9,716 research outputs found
The Swap Matching Problem Revisited
In this paper, we revisit the much studied problem of Pattern Matching with
Swaps (Swap Matching problem, for short). We first present a graph-theoretic
model, which opens a new and so far unexplored avenue to solve the problem.
Then, using the model, we devise two efficient algorithms to solve the swap
matching problem. The resulting algorithms are adaptations of the classic
shift-and algorithm. For patterns having length similar to the word-size of the
target machine, both the algorithms run in linear time considering a fixed
alphabet.Comment: 23 pages, 3 Figures and 17 Table
TAPER: query-aware, partition-enhancement for large, heterogenous, graphs
Graph partitioning has long been seen as a viable approach to address Graph
DBMS scalability. A partitioning, however, may introduce extra query processing
latency unless it is sensitive to a specific query workload, and optimised to
minimise inter-partition traversals for that workload. Additionally, it should
also be possible to incrementally adjust the partitioning in reaction to
changes in the graph topology, the query workload, or both. Because of their
complexity, current partitioning algorithms fall short of one or both of these
requirements, as they are designed for offline use and as one-off operations.
The TAPER system aims to address both requirements, whilst leveraging existing
partitioning algorithms. TAPER takes any given initial partitioning as a
starting point, and iteratively adjusts it by swapping chosen vertices across
partitions, heuristically reducing the probability of inter-partition
traversals for a given pattern matching queries workload. Iterations are
inexpensive thanks to time and space optimisations in the underlying support
data structures. We evaluate TAPER on two different large test graphs and over
realistic query workloads. Our results indicate that, given a hash-based
partitioning, TAPER reduces the number of inter-partition traversals by around
80%; given an unweighted METIS partitioning, by around 30%. These reductions
are achieved within 8 iterations and with the additional advantage of being
workload-aware and usable online.Comment: 12 pages, 11 figures, unpublishe
Fine-Grained Complexity of k-OPT in Bounded-Degree Graphs for Solving TSP
The Traveling Salesman Problem asks to find a minimum-weight Hamiltonian cycle in an edge-weighted complete graph. Local search is a widely-employed strategy for finding good solutions to TSP. A popular neighborhood operator for local search is k-opt, which turns a Hamiltonian cycle C into a new Hamiltonian cycle C\u27 by replacing k edges. We analyze the problem of determining whether the weight of a given cycle can be decreased by a k-opt move. Earlier work has shown that (i) assuming the Exponential Time Hypothesis, there is no algorithm that can detect whether or not a given Hamiltonian cycle C in an n-vertex input can be improved by a k-opt move in time f(k) n^o(k / log k) for any function f, while (ii) it is possible to improve on the brute-force running time of O(n^k) and save linear factors in the exponent. Modern TSP heuristics are very successful at identifying the most promising edges to be used in k-opt moves, and experiments show that very good global solutions can already be reached using only the top-O(1) most promising edges incident to each vertex. This leads to the following question: can improving k-opt moves be found efficiently in graphs of bounded degree? We answer this question in various regimes, presenting new algorithms and conditional lower bounds. We show that the aforementioned ETH lower bound also holds for graphs of maximum degree three, but that in bounded-degree graphs the best improving k-move can be found in time O(n^((23/135+epsilon_k)k)), where lim_{k -> infty} epsilon_k = 0. This improves upon the best-known bounds for general graphs. Due to its practical importance, we devote special attention to the range of k in which improving k-moves in bounded-degree graphs can be found in quasi-linear time. For k <= 7, we give quasi-linear time algorithms for general weights. For k=8 we obtain a quasi-linear time algorithm when the weights are bounded by O(polylog n). On the other hand, based on established fine-grained complexity hypotheses about the impossibility of detecting a triangle in edge-linear time, we prove that the k = 9 case does not admit quasi-linear time algorithms. Hence we fully characterize the values of k for which quasi-linear time algorithms exist for polylogarithmic weights on bounded-degree graphs
Hedging and invoicing strategies to reduce exchange rate exposure - a euro-area perspective
Domestic-currency invoicing and hedging allow internationally active firms to reduce their exposure to exchange rate variations. This paper argues that domestic-currency invoicing and hedging with exchange rate derivatives allow a fairly straightforward management of transaction and translation risk. Broader economic risk (which takes into account the impact of the exchange rate on competitiveness) is by its very nature harder to manage, but the paper argues that natural hedging provides possibilities for doing so. A novelty of this paper is a survey of actual hedging strategies and techniques of large euro-area corporations. The paper finds that euro-area exporters make ample use of instruments to limit the adverse impact of euro appreciation.Exchange rate risk, invoicing, hedging, derivatives, Hedging and invoicing strategies to reduce exchange rate exposure - a euro-area perspective, Economic Paper, D�hring
Custom, Contract, and Kidney Exchange
In this Essay, we examine a case in which the organizational and logistical demands of a novel form of organ exchange (the nonsimultaneous, extended, altruistic donor (NEAD) chain) do not map cleanly onto standard cultural schemas for either market or gift exchange, resulting in sociological ambiguity and legal uncertainty. In some ways, a NEAD chain resembles a form of generalized exchange, an ancient and widespread instance of the norm of reciprocity that can be thought of simply as the obligation to pay it forward rather than the obligation to reciprocate directly with the original giver. At the same time, a NEAD chain resembles a string of promises and commitments to deliver something in exchange for some valuable consideration—that is, a series of contracts.
Neither of these salient social imaginaries of exchange—gift giving or formal contract—perfectly meets the practical demands of the NEAD system. As a result, neither contract nor generalized exchange drives the practice of NEAD chains. Rather, the majority of actual exchanges still resemble a simpler form of exchange: direct, simultaneous exchange between parties with no time delay or opportunity to back out. If NEAD chains are to reach their full promise for large-scale, nonsimultaneous organ transfer, legal uncertainties and sociological ambiguities must be finessed, both in the practices of the coordinating agencies and in the minds of NEAD-chain participants. This might happen either through the further elaboration of gift-like language and practices, or through a creative use of the cultural form and motivational vocabulary, but not necessarily the legal and institutional machinery, of contract
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
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