Closing the gap in the capacity of random wireless networks via percolation theory

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We develop techniques to study the phase transition for planar Gaussian percolation models that are not (necessarily) positively correlated. These models lack the property of positive associations (also known as the 'FKG inequality'), and hence many classi ...

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

We study the performance of Markov chains for the q-state ferromagnetic Potts model on random regular graphs. While the cases of the grid and the complete graph are by now well-understood, the case of random regular graphs has resisted a detailed analysis ...

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

The intrinsic random amplitude and phase modulation of 40 distinct lines of a microresonator frequency comb operated in the modulation instability regime are used to realize massively parallel random-modulation continuous-wave light detection and ranging, ...

We examine the influence of surface charge on the percolation, gel-point and phase behavior of cellulose nanocrystal (CNC) suspensions in relation to their nonlinear rheological material response. Desulfation decreases CNC surface charge density which lead ...

Closing the gap in the capacity of random wireless networks via percolation theory

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THE PHASE TRANSITION FOR PLANAR GAUSSIAN PERCOLATION MODELS WITHOUT FKG

STREAMING TENSOR TRAIN APPROXIMATION

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Metastability of the Potts Ferromagnet on Random Regular Graphs

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Quantization for Decentralized Learning Under Subspace Constraints

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Chaotic microcomb-based parallel ranging

Percolation and phase behavior in cellulose nanocrystal suspensions from nonlinear rheological analysis