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Lu Wang

Publications and source records attributed to Lu Wang.

At least 397 records · Page 22Linked to original sources
arXiv2017

Impact of impurities on zonal flow driven by trapped electron mode turbulence

The impact of impurities on the generation of zonal flow (ZF) driven by collisonless trapped electron mode (CTEM) turbulence in deuterium (D)-tritium (T) plasmas is investigated. The expression for ZF growth rate with impurities is derived by balancing the ZF potential shielded by polarization effects and the ZF modulated radial turbulent current. Then, it is shown that the maximum normalized ZF growth rate is reduced by the presence of the fully ionized non-trace light impurities with relatively flat density profile, and slightly reduced by highly ionized trace tungsten (W). While, the maximum normalized ZF growth rate can be also enhanced by fully ionized non-trace light impurities with relatively steep density profile. In particular, the effects of high temperature helium from D-T reaction on ZF depend on the temperature ratio between electron and high temperature helium. The possible relevance of our findings to recent experimental results and future burning plasmas is also discussed.

physics.plasm-ph↗
arXiv2017

A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization

We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.

cs.CL↗
arXiv2017

Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.

cs.CL↗
arXiv2017

Joint Modeling of Content and Discourse Relations in Dialogues

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members' understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.

cs.CL↗
arXiv2017

Understanding and Detecting Supporting Arguments of Diverse Types

We investigate the problem of sentence-level supporting argument detection from relevant documents for user-specified claims. A dataset containing claims and associated citation articles is collected from online debate website idebate.org. We then manually label sentence-level supporting arguments from the documents along with their types as study, factual, opinion, or reasoning. We further characterize arguments of different types, and explore whether leveraging type information can facilitate the supporting arguments detection task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses features informed by argument types yields better performance than the same ranker trained without type information.

cs.CL↗
arXiv2017

Polaron dynamics with off-diagonal coupling: beyond the Ehrenfest approximation

Treated traditionally by the Ehrenfest approximation, dynamics of a one-dimensional molecular crystal model with off-diagonal exciton-phonon coupling is investigated in this work using the Dirac-Frenkel time-dependent variational principle with the multi-D$_2$ {\it Ansatz}. It is shown that the Ehrenfest method is equivalent to our variational method with the single D$_2$ {\it Ansatz}, and with the multi-D$_2$ {\it Ansatz}, the accuracy of our simulated dynamics is significantly enhanced in comparison with the semi-classical Ehrenfest dynamics. The multi-D$_2$ {\it Ansatz} is able to capture numerically accurate exciton momentum probability and help clarify the relation between the exciton momentum redistribution and the exciton energy relaxation. The results demonstrate that the exciton momentum distributions in the steady state are determined by a combination of the transfer integral and the off-diagonal coupling strength, independent of the excitonic initial conditions. We also probe the effect of the transfer integral and the off-diagonal coupling on exciton transport in both real and reciprocal space representations. Finally, the variational method with importance sampling is employed to investigate temperature effects on exciton transport using the multi-$\rm D_2$ {\it Ansatz}, and it is demonstrated that the variational approach is valid in both low and high temperature regimes.

physics.chem-ph↗
arXiv2017

Finite-temperature time-dependent variation with multiple Davydov states

The Dirac-Frenkel time-dependent variational approach with Davydov Ansätze is a sophisticated, yet efficient technique to obtain an acuurate solution to many-body Schrödinger equations for energy and charge transfer dy- namics in molecular aggregates and light-harvesting complexes. We extend this variational approach to finite temperatures dynamics of the spin-boson model by adopting a Monte Carlo importance sampling method. In or- der to demonstrate the applicability of this approach, we compare real-time quantum dynamics of the spin-boson model calculated with that from numerically exact iterative quasiadiabatic propagator path integral (QUAPI) technique. The comparison shows that our variational approach with the single Davydov Ansätze is in excellent agreement with the QUAPI method at high temperatures, while the two differ at low temperatures. Accuracy in dynamics calculations employing a multitude of Davydov trial states is found to improve substantially over the single Davydov Ansatz, especially at low temperatures. At a moderate computational cost, our variational approach with the multiple Davydov Ansatz is shown to provide accurate spin-boson dynamics over a wide range of temperatures and bath spectral densities.

quant-ph↗
arXiv2017

Bayesian Network--Response Regression

There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance.

stat.ME↗
arXiv2017

Electromagnetic theory of turbulent acceleration of parallel flow and momentum conservation

Intrinsic flow in plasma physics is a long-standing puzzle, since it is difficult to understand its origin without contradiction to momentum conservation in conventional wisdom. It is proved that the electromagnetic turbulent acceleration as a candidate for intrinsic parallel flow generation driven by pressure gradient along the total magnetic field line does not contradict momentum conservation. The conserved quantity corresponding to axial symmetry is the total gyrocenter parallel canonical momentum carried by both species or the total gyrocenter parallel momentum including the ion gyrocenter kinematic momentum and electromagnetic fields momentum, but not the ion kinematic momentum, or even the ion parallel flow. A conservation equation of total parallel momentum including the ion particles' kinematic momentum and electromagnetic fields momentum is also presented.

physics.plasm-ph↗
arXiv2016

Intrinsic parallel rotation drive by electromagnetic ion temperature gradient turbulence

The quasilinear intrinsic parallel flow drive including parallel residual stress, kinetic stress, cross Maxwell stress and parallel turbulent acceleration by electromagnetic ion temperature gradient (ITG) turbulence is calculated analytically using electromagnetic gyrokinetic theory. Both the kinetic stress and cross Maxwell stress also enter the mean parallel flow velocity equation via their divergence, as for the usual residual stress. The turbulent acceleration driven by ion pressure gradient along the total magnetic field (including equilibrium magnetic field and fluctuating radial magnetic field) cannot be written as a divergence of stress, and so should be treated as a local source/sink. All these terms can provide intrinsic parallel rotation drive. Electromagnetic effects reduce the non-resonant electrostatic stress force and even reverse it, but enhance the resonant stress force. Both the non-resonant and resonant turbulent acceleration terms are also enhanced by electromagnetic effects. The possible implications of our results for experimental observations are discussed.

physics.plasm-ph↗
arXiv2016

Isotopic dependence of impurity transport driven by ion temperature gradient turbulence

Hydrogenic ion mass effects, namely the isotopic effects on impurity transport driven by ion temperature gradient (ITG) turbulence are investigated using gyrokinetic theory. For non-trace impurities, changing from hydrogen (H) to deuterium (D), and to tritium (T) plasmas, the outward flux for lower (higher) ionized impurities or for lighter (heavier) impurities is found to decrease (increase), although isotopic dependence of ITG linear growth rate is weak. This is mainly due to the decrease of outward (inward) convection, while the isotopic dependence of diffusion is relatively weak. In addition, the isotopic effects reduce (enhance) the impurity flux of fully ionized carbon (C6+) for weaker (stronger) magnetic shear. In trace impurity limit, the isotopic effects are found to reduce the accumulation of high-Z tungsten (W). Moreover, the isotopic effects on the peaking factor (PF) of trace high-Z W get stronger with stronger magnetic shear.

physics.plasm-ph↗
arXiv2016

Asymptotic structure of self-shrinkers

We show that each end of a noncompact self-shrinker in $\mathbb{R}^3$ of finite topology is smoothly asymptotic to either a regular cone or a self-shrinking round cylinder.

math.DG↗
arXiv2016

Poloidal rotation driven by nonlinear momentum transport in strong electrostatic turbulence

Virtually, all existing theoretical works on turbulent poloidal momentum transport are based on quasilinear theory. Nonlinear poloidal momentum flux - $\langle \tilde{v}_r \tilde{n} \tilde{v}_θ \rangle$ is universally neglected. However, in the strong turbulence regime where relative fluctuation amplitude is no longer small, quasilinear theory is invalid. This is true at the all-important plasma edge. In this work, nonlinear poloidal momentum flux $ \langle \tilde{v}_r \tilde{n} \tilde{v}_θ \rangle $ in strong electrostatic turbulence is calculated using Hasegawa-Mima equation, and is compared with quasilinear poloidal Reynolds stress. A novel property is that symmetry breaking in fluctuation spectrum is not necessary for a nonlinear poloidal momentum flux. This is fundamentally different from the quasilinear Reynold stress. Furthermore, the comparison implies that the poloidal rotation drive from the radial gradient of nonlinear momentum flux is comparable to that from the quasilinear Reynolds force. Nonlinear poloidal momentum transport in strong electrostatic turbulence is thus not negligible for poloidal rotation drive, and so may be significant to transport barrier formation.

physics.plasm-ph↗
arXiv2016

A Sharp Lower Bound for the Entropy of Closed Hypersurfaces up to Dimension Six

In [5], Colding-Ilmanen-Minicozzi-White showed that within the class of closed smooth self-shrinkers in $\mathbb{R}^{n+1}$, the entropy is uniquely minimized at the round sphere. They conjectured that, for $2\leq n\leq 6$, the round sphere minimizes the entropy among all closed smooth hypersurfaces. Using an appropriate weak mean curvature flow, we prove their conjecture. For these dimensions, our approach also gives a new proof of the main result of [5] and extends its conclusions to compact singular self-shrinkers.

math.DG↗
arXiv2016

A Topological Property of Asymptotically Conical Self-Shrinkers of Small Entropy

For any asymptotically conical self-shrinker with entropy less than or equal to that of a cylinder we show that the link of the asymptotic cone must separate the unit sphere into exactly two connected components, both diffeomorphic to the self-shrinker. Combining this with recent work of Brendle, we conclude that the round sphere uniquely minimizes the entropy among all non-flat two-dimensional self-shrinkers. This confirms a conjecture of Colding-Ilmanen-Minicozzi-White in dimension two.

math.DG↗
arXiv2016

Topology of Closed Hypersurfaces of Small Entropy

We use a weak mean curvature flow together with a surgery procedure to show that all closed hypersurfaces in $\mathbb{R}^4$ with entropy less than or equal to that of $\mathbb{S}^2\times \mathbb{R}$, the round cylinder in $\mathbb{R}^4$, are diffeomorphic to $\mathbb{S}^3$.

math.DG↗
arXiv2016

Summarizing Decisions in Spoken Meetings

This paper addresses the problem of summarizing decisions in spoken meetings: our goal is to produce a concise {\it decision abstract} for each meeting decision. We explore and compare token-level and dialogue act-level automatic summarization methods using both unsupervised and supervised learning frameworks. In the supervised summarization setting, and given true clusterings of decision-related utterances, we find that token-level summaries that employ discourse context can approach an upper bound for decision abstracts derived directly from dialogue acts. In the unsupervised summarization setting,we find that summaries based on unsupervised partitioning of decision-related utterances perform comparably to those based on partitions generated using supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23 using SVMs).

cs.CL↗