We have developed particle detectors based on fused silica (quartz) Cherenkov
radiators read out with micro-channel plate photomultipliers (MCP-PMTs) or
silicon photomultipliers (SiPMs) for high precision timing (Sigma(t) about
10-15 ps). One application is to measure the times of small angle protons from
exclusive reactions, e.g. p + p - p + H + p, at the Large Hadron Collider, LHC.
They may also be used to measure directional particle fluxes close to external
or stored beams. The detectors have small areas (square cm), but need to be
active very close (a few mm) to the intense LHC beam, and so must be radiation
hard and nearly edgeless. We present results of tests of detectors with quartz
bars inclined at the Cherenkov angle, and with bars in the form of an "L" (with
a 90 degree corner). We also describe a possible design for a fast timing
hodoscope with elements of a few square mm.Comment: 24 pages, 14 figure
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