1,479 research outputs found
Range Queries on Uncertain Data
Given a set of uncertain points on the real line, each represented by
its one-dimensional probability density function, we consider the problem of
building data structures on to answer range queries of the following three
types for any query interval : (1) top- query: find the point in that
lies in with the highest probability, (2) top- query: given any integer
as part of the query, return the points in that lie in
with the highest probabilities, and (3) threshold query: given any threshold
as part of the query, return all points of that lie in with
probabilities at least . We present data structures for these range
queries with linear or nearly linear space and efficient query time.Comment: 26 pages. A preliminary version of this paper appeared in ISAAC 2014.
In this full version, we also present solutions to the most general case of
the problem (i.e., the histogram bounded case), which were left as open
problems in the preliminary versio
The Total s-Energy of a Multiagent System
We introduce the "total s-energy" of a multiagent system with time-dependent
links. This provides a new analytical lens on bidirectional agreement dynamics,
which we use to bound the convergence rates of dynamical systems for
synchronization, flocking, opinion dynamics, and social epistemology
Finding Pairwise Intersections Inside a Query Range
We study the following problem: preprocess a set O of objects into a data
structure that allows us to efficiently report all pairs of objects from O that
intersect inside an axis-aligned query range Q. We present data structures of
size and with query time
time, where k is the number of reported pairs, for two classes of objects in
the plane: axis-aligned rectangles and objects with small union complexity. For
the 3-dimensional case where the objects and the query range are axis-aligned
boxes in R^3, we present a data structures of size and query time . When the objects and
query are fat, we obtain query time using storage
Inertial Hegselmann-Krause Systems
We derive an energy bound for inertial Hegselmann-Krause (HK) systems, which
we define as a variant of the classic HK model in which the agents can change
their weights arbitrarily at each step. We use the bound to prove the
convergence of HK systems with closed-minded agents, which settles a conjecture
of long standing. This paper also introduces anchored HK systems and show their
equivalence to the symmetric heterogeneous model
Orthogonal Range Reporting and Rectangle Stabbing for Fat Rectangles
In this paper we study two geometric data structure problems in the special
case when input objects or queries are fat rectangles. We show that in this
case a significant improvement compared to the general case can be achieved.
We describe data structures that answer two- and three-dimensional orthogonal
range reporting queries in the case when the query range is a \emph{fat}
rectangle. Our two-dimensional data structure uses words and supports
queries in time, where is the number of points in the
data structure, is the size of the universe and is the number of points
in the query range. Our three-dimensional data structure needs
words of space and answers queries in time. We also consider the rectangle stabbing problem on a set of
three-dimensional fat rectangles. Our data structure uses space and
answers stabbing queries in time.Comment: extended version of a WADS'19 pape
A Sharp Bound on the -Energy and Its Applications to Averaging Systems
The {\em -energy} is a generating function of wide applicability in
network-based dynamics. We derive an (essentially) optimal bound of on the -energy of an -agent symmetric averaging system, for any
positive real , where~ is a lower bound on the nonzero weights.
This is done by introducing the new dynamics of {\em twist systems}. We show
how to use the new bound on the -energy to tighten the convergence rate of
systems in opinion dynamics, flocking, and synchronization
Toward a Theory of Markov Influence Systems and their Renormalization
Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social sciences. In "Markov influence systems" (MIS), the transition probabilities of the chains change as a function of the current state distribution. This work introduces a renormalization framework for analyzing the dynamics of MIS. It comes in two independent parts: first, we generalize the standard classification of Markov chain states to the dynamic case by showing how to "parse" graph sequences. We then use this framework to
carry out the bifurcation analysis of a few important MIS families.
In particular, we show that irreducible MIS are almost always
asymptotically periodic. We also give an example of "hyper-torpid" mixing, where a stationary distribution is reached in super-exponential time, a timescale that cannot be achieved by any Markov chain
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