538 research outputs found
From Lock Freedom to Progress Using Session Types
Inspired by Kobayashi's type system for lock freedom, we define a behavioral
type system for ensuring progress in a language of binary sessions. The key
idea is to annotate actions in session types with priorities representing the
urgency with which such actions must be performed and to verify that processes
perform such actions with the required priority. Compared to related systems
for session-based languages, the presented type system is relatively simpler
and establishes progress for a wider range of processes.Comment: In Proceedings PLACES 2013, arXiv:1312.221
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
Exact Results for Diffusion-Limited Reactions with Synchronous Dynamics
A new method is introduced allowing to solve exactly the reactions A+A->inert
and A+A->A on the 1D lattice with synchronous diffusional dynamics
(simultaneous hopping of all particles). Exact connections are found relating
densities and certain correlation properties of these two reactions at all
times. Asymptotic behavior at large times as well as scaling form describing
the regime of low initial density, are derived explicitly.Comment: 12 pages in plain Te
A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics
The analysis of the intraday dynamics of correlations among high-frequency
returns is challenging due to the presence of asynchronous trading and market
microstructure noise. Both effects may lead to significant data reduction and
may severely underestimate correlations if traditional methods for
low-frequency data are employed. We propose to model intraday log-prices
through a multivariate local-level model with score-driven covariance matrices
and to treat asynchronicity as a missing value problem. The main advantages of
this approach are: (i) all available data are used when filtering correlations,
(ii) market microstructure noise is taken into account, (iii) estimation is
performed through standard maximum likelihood methods. Our empirical analysis,
performed on 1-second NYSE data, shows that opening hours are dominated by
idiosyncratic risk and that a market factor progressively emerges in the second
part of the day. The method can be used as a nowcasting tool for high-frequency
data, allowing to study the real-time response of covariances to macro-news
announcements and to build intraday portfolios with very short optimization
horizons.Comment: 30 pages, 10 figures, 7 table
Multiuser detection in a dynamic environment Part I: User identification and data detection
In random-access communication systems, the number of active users varies
with time, and has considerable bearing on receiver's performance. Thus,
techniques aimed at identifying not only the information transmitted, but also
that number, play a central role in those systems. An example of application of
these techniques can be found in multiuser detection (MUD). In typical MUD
analyses, receivers are based on the assumption that the number of active users
is constant and known at the receiver, and coincides with the maximum number of
users entitled to access the system. This assumption is often overly
pessimistic, since many users might be inactive at any given time, and
detection under the assumption of a number of users larger than the real one
may impair performance.
The main goal of this paper is to introduce a general approach to the problem
of identifying active users and estimating their parameters and data in a
random-access system where users are continuously entering and leaving the
system. The tool whose use we advocate is Random-Set Theory: applying this, we
derive optimum receivers in an environment where the set of transmitters
comprises an unknown number of elements. In addition, we can derive
Bayesian-filter equations which describe the evolution with time of the a
posteriori probability density of the unknown user parameters, and use this
density to derive optimum detectors. In this paper we restrict ourselves to
interferer identification and data detection, while in a companion paper we
shall examine the more complex problem of estimating users' parameters.Comment: To be published on IEEE Transactions on Information Theor
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