3,718 research outputs found

### Compressed Sensing over $\ell_p$-balls: Minimax Mean Square Error

We consider the compressed sensing problem, where the object x_0 \in \bR^N
is to be recovered from incomplete measurements $y = Ax_0 + z$; here the
sensing matrix $A$ is an $n \times N$ random matrix with iid Gaussian entries
and $n < N$. A popular method of sparsity-promoting reconstruction is
$\ell^1$-penalized least-squares reconstruction (aka LASSO, Basis Pursuit).
It is currently popular to consider the strict sparsity model, where the
object $x_0$ is nonzero in only a small fraction of entries. In this paper, we
instead consider the much more broadly applicable $\ell_p$-sparsity model,
where $x_0$ is sparse in the sense of having $\ell_p$ norm bounded by $\xi
\cdot N^{1/p}$ for some fixed $0 0$.
We study an asymptotic regime in which $n$ and $N$ both tend to infinity with
limiting ratio $n/N = \delta \in (0,1)$, both in the noisy ($z \neq 0$) and
noiseless ($z=0$) cases. Under weak assumptions on $x_0$, we are able to
precisely evaluate the worst-case asymptotic minimax mean-squared
reconstruction error (AMSE) for $\ell^1$ penalized least-squares: min over
penalization parameters, max over $\ell_p$-sparse objects $x_0$. We exhibit the
asymptotically least-favorable object (hardest sparse signal to recover) and
the maximin penalization.
Our explicit formulas unexpectedly involve quantities appearing classically
in statistical decision theory. Occurring in the present setting, they reflect
a deeper connection between penalized $\ell^1$ minimization and scalar soft
thresholding. This connection, which follows from earlier work of the authors
and collaborators on the AMP iterative thresholding algorithm, is carefully
explained.
Our approach also gives precise results under weak-$\ell_p$ ball coefficient
constraints, as we show here.Comment: 41 pages, 11 pdf figure

### Message Passing Algorithms for Compressed Sensing

Compressed sensing aims to undersample certain high-dimensional signals, yet
accurately reconstruct them by exploiting signal characteristics. Accurate
reconstruction is possible when the object to be recovered is sufficiently
sparse in a known basis. Currently, the best known sparsity-undersampling
tradeoff is achieved when reconstructing by convex optimization -- which is
expensive in important large-scale applications. Fast iterative thresholding
algorithms have been intensively studied as alternatives to convex optimization
for large-scale problems. Unfortunately known fast algorithms offer
substantially worse sparsity-undersampling tradeoffs than convex optimization.
We introduce a simple costless modification to iterative thresholding making
the sparsity-undersampling tradeoff of the new algorithms equivalent to that of
the corresponding convex optimization procedures. The new
iterative-thresholding algorithms are inspired by belief propagation in
graphical models. Our empirical measurements of the sparsity-undersampling
tradeoff for the new algorithms agree with theoretical calculations. We show
that a state evolution formalism correctly derives the true
sparsity-undersampling tradeoff. There is a surprising agreement between
earlier calculations based on random convex polytopes and this new, apparently
very different theoretical formalism.Comment: 6 pages paper + 9 pages supplementary information, 13 eps figure.
Submitted to Proc. Natl. Acad. Sci. US

### How do we remember the past in randomised strategies?

Graph games of infinite length are a natural model for open reactive
processes: one player represents the controller, trying to ensure a given
specification, and the other represents a hostile environment. The evolution of
the system depends on the decisions of both players, supplemented by chance.
In this work, we focus on the notion of randomised strategy. More
specifically, we show that three natural definitions may lead to very different
results: in the most general cases, an almost-surely winning situation may
become almost-surely losing if the player is only allowed to use a weaker
notion of strategy. In more reasonable settings, translations exist, but they
require infinite memory, even in simple cases. Finally, some traditional
problems becomes undecidable for the strongest type of strategies

### Threshold values of Random K-SAT from the cavity method

Using the cavity equations of
\cite{mezard:parisi:zecchina:02,mezard:zecchina:02}, we derive the various
threshold values for the number of clauses per variable of the random
$K$-satisfiability problem, generalizing the previous results to $K \ge 4$. We
also give an analytic solution of the equations, and some closed expressions
for these thresholds, in an expansion around large $K$. The stability of the
solution is also computed. For any $K$, the satisfiability threshold is found
to be in the stable region of the solution, which adds further credit to the
conjecture that this computation gives the exact satisfiability threshold.Comment: 38 pages; extended explanations and derivations; this version is
going to appear in Random Structures & Algorithm

### The Long-Baseline Neutrino Facility

The Deep Underground Neutrino Experiment (DUNE) collaboration is developing an international multi-kiloton Long-Baseline Neutrino experiment to be located about a mile underground at the Sanford Underground Research Facility (SURF), in Lead, SD, USA. In the current configuration four cryostats will contain a modular detector and a total of 68,400 ton of ultra pure liquid argon, with a level of impurities lower than 100 parts per trillion (ppt) of oxygen equivalent contamination. The Long-Baseline Neutrino Facility (LBNF) provides the conventional facilities and the cryogenic infrastructure (including the cryostats housing the detector) to support DUNE. This contribution presents the modes of operations, layout and main features of the LBNF cryogenic system.
The system is comprised of three sub-systems: External/Infrastructure (or LN2), Proximity (or LAr) and Internal cryogenics. The External/Infrastructure provides the infrastructure and equipment to store, produce and distribute the cryogenic fluids needed for the operation of the Proximity Cryogenics, which delivers them to the Internal at the pressure, temperature, mass flow rate, quality and purity required by the detector inside the cryostat. The External/Infrastructure cryogenics includes the LN2 refrigeration system and the surface facilities, with the receiving stations, the LN2 and LAr storage tanks and the vaporizers. The Proximity Cryogenics includes the LAr and GAr purification systems, the phase separators, the condensers, and the piping connecting the various parts. The Internal Cryogenics consists of all the cryogenic equipment located inside the cryostat, namely the GAr and LAr distribution systems and the systems to cool down the cryostats and the detectors. An international engineering team will design, manufacture, commission, and qualify the LBNF cryogenic system. The expected performance, the functional requirements and the status of the design are presented in this contribution

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