Asymmetric simple exclusion process on a ring conditioned on enhanced flux

We show that in the asymmetric simple exclusion process (ASEP) on a ring, conditioned on carrying a large flux, the particle experience an effective long-range potential which in the limit of very large flux takes the simple form $U= -2\sum_{i\neq j}\log|\sin\pi(n_{i}/L-n_{j}/L)|$, where $n_{1}n_{2},\ldots n_{N}$ are the particle positions, similar to the effective potential between the eigenvalues of the circular unitary ensemble in random matrices. Effective hopping rates and various quasistationary probabilities under such a conditioning are found analytically using the Bethe ansatz and determinantal free fermion techniques. Our asymptotic results extend to the limit of large current and large activity for a family of reaction-diffusion processes with on-site exclusion between particles. We point out an intriguing generic relation between classical stationary probability distributions for conditioned dynamics and quantum ground state wave functions, in particular, in the case of exclusion processes, for free fermions.Comment: submitted to J. Stat. Mec

Dirty black holes: Symmetries at stationary non-static horizons

We establish that the Einstein tensor takes on a highly symmetric form near the Killing horizon of any stationary but non-static (and non-extremal) black hole spacetime. [This follows up on a recent article by the current authors, gr-qc/0402069, which considered static black holes.] Specifically, at any such Killing horizon -- irrespective of the horizon geometry -- the Einstein tensor block-diagonalizes into ``transverse'' and ``parallel'' blocks, and its transverse components are proportional to the transverse metric. Our findings are supported by two independent procedures; one based on the regularity of the on-horizon geometry and another that directly utilizes the elegant nature of a bifurcate Killing horizon. It is then argued that geometrical symmetries will severely constrain the matter near any Killing horizon. We also speculate on how this may be relevant to certain calculations of the black hole entropy.Comment: 21 pages; plain LaTe

Overcoming device unreliability with continuous learning in a population coding based computing system

The brain, which uses redundancy and continuous learning to overcome the unreliability of its components, provides a promising path to building computing systems that are robust to the unreliability of their constituent nanodevices. In this work, we illustrate this path by a computing system based on population coding with magnetic tunnel junctions that implement both neurons and synaptic weights. We show that equipping such a system with continuous learning enables it to recover from the loss of neurons and makes it possible to use unreliable synaptic weights (i.e. low energy barrier magnetic memories). There is a tradeoff between power consumption and precision because low energy barrier memories consume less energy than high barrier ones. For a given precision, there is an optimal number of neurons and an optimal energy barrier for the weights that leads to minimum power consumption

On the Fairness of Centralised Decision-Making Strategies in multi-TSO Power Systems

In this paper, we consider an interconnected power system, where the different Transmission System Operators (TSOs) have agreed to transferring some of their competences to a Centralised Control Center (CCC). In such a context, a recurrent difficulty for the CCC is to define decision-making strategies which are fair enough to every TSO of the interconnected system. We address this multiobjective problem when the objective of every TSO can be represented by a real-valued function. We propose an algorithm to elect the solution that leads to the minimisation of the distance with the utopian minimum after having normalised the different objectives. We analyse the fairness of this solution in the sense of economics. We illustrate the approach with the IEEE 118 bus system partitioned in 3 areas having as local objective the minimisation of active power losses, the maximisation of reactive power reserves, or a combination of both criteria.multi-area power system, centralised control, multi-objective optimisation, fairness.

Reheating predictions in single field inflation

Reheating is a transition era after the end of inflation, during which the inflaton is converted into the particles that populate the Universe at later times. No direct cosmological observables are normally traceable to this period of reheating. Indirect bounds can however be derived. One possibility is to consider cosmological evolution for observable CMB scales from the time of Hubble crossing to the present time. Depending upon the model, the duration and final temperature after reheating, as well as its equation of state, may be directly linked to inflationary observables. For single-field inflationary models, if we approximate reheating by a constant equation of state, one can derive relations between the reheating duration (or final temperature), its equation of state parameter, and the scalar power spectrum amplitude and spectral index. While this is a simple approximation, by restricting the equation of state to lie within a broad physically allowed range, one can in turn bracket an allowed range of $n_s$ and $r$ for these models. The added constraints can help break degeneracies between inflation models that otherwise overlap in their predictions for $n_s$ and $r$.Comment: 32 pages, 15 figures. Revised in response to comments on the original version, and in preparation for submission for publication. More references and a new figure were adde