768 research outputs found
A New FronTIR in Targeted Protein Degradation and Plant Development
Three papers, two in a recent issue of Nature and one in the July issue of Developmental Cell, identify a family of F box proteins as the long-sought receptors for the plant growth hormone auxin. The new studies reveal that auxin, a small molecule, regulates F box proteins, which are involved in ubiquitin-mediated protein degradation. This finding has profound implications for understanding plant physiology and development and for defining new modes of regulation of SCF ubiquitin ligase complexes
Quantum Separability and Entanglement Detection via Entanglement-Witness Search and Global Optimization
We focus on determining the separability of an unknown bipartite quantum
state by invoking a sufficiently large subset of all possible
entanglement witnesses given the expected value of each element of a set of
mutually orthogonal observables. We review the concept of an entanglement
witness from the geometrical point of view and use this geometry to show that
the set of separable states is not a polytope and to characterize the class of
entanglement witnesses (observables) that detect entangled states on opposite
sides of the set of separable states. All this serves to motivate a classical
algorithm which, given the expected values of a subset of an orthogonal basis
of observables of an otherwise unknown quantum state, searches for an
entanglement witness in the span of the subset of observables. The idea of such
an algorithm, which is an efficient reduction of the quantum separability
problem to a global optimization problem, was introduced in PRA 70 060303(R),
where it was shown to be an improvement on the naive approach for the quantum
separability problem (exhaustive search for a decomposition of the given state
into a convex combination of separable states). The last section of the paper
discusses in more generality such algorithms, which, in our case, assume a
subroutine that computes the global maximum of a real function of several
variables. Despite this, we anticipate that such algorithms will perform
sufficiently well on small instances that they will render a feasible test for
separability in some cases of interest (e.g. in 3-by-3 dimensional systems)
Column generation for airline problems
Issued as Progress report, and Final project report, Project no. E-24-66
Submodular Maximization Meets Streaming: Matchings, Matroids, and More
We study the problem of finding a maximum matching in a graph given by an
input stream listing its edges in some arbitrary order, where the quantity to
be maximized is given by a monotone submodular function on subsets of edges.
This problem, which we call maximum submodular-function matching (MSM), is a
natural generalization of maximum weight matching (MWM), which is in turn a
generalization of maximum cardinality matching (MCM). We give two incomparable
algorithms for this problem with space usage falling in the semi-streaming
range---they store only edges, using working memory---that
achieve approximation ratios of in a single pass and in
passes respectively. The operations of these algorithms
mimic those of Zelke's and McGregor's respective algorithms for MWM; the
novelty lies in the analysis for the MSM setting. In fact we identify a general
framework for MWM algorithms that allows this kind of adaptation to the broader
setting of MSM.
In the sequel, we give generalizations of these results where the
maximization is over "independent sets" in a very general sense. This
generalization captures hypermatchings in hypergraphs as well as independence
in the intersection of multiple matroids.Comment: 18 page
Mixed state discrimination using optimal control
We present theory and experiment for the task of discriminating two
nonorthogonal states, given multiple copies. We implement several local
measurement schemes, on both pure states and states mixed by depolarizing
noise. We find that schemes which are optimal (or have optimal scaling) without
noise perform worse with noise than simply repeating the optimal single-copy
measurement. Applying optimal control theory, we derive the globally optimal
local measurement strategy, which outperforms all other local schemes, and
experimentally implement it for various levels of noise.Comment: Corrected ref 1 date; 4 pages & 4 figures + 2 pages & 3 figures
supplementary materia
Complexity of Strong Implementability
We consider the question of implementability of a social choice function in a
classical setting where the preferences of finitely many selfish individuals
with private information have to be aggregated towards a social choice. This is
one of the central questions in mechanism design. If the concept of weak
implementation is considered, the Revelation Principle states that one can
restrict attention to truthful implementations and direct revelation
mechanisms, which implies that implementability of a social choice function is
easy to check. For the concept of strong implementation, however, the
Revelation Principle becomes invalid, and the complexity of deciding whether a
given social choice function is strongly implementable has been open so far. In
this paper, we show by using methods from polyhedral theory that strong
implementability of a social choice function can be decided in polynomial space
and that each of the payments needed for strong implementation can always be
chosen to be of polynomial encoding length. Moreover, we show that strong
implementability of a social choice function involving only a single selfish
individual can be decided in polynomial time via linear programming
An oil pipeline design problem
Copyright @ 2003 INFORMSWe consider a given set of offshore platforms and onshore wells producing known (or estimated) amounts of oil to be connected to a port. Connections may take place directly between platforms, well sites, and the port, or may go through connection points at given locations. The configuration of the network and sizes of pipes used must be chosen to minimize construction costs. This problem is expressed as a mixed-integer program, and solved both heuristically by Tabu Search and Variable Neighborhood Search methods and exactly by a branch-and-bound method. Two new types of valid inequalities are introduced. Tests are made with data from the South Gabon oil field and randomly generated problems.The work of the first author was supported by NSERC grant #OGP205041. The work of the second author was supported by FCAR (Fonds pour la Formation des Chercheurs et l’Aide à la Recherche) grant #95-ER-1048, and NSERC grant #GP0105574
Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach
Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution
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