We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks.

Governments all over the world are rapidly embracing digital technologies for information collection, governance, and social control. Recent studies suggest citizens may accept or even support digital surveillance. By using an online survey dataset on public opinion about facial recognition technology, contact tracing apps, and the social credit system in China, Germany, the US, and the UK, this article shows that these studies have overlooked a small yet significant group of digital technology doubters. Our results show that while up to 10% of Chinese citizens belong to the group of “digital doubters,” this group is the largest in Germany with 30% of citizens. The US and the UK are in the middle with approximately 20%. While citizens who belong to this group of digital doubters worry about privacy and surveillance issues, their attitudes can also be explained by them not being convinced of the benefits of digital technologies, including improved efficiency, security, or convenience. We find that the more citizens lack trust in their government, the more likely they are to belong to the group of digital doubters. Our findings demonstrate that in both democratic and authoritarian states, there are citizens opposing the adoption of certain digital technologies. This underscores the importance of initiating societal debate to determine the appropriate regulations that align with these societal preferences.

We extend the classical setting of an optimal stopping problem under full information to include problems with an unknown state. The framework allows the unknown state to influence (i) the drift of the underlying process, (ii) the payoff functions, and (iii) the distribution of the time horizon. Since the stopper is assumed to observe the underlying process and the random horizon, this is a two-source learning problem. Assigning a prior distribution for the unknown state, standard filtering theory can be employed to embed the problem in a Markovian framework with one additional state variable representing the posterior of the unknown state. We provide a convenient formulation of this Markovian problem, based on a measure change technique that decouples the underlying process from the new state variable. Moreover, we show by means of several novel examples that this reduced formulation can be used to solve problems explicitly.

We propose a modification to the random destruction of graphs: given a finite network with a distinguished set of sources and targets, remove (cut) vertices at random, discarding components that do not contain a source node. We investigate the number of cuts required until all targets are removed, and the size of the remaining graph. This model interpolates between the random cutting model going back to Meir and Moon (J. Austral. Math. Soc. 11, 1970) and site percolation. We prove several general results, including that the size of the remaining graph is a tight family of random variables for compatible sequences of expander-type graphs, and determine limiting distributions for binary caterpillar trees and complete binary trees.

We consider parallel single-server queues in heavy traffic with randomly split Hawkes arrival processes. The service times are assumed to be independent and identically distributed (i.i.d.) in each queue and are independent in different queues. In the critically loaded regime at each queue, it is shown that the diffusion-scaled queueing and workload processes converge to a multidimensional reflected Brownian motion in the non-negative orthant with orthonormal reflections. For the model with abandonment, we also show that the corresponding limit is a multidimensional reflected Ornstein–Uhlenbeck diffusion in the non-negative orthant.