7,775 research outputs found
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018
Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources
The growing deployment of sensors as part of Internet of Things (IoT) is
generating thousands of event streams. Complex Event Processing (CEP) queries
offer a useful paradigm for rapid decision-making over such data sources. While
often centralized in the Cloud, the deployment of capable edge devices on the
field motivates the need for cooperative event analytics that span Edge and
Cloud computing. Here, we identify a novel problem of query placement on edge
and Cloud resources for dynamically arriving and departing analytic dataflows.
We define this as an optimization problem to minimize the total makespan for
all event analytics, while meeting energy and compute constraints of the
resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for
such dynamic dataflows, and validate them using detailed simulations for 100 -
1000 edge devices and VMs. The results show that our heuristics offer
O(seconds) planning time, give a valid and high quality solution in all cases,
and reduce the number of query migrations. Furthermore, rebalance strategies
when applied in these heuristics have significantly reduced the makespan by
around 20 - 25%.Comment: 11 pages, 7 figure
MIHash: Online Hashing with Mutual Information
Learning-based hashing methods are widely used for nearest neighbor
retrieval, and recently, online hashing methods have demonstrated good
performance-complexity trade-offs by learning hash functions from streaming
data. In this paper, we first address a key challenge for online hashing: the
binary codes for indexed data must be recomputed to keep pace with updates to
the hash functions. We propose an efficient quality measure for hash functions,
based on an information-theoretic quantity, mutual information, and use it
successfully as a criterion to eliminate unnecessary hash table updates. Next,
we also show how to optimize the mutual information objective using stochastic
gradient descent. We thus develop a novel hashing method, MIHash, that can be
used in both online and batch settings. Experiments on image retrieval
benchmarks (including a 2.5M image dataset) confirm the effectiveness of our
formulation, both in reducing hash table recomputations and in learning
high-quality hash functions.Comment: International Conference on Computer Vision (ICCV), 201
Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming
Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
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