7,694 research outputs found
Weighted Spectral Embedding of Graphs
We present a novel spectral embedding of graphs that incorporates weights
assigned to the nodes, quantifying their relative importance. This spectral
embedding is based on the first eigenvectors of some properly normalized
version of the Laplacian. We prove that these eigenvectors correspond to the
configurations of lowest energy of an equivalent physical system, either
mechanical or electrical, in which the weight of each node can be interpreted
as its mass or its capacitance, respectively. Experiments on a real dataset
illustrate the impact of weighting on the embedding
Robust Multi-Cellular Developmental Design
This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange "chemicals" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics
The Effects of Transmission Uncertainty on the Flexibility-Credibility Tradeoff in Monetary Policy
In this paper we address the issue of how parameter uncertainty affects the optimal degree of central bank conservatism. The analysis is conducted in the standard macroeconomic model of a monetary policy game embedding an expectational Phillips-curve. Multiplicative "Brainard" uncertainty is added to the model. This means that the central bank's policy instrument has a stochastic impact on inflation. This type of uncertainty is particularly interesting, since it affects the credibilityâflexibility tradeoff in monetary policymaking. We show that if the flexibility problem dominates, an increase in uncertainty reduces optimal conservatism. However, increases in uncertainty can also require increases in the optimal degree of conservatism. This happens when the central bank has a sufficiently large credibility problem. This is particularly clear in the case of the introduction of uncertainty at the margin. Furthermore, the coefficient of variation of inflation appears to contain useful information about the relative size of the credibility problem and, hence, about how incipient uncertainty can affect optimal conservatism in actual economies.credibility; flexibility; monetary policy; conservatism; uncertainty
Streaming, Memory Limited Algorithms for Community Detection
In this paper, we consider sparse networks consisting of a finite number of
non-overlapping communities, i.e. disjoint clusters, so that there is higher
density within clusters than across clusters. Both the intra- and inter-cluster
edge densities vanish when the size of the graph grows large, making the
cluster reconstruction problem nosier and hence difficult to solve. We are
interested in scenarios where the network size is very large, so that the
adjacency matrix of the graph is hard to manipulate and store. The data stream
model in which columns of the adjacency matrix are revealed sequentially
constitutes a natural framework in this setting. For this model, we develop two
novel clustering algorithms that extract the clusters asymptotically
accurately. The first algorithm is {\it offline}, as it needs to store and keep
the assignments of nodes to clusters, and requires a memory that scales
linearly with the network size. The second algorithm is {\it online}, as it may
classify a node when the corresponding column is revealed and then discard this
information. This algorithm requires a memory growing sub-linearly with the
network size. To construct these efficient streaming memory-limited clustering
algorithms, we first address the problem of clustering with partial
information, where only a small proportion of the columns of the adjacency
matrix is observed and develop, for this setting, a new spectral algorithm
which is of independent interest.Comment: NIPS 201
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