output
20002026
most cited11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks

1.5k citations

Showing 2019Show all

77 papers · 1 filter

cs.SI2019★ 22 cited

Detection of Community Structures in Networks with Nodal Features based on Generative Probabilistic Approach

Hadi Zare, Mahdi Hajiabadi, Mahdi Jalili

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based…

physics.app-ph2019★ 437 cited

Phase-encoded RF signal generation based on an integrated 49GHz micro-comb optical source

Xingyuan Xu, Mengxi Tan, Jiayang Wu +8

We demonstrate photonic RF phase encoding based on an integrated micro-comb source. By assembling single-cycle Gaussian pulse replicas using a transversal filtering structure, phas…

eess.SP2019★ 154 cited

Internet of Things in Geospatial Analytics

Carlos Granell, Andreas Kamilaris, Alexander Kotsev +2

Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space and by using…

cs.LG2019★ 1 cited

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Zining Liu, Chong Long, Xiaolu Lu +3

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multipl…

cs.IT2019★ 1 cited

Intermittent Information-Driven Search for Underwater Targets

Branko Ristic, Alex Skvortsov

The problem is area-restricted search for targets using an autonomous mobile sensing platform. Detection is imperfect: the probability of detection depends on the range to the targ…

physics.flu-dyn2019★ 3 cited

Global Organization of Three-Dimensional, Volume-Preserving Flows: Constraints, Degenerate Points, and Lagrangian Structure

Bharath Ravu, Guy Metcalfe, Murray Rudman +2

Global organization of 3-dimensional (3D) Lagrangian chaotic transport is difficult to infer without extensive computation. For 3D time-periodic flows with one invariant we show ho…