arXiv subjects

Cheng Luo

Publications and source records attributed to Cheng Luo.

101 records · Page 6Linked to original sources

arXiv2020

Let's be Humorous: Knowledge Enhanced Humor Generation

The generation of humor is an under-explored and challenging problem. Previous works mainly utilize templates or replace phrases to generate humor. However, few works focus on freer forms and the background knowledge of humor. The linguistic theory of humor defines the structure of a humor sentence as set-up and punchline. In this paper, we explore how to generate a punchline given the set-up with the relevant knowledge. We propose a framework that can fuse the knowledge to end-to-end models. To our knowledge, this is the first attempt to generate punchlines with knowledge enhanced model. Furthermore, we create the first humor-knowledge dataset. The experimental results demonstrate that our method can make use of knowledge to generate fluent, funny punchlines, which outperforms several baselines.

cs.CL↗

arXiv2019

Neural Document Expansion with User Feedback

This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries which lead to clicks on the document and weights these expansion terms with learned attention. It is plugged into a standard neural ranker and learned end-to-end. Experiments on a commercial search log demonstrate that NeuDEF significantly improves the accuracy of state-of-the-art neural rankers and expansion methods on queries with different frequencies. Further studies show the contribution of click queries and learned expansion weights, as well as the influence of document popularity of NeuDEF's effectiveness.

cs.IR↗

arXiv2019

Temporal Relational Ranking for Stock Prediction

Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.

cs.CE↗

arXiv2018

Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity weighting. Despite their differences, most existing studies separate the estimation of click bias (namely the \textit{propensity model}) from the learning of ranking algorithms. To estimate click propensities, they either conduct online result randomization, which can negatively affect the user experience, or offline parameter estimation, which has special requirements for click data and is optimized for objectives (e.g. click likelihood) that are not directly related to the ranking performance of the system. In this work, we address those problems by unifying the learning of propensity models and ranking models. We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank. Based on this observation, we propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker and an \textit{unbiased propensity model}. DLA is an automatic unbiased learning-to-rank framework as it directly learns unbiased ranking models from biased click data without any preprocessing. It can adapt to the change of bias distributions and is applicable to online learning. Our empirical experiments with synthetic and real-world data show that the models trained with DLA significantly outperformed the unbiased learning-to-rank algorithms based on result randomization and the models trained with relevance signals extracted by click models.

cs.IR↗

arXiv2016

Spin and quadrupolar orders in the spin-1 bilinear-biquadratic model for iron-based superconductors

Motivated by recent experimental and theoretical progresses of the magnetic properties of iron-based superconductors, we provide a comprehensive analysis of the spin-1 bilinear-biquadratic (BBQ) model on the square lattice. Using variational approach in the mean-field level, we identify the existence of various magnetic phases, including conventional spin dipolar orderings (ferro- or antiferromagnet), novel quadrupolar (spin nematic) orderings and mixed dipolar-quadrupolar orderings. In contrast to the usual Heisenberg model, the elementary excitations of the spin-1 BBQ model are described by the flavor-wave theory within the SU(3) representation. By fitting the experimental spin-wave dispersion, we determine the refined exchange couplings corresponding to the collinear antiferromagnetic iron pnictides. We also present the dynamic structure factors of both spin dipolar and quadrupolar components with connections to the future experiments.

cond-mat.str-el↗

arXiv2016

The Dependent Random Measures with Independent Increments in Mixture Models

When observations are organized into groups where commonalties exist amongst them, the dependent random measures can be an ideal choice for modeling. One of the propositions of the dependent random measures is that the atoms of the posterior distribution are shared amongst groups, and hence groups can borrow information from each other. When normalized dependent random measures prior with independent increments are applied, we can derive appropriate exchangeable probability partition function (EPPF), and subsequently also deduce its inference algorithm given any mixture model likelihood. We provide all necessary derivation and solution to this framework. For demonstration, we used mixture of Gaussians likelihood in combination with a dependent structure constructed by linear combinations of CRMs. Our experiments show superior performance when using this framework, where the inferred values including the mixing weights and the number of clusters both respond appropriately to the number of completely random measure used.

stat.ML↗

arXiv2016

Smoothed Hierarchical Dirichlet Process: A Non-Parametric Approach to Constraint Measures

Time-varying mixture densities occur in many scenarios, for example, the distributions of keywords that appear in publications may evolve from year to year, video frame features associated with multiple targets may evolve in a sequence. Any models that realistically cater to this phenomenon must exhibit two important properties: the underlying mixture densities must have an unknown number of mixtures, and there must be some "smoothness" constraints in place for the adjacent mixture densities. The traditional Hierarchical Dirichlet Process (HDP) may be suited to the first property, but certainly not the second. This is due to how each random measure in the lower hierarchies is sampled independent of each other and hence does not facilitate any temporal correlations. To overcome such shortcomings, we proposed a new Smoothed Hierarchical Dirichlet Process (sHDP). The key novelty of this model is that we place a temporal constraint amongst the nearby discrete measures $\{G_j\}$ in the form of symmetric Kullback-Leibler (KL) Divergence with a fixed bound $B$. Although the constraint we place only involves a single scalar value, it nonetheless allows for flexibility in the corresponding successive measures. Remarkably, it also led us to infer the model within the stick-breaking process where the traditional Beta distribution used in stick-breaking is now replaced by a new constraint calculated from $B$. We present the inference algorithm and elaborate on its solutions. Our experiment using NIPS keywords has shown the desirable effect of the model.

stat.ML↗

arXiv2015

A Program Logic for Verifying Secure Routing Protocols

The Internet, as it stands today, is highly vulnerable to attacks. However, little has been done to understand and verify the formal security guarantees of proposed secure inter-domain routing protocols, such as Secure BGP (S-BGP). In this paper, we develop a sound program logic for SANDLog-a declarative specification language for secure routing protocols for verifying properties of these protocols. We prove invariant properties of SANDLog programs that run in an adversarial environment. As a step towards automated verification, we implement a verification condition generator (VCGen) to automatically extract proof obligations. VCGen is integrated into a compiler for SANDLog that can generate executable protocol implementations; and thus, both verification and empirical evaluation of secure routing protocols can be carried out in this unified framework. To validate our framework, we encoded several proposed secure routing mechanisms in SANDLog, verified variants of path authenticity properties by manually discharging the generated verification conditions in Coq, and generated executable code based on SANDLog specification and ran the code in simulation.

cs.LO↗

arXiv2015

Signatures of indirect K-edge resonant inelastic x-ray scattering on magnetic excitations in triangular lattice antiferromagnet

We compute the K-edge indirect resonant inelastic x-ray scattering (RIXS) spectrum of a triangular lattice antiferromagnet in its ordered coplanar 3- sublattice 120 degree magnetic state. By considering the first order self$-$energy corrections to the spin wave spectrum, magnon decay rate, bimagnon interactions within the ladder approximation Bethe-Salpeter scheme, and the effect of three-magnon contributions up to 1/S- order we find that the RIXS spectra is non-trivially affected. For a purely isotropic triangular lattice model, the peak splitting mechanism and the appearance of a multipeak RIXS structure is primarily dictated by the damping of magnon modes. At a scattering wavevector corresponding to the zone center Γpoint and at the roton point q=M, where the magnon decay rate is zero, a stable single peak forms. At the $Γ$ point, the contribution is purely trimagnon at the 1/S level and occurs approximately at the trimagnon energy of 6JS. The roton peak occurs at a lower energy of 4JS. The K-edge single peak RIXS spectra at the roton momentum can be utilized as an experimental signature to detect the presence of roton excitations. A unique feature of the triangular lattice K-edge RIXS spectra is the nonvanishing RIXS intensity at both the zone center $Γ$ point and the antiferromagnetic wavevector K point. This result is in sharp contrast to the vanishing K-edge RIXS intensity of the collinear magnetic phases on the square lattice. We find that including XXZ anisotropy leads to additional peak splitting, including at the roton scattering wavevector where the single peak destabilizes towards a two-peak structure. The observed splitting is consistent with our earlier theoretical prediction of the effects of spatial anisotropy on the RIXS spectra of a frustrated quantum magnet [Luo, Datta, and Yao, Phys. Rev. B 89, 165103 (2014)].

cond-mat.mtrl-sci↗

arXiv2014

Spectrum splitting of bimagnon excitations in a spatially frustrated Heisenberg antiferromagnet revealed by resonant inelastic x-ray scattering

We perform a comprehensive analysis of the bimagnon resonant inelastic x-ray scattering (RIXS) intensity spectra of the spatially frustrated Jx-Jy-J2 Heisenberg model on a square lattice in both the antiferromagnetic and the collinear antiferromagnetic phase. We study the model for strong frustration and significant spatial anisotropy to highlight the key signatures of RIXS spectrum splitting which may be experimentally discernible. Based on an interacting spin wave theory study within the ladder approximation Bethe-Salpeter scheme, we find the appearance of a robust two-peak structure over a wide range of the transferred momenta in both magnetically ordered phases. The unfrustrated model has a single-peak structure with a two-peak splitting originating due to spatial anisotropy and frustrated interactions. Our predicted two-peak structure from both magnetically ordered regime can be realized in iron pnictides.

cond-mat.str-el↗

arXiv2014

Bidirectional Control of Absence Seizures by the Basal Ganglia: A Computational Evidence

Absence epilepsy is believed to be associated with the abnormal interactions between the cerebral cortex and thalamus. Besides the direct coupling, anatomical evidence indicates that the cerebral cortex and thalamus also communicate indirectly through an important intermediate bridge--basal ganglia. It has been thus postulated that the basal ganglia might play key roles in the modulation of absence seizures, but the relevant biophysical mechanisms are still not completely established. Using a biophysically based model, we demonstrate here that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. Under certain conditions, these two types of seizure control are observed to coexist in the same network. More importantly, due to the competition between the inhibitory SNr-TRN and SNr-SRN pathways, we find that both decreasing and increasing the activation of SNr neurons from the normal level may considerably suppress the generation of SWDs in the coexistence region. Overall, these results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder.

q-bio.NC↗