37,643 research outputs found
Towards Optimal Moment Estimation in Streaming and Distributed Models
One of the oldest problems in the data stream model is to approximate the p-th moment ||X||_p^p = sum_{i=1}^n X_i^p of an underlying non-negative vector X in R^n, which is presented as a sequence of poly(n) updates to its coordinates. Of particular interest is when p in (0,2]. Although a tight space bound of Theta(epsilon^-2 log n) bits is known for this problem when both positive and negative updates are allowed, surprisingly there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is O(epsilon^-2 log n) bits, while the lower bound is only Omega(epsilon^-2 + log n) bits. Recently, an upper bound of O~(epsilon^-2 + log n) bits was obtained under the assumption that the updates arrive in a random order.
We show that for p in (0, 1], the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of O~(epsilon^-2 + log n) bits for estimating |X |_p^p. Our techniques also give new upper bounds for estimating the empirical entropy in a stream. On the other hand, we show that for p in (1,2], in the natural coordinator and blackboard distributed communication topologies, there is an O~(epsilon^-2) bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies G, obtaining an O~(epsilon^2 log d) max-communication upper bound, where d is the diameter of G. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an Omega(epsilon^-2 log n) bit lower bound for p in (1,2] for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter
Technical Report: Cooperative Multi-Target Localization With Noisy Sensors
This technical report is an extended version of the paper 'Cooperative
Multi-Target Localization With Noisy Sensors' accepted to the 2013 IEEE
International Conference on Robotics and Automation (ICRA).
This paper addresses the task of searching for an unknown number of static
targets within a known obstacle map using a team of mobile robots equipped with
noisy, limited field-of-view sensors. Such sensors may fail to detect a subset
of the visible targets or return false positive detections. These measurement
sets are used to localize the targets using the Probability Hypothesis Density,
or PHD, filter. Robots communicate with each other on a local peer-to-peer
basis and with a server or the cloud via access points, exchanging measurements
and poses to update their belief about the targets and plan future actions. The
server provides a mechanism to collect and synthesize information from all
robots and to share the global, albeit time-delayed, belief state to robots
near access points. We design a decentralized control scheme that exploits this
communication architecture and the PHD representation of the belief state.
Specifically, robots move to maximize mutual information between the target set
and measurements, both self-collected and those available by accessing the
server, balancing local exploration with sharing knowledge across the team.
Furthermore, robots coordinate their actions with other robots exploring the
same local region of the environment.Comment: Extended version of paper accepted to 2013 IEEE International
Conference on Robotics and Automation (ICRA
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
"Graph Entropy, Network Coding and Guessing games"
We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is identical to its guessing number and can be bounded from below with the number of vertices minus the size of the graphâs shortest index code. We show that the Network Coding solvability of each speciïŹc multiple unicast network is completely determined by the entropy (as well as by the shortest index code) of the directed graph that occur by identifying each source node with each corresponding target node. Shannonâs information inequalities can be used to calculate up- per bounds on a graphâs entropy as well as calculating the size of the minimal index code. Recently, a number of new families of so-called non-shannon-type information inequalities have been discovered. It has been shown that there exist communication networks with a ca- pacity strictly ess than required for solvability, but where this fact cannot be derived using Shannonâs classical information inequalities. Based on this result we show that there exist graphs with an entropy that cannot be calculated using only Shannonâs classical information inequalities, and show that better estimate can be obtained by use of certain non-shannon-type information inequalities
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