output
20072025
most citedOptimal Power Allocation for Distributed Detection over MIMO Channels in Wireless Sensor Networks

105 citations

15 papers

physics.soc-ph2025★ 1 cited

Interpretable Early Warnings using Machine Learning in an Online Game-experiment

Guillaume Falmagne, Anna B. Stephenson, Simon A. Levin

Stemming from physics and later applied to other fields such as ecology, the theory of critical transitions suggests that some regime shifts are preceded by statistical early warni…

physics.flu-dyn2022★ 22 cited

Experimental observations and modeling of sub-Hinze bubble production by turbulent bubble break-up

Daniel J. Ruth, Aditya K. Aiyer, Aliénor Rivière +2

We present experiments on large air cavities spanning a wide range of sizes relative to the Hinze scale , the scale at which turbulent stresses are balanced by surfac…

physics.flu-dyn2020★ 57 cited

Bubble deformation by a turbulent flow

Stéphane Perrard, Aliénor Rivière, Wouter Mostert +1

We investigate the modes of deformation of an initially spherical bubble immersed in a homogeneous and isotropic turbulent background flow. We perform direct numerical simulations…

eess.SP2020★ 4 cited

Sensing and Communication Tradeoff Design for AoI Minimization in a Cellular Internet of UAVs

Shuhang Zhang, Hongliang Zhang, Lingyang Song +2

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data for multiple tasks and transmit the data to the base station (BS). To qua…

cs.NI2017★ 12 cited

Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing

Chen-Feng Liu, Mehdi Bennis, H. Vincent Poor

While mobile edge computing (MEC) alleviates the computation and power limitations of mobile devices, additional latency is incurred when offloading tasks to remote MEC servers. In…

stat.ML2016

Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models

Xinyu He, Warren B. Powell

We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameter…