arXiv subjects

Deepthi Gopal

Publications and source records attributed to Deepthi Gopal.

3 recordsLinked to original sources

arXiv2024

Estimating transmission noise on networks from stationary local order

In this paper we study networks of nodes characterised by binary traits that change both endogenously and through nearest-neighbour interaction. Our analytical results show that those traits can be ranked according to the noisiness of their transmission using only measures of order in the stationary state. Crucially, this ranking is independent of network topology. As an example, we explain why, in line with a long-standing hypothesis, the relative stability of the structural traits of languages can be estimated from their geospatial distribution. We conjecture that similar inferences may be possible in a more general class of Markovian systems. Consequently, in many empirical domains where longitudinal information is not easily available the propensities of traits to change could be estimated from spatial data alone.

cond-mat.stat-mech↗

arXiv2018

Geospatial distributions reflect rates of evolution of features of language

Quantifying the speed of linguistic change is challenging due to the fact that the historical evolution of languages is sparsely documented. Consequently, traditional methods rely on phylogenetic reconstruction. In this paper, we propose a model-based approach to the problem through the analysis of language change as a stochastic process combining vertical descent, spatial interactions, and mutations in both dimensions. A notion of linguistic temperature emerges naturally from this analysis as a dimensionless measure of the propensity of a linguistic feature to undergo change. We demonstrate how temperatures of linguistic features can be inferred from their present-day geospatial distributions, without recourse to information about their phylogenies. Thus the evolutionary dynamics of language, operating across thousands of years, leaves a measurable geospatial signature. This signature licenses inferences about the historical evolution of languages even in the absence of longitudinal data.

physics.soc-ph↗

arXiv2011

Using post-measurement information in state discrimination

We consider a special form of state discrimination in which after the measurement we are given additional information that may help us identify the state. This task plays a central role in the analysis of quantum cryptographic protocols in the noisy-storage model, where the identity of the state corresponds to a certain bit string, and the additional information is typically a choice of encoding that is initially unknown to the cheating party. We first provide simple optimality conditions for measurements for any such problem, and show upper and lower bounds on the success probability. For a certain class of problems, we furthermore provide tight bounds on how useful post-measurement information can be. In particular, we show that for this class finding the optimal measurement for the task of state discrimination with post-measurement information does in fact reduce to solving a different problem of state discrimination without such information. However, we show that for the corresponding classical state discrimination problems with post-measurement information such a reduction is impossible, by relating the success probability to the violation of Bell inequalities. This suggests the usefulness of post-measurement information as another feature that distinguishes the classical from a quantum world.

quant-ph↗