arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 433 records · Page 24Linked to original sources

arXiv2019

Observation of Rydberg exciton polaritons and their condensate in a perovskite cavity

The condensation of half-light half-matter exciton polaritons in semiconductor optical cavities is a striking example of macroscopic quantum coherence in a solid state platform. Quantum coherence is possible only when there are strong interactions between the exciton polaritons provided by their excitonic constituents. Rydberg excitons with high principle value exhibit strong dipole-dipole interactions in cold atoms. However, polaritons with the excitonic constituent that is an excited state, namely Rydberg exciton polaritons (REPs), have not yet been experimentally observed. Here, for the first time, we observe the formation of REPs in a single crystal CsPbBr3 perovskite cavity without any external fields. These polaritons exhibit strong nonlinear behavior that leads to a coherent polariton condensate with a prominent blue shift. Furthermore, the REPs in CsPbBr3 are highly anisotropic and have a large extinction ratio, arising from the perovskite's orthorhombic crystal structure. Our observation not only sheds light on the importance of many-body physics in coherent polariton systems involving higher-order excited states, but also paves the way for exploring these coherent interactions for solid state quantum optical information processing.

cond-mat.mtrl-sci↗

arXiv2019

Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants

Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one of the active topics in medical image segmentation in recent years. In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN). We propose a heuristic RPN (H-RPN) which can utilize surrounding information around the PWMLs for heuristic segmentation. Also, we design a lightweight segmentation network to segment the lesion in a fast way. Densely connected conditional random field (DCRF) is used to optimize the segmentation results. We only use T1w MRIs to segment PWMLs. The result shows that our model can well segment the lesion of ordinary size or even pixel size. The Dice similarity coefficient reaches 0.6616, the sensitivity is 0.7069, the specificity is 0.9997, and the Hausdorff distance is 52.9130. The proposed method outperforms the state-of-the-art algorithm. (The code of this paper is available on https://github.com/YalongLiu/Refined-Segmentation-R-CNN)

eess.IV↗

arXiv2019

Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues. The extensive experiments demonstrate that the proposed approach obtains more accurate foreground and background maps with the assistance of the breast anatomy; especially, for the images having large or small tumors; meanwhile, the new objective function can handle the images without tumors. The newly proposed method achieves state-of-the-art performance when compared to eight tumor saliency estimation approaches using two breast ultrasound datasets.

cs.CV↗

arXiv2019

Neural Response Generation with Meta-Words

We present open domain response generation with meta-words. A meta-word is a structured record that describes various attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we enhance the sequence-to-sequence architecture with a goal tracking memory network that formalizes meta-word expression as a goal and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results on two large-scale datasets indicate that our model can significantly outperform several state-of-the-art generation models in terms of response relevance, response diversity, accuracy of one-to-many modeling, accuracy of meta-word expression, and human evaluation.

cs.CL↗

arXiv2019

Collective oscillation modes of a superfluid Bose-Fermi mixture

In this work, we present a theoretical study for the collective oscillation modes, i.e. quadrupole, radial and axial mode, of a mixture of Bose and Fermi superfluids in the crossover from a Bardeen-Cooper-Schrieffer (BCS) superfluid to a molecular Bose-Einstein condensate (BEC) in harmonic trapping potentials with cylindrical symmetry of experimental interest. To this end, we start from the coupled superfluid hydrodynamic equations for the dynamics of Bose-Fermi superfluid mixtures and use the scaling theory that has been developed for a coupled system. The collective oscillation modes of Bose-Fermi superfluid mixtures are found to crucially depend on the overlap integrals of the spatial derivations of density profiles of the Bose and Fermi superfluids at equilibrium. We not only present the explicit expressions for the overlap density integrals, as well as the frequencies of the collective modes provided that the effective Bose-Fermi coupling is weak, but also test the valid regimes of the analytical approximations by numerical calculations in realistic experimental conditions. In the presence of a repulsive Bose-Fermi interaction, we find that the frequencies of the three collective modes of the Bose and Fermi superfluids are all upshifted, and the change speeds of the frequency shifts in the BCS-BEC crossover can characterize the different groundstate phases of the Bose-Fermi superfluid mixtures for different trap geometries.

cond-mat.quant-gas↗

arXiv2019

Direct CO2 Electroreduction from Carbonate

The process of CO2 valorization, all the way from capture of CO2 to its electrochemical upgrade, requires significant inputs in each of the capture, upgrade, and separation steps. The gas phase CO2 feed following the capture-and-release stage and into the CO2 electroreduction stage produce a large waste of CO2, between 80 and 95% of CO2 is wasted due to carbonate formation or electrolyte crossover, that adds cost and energy consumption to the CO2 management aspect of the system. Here we report an electrolyzer that instead directly upgrades carbonate electrolyte from CO2 capture solution to syngas, achieving 100% carbon utilization across the system. A bipolar membrane is used to produce proton in situ, under applied potential, which facilitates CO2 releasing at the membrane:catalyst interface from the carbonate solution. Using an Ag catalyst, we generate pure syngas at a 3 to 1 H2 to CO ratio, with no CO2 dilution at the gas outlet, at a current density of 150 mA cm-2, and achieve a full cell energy efficiency of 35%. The direct carbonate cell was stable under a continuous 145 h of catalytic operation at ca. 180 mA cm-2. The work demonstrates that coupling CO2 electrolysis directly with a CO2 capture system can accelerate the path towards viable CO2 conversion technologies.

physics.chem-ph↗

arXiv2019

Double Helical Conformation and Extreme Rigidity in a Rodlike Polyelectrolyte

The ubiquitous biomacromolecule DNA has an axial rigidity persistence length of ~50 nm, driven by its elegant double helical structure. While double and multiple helix structures appear widely in nature, only rarely are these found in synthetic non-chiral macromolecules. Here we describe a double helical conformation in the densely charged aromatic polyamide poly(2,2'-disulfonyl-4,4'-benzidine terephthalamide) or PBDT. This double helix macromolecule represents one of the most rigid simple molecular structures known, exhibiting an extremely high axial persistence length (~1 micrometer). We present X-ray diffraction, NMR spectroscopy, and molecular dynamics (MD) simulations that reveal and confirm the double helical conformation. The discovery of this extreme rigidity in combination with high charge density gives insight into the self-assembly of molecular ionic composites with high mechanical modulus (~1 GPa) yet with liquid-like ion motions inside, and provides fodder for formation of new 1D-reinforced composites.

cond-mat.soft↗

arXiv2019

Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program. The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few semidefinite programs under two conditions. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.

math.OC↗

arXiv2018

Hankel determinants for convolution powers of Catalan numbers

The Hankel determinants $\left(\frac{r}{2(i+j)+r}\binom{2(i+j)+r}{i+j}\right)_{0\leq i,j \leq n-1}$ of the convolution powers of Catalan numbers were considered by Cigler and by Cigler and Krattenthaler. We evaluate these determinants for $r\le 31$ by finding shifted periodic continued fractions, which arose in application of Sulanke and Xin's continued fraction method. These include some of the conjectures of Cigler as special cases. We also conjectured a polynomial characterization of these determinants. The same technique is used to evaluate the Hankel determinants $\left(\binom{2(i+j)+r}{i+j}\right)_{0\leq i,j \leq n-1} $. Similar results are obtained.

math.CO↗

arXiv2018

Higgs production in association with bottom quark pair at LHC

The direct observation of the Yukawa interaction related to Standard Model Higgs and the bottom quark is obtained from the $h\to b\bar{b}$ process recently. The b-tagging becomes an important tool to search for new heavy resonance production at LHC. We use the b-tagging method to investigate the neutral Higgs production in association with a bottom quark pair, and perform the detector simulation for the signal process with the Higgs decaying into $b\bar{b}$ or $τ^+τ^-$ together with the dominant backgrounds. Our results show that $b\bar{b}b\bar{b}$ and $b\bar{b}τ^+τ^-$ processes will play an important role to search for the exotic heavy Higgs production at LHC and future hadron colliders.

hep-ph↗

arXiv2018

From planes to bowls: photodissociation of the bisanthenequinone cation

We present a combined experimental and theoretical study of the photodissociation of the bisanthenequinone (C28H12O2) cation, Bq+. The experiments show that, upon photolysis, the Bq+ cation does not dehydrogenate, but instead fragments through the sequential loss of the two neutral carbonyl groups, causing the formation of five-membered carbon cycles. Quantum chemical calculations confirm this Bq+ -> [Bq - CO]+ -> [Bq - 2CO]+ sequence as the energetically most favorable reaction pathway. For the first CO loss, a transition state with a barrier of ~3.2 eV is found, substantially lower than the lowest calculated H loss dissociation pathway (~ 4.9 eV). A similar situation applies for the second CO loss channel (~3.8 eV vs. ~4.7 eV), but where the first dissociation step does not strongly alter the planar PAH geometry, the second step transforms the molecule into a bowl-shaped one.

physics.atm-clus↗

arXiv2018

Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm

We propose a monospectral image encryption method in which the multispectral color image acquisition by using heterogeneous monospectral cameras. Because the captured monospectral elemental images (EIs) belongs to grayscale image, it is means that the captured EIs can be directly encrypted by the proposed encoding method. Subsequently, the linear cellular automata (CA) and hyperchaotic encoding algorithm are employed to encrypt the captured EIs. Different from previous methods, the proposed method can directly encrypt the multispectral color information rather than having to divide into three color channels (R, G and B), thereby, the proposed method can greatly reduce the encryption calculation.

cs.CR↗

arXiv2018

Restricting Greed in Training of Generative Adversarial Network

Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning. Variations of GAN have achieved competitive results on specific tasks. However, the stability of training and diversity of generated instances are still worth studying further. Training of GAN can be thought of as a greedy procedure, in which the generative net tries to make the locally optimal choice (minimizing loss function of discriminator) in each iteration. Unfortunately, this often makes generated data resemble only a few modes of real data and rotate between modes. To alleviate these problems, we propose a novel training strategy to restrict greed in training of GAN. With help of our method, the generated samples can cover more instance modes with more stable training process. Evaluating our method on several representative datasets, we demonstrate superiority of improved training strategy on typical GAN models with different distance metrics.

cs.CV↗

arXiv2018

Hankel determinants and shifted periodic continued fractions

Sulanke and Xin developed a continued fraction method that applies to evaluate Hankel determinants corresponding to quadratic generating functions. We use their method to give short proofs of Cigler's Hankel determinant conjectures, which were proved recently by Chang-Hu-Zhang using direct determinant computation. We find that shifted periodic continued fractions arise in our computation. We also discover and prove some new nice Hankel determinants relating to lattice paths with step set $\{(1,1),(q,0), (\ell-1,-1)\}$ for integer parameters $m,q,\ell$. Again shifted periodic continued fractions appear.

math.CO↗

arXiv2018

A Hybrid Framework for Tumor Saliency Estimation

Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality. Most tumor segmentation approaches achieve good performance on BUS images collected in controlled settings; however, the performance degrades greatly with BUS images from different sources. Tumor saliency estimation (TSE) has attracted increasing attention to solving the problem by modeling radiologists' attention mechanism. In this paper, we propose a novel hybrid framework for TSE, which integrates both high-level domain-knowledge and robust low-level saliency assumptions and can overcome drawbacks caused by direct mapping in traditional TSE approaches. The new framework integrated the Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the layer structure-based weighted map. The experimental results demonstrate that the proposed approach outperforms state-of-the-art TSE methods.

cs.CV↗

arXiv2018

Optical Signatures of Dirac Nodal-lines in NbAs$_2$

Using polarized optical and magneto-optical spectroscopy, we have demonstrated universal aspects of electrodynamics associated with Dirac nodal-lines. We investigated anisotropic electrodynamics of NbAs$_2$ where the spin-orbit interaction triggers energy gaps along the nodal-lines, which manifest as sharp steps in the optical conductivity spectra. We show experimentally and theoretically that shifted 2D Dirac nodal-lines feature linear scaling $σ_1 (ω)\simω$, similar to 3D nodal-points. Massive Dirac nature of the nodal-lines are confirmed by magneto-optical data, which may also be indicative of theoretically predicted surface states. Optical data also offer a natural explanation for the giant magneto-resistance in NbAs$_2$.

cond-mat.mtrl-sci↗

arXiv2018

Study Standard Model and Majorana Neutrino Contributions to $B^{+} \to K^{(*)\pm}μ^+μ^{\mp}$

Lepton number violation processes can be induced by the Majorana neutrino exchange, which provide evidence for the Majorana nature of neutrinos. In addition to the natural explanation of the small neutrino masses, Type-I seesaw mechanism predicts the existence of Majorana neutrinos. The aim of this work is to study the B meson rare decays $B^{+} \to K^{(*)+}μ^+μ^-$ in the standard model and its extensions, and then to investigate the same-sign decay processes $B^{+}\to K^{(*)-}μ^{+}μ^+$. The corresponding dilepton invariant mass distributions are predicted. It is found that the dilepton angular distributions illustrate the properties of new interactions induced by the Majorana neutrinos.

hep-ph↗

arXiv2018

Delay Analysis of Random Scheduling and Round Robin in Small Cell Networks

We analyze the delay performance of small cell networks operating under random scheduling (RS) and round robin (RR) protocols. Based on stochastic geometry and queuing theory, we derive accurate and tractable expressions for the distribution of mean delay, which accounts for the impact of random traffic arrivals, queuing interactions, and failed packet retransmissions. Our analysis asserts that RR outperforms RS in terms of mean delay, regardless of traffic statistic. Moreover, the gain from RR is more pronounced in the presence of heavy traffic, which confirms the importance of accounting fairness in the design of scheduling policy. We also find that constrained on the same delay outage probability, RR is able to support more user equipments (UEs) than that of RS, demonstrating it as an appropriate candidate for the traffic scheduling policy of internet-of-things (IoT) network.

cs.IT↗