9,298 research outputs found
SAT Modulo Monotonic Theories
We define the concept of a monotonic theory and show how to build efficient
SMT (SAT Modulo Theory) solvers, including effective theory propagation and
clause learning, for such theories. We present examples showing that monotonic
theories arise from many common problems, e.g., graph properties such as
reachability, shortest paths, connected components, minimum spanning tree, and
max-flow/min-cut, and then demonstrate our framework by building SMT solvers
for each of these theories. We apply these solvers to procedural content
generation problems, demonstrating major speed-ups over state-of-the-art
approaches based on SAT or Answer Set Programming, and easily solving several
instances that were previously impractical to solve
Interactive inspection of complex multi-object industrial assemblies
The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft
Generalized sequential tree-reweighted message passing
This paper addresses the problem of approximate MAP-MRF inference in general
graphical models. Following [36], we consider a family of linear programming
relaxations of the problem where each relaxation is specified by a set of
nested pairs of factors for which the marginalization constraint needs to be
enforced. We develop a generalization of the TRW-S algorithm [9] for this
problem, where we use a decomposition into junction chains, monotonic w.r.t.
some ordering on the nodes. This generalizes the monotonic chains in [9] in a
natural way. We also show how to deal with nested factors in an efficient way.
Experiments show an improvement over min-sum diffusion, MPLP and subgradient
ascent algorithms on a number of computer vision and natural language
processing problems
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
position of a record within a sorted array, a Hash-Index as a model to map a
key to a position of a record within an unsorted array, and a BitMap-Index as a
model to indicate if a data record exists or not. In this exploratory research
paper, we start from this premise and posit that all existing index structures
can be replaced with other types of models, including deep-learning models,
which we term learned indexes. The key idea is that a model can learn the sort
order or structure of lookup keys and use this signal to effectively predict
the position or existence of records. We theoretically analyze under which
conditions learned indexes outperform traditional index structures and describe
the main challenges in designing learned index structures. Our initial results
show, that by using neural nets we are able to outperform cache-optimized
B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
several real-world data sets. More importantly though, we believe that the idea
of replacing core components of a data management system through learned models
has far reaching implications for future systems designs and that this work
just provides a glimpse of what might be possible
Submodular Inference of Diffusion Networks from Multiple Trees
Diffusion and propagation of information, influence and diseases take place
over increasingly larger networks. We observe when a node copies information,
makes a decision or becomes infected but networks are often hidden or
unobserved. Since networks are highly dynamic, changing and growing rapidly, we
only observe a relatively small set of cascades before a network changes
significantly. Scalable network inference based on a small cascade set is then
necessary for understanding the rapidly evolving dynamics that govern
diffusion. In this article, we develop a scalable approximation algorithm with
provable near-optimal performance based on submodular maximization which
achieves a high accuracy in such scenario, solving an open problem first
introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real
diffusion data show that our algorithm in practice achieves an optimal
trade-off between accuracy and running time.Comment: To appear in the 29th International Conference on Machine Learning
(ICML), 2012. Website:
http://www.stanford.edu/~manuelgr/network-inference-multitree
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