114,507 research outputs found
Gossip in a Smartphone Peer-to-Peer Network
In this paper, we study the fundamental problem of gossip in the mobile
telephone model: a recently introduced variation of the classical telephone
model modified to better describe the local peer-to-peer communication services
implemented in many popular smartphone operating systems. In more detail, the
mobile telephone model differs from the classical telephone model in three
ways: (1) each device can participate in at most one connection per round; (2)
the network topology can undergo a parameterized rate of change; and (3)
devices can advertise a parameterized number of bits about their state to their
neighbors in each round before connection attempts are initiated. We begin by
describing and analyzing new randomized gossip algorithms in this model under
the harsh assumption of a network topology that can change completely in every
round. We prove a significant time complexity gap between the case where nodes
can advertise bits to their neighbors in each round, and the case where
nodes can advertise bit. For the latter assumption, we present two
solutions: the first depends on a shared randomness source, while the second
eliminates this assumption using a pseudorandomness generator we prove to exist
with a novel generalization of a classical result from the study of two-party
communication complexity. We then turn our attention to the easier case where
the topology graph is stable, and describe and analyze a new gossip algorithm
that provides a substantial performance improvement for many parameters. We
conclude by studying a relaxed version of gossip in which it is only necessary
for nodes to each learn a specified fraction of the messages in the system.Comment: Extended Abstract to Appear in the Proceedings of the ACM Conference
on the Principles of Distributed Computing (PODC 2017
Invariant Models for Causal Transfer Learning
Methods of transfer learning try to combine knowledge from several related
tasks (or domains) to improve performance on a test task. Inspired by causal
methodology, we relax the usual covariate shift assumption and assume that it
holds true for a subset of predictor variables: the conditional distribution of
the target variable given this subset of predictors is invariant over all
tasks. We show how this assumption can be motivated from ideas in the field of
causality. We focus on the problem of Domain Generalization, in which no
examples from the test task are observed. We prove that in an adversarial
setting using this subset for prediction is optimal in Domain Generalization;
we further provide examples, in which the tasks are sufficiently diverse and
the estimator therefore outperforms pooling the data, even on average. If
examples from the test task are available, we also provide a method to transfer
knowledge from the training tasks and exploit all available features for
prediction. However, we provide no guarantees for this method. We introduce a
practical method which allows for automatic inference of the above subset and
provide corresponding code. We present results on synthetic data sets and a
gene deletion data set
Scalable Greedy Algorithms for Transfer Learning
In this paper we consider the binary transfer learning problem, focusing on
how to select and combine sources from a large pool to yield a good performance
on a target task. Constraining our scenario to real world, we do not assume the
direct access to the source data, but rather we employ the source hypotheses
trained from them. We propose an efficient algorithm that selects relevant
source hypotheses and feature dimensions simultaneously, building on the
literature on the best subset selection problem. Our algorithm achieves
state-of-the-art results on three computer vision datasets, substantially
outperforming both transfer learning and popular feature selection baselines in
a small-sample setting. We also present a randomized variant that achieves the
same results with the computational cost independent from the number of source
hypotheses and feature dimensions. Also, we theoretically prove that, under
reasonable assumptions on the source hypotheses, our algorithm can learn
effectively from few examples
Heuristics with Performance Guarantees for the Minimum Number of Matches Problem in Heat Recovery Network Design
Heat exchanger network synthesis exploits excess heat by integrating process
hot and cold streams and improves energy efficiency by reducing utility usage.
Determining provably good solutions to the minimum number of matches is a
bottleneck of designing a heat recovery network using the sequential method.
This subproblem is an NP-hard mixed-integer linear program exhibiting
combinatorial explosion in the possible hot and cold stream configurations. We
explore this challenging optimization problem from a graph theoretic
perspective and correlate it with other special optimization problems such as
cost flow network and packing problems. In the case of a single temperature
interval, we develop a new optimization formulation without problematic big-M
parameters. We develop heuristic methods with performance guarantees using
three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy
packing. Numerical results from a collection of 51 instances substantiate the
strength of the methods
Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network
Multinational corporations use highly complex structures of parents and
subsidiaries to organize their operations and ownership. Offshore Financial
Centers (OFCs) facilitate these structures through low taxation and lenient
regulation, but are increasingly under scrutiny, for instance for enabling tax
avoidance. Therefore, the identification of OFC jurisdictions has become a
politicized and contested issue. We introduce a novel data-driven approach for
identifying OFCs based on the global corporate ownership network, in which over
98 million firms (nodes) are connected through 71 million ownership relations.
This granular firm-level network data uniquely allows identifying both
sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while
conduit-OFCs are attractive intermediate destinations in the routing of
international investments and enable the transfer of capital without taxation.
We identify 24 sink-OFCs. In addition, a small set of five countries -- the
Netherlands, the United Kingdom, Ireland, Singapore and Switzerland -- canalize
the majority of corporate offshore investment as conduit-OFCs. Each conduit
jurisdiction is specialized in a geographical area and there is significant
specialization based on industrial sectors. Against the idea of OFCs as exotic
small islands that cannot be regulated, we show that many sink and conduit-OFCs
are highly developed countries
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