10,178 research outputs found
T-Cell activation: a queuing theory analysis at low agonist density
We analyze a simple linear triggering model of the T-cell receptor (TCR) within the framework of queuing theory, in which TCRs enter the queue upon full activation and exit by downregulation. We fit our model to four experimentally characterized threshold activation criteria and analyze their specificity and sensitivity: the initial calcium spike, cytotoxicity, immunological synapse formation, and cytokine secretion. Specificity characteristics improve as the time window for detection increases, saturating for time periods on the timescale of downregulation; thus, the calcium spike (30 s) has low specificity but a sensitivity to single-peptide MHC ligands, while the cytokine threshold (1 h) can distinguish ligands with a 30% variation in the complex lifetime. However, a robustness analysis shows that these properties are degraded when the queue parameters are subject to variation—for example, under stochasticity in the ligand number in the cell-cell interface and population variation in the cellular threshold. A time integration of the queue over a period of hours is shown to be able to control parameter noise efficiently for realistic parameter values when integrated over sufficiently long time periods (hours), the discrimination characteristics being determined by the TCR signal cascade kinetics (a kinetic proofreading scheme). Therefore, through a combination of thresholds and signal integration, a T cell can be responsive to low ligand density and specific to agonist quality. We suggest that multiple threshold mechanisms are employed to establish the conditions for efficient signal integration, i.e., coordinate the formation of a stable contact interface
Variation in habitat choice and delayed reproduction: Adaptive queuing strategies or individual quality differences?
In most species, some individuals delay reproduction or occupy inferior breeding positions. The queue hypothesis tries to explain both patterns by proposing that individuals strategically delay breeding (queue) to acquire better breeding or social positions. In 1995, Ens, Weissing, and Drent addressed evolutionarily stable queuing strategies in situations with habitat heterogeneity. However, their model did not consider the non - mutually exclusive individual quality hypothesis, which suggests that some individuals delay breeding or occupy inferior breeding positions because they are poor competitors. Here we extend their model with individual differences in competitive abilities, which are probably plentiful in nature. We show that including even the smallest competitive asymmetries will result in individuals using queuing strategies completely different from those in models that assume equal competitors. Subsequently, we investigate how well our models can explain settleme! nt patterns in the wild, using a long-term study on oystercatchers. This long-lived shorebird exhibits strong variation in age of first reproduction and territory quality. We show that only models that include competitive asymmetries can explain why oystercatchers' settlement patterns depend on natal origin. We conclude that predictions from queuing models are very sensitive to assumptions about competitive asymmetries, while detecting such differences in the wild is often problematic.
Bayesian inference for queueing networks and modeling of internet services
Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle
billions of requests per day on clusters of thousands of computers. Because
these services operate under strict performance requirements, a statistical
understanding of their performance is of great practical interest. Such
services are modeled by networks of queues, where each queue models one of the
computers in the system. A key challenge is that the data are incomplete,
because recording detailed information about every request to a heavily used
system can require unacceptable overhead. In this paper we develop a Bayesian
perspective on queueing models in which the arrival and departure times that
are not observed are treated as latent variables. Underlying this viewpoint is
the observation that a queueing model defines a deterministic transformation
between the data and a set of independent variables called the service times.
With this viewpoint in hand, we sample from the posterior distribution over
missing data and model parameters using Markov chain Monte Carlo. We evaluate
our framework on data from a benchmark Web application. We also present a
simple technique for selection among nested queueing models. We are unaware of
any previous work that considers inference in networks of queues in the
presence of missing data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Tools for modelling and simulating migration-based preservation
This report describes two tools for modelling and simulating the costs and risks of using IT storage systems for the long-term archiving of file-based AV assets. The tools include a model of storage costs, the ingest and access of files, the possibility of data corruption and loss from a range of mechanisms, and the impact of having limited resources with which to fulfill access requests and preservation actions. Applications include archive planning, development of a technology strategy, cost estimation for business planning, operational decision support, staff training and generally promoting awareness of the issues and challenges archives face in digital preservation
On the Impact of Wireless Jamming on the Distributed Secondary Microgrid Control
The secondary control in direct current microgrids (MGs) is used to restore
the voltage deviations caused by the primary droop control, where the latter is
implemented locally in each distributed generator and reacts to load
variations. Numerous recent works propose to implement the secondary control in
a distributed fashion, relying on a communication system to achieve consensus
among MG units. This paper shows that, if the system is not designed to cope
with adversary communication impairments, then a malicious attacker can apply a
simple jamming of a few units of the MG and thus compromise the secondary MG
control. Compared to other denial-of-service attacks that are oriented against
the tertiary control, such as economic dispatch, the attack on the secondary
control presented here can be more severe, as it disrupts the basic
functionality of the MG
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating
the problem of traffic congestion. In particular, deep RL algorithms have been
shown to produce adaptive traffic signal controllers that outperform
conventional systems. However, in order to be reliable in highly dynamic urban
areas, such controllers need to be robust with the respect to a series of
exogenous sources of uncertainty. In this paper, we develop an open-source
callback-based framework for promoting the flexible evaluation of different
deep RL configurations under a traffic simulation environment. With this
framework, we investigate how deep RL-based adaptive traffic controllers
perform under different scenarios, namely under demand surges caused by special
events, capacity reductions from incidents and sensor failures. We extract
several key insights for the development of robust deep RL algorithms for
traffic control and propose concrete designs to mitigate the impact of the
considered exogenous uncertainties.Comment: 8 page
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