255 research outputs found
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
Reinforcement learning provides a mathematical framework for learning-based
control, whose success largely depends on the amount of data it can utilize.
The efficient utilization of historical trajectories obtained from previous
policies is essential for expediting policy optimization. Empirical evidence
has shown that policy gradient methods based on importance sampling work well.
However, existing literature often neglect the interdependence between
trajectories from different iterations, and the good empirical performance
lacks a rigorous theoretical justification. In this paper, we study a variant
of the natural policy gradient method with reusing historical trajectories via
importance sampling. We show that the bias of the proposed estimator of the
gradient is asymptotically negligible, the resultant algorithm is convergent,
and reusing past trajectories helps improve the convergence rate. We further
apply the proposed estimator to popular policy optimization algorithms such as
trust region policy optimization. Our theoretical results are verified on
classical benchmarks
Prediction of PEV Adoption with Agent-Based Parameterized Bass Network Diffusion Model
Although the growing electric vehicle (EV) population is leading us into a
more sustainable world, it is also bringing challenges for the manufacturers's
production planning, the charging facility providers's expansion plan, and the
energy system's adaption to greater electricity demand. To tackle these
challenges, a model to predict EV growth in geographical scope would be
helpful. In this study, an agent-based parameterized bass network diffusion
model was developed for EV population data in Washington. The model included
income levels and number of neighbors adopted as two key factors in determining
EV diffusion probabilities. With the parameters estimated from simulation, the
resulting model achieve a high estimation accuracy for EV adoption in
Washington in both temporal and geographical scopes. This model could be used
to predict EV growth in Washington, and to be adopted to other geographical
areas
Episodic Bayesian Optimal Control with Unknown Randomness Distributions
Stochastic optimal control with unknown randomness distributions has been
studied for a long time, encompassing robust control, distributionally robust
control, and adaptive control. We propose a new episodic Bayesian approach that
incorporates Bayesian learning with optimal control. In each episode, the
approach learns the randomness distribution with a Bayesian posterior and
subsequently solves the corresponding Bayesian average estimate of the true
problem. The resulting policy is exercised during the episode, while additional
data/observations of the randomness are collected to update the Bayesian
posterior for the next episode. We show that the resulting episodic value
functions and policies converge almost surely to their optimal counterparts of
the true problem if the parametrized model of the randomness distribution is
correctly specified. We further show that the asymptotic convergence rate of
the episodic value functions is of the order . We develop an
efficient computational method based on stochastic dual dynamic programming for
a class of problems that have convex value functions. Our numerical results on
a classical inventory control problem verify the theoretical convergence
results and demonstrate the effectiveness of the proposed computational method
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.Comment: to appear in AAAI (2019). The first two authors contributed equally
to the paper. Corresponding Author: Feng L
Microfiber-based polarization beam splitter and its application for passively mode-locked all-fiber laser
Nonlinear polarization evolution based on polarization beam splitter (PBS) is a classical technique for passive mode-locking of fiber lasers. Different from commonly used bulky PBS, in this paper all-fiber PBSs composed of two parallel coupled microfibers have been proposed and fabricated under the condition of appropriate microfiber diameter and coupling length. Using our fabricated microfiber PBSs, passively mode-locked all-fiber lasers have also been demonstrated. The results indicate that the microfiber-based PBS has advantages of simple fabrication, compact size, and most importantly, variable polarization extinction ratio and operation bandwidth. The all-fiber mode-locked lasers with the microfiber PBSs generating stable pulses at both 1.0 μm and 1.5 μm wavelength bands have comparable performance with their counterparts based on bulky PBSs. It may be a step towards true all-fiber mode-locked laser and other all-fiber systems
Ultrathin MgB2 films fabricated on Al2O3 substrate by hybrid physical-chemical vapor deposition with high Tc and Jc
Ultrathin MgB2 superconducting films with a thickness down to 7.5 nm are
epitaxially grown on (0001) Al2O3 substrate by hybrid physical-chemical vapor
deposition method. The films are phase-pure, oxidation-free and continuous. The
7.5 nm thin film shows a Tc(0) of 34 K, which is so far the highest Tc(0)
reported in MgB2 with the same thickness. The critical current density of
ultrathin MgB2 films below 10 nm is demonstrated for the first time as Jc ~
10^6 A cm^{-2} for the above 7.5 nm sample at 16 K. Our results reveal the
excellent superconducting properties of ultrathin MgB2 films with thicknesses
between 7.5 and 40 nm on Al2O3 substrate.Comment: 7 pages, 4 figures, 2 table
- …