arXiv subjects

Xi Chen

Publications and source records attributed to Xi Chen.

At least 1,099 records · Page 61Linked to original sources

arXiv2017

HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation

Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains. However, task requester usually has a limited amount of budget, thus it is desirable to have a policy to wisely allocate the budget to achieve better quality. In this paper, we study the principle of information maximization for active sampling strategies in the framework of HodgeRank, an approach based on Hodge Decomposition of pairwise ranking data with multiple workers. The principle exhibits two scenarios of active sampling: Fisher information maximization that leads to unsupervised sampling based on a sequential maximization of graph algebraic connectivity without considering labels; and Bayesian information maximization that selects samples with the largest information gain from prior to posterior, which gives a supervised sampling involving the labels collected. Experiments show that the proposed methods boost the sampling efficiency as compared to traditional sampling schemes and are thus valuable to practical crowdsourcing experiments.

stat.ML↗

arXiv2017

Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution

With exploiting contextual information over large image regions in an efficient way, the deep convolutional neural network has shown an impressive performance for single image super-resolution (SR). In this paper, we propose a deep convolutional network by cascading the well-designed inception-residual blocks within the deep Laplacian pyramid framework to progressively restore the missing high-frequency details of high-resolution (HR) images. By optimizing our network structure, the trainable depth of the proposed network gains a significant improvement, which in turn improves super-resolving accuracy. With our network depth increasing, however, the saturation and degradation of training accuracy continues to be a critical problem. As regard to this, we propose an effective two-stage training strategy, in which we firstly use images downsampled from the ground-truth HR images as the optimal objective to train the inception-residual blocks in each pyramid level with an extremely high learning rate enabled by gradient clipping, and then the ground-truth HR images are used to fine-tune all the pre-trained inception-residual blocks for obtaining the final SR model. Furthermore, we present a new loss function operating in both image space and local rank space to optimize our network for exploiting the contextual information among different output components. Extensive experiments on benchmark datasets validate that the proposed method outperforms existing state-of-the-art SR methods in terms of the objective evaluation as well as the visual quality.

cs.CV↗

arXiv2017

Effects of temperature and strain rate on mechanical behaviors of Stone-Wales defective monolayer black phosphorene

The mechanical behaviors of monolayer black phosphorene (MBP) are explored by molecular dynamics (MD) simulations using reactive force field. It is revealed that the temperature and strain rate have significant influence on mechanical behaviors of MBP, and they are further weakened by SW (Stone-Wales) defects. In general, the tensile strength for both of the pristine and SW defective MBP decreases with the increase of temperature or decreasing of strain rate. Surprisingly, for relatively high temperature and low strain rate, phase transition from the black phosphorene to a mixture of β-phase (β-P) and γ-phase (γ-P) is observed for the SW-2 defective MBP under armchair tension, while self-healing of the SW-2 defect is observed under zigzag tension. A deformation map of SW-2 defective MBP under armchair tension at different temperature and strain rate is established, which is useful for the design of phosphorene allotropes by strain. The results presented herein yield useful insights for designing and tuning the structure, and the mechanical and physical properties of phosphorene.

physics.comp-ph↗

arXiv2017

Meta Learning Shared Hierarchies

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.

cs.LG↗

arXiv2017

An Effective Training Method For Deep Convolutional Neural Network

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the model during training. The proposed method can be considered an unusual form of regularization: the model parameters are obtained by training a relatively low-capacity model, that is relatively easy to optimize at the beginning, with only a few iterations, and these parameters are reused for the initialization of a higher-capacity model. We derive the upper and lower bounds of variance of the weight variation, and show that the initial symmetric structure of NGs helps stabilize training. We evaluate the proposed method on different frameworks of convolutional neural networks over two object recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results showed that the proposed method allows us to (1) speed up the convergence of training, (2) allow for less careful weight initialization, (3) improve or at least maintain the performance of the model at negligible extra computational cost, and (4) easily train a very deep model.

cs.LG↗

arXiv2017

Asymptotically Optimal Sequential Design for Rank Aggregation

A sequential design problem for rank aggregation is commonly encountered in psychology, politics, marketing, sports, etc. In this problem, a decision maker is responsible for ranking $K$ items by sequentially collecting pairwise noisy comparison from judges. The decision maker needs to choose a pair of items for comparison in each step, decide when to stop data collection, and make a final decision after stopping, based on a sequential flow of information. Due to the complex ranking structure, existing sequential analysis methods are not suitable. In this paper, we formulate the problem under a Bayesian decision framework and propose sequential procedures that are asymptotically optimal. These procedures achieve asymptotic optimality by seeking for a balance between exploration (i.e. finding the most indistinguishable pair of items) and exploitation (i.e. comparing the most indistinguishable pair based on the current information). New analytical tools are developed for proving the asymptotic results, combining advanced change of measure techniques for handling the level crossing of likelihood ratios and classic large deviation results for martingales, which are of separate theoretical interest in solving complex sequential design problems. A mirror-descent algorithm is developed for the computation of the proposed sequential procedures.

stat.ME↗

arXiv2017

Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an optimal classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public data set and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities.

cs.LG↗

arXiv2017

A Novel Deformation Method for Higher Order Mesh Generation

The development of higher order finite elements methods has become an active research area. The deformation method for mesh generation has achieved a prescribed positive Jacobian determinant constraint and it has been a useful method for mesh adaptation. In this work, the deformation method is combined with local refinement to generate higher order meshes. 2D and 3D numerical examples of element order p=3 are shown to demonstrate the effectiveness of our new algorithm.

cs.CG↗

arXiv2017

Widespread Presence of Glycolaldehyde and Ethylene Glycol Around Sagittarius B2

We report the detection of widespread CH$_2$OHCHO and HOCH$_2$CH$_2$OH emission in Galactic center giant molecular cloud Sagittarius B2 using the Shanghai Tianma 65m Radio Telescope. Our observations show for the first time that the spatial distribution of these two important prebiotic molecules extends over 15 arc-minutes, corresponding to a linear size of approximately 36 pc. These two molecules are not just distributed in or near the hot cores. The abundance of these two molecules seems to decrease from the cold outer region to the central region associated with star-formation activity. Results present here suggest that these two molecules are likely to form through a low temperature process. Recent theoretical and experimental studies demonstrated that prebiotic molecules can be efficiently formed in icy grain mantles through several pathways. However, these complex ice features cannot be directly observed, and most constraints on the ice compositions come from millimeter observations of desorbed ice chemistry products. These results, combined with laboratory studies, strongly support the existence of abundant prebiotic molecules in ices.

astro-ph.GA↗

arXiv2017

Molecular Beam Epitaxy Growth of Tetragonal FeS Film on SrTiO3(001) Substrate

We report the successful growth of tetragonal FeS film with one or two unit-cell (UC) thickness on SrTiO3(001) substrate by molecular beam epitaxy. Large lattice constant mismatch with the substrate leads to high density of defects in single UC FeS, while it has been significantly reduced in double UC thick film due to the lattice relaxation. The scanning tunneling spectra on the surface of FeS thin film reveal the electronic doping effect of single UC FeS from the substrate. In addition, at the Fermi level, the energy gaps of approximate 1.5 meV are observed in films of both thicknesses at 4.6 K and below. The absence of coherence peaks of gap spectra may be related to the preformed Cooper-pairs without phase coherence.

cond-mat.supr-con↗

arXiv2017

Xiao's Conjecture on Canonically Fibered Surfaces

A canonically fibered surface is a surface whose canonical series maps it to a curve. Using Miyaoka-Yau inequality, A. Beauville proved that a canonically fibered surface has relative genus at most 5 when its geometric genus is sufficiently large. G. Xiao further conjectured that the relative genus cannot exceed 4. We give a proof of this conjecture.

math.AG↗

arXiv2017

Direct observation of magnon-phonon coupling in yttrium iron garnet

The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic transition temperature of $\sim$560 K has been widely used in microwave and spintronic devices. Anomalous features in the spin Seeback effect (SSE) voltages have been observed in Pt/YIG and attributed to the magnon-phonon coupling. Here we use inelastic neutron scattering to map out low-energy spin waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic field. By comparing the zero and 9.1 T data, we find that instead of splitting and opening up gaps at the spin wave and acoustic phonon dispersion intersecting points, magnon-phonon coupling in YIG enhances the hybridized scattering intensity. These results are different from expectations of conventional spin-lattice coupling, calling for new paradigms to understand the scattering process of magnon-phonon interactions and the resulting magnon-polarons.

cond-mat.str-el↗

arXiv2017

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

stat.ML↗

arXiv2017

A 95 GHz Methanol Emission Survey Toward Eight Small Supernova Remnants

We report on a 95 GHz ($8_0-7_1$ A$^{+}$) methanol (CH$_3$OH) emission survey with the Purple Mountain Observatory Delingha 13.7 m telescope. Eight supernova remnants (SNRs) with angular size $\lesssim$ 10' were observed, but emission was only detected in three SNRs near the Galactic center (Sgr A East, G 0.1-0.1, and G 359.92-0.09). CH$_3$OH emission mainly surrounds the SNRs and can be decomposed into nine spatial peaks with velocity range of eight peaks being (-30, 70) km s$^{-1}$, and the other (70, 120) km s$^{-1}$. They are probably excited by interaction with these SNRs and adjacent molecular gas in the central molecular zone (CMZ), although star formation may play an important role in exciting CH$_3$OH emission in some regions of CMZ. We infer that tidal action is unlikely to be an excitation source for CH$_3$OH emission.

astro-ph.GA↗

arXiv2017

Graph Estimation for Matrix-variate Gaussian Data

Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among matrix entries, conducting support recovery yields valuable information. A commonly used approach is the penalized log-likelihood method. However, due to the complicated structure of precision matrices in the form of Kronecker product, the log-likelihood is non-convex, which presents challenges for both computation and theoretical analysis. In this paper, we propose an alternative approach by formulating the support recovery problem into a multiple testing problem. A new test statistic is developed and based on that, we use the popular Benjamini and Hochberg's procedure to control false discovery rate (FDR) asymptotically. Our method involves only convex optimization, making it computationally attractive. Theoretically, our method allows very weak conditions, i.e., even when the sample size is finite and the dimensions go to infinity, the asymptotic normality of the test statistics and FDR control can still be guaranteed. We further provide the power analysis result. The finite sample performance of the proposed method is illustrated by both simulated and real data analysis.

stat.ME↗

arXiv2017

Optimal Stopping and Worker Selection in Crowdsourcing: an Adaptive Sequential Probability Ratio Test Framework

In this paper, we aim at solving a class of multiple testing problems under the Bayesian sequential decision framework. Our motivating application comes from binary labeling tasks in crowdsourcing, where the requestor needs to simultaneously decide which worker to choose to provide the label and when to stop collecting labels under a certain budget constraint. We start with the binary hypothesis testing problem to determine the true label of a single object, and provide an optimal solution by casting it under the adaptive sequential probability ratio test (Ada-SPRT) framework. We characterize the structure of the optimal solution, i.e., optimal adaptive sequential design, which minimizes the Bayes risk through log-likelihood ratio statistic. We also develop a dynamic programming algorithm that can efficiently approximate the optimal solution. For the multiple testing problem, we further propose to adopt an empirical Bayes approach for estimating class priors and show that our method has an averaged loss that converges to the minimal Bayes risk under the true model. The experiments on both simulated and real data show the robustness of our method and its superiority in labeling accuracy as compared to several other recently proposed approaches.

stat.ME↗

arXiv2017

Beyond Talagrand Functions: New Lower Bounds for Testing Monotonicity and Unateness

We prove a lower bound of $\tildeΩ(n^{1/3})$ for the query complexity of any two-sided and adaptive algorithm that tests whether an unknown Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ is monotone or far from monotone. This improves the recent bound of $\tildeΩ(n^{1/4})$ for the same problem by Belovs and Blais [BB15]. Our result builds on a new family of random Boolean functions that can be viewed as a two-level extension of Talagrand's random DNFs. Beyond monotonicity, we also prove a lower bound of $\tildeΩ(n^{2/3})$ for any two-sided and adaptive algorithm, and a lower bound of $\tildeΩ(n)$ for any one-sided and non-adaptive algorithm for testing unateness, a natural generalization of monotonicity. The latter matches the recent linear upper bounds by Khot and Shinkar [KS15] and by Chakrabarty and Seshadhri [CS16].

cs.CC↗

arXiv2017

Boolean Unateness Testing with $\widetilde{O}(n^{3/4})$ Adaptive Queries

We give an adaptive algorithm which tests whether an unknown Boolean function $f\colon \{0, 1\}^n \to\{0, 1\}$ is unate, i.e. every variable of $f$ is either non-decreasing or non-increasing, or $ε$-far from unate with one-sided error using $\widetilde{O}(n^{3/4}/ε^2)$ queries. This improves on the best adaptive $O(n/ε)$-query algorithm from Baleshzar, Chakrabarty, Pallavoor, Raskhodnikova and Seshadhri when $1/ε\ll n^{1/4}$. Combined with the $\widetildeΩ(n)$-query lower bound for non-adaptive algorithms with one-sided error of [CWX17, BCPRS17], we conclude that adaptivity helps for the testing of unateness with one-sided error. A crucial component of our algorithm is a new subroutine for finding bi-chromatic edges in the Boolean hypercube called adaptive edge search.

cs.CC↗