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Jun Ma

Publications and source records attributed to Jun Ma.

At least 397 records · Page 22Linked to original sources
arXiv2020

A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation

Unstructured Persona-oriented Dialogue Systems (UPDS) has been demonstrated effective in generating persona consistent responses by utilizing predefined natural language user persona descriptions (e.g., "I am a vegan"). However, the predefined user persona descriptions are usually short and limited to only a few descriptive words, which makes it hard to correlate them with the dialogues. As a result, existing methods either fail to use the persona description or use them improperly when generating persona consistent responses. To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses. PEE consists of two main modules: persona exploration and persona exploitation. The former learns to extend the predefined user persona description by mining and correlating with existing dialogue corpus using a variational auto-encoder (VAE) based topic model. The latter learns to generate persona consistent responses by utilizing the predefined and extended user persona description. In order to make persona exploitation learn to utilize user persona description more properly, we also introduce two persona-oriented loss functions: Persona-oriented Matching (P-Match) loss and Persona-oriented Bag-of-Words (P-BoWs) loss which respectively supervise persona selection in encoder and decoder. Experimental results show that our approach outperforms state-of-the-art baselines, in terms of both automatic and human evaluations.

cs.CL↗
arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗
arXiv2020

The Nature of the Double Nuclei in the Barred S0 Galaxy IC676

The lenticular galaxy IC 676 is a barred galaxy with double nuclei and active star formation in the central region. In this work we present the long-slit spectroscopy and archival multi-wavelength images to investigate the nature and origin of the double nuclei in IC 676. Through photometric 1D brightness profiles and 2D image decomposition, we show that this galaxy consists of a stellar bar with the length of $\sim$ 2.5 kpc and two Sérsic disks both of which with Sérsic index $\it n \sim$ 1.3. There is probably little or no bulge component assembled in IC 676. The luminosities of the double nuclei are primarily dominated by young stellar populations within the ages of 1-10 Myr. The northern nucleus has stronger star formation activity than the southern one. The surface densities of the star formation rate in the double nuclei are similar to those in starburst galaxies or the circumnuclear star forming regions in spiral galaxies. Each of the double nuclei in IC 676 likely consists of young massive star clusters, which can be resolved as bright knots in the HST high resolution image. Our results suggest that IC 676 likely has a complex formation and evolutionary history. The secular processes driven by the stellar bar and external accretion may dominate the formation and evolution of its double nuclei. This indicates that the secular evolution involving the internal and external drivers may have an important contribution for the evolution of lenticular galaxies.

astro-ph.GA↗
arXiv2020

The 1/k-Eulerian polynomials of type B

In this paper, we give a type B analogue of the 1/k-Eulerian polynomials. Properties of this kind of polynomials, including combinatorial interpretations, recurrence relations and gamma-positivity are studied. In particular, we show that the 1/k-Eulerian polynomials of type B are gamma-positive when $k>0$. Moreover, we obtain the corresponding results for derangements of type B. We show that a type B 1/k-derangement polynomials $d_n^B(x;k)$ are bi-gamma-positive when $k\geq 1/2$. In particular, we get a symmetric decomposition of $d_n^B(x;1/2)$ in terms of the classical derangement polynomials.

math.CO↗
arXiv2020

On the unimodality of the Taylor expansion coefficients of Jacobian elliptic functions

The Jacobian elliptic functions are standard forms of elliptic functions, and they were independently introduced by C.G.J. Jacobi and N.H. Abel. In this paper, we study the unimodality of Taylor expansion coefficients of the Jacobian elliptic functions sn(u,k) and cn(u,k). By using the theory of gamma-positivity, we obtain that the Taylor expansion coefficients of sn(u,k) are symmetric and unimodal, and that of cn(u,k) are unimodal and alternatingly increasing.

math.CO↗
arXiv2019

RefNet: A Reference-aware Network for Background Based Conversation

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversations (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, they either cannot generate natural responses or have difficulty in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address the two issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly cite a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.

cs.CL↗
arXiv2019

Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation

Background Based Conversations (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background knowledge (a text fragment containing related facts or comments, etc.) based on which to generate the next response. Previous work addresses KS by employing attention and/or pointer mechanisms. These mechanisms use a local perspective, i.e., they select a token at a time based solely on the current decoding state. We argue for the adoption of a global perspective, i.e., pre-selecting some text fragments from the background knowledge that could help determine the topic of the next response. We enhance KS in BBCs by introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a conversational context and background knowledge, we first learn a topic transition vector to encode the most likely text fragments to be used in the next response, which is then used to guide the local KS at each decoding timestamp. In order to effectively learn the topic transition vector, we propose a distantly supervised learning schema. Experimental results show that the GLKS model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluation. More importantly, GLKS achieves this without requiring any extra annotations, which demonstrates its high degree of scalability.

cs.CL↗
arXiv2019

Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions

We propose a new nonparametric estimator for first-price auctions with independent private values that imposes the monotonicity constraint on the estimated inverse bidding strategy. We show that our estimator has a smaller asymptotic variance than that of Guerre, Perrigne and Vuong's (2000) estimator. In addition to establishing pointwise asymptotic normality of our estimator, we provide a bootstrap-based approach to constructing uniform confidence bands for the density function of latent valuations.

econ.EM↗
arXiv2019

Improving Outfit Recommendation with Co-supervision of Fashion Generation

The task of fashion recommendation includes two main challenges: visual understanding and visual matching. Visual understanding aims to extract effective visual features. Visual matching aims to model a human notion of compatibility to compute a match between fashion items. Most previous studies rely on recommendation loss alone to guide visual understanding and matching. Although the features captured by these methods describe basic characteristics (e.g., color, texture, shape) of the input items, they are not directly related to the visual signals of the output items (to be recommended). This is problematic because the aesthetic characteristics (e.g., style, design), based on which we can directly infer the output items, are lacking. Features are learned under the recommendation loss alone, where the supervision signal is simply whether the given two items are matched or not. To address this problem, we propose a neural co-supervision learning framework, called the FAshion Recommendation Machine (FARM). FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information. FARM enhances visual matching by introducing a novel layer-to-layer matching mechanism to fuse aesthetic information more effectively, and meanwhile avoiding paying too much attention to the generation quality and ignoring the recommendation performance. Extensive experiments on two publicly available datasets show that FARM outperforms state-of-the-art models on outfit recommendation, in terms of AUC and MRR. Detailed analyses of generated and recommended items demonstrate that FARM can encode better features and generate high quality images as references to improve recommendation performance.

cs.IR↗
arXiv2019

New nearby hypervelocity stars and their spatial distribution from Gaia DR2

Base on about 4,500 large tangential velocity ($V_\mathrm{tan}>0.75V_\mathrm{esc}$) with high-precision proper motions and $5σ$ parallaxes in Gaia DR2 5D information derived from parallax and proper motion, we identify more than 600 high velocity stars with $50\%$ unbound probability. Of these, 28 nearby (less than 6 kpc) late-type Hypervelocity stars (HVSs) with over $99\%$ possibility of unbound are discovered. In order to search for the unbound stars from the full Gaia DR2 6D phase space information derived from parallax, proper motion and radial velocity, we also identify 28 stars from the total velocity ($V_\mathrm{gc}>0.75V_\mathrm{esc}$) that have probabilities greater than $50\%$ of being unbound from the Galaxy. Of these, only three have a nearly $99\%$ probabilities of being unbound. On the whole HVSs subsample, there is 12 sources reported by other surveys. We study the spatial distribution of angular positions and angular separation of HVSs. We find the unbound HVSs are spatially anisotropic that is most significant in the Galactic longitude at more than $3σ$ level, and lower unbound probability HVSs are systematically more isotropic. The spatial distribution can reflect the origin of HVSs and we discuss the possible origin link with the anisotropy.

astro-ph.GA↗
arXiv2019

Chemical and Kinematic Properties of the Galactic Disk from the LAMOST and Gaia Sample Stars

We determined the chemical and kinematic properties of the Galactic thin and thick disk using a sample of 307,246 A/F/G/K-type giant stars from the LAMOST spectroscopic survey and Gaia DR2 survey. Our study found that the thick disk globally exhibits no metallicity radial gradient, but the inner disk ($R \le 8$ kpc) and the outer disk ($R>8$ kpc) have different gradients when they are studied separately. The thin disk also shows two different metallicity radial gradients for the inner disk and the outer disk, and has steep metallicity vertical gradient of d[Fe/H]/d$|z|$ $=-0.12\pm0.0007$ dex kpc$^{-1}$, but it becomes flat when it is measured at increasing radial distance, while the metallicity radial gradient becomes weaker with increasing vertical distance. Adopting a galaxy potential model, we derived the orbital eccentricity of sample stars and found a downtrend of average eccentricity with increasing metallicity for the thick disk. The variation of the rotation velocity with the metallicity shows a positive gradient for the thick disk stars and a negative one for the thin disk stars. Comparisons of our observed results with models of disk formation suggest that radial migration could have influenced the chemical evolution of the thin disk. The formation of the thick disk could be affected by more than one processes: the accretion model could play an indispensable role, while other formation mechanisms, such as the radial migration or heating scenario model could also have a contribution.

astro-ph.GA↗
arXiv2019

Field-free switching of a Spin-orbit torque device aided by interlayer-coupling induced domain walls

The spin-orbit torque device is promising as a candidate for next generation magnetic memory, while the static in-plane field needed to induce deterministic switching is a main obstacle for its application in highly integrated circuits. Instead of introducing effective field into the device, in this work we present an alternative way to achieve the field-free current-driven magnetization switching. By adding Tb/Co multilayers at two ends of the current channel, assisting domain wall is created by interlayer exchange coupling. The field-free deterministic switching is achieved by the movement of the domain wall driven by current. By loop shift measurement we find the driven force exerted on the domain wall is determined by the direction of the in-plane moment in the domain wall. Finally, we modify the device into a synthetic antiferromagnetic structure to solve the problem of reading out signal. The present work broadens the choice of field-free spin-orbit torque device design and clearly depicts the difference between two different switching mechanism.

physics.app-ph↗
arXiv2019

The alternating run polynomials of permutations

In this paper, we first consider a generalization of the David-Barton identity which relate the alternating run polynomials to Eulerian polynomials. By using context-free grammars, we then present a combinatorial interpretation of a family of q-alternating run polynomials. Furthermore, we introduce the definition of semi-gamma-positive polynomial and we show the semi-gamma-positivity of the alternating run polynomials of dual Stirling permutations. A connection between the up-down run polynomials of permutations and the alternating run polynomials of dual Stirling permutations is established.

math.CO↗
arXiv2019

Proportional hazards model with partly interval censoring and its penalized likelihood estimation

This paper considers the problem of semi-parametric proportional hazards model fitting for interval, left and right censored survival times. We adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously, where the penalty is introduced in order to regularize the baseline hazard estimate. We present asymptotic properties of our estimate, allowing for the possibility that it may lie on the boundary of the parameter space. We also provide a computational method based on marginal likelihood, which allows the regularization parameter to be determined automatically. Comparisons of our method with other approaches are given in simulations which demonstrate that our method has favourable performance. A real data application involving a model for melanoma recurrence is presented and an R package implementing the methods is available.

stat.ME↗
arXiv2019

Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator

We consider inference on the probability density of valuations in the first-price sealed-bid auctions model within the independent private value paradigm. We show the asymptotic normality of the two-step nonparametric estimator of Guerre, Perrigne, and Vuong (2000) (GPV), and propose an easily implementable and consistent estimator of the asymptotic variance. We prove the validity of the pointwise percentile bootstrap confidence intervals based on the GPV estimator. Lastly, we use the intermediate Gaussian approximation approach to construct bootstrap-based asymptotically valid uniform confidence bands for the density of the valuations.

econ.EM↗
arXiv2019

Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation

Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. NOR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.

cs.IR↗
arXiv2019

Gamma-positivity and partial gamma-positivity of descent-type polynomials

In this paper, we study gamma-positivity of descent-type polynomials by introducing the change of context-free grammars method. We first present grammatical proofs of the gamma-positivity of the Eulerian polynomials, type B Eulerian polynomials, derangement polynomials, Narayana polynomials and type B Narayana polynomials. We then provide partial gamma-positive expansions for several multivariate polynomials associated to Stirling permutations, Legendre-Stirling permutations, Jacobi-Stirling permutations and type B derangements, and the recurrences for the partial gamma-coefficients of these expansions are also obtained. Moreover, we define variants of the Foata-Strehl group action which are used to give combinatorial interpretations for the coefficients of most of these partial gamma-positive expansions.

math.CO↗