Optimal path for a quantum teleportation protocol in entangled networks

Bellman's optimality principle has been of enormous importance in the development of whole branches of applied mathematics, computer science, optimal control theory, economics, decision making, and classical physics. Examples are numerous: dynamic programming, Markov chains, stochastic dynamics, calculus of variations, and the brachistochrone problem. Here we show that Bellman's optimality principle is violated in a teleportation problem on a quantum network. This implies that finding the optimal fidelity route for teleporting a quantum state between two distant nodes on a quantum network with bi-partite entanglement will be a tough problem and will require further investigation.Comment: 4 pages, 1 figure, RevTeX

Learning physical descriptors for materials science by compressed sensing

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors

Analytical computation of the epidemic threshold on temporal networks

The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the Susceptible-Infectious-Susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is both of fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.Comment: 22 pages, 6 figure

Small worlds and clustering in spatial networks

Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few reproduce even the most basic properties of real-world networks. Here, we focus on three such properties sparsity, small worldness, and clustering and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach in which network links correspond to noninteracting fermions whose energy depends on spatial distances between nodes

Establishing Research Competitiveness in Biophysical Sciences in Maine

The Maine EPSCoR Research Infrastructure Improvement award is designed to enhance Maine\u27s competitiveness in molecular biophysical sciences through a partnership between the University of Maine and Maine\u27s non-profit research organizations. The proposed Biophysical Sciences Institute brings together University of Maine faculty in physics, chemistry, biology, mathematics, and spatial engineering, with biomedical researchers at the Jackson Laboratory and Maine Medical Center Research Institute. Maine EPSCoR proposes to hire additional tenure-track faculty in the fields of biophysics and advanced optics, biochemistry, structural biology, applied mathematics, computer science, image analysis and visualization, and material science. The new and existing investigators will form research teams to develop new measurement techniques, new sensors, and innovative approaches to data processing and interpretation in intracellular structures and dynamics, functional materials as a means to manipulate cellular reactions, and biocomputing. In addition to establishing the institute, Maine EPSCoR will integrate research and education through improvements to graduate training

E-polis: A serious game for the gamification of sociological surveys

E-polis is a multi-platform serious game that gamifies a sociological survey for studying young people's opinions regarding their ideal society. The gameplay is based on a user navigating through a digital city, experiencing the changes inflicted, triggered by responses to social and pedagogical surveys, known as "dilemmas". The game integrates elements of adventure, exploration, and simulation. Unity was the selected game engine used for the development of the game, while a middleware component was also developed to gather and process the users' data. At the end of each game, users are presented with a blueprint of the city they navigated to showcase how their choices influenced its development. This motivates them to reflect on their answers and validate them. The game can be used to collect data on a variety of topics, such as social justice, and economic development, or to promote civic engagement and encourage young people to think critically about the world around them.Comment: 8 pages, 11 figures, Proceedings of the International Conference on Applied Mathematics & Computer Science (ICAMCS) 202