arXiv subjects

Yue Luo

Publications and source records attributed to Yue Luo.

24 records · Page 2Linked to original sources

arXiv2019

Single photon emission in $WSe_2$ up 160 K by quantum yield control

Two dimensional semiconductors hosting strain-induced quantum emitters offer unique abilities to achieve scalable architectures for deterministic coupling to nanocavities and waveguides that are required to enable chip-based quantum information processing technologies. A severe drawback remains that exciton emission from quantum emitters in $WSe_2$ quenches beyond 30 K, which requires cryogenic cooling. Here we demonstrate an approach to increase the temperature survival of exciton quantum emitters in $WSe_2$ that is based on maximizing the emitter quantum yield. Utilizing optimized material growth that leads to reduced density of nonradiative defects as well as coupling of the exciton emission to plasmonic nanocavities modes, we achieve average quantum yields up to 44%, thermal activation energies up to 92 meV, and single photon emission signatures up to temperatures of 160 K. At these values non-cryogenic cooling with thermo-electric chips becomes already feasible, while quantitative analysis shows that room temperature operation is within reach through active strain engineering.

cond-mat.mtrl-sci↗

arXiv2018

Approximation algorithms for the three-machine proportionate mixed shop scheduling

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flow-shop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as $M3 \mid prpt \mid C_{\max}$, in which each job has equal processing times on all three machines. Koulamas and Kyparisis [{\it European Journal of Operational Research}, 243:70--74,2015] showed that the problem is solvable in polynomial time in some very special cases; for the non-solvable case, they proposed a $5/3$-approximation algorithm. In this paper, we present an improved $4/3$-approximation algorithm and show that this ratio of $4/3$ is asymptotically tight; when the largest job is a flow-shop job, we present a fully polynomial-time approximation scheme (FPTAS). On the negative side, while the $F3 \mid prpt \mid C_{\max}$ problem is polynomial-time solvable, we show an interesting hardness result that adding one open-shop job to the job set makes the problem NP-hard if this open-shop job is larger than any flow-shop job. We are able to design an FPTAS for this special case too.

cs.DS↗

arXiv2018

Deterministic coupling of site-controlled quantum emitters in monolayer semiconductors to plasmonic nanocavities

Solid-state single-quantum emitters are a crucial resource for on-chip photonic quantum technologies and require efficient cavity-emitter coupling to realize quantum networks beyond the single-node level. Previous approaches to enhance light-matter interactions rely on forming nanocavities around randomly located quantum dots or color centers but lack spatial control of the quantum emitter itself that is required for scaling. Here we demonstrate a deterministic approach to achieve Purcell-enhancement at lithographically defined locations using the sharp corner of a metal nanocube for both electric field enhancement and to deform a two-dimensional material. For a 3 by 4 array of strain-induced exciton quantum emitters formed into monolayer WSe2 we show spontaneous emission rate enhancement with Purcell-factors (FP) up to FP=1050 (average FP=272), single-photon purification, and cavity-enhanced quantum yields increasing from initially 1 % to 15 %. The utility of our nanoplasmonic platform is applicable to other 2D material, including boron nitride, opening new inroads in quantum photonics.

cond-mat.mes-hall↗

arXiv2018

LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.

cs.CV↗

arXiv2018

Single View Stereo Matching

Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss functions during training, the inference procedure does not explicitly impose any geometrical constraint. Therefore these models purely rely on the quality of data and the effectiveness of learning to generalize. This either leads to suboptimal results or the demand of huge amount of expensive ground truth labelled data to generate reasonable results. In this paper, we show for the first time that the monocular depth estimation problem can be reformulated as two sub-problems, a view synthesis procedure followed by stereo matching, with two intriguing properties, namely i) geometrical constraints can be explicitly imposed during inference; ii) demand on labelled depth data can be greatly alleviated. We show that the whole pipeline can still be trained in an end-to-end fashion and this new formulation plays a critical role in advancing the performance. The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data. The model also generalizes well to other monocular depth estimation benchmarks. We also discuss the implications and the advantages of solving monocular depth estimation using stereo methods.

cs.CV↗

arXiv2017

Near-unity quantum yield from carbon nanotube excitons coupled to plasmonic nanocavities

Single-walled carbon nanotubes (SWCNTs) are promising absorbers and emitters to enable novel photonic and optoelectronic applications but are also known to severely suffer from low optical quantum yields. Here we demonstrate SWCNTs excitons coupled to plasmonic nanocavities reaching deeply into the Purcell regime with FP=234 (average FP=76), near unity quantum yields of 70% (average 41%), and a photon emission rate of 1.7 MHz into the first lens. The measured ultra-narrow exciton linewidth (18 micro eV) implies furthermore generation of indistinguishable single photons from a SWCNT. To demonstrate utility beyond quantum light sources we show that nanocavity-coupled SWCNTs perform as single-molecule thermometers detecting plasmonically induced heat (Delta T=150K) in a unique interplay of excitons, phonons, and plasmons at the nanoscale.

cond-mat.mes-hall↗