J. Yang

J. Yang
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Computer Science - Computer Vision and Pattern Recognition (11)
 
Computer Science - Learning (4)
 
High Energy Astrophysical Phenomena (4)
 
Physics - Materials Science (3)
 
Astrophysics of Galaxies (3)
 
Mathematics - Analysis of PDEs (3)
 
Computer Science - Neural and Evolutionary Computing (2)
 
Computer Science - Networking and Internet Architecture (2)
 
Computer Science - Computation and Language (2)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (2)
 
Computer Science - Graphics (2)
 
Nonlinear Sciences - Pattern Formation and Solitons (2)
 
Physics - Accelerator Physics (1)
 
Computer Science - Information Retrieval (1)
 
Physics - Optics (1)
 
Mathematics - Algebraic Geometry (1)
 
Mathematics - Number Theory (1)
 
Physics - Strongly Correlated Electrons (1)
 
Computer Science - Computational Geometry (1)
 
Computer Science - Artificial Intelligence (1)
 
Physics - Disordered Systems and Neural Networks (1)
 
High Energy Physics - Phenomenology (1)
 
Mathematics - Classical Analysis and ODEs (1)
 
Computer Science - Distributed; Parallel; and Cluster Computing (1)
 
Computer Science - Operating Systems (1)
 
Computer Science - Performance (1)
 
Nonlinear Sciences - Chaotic Dynamics (1)
 
Physics - Statistical Mechanics (1)
 
Mathematics - Dynamical Systems (1)
 
Computer Science - Cryptography and Security (1)
 
Computer Science - Software Engineering (1)
 
Physics - Instrumentation and Detectors (1)
 
Nonlinear Sciences - Adaptation and Self-Organizing Systems (1)
 
Quantitative Biology - Quantitative Methods (1)
 
High Energy Physics - Experiment (1)
 
Physics - Atmospheric and Oceanic Physics (1)
 
General Relativity and Quantum Cosmology (1)

Publications Authored By J. Yang

Universal style transfer aims to transfer any arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. Read More

The study of energy transport properties in heterogeneous materials has attracted scientific interest for more than a century, and it continues to offer fundamental and rich questions. One of the unanswered challenges is to extend Anderson theory for uncorrelated and fully disordered lattices in condensed-matter systems to physical settings in which additional effects compete with disorder. Specifically, the effect of strong nonlinearity has been largely unexplored experimentally, partly due to the paucity of testbeds that can combine the effect of disorder and nonlinearity in a controllable manner. Read More

The growing pressure on cloud application scalability has accentuated storage performance as a critical bottle- neck. Although cache replacement algorithms have been extensively studied, cache prefetching - reducing latency by retrieving items before they are actually requested remains an underexplored area. Existing approaches to history-based prefetching, in particular, provide too few benefits for real systems for the resources they cost. Read More

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84. Read More

Deep learning (DL) systems are increasingly deployed in security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner-case inputs are of great importance. However, systematic testing of large-scale DL systems with thousands of neurons and millions of parameters for all possible corner-cases is a hard problem. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose different erroneous behaviors for rare inputs. Read More

2017May
Authors: R. U. Abbasi, T. Abu-Zayyad, M. Allen, R. Azuma, E. Barcikowski, J. W. Belz, D. R. Bergman, S. A. Blake, M. Byrne, R. Cady, B. G. Cheon, J. Chiba, M. Chikawa, T. Fujii, M. Fukushima, T. Goto, W. Hanlon, Y. Hayashi, N. Hayashida, K. Hibino, K. Honda, D. Ikeda, N. Inoue, T. Ishii, R. Ishimori, H. Ito, D. Ivanov, S. Jeong, C. C. H. Jui, K. Kadota, F. Kakimoto, O. Kalashev, K. Kasahara, H. Kawai, S. Kawakami, S. Kawana, K. Kawata, E. Kido, H. B. Kim, J. H. Kim, J. H. Kim, S. S. Kishigami, S. Kitamura, Y. Kitamura, P. R. Krehbiel, V. Kuzmin, Y. J. Kwon, J. Lan, R. LeVon, J. P. Lundquist, K. Machida, K. Martens, T. Matsuda, T. Matsuyama, J. N. Matthews, M. Minamino, K. Mukai, I. Myers, K. Nagasawa, S. Nagataki, R. Nakamura, T. Nakamura, T. Nonaka, S. Ogio, J. Ogura, M. Ohnishi, H. Ohoka, K. Oki, T. Okuda, M. Ono, R. Onogi, A. Oshima, S. Ozawa, I. H. Park, M. S. Pshirkov, J. Remington, W. Rison, D. Rodeheffer, D. C. Rodriguez, G. Rubtsov, D. Ryu, H. Sagawa, K. Saito, N. Sakaki, N. Sakurai, T. Seki, K. Sekino, P. D. Shah, F. Shibata, T. Shibata, H. Shimodaira, B. K. Shin, H. S. Shin, J. D. Smith, P. Sokolsky, R. W. Springer, B. T. Stokes, T. A. Stroman, T. Suzawa, H. Takai, M. Takeda, R. Takeishi, A. Taketa, M. Takita, Y. Tameda, H. Tanaka, K. Tanaka, M. Tanaka, R. J. Thomas, S. B. Thomas, G. B. Thomson, P. Tinyakov, I. Tkachev, H. Tokuno, T. Tomida, S. Troitsky, Y. Tsunesada, K. Tsutsumi, Y. Uchihori, S. Udo, F. Urban, G. Vasiloff, T. Wong, M. Yamamoto, R. Yamane, H. Yamaoka, K. Yamazaki, J. Yang, K. Yashiro, Y. Yoneda, S. Yoshida, H. Yoshii, Z. Zundel

Bursts of energetic particle showers have been observed in coincidence with downward propagating negative leaders in lightning flashes by the Telescope Array Surface Detector (TASD). The TASD is a 700 square kilometer cosmic ray observatory located in western Utah. Lightning position, time, and electric field information was collected by a lightning mapping array and slow antenna colocated with the TASD. Read More

The correlation of co-located atomic clocks is difficult to measure because their common-mode noise induced by environment would be canceled out during the comparison measurement. It is like two people sitting on a bus will have no idea that they are moving by simply comparing between themselves until they look outside the window. With the development of fiber-based high-precision frequency transfer technique, we can directly measure the correlation of co-located atomic clocks with the help of remote ones. Read More

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. Read More

Applying the Picard-Fuchs equation to the discontinuous differential system, we obtain the upper bounds of the number of zeros for Abelian integrals of four kinds of quadratic differential systems when it is perturbed inside all discontinuous polynomials with degree $n$. Furthermore, by using the {\it Chebyshev criterion}, we obtain the sharp upper bounds on each period annulus for $n=2$. Read More

We present a large-scale mapping toward the GEM OB1 association in the galactic anti-center direction. The 9^deg * 6.5^deg area was mapped in 12CO, 13CO, and C18O with 50" angular resolution at 30" sampling. Read More

Recently, a number of nonlocal integrable equations, such as the PT-symmetric nonlinear Schrodinger (NLS) equation and PT-symmetric Davey-Stewartson equations, were proposed and studied. Here we show that many of such nonlocal integrable equations can be converted to local integrable equations through simple variable transformations. Examples include these nonlocal NLS and Davey-Stewartson equations, a nonlocal derivative NLS equation, the reverse space-time complex modified Korteweg-de Vries (CMKdV) equation, and many others. Read More

Chimera states, which consist of coexisting domains of spatially coherent and incoherent dynamics, have been widely found in nonlocally coupled oscillatory systems. We demonstrate for the first time that chimera states can emerge from excitable systems under nonlocal coupling in which isolated units only allow for the equilibrium. We theoretically reveal that nonlocal coupling induced collective oscillation is behind the occurrence of the chimera states. Read More

Solving the global method of Weighted Least Squares (WLS) model in image filtering is both time- and memory-consuming. In this paper, we present an alternative approximation in a time- and memory- efficient manner which is denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a large linear system, we propose to iteratively solve a sequence of subsystems which are one-dimensional WLS models. Read More

In this paper, we show that the global monopole spacetime is one of the exact solutions of Einstein equations by means of the method treating the matter field as a non-linear sigma model, without the weak field approximation applied in the original derivation by Barriola and Vilenkin in 1989. Further more, we find the physical original of the topological charge in the global monopole spacetime, which is known as the sigma model. Finally, we generalize the proposal which generates spacetime from thermodynamical laws to the case that spacetime with global monopole charge. Read More

Semi-supervised learning plays an important role in large-scale machine learning. Properly using additional unlabeled data (largely available nowadays) often can improve the machine learning accuracy. However, if the machine learning model is misspecified for the underlying true data distribution, the model performance could be seriously jeopardized. Read More

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. Read More

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Read More

An effective way to suppress the cascading failure risk is the branch capacity upgrade, whose optimal decision making, however, may incur high computational burden. A practical way is to find out some critical branches as the candidates in advance. This paper proposes a simulation data oriented approach to identify the critical branches with higher importance in cascading failure propagation. Read More

We propose a hierarchical approach for making long-term predictions of future frames. To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions. Long-term video prediction is difficult to perform by recurrently observing the predicted frames because the small errors in pixel space exponentially amplify as predictions are made deeper into the future. Read More

In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g. Read More

We present Component-Based Simplex Architecture (CBSA), a new framework for assuring the runtime safety of component-based cyber-physical systems (CPSs). CBSA integrates Assume-Guarantee (A-G) reasoning with the core principles of the Simplex control architecture to allow component-based CPSs to run advanced, uncertified controllers while still providing runtime assurance that A-G contracts and global properties are satisfied. In CBSA, multiple Simplex instances, which can be composed in a nested, serial or parallel manner, coordinate to assure system-wide properties. Read More

This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Read More

In this paper, we consider the existence of multiple nodal solutions of the nonlinear Choquard equation \begin{equation*} \ \ \ \ (P)\ \ \ \ \begin{cases} -\Delta u+u=(|x|^{-1}\ast|u|^p)|u|^{p-2}u \ \ \ \text{in}\ \mathbb{R}^3, \ \ \ \ \\ u\in H^1(\mathbb{R}^3),\\ \end{cases} \end{equation*} where $p\in (\frac{5}{2},5)$. We show that for any positive integer $k$, problem $(P)$ has at least a radially symmetrical solution changing sign exactly $k$-times. Read More

Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neighborhoods, and users. On the basis of this model, we further propose a Spatio-Temporal Embedding Similarity algorithm (STES) for location recommendation. Read More

In this paper, we investigate the constrained minimization problem \begin{equation}\label{eq:0.1} e(a):=\inf_{\{u\in \mathcal{H},\|u\|_2^2=1\}}E_a(u), \end{equation} where the energy functional \begin{equation} \label{eq:0.2} E_a(u)=\int_{\mathbb{R}^3}(u\sqrt{-\Delta+m^2}\,u+Vu^2)\,dx -\frac{a}{2}\int_{\mathbb{R}^3}(|x|^{-1}*u^2)u^2\,dx \end{equation} with $m\in \mathbb{R}$, $a>0$, is defined on a Sobolev space $\mathcal{H}$. Read More

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. Read More

Laser cooling of relativistic heavy ion beams of Li-like C$^{3+}$ and O$^{4+}$ is being in preparation at the experimental Cooler Storage Ring (CSRe). Recently, a preparatory experiment to test important prerequisites for laser cooling of relativistic $^{12}$C$^{3+}$ ion beams using a pulsed laser system has been performed at the CSRe. Unfortunately, the interaction between the ions and the pulsed laser cannot be detected. Read More

In this paper, using the stochastic geometry theory, we present a framework for analyzing the performance of device-to-device (D2D) communications underlaid uplink (UL) cellular networks. In our analysis, we consider a D2D mode selection criterion based on an energy threshold for each user equipment (UE). Specifically, a UE will operate in a cellular mode, if its received signal strength from the strongest base station (BS) is large than a threshold \beta. Read More

We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. Read More

Optical frequency combs are crucial for both fundamental science and applications demanding wide frequency spanning and ultra-precision resolutions. Recent advancements of nonlinear Kerr effect based optical frequency combs in microcavities open up new opportunities in a compact platform, however, internal cavity-enhanced nonlinearities are still unclear. Here we demonstrate transient nonlinear dynamics during optical frequency comb formation inside a Kerr microcavity. Read More

General messenger-matter interactions with complete or incomplete GUT multiplet messengers are introduced in the deflected anomaly mediated SUSY breaking scenario to explain the muon $g-2$ anomaly. We find that while the muon $g-2$ anomaly can be solved in both scenarios under current constraints including the LHC bounds on gluino mass, the scenarios with incomplete GUT multiplet messengers are more favored. At the same time, we find that the gluino mass is upper bounded by about 2. Read More

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i. Read More

A patchwork method is used to study the dynamics of loss and recovery of an initial configuration in spin glass models in dimensions d=1 and d=2. The patchwork heuristic is used to accelerate the dynamics to investigate how models might reproduce the remarkable memory effects seen in experiment. Starting from a ground state configuration computed for one choice of nearest neighbor spin couplings, the sample is aged up to a given scale under new random couplings, leading to the partial erasure of the original ground state. Read More

The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. Read More

One of the most striking features of topological matter is the unusual surface state, protecting the topologically nontrivial phase from its trivial counterpart. The recently emergent Weyl semimetal has disconnected Fermi arc surface states, due to the separated Weyl nodes with opposite chiralities. In the presence of a static magnetic field oriented perpendicular to the two opposite surfaces of a Weyl semimetal, those disjointed Fermi arcs from two opposite surfaces can intertwine with chiral bulk modes and participate in unusual closed magnetic orbits. Read More

Recently, a new type of two-dimensional layered material, i.e. C3N, has been fabricated by polymerization of 2,3-diaminophenazine and used to fabricate a field-effect transistor device with an on/off current ratio reaching 5. Read More

In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Read More

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. Read More

We present the first discoveries from a survey of $z\gtrsim6$ quasars using imaging data from the DECam Legacy Survey (DECaLS) in the optical, the UKIRT Deep Infrared Sky Survey (UKIDSS) and a preliminary version of the UKIRT Hemisphere Survey (UHS) in the near-IR, and ALLWISE in the mid-IR. DECaLS will image 9000 deg$^2$ of sky down to $z_{\rm AB}\sim23.0$, and UKIDSS and UHS, which will map the northern sky at $0Read More

We report an experimental investigation of the two-dimensional Jeff = 1/2 antiferromagnetic Mott insulator by varying the interlayer exchange coupling in [(SrIrO3)1, (SrTiO3)m] (m = 1, 2 and 3) superlattices. Although all samples exhibited an insulating ground state with long-range magnetic order, temperature-dependent resistivity measurements showed a stronger insulating behavior in the m = 2 and m = 3 samples than the m = 1 sample which displayed a clear kink at the magnetic transition. This difference indicates that the blocking effect of the excessive SrTiO3 layer enhances the effective electron-electron correlation and strengthens the Mott phase. Read More

Using hundreds of XMM-Newton and Chandra archival observations and nearly a thousand RXTE observations, we have generated a comprehensive library of the known pulsars in the Small and Large Magellanic Clouds (SMC, LMC). The pulsars are detected multiple times across the full parameter spaces of X-ray luminosity ($L_X= 10^{31-38}$~erg/s) and spin period ( P$<$1s -- P$>$1000s) and the library enables time-domain studies at a range of energy scales. The high time-resolution and sensitivity of the EPIC cameras are complemented by the angular resolution of Chandra and the regular monitoring of RXTE. Read More

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with dual sparsities on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. Read More

This work concerns the semilinear wave equation in three space dimensions with a power-like nonlinearity which is greater than cubic, and not quintic (i.e. not energy-critical). Read More

We have collected and analyzed the complete archive of {\itshape XMM-Newton\} (116), {\itshape Chandra\} (151), and {\itshape RXTE\} (952) observations of the Small Magellanic Cloud (SMC), spanning 1997-2014. The resulting observational library provides a comprehensive view of the physical, temporal and statistical properties of the SMC pulsar population across the luminosity range of $L_X= 10^{31.2}$--$10^{38}$~erg~s$^{-1}$. Read More

Quantum wells constitute one of the most important classes of devices in the study of 2D systems. In a double layer QW, the additional "which-layer" degree of freedom gives rise to celebrated phenomena such as Coulomb drag, Hall drag and exciton condensation. Here we demonstrate facile formation of wide QWs in few-layer black phosphorus devices that host double layers of charge carriers. Read More

In this paper, we propose an efficient super-resolution (SR) method based on deep convolutional neural network (CNN), namely gradual upsampling network (GUN). Recent CNN based SR methods either preliminarily magnify the low resolution (LR) input to high resolution (HR) and then reconstruct the HR input, or directly reconstruct the LR input and then recover the HR result at the last layer. The proposed GUN utilizes a gradual process instead of these two kinds of frameworks. Read More

Recent learning-based super-resolution (SR) methods often focus on the dictionary learning or network training. In this paper, we detailedly discuss a new SR framework based on local classification instead of traditional dictionary learning. The proposed efficient and extendible SR framework is named as local patch classification (LPC) based framework. Read More

In this article, we discuss and survey the recent progress towards the Schottky problem, and make some comments on the relations between the Andr{\'e}-Oort conjecture, Okounkov convex bodies, Coleman's conjecture, stable modular forms, Siegel-Jacobi spaces, stable Jacobi forms and the Schottky problem. Read More

The 80 high-mass X-ray binary (HMXB) pulsars that are known to reside in the Magellanic Clouds (MCs) have been observed by the XMM-Newton and Chandra X-ray telescopes on a regular basis for 15 years, and the XMM-Newton and Chandra archives contain nearly complete information about the duty cycles of the sources with spin periods P_S < 100 s. We have rerprocessed the archival data from both observatories and we combined the output products with all the published observations of 31 MC pulsars with P_S < 100 s in an attempt to investigate the faintest X-ray emission states of these objects that occur when accretion to the polar caps proceeds at the smallest possible rates. These states determine the so-called propeller lines of the accreting pulsars and yield information about the magnitudes of their surface magnetic fields. Read More