5,133 research outputs found
Chaos or Noise - Difficulties of a Distinction
In experiments, the dynamical behavior of systems is reflected in time
series. Due to the finiteness of the observational data set it is not possible
to reconstruct the invariant measure up to arbitrary fine resolution and
arbitrary high embedding dimension. These restrictions limit our ability to
distinguish between signals generated by different systems, such as regular,
chaotic or stochastic ones, when analyzed from a time series point of view. We
propose to classify the signal behavior, without referring to any specific
model, as stochastic or deterministic on a certain scale of the resolution
, according to the dependence of the -entropy,
, and of the finite size Lyapunov exponent,
, on .Comment: 24 pages RevTeX, 9 eps figures included, two references added, minor
corrections, one section has been split in two (submitted to PRE
A multiresolution Discrete Element Method for triangulated objects with implicit time stepping
Simulations of many rigid bodies colliding with each other sometimes yield particularly interesting results if the colliding objects differ significantly in size and are nonspherical. The most expensive part within such a simulation code is the collision detection. We propose a family of novel multiscale collision detection algorithms that can be applied to triangulated objects within explicit and implicit time stepping methods. They are well suited to handle objects that cannot be represented by analytical shapes or assemblies of analytical objects. Inspired by multigrid methods and adaptive mesh refinement, we determine collision points iteratively over a resolution hierarchy and combine a functional minimization plus penalty parameters with the actual comparision-based geometric distance calculation. Coarse surrogate geometry representations identify “no collision” scenarios early on and otherwise yield an educated guess which triangle subsets of the next finer level might yield collisions. They prune the search tree and furthermore feed conservative contact force estimates into the iterative solve behind an implicit time stepping. Implicit time stepping and nonanalytical shapes often yield prohibitive high compute cost for rigid body simulations. Our approach reduces the object-object comparison cost algorithmically by one to two orders of magnitude. It also exhibits high vectorization efficiency due to its iterative nature
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas
We investigate the approximability of several classes of real-valued
functions by functions of a small number of variables ({\em juntas}). Our main
results are tight bounds on the number of variables required to approximate a
function within -error over
the uniform distribution: 1. If is submodular, then it is -close
to a function of variables.
This is an exponential improvement over previously known results. We note that
variables are necessary even for linear
functions. 2. If is fractionally subadditive (XOS) it is -close
to a function of variables. This result holds for all
functions with low total -influence and is a real-valued analogue of
Friedgut's theorem for boolean functions. We show that
variables are necessary even for XOS functions.
As applications of these results, we provide learning algorithms over the
uniform distribution. For XOS functions, we give a PAC learning algorithm that
runs in time . For submodular functions we give
an algorithm in the more demanding PMAC learning model (Balcan and Harvey,
2011) which requires a multiplicative factor approximation with
probability at least over the target distribution. Our uniform
distribution algorithm runs in time .
This is the first algorithm in the PMAC model that over the uniform
distribution can achieve a constant approximation factor arbitrarily close to 1
for all submodular functions. As follows from the lower bounds in (Feldman et
al., 2013) both of these algorithms are close to optimal. We also give
applications for proper learning, testing and agnostic learning with value
queries of these classes.Comment: Extended abstract appears in proceedings of FOCS 201
Interactive Channel Capacity Revisited
We provide the first capacity approaching coding schemes that robustly
simulate any interactive protocol over an adversarial channel that corrupts any
fraction of the transmitted symbols. Our coding schemes achieve a
communication rate of over any
adversarial channel. This can be improved to for
random, oblivious, and computationally bounded channels, or if parties have
shared randomness unknown to the channel.
Surprisingly, these rates exceed the interactive channel capacity bound
which [Kol and Raz; STOC'13] recently proved for random errors. We conjecture
and to be the optimal rates for their respective settings
and therefore to capture the interactive channel capacity for random and
adversarial errors.
In addition to being very communication efficient, our randomized coding
schemes have multiple other advantages. They are computationally efficient,
extremely natural, and significantly simpler than prior (non-capacity
approaching) schemes. In particular, our protocols do not employ any coding but
allow the original protocol to be performed as-is, interspersed only by short
exchanges of hash values. When hash values do not match, the parties backtrack.
Our approach is, as we feel, by far the simplest and most natural explanation
for why and how robust interactive communication in a noisy environment is
possible
Learning-aided Stochastic Network Optimization with Imperfect State Prediction
We investigate the problem of stochastic network optimization in the presence
of imperfect state prediction and non-stationarity. Based on a novel
distribution-accuracy curve prediction model, we develop the predictive
learning-aided control (PLC) algorithm, which jointly utilizes historic and
predicted network state information for decision making. PLC is an online
algorithm that requires zero a-prior system statistical information, and
consists of three key components, namely sequential distribution estimation and
change detection, dual learning, and online queue-based control.
Specifically, we show that PLC simultaneously achieves good long-term
performance, short-term queue size reduction, accurate change detection, and
fast algorithm convergence. In particular, for stationary networks, PLC
achieves a near-optimal , utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an
utility-backlog tradeoff for distributions that last
time, where
is the prediction accuracy and is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an length
for the same utility performance with a larger backlog). Moreover, PLC detects
distribution change slots faster with high probability ( is the
prediction size) and achieves an convergence time. Our results demonstrate
that state prediction (even imperfect) can help (i) achieve faster detection
and convergence, and (ii) obtain better utility-delay tradeoffs
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