arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

At least 775 records · Page 43Linked to original sources

arXiv2019

A kernel regression model for panel count data with time-varying coefficients

For the conditional mean function of panel count model with time-varying coefficients, we propose to use local kernel regression method for estimation. Partial log-likelihood with local polynomial is formed for estimation. Under some regularity conditions, strong uniform consistency rates are obtained for the local estimator. At target time point, we show that the local estimator converges in distribution to normal distribution. The baseline mean function estimator is also shown to be consistent. Simulation studies show that the time-varying coefficient estimator is close to the true value, the empirical coverage probabilities of the confidence interval is close to the nominal level. We also applied the proposed method to analyze a clinical study on childhood wheezing.

math.ST↗

arXiv2019

Quantifying dynamics of failure across science, startups, and security

Human achievements are often preceded by repeated attempts that initially fail, yet little is known about the mechanisms governing the dynamics of failure. Here, building on the rich literature on innovation, human dynamics and learning, we develop a simple one-parameter model that mimics how successful future attempts build on those past. Analytically solving this model reveals a phase transition that separates dynamics of failure into regions of stagnation or progression, predicting that near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Below the critical point, we see those who explore disjoint opportunities without a pattern of improvement, and above it, those who exploit incremental refinements to systematically advance toward success. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may be initially similar, yet are characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality of each subsequent attempt. We collected large-scale data from three disparate domains, tracing repeated attempts by (i) NIH investigators to fund their research, (ii) innovators to successfully exit their startup ventures, and (iii) terrorist organizations to post casualties in violent attacks, finding broadly consistent empirical support across all three domains. Together, our findings unveil identifiable yet previously unknown early signals that allow us to identify failure dynamics that will lead to ultimate victory or defeat. Given the ubiquitous nature of failures and the paucity of quantitative approaches to understand them, these results represent a crucial step toward deeper understanding of the complex dynamics beneath failures, the essential prerequisites for success.

physics.soc-ph↗

arXiv2019

Early-career setback and future career impact

Setbacks are an integral part of a scientific career, yet little is known about whether an early-career setback may augment or hamper an individual's future career impact. Here we examine junior scientists applying for U.S. National Institutes of Health (NIH) R01 grants. By focusing on grant proposals that fell just below and just above the funding threshold, we compare "near-miss" with "near-win" individuals to examine longer-term career outcomes. Our analyses reveal that an early-career near miss has powerful, opposing effects. On one hand, it significantly increases attrition, with one near miss predicting more than a 10% chance of disappearing permanently from the NIH system. Yet, despite an early setback, individuals with near misses systematically outperformed those with near wins in the longer run, as their publications in the next ten years garnered substantially higher impact. We further find that this performance advantage seems to go beyond a screening mechanism, whereby a more selected fraction of near-miss applicants remained than the near winners, suggesting that early-career setback appears to cause a performance improvement among those who persevere. Overall, the findings are consistent with the concept that "what doesn't kill me makes me stronger." Whereas science is often viewed as a setting where early success begets future success, our findings unveil an intimate yet previously unknown relationship where early-career setback can become a marker for future achievement, which may have broad implications for identifying, training and nurturing junior scientists whose career will have lasting impact.

physics.soc-ph↗

arXiv2019

Crowd Counting Using Scale-Aware Attention Networks

In this paper, we consider the problem of crowd counting in images. Given an image of a crowded scene, our goal is to estimate the density map of this image, where each pixel value in the density map corresponds to the crowd density at the corresponding location in the image. Given the estimated density map, the final crowd count can be obtained by summing over all values in the density map. One challenge of crowd counting is the scale variation in images. In this work, we propose a novel scale-aware attention network to address this challenge. Using the attention mechanism popular in recent deep learning architectures, our model can automatically focus on certain global and local scales appropriate for the image. By combining these global and local scale attention, our model outperforms other state-of-the-art methods for crowd counting on several benchmark datasets.

cs.CV↗

arXiv2019

Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i.e., learning-to-rank. Recently, a number of authors have proposed new techniques referred to as 'unbiased learning-to-rank', which can reduce position bias and train a relatively high-performance ranker using click data. Most of the algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for pairwise learning-to-rank that can jointly conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel algorithm, which can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that our algorithm can significantly outperform existing algorithms. In addition, an online A/B Testing at a commercial search engine shows that our algorithm can effectively conduct debiasing of click data and enhance relevance ranking.

cs.IR↗

arXiv2019

Wasserstein-Wasserstein Auto-Encoders

To address the challenges in learning deep generative models (e.g.,the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE). We formulate WWAE as minimization of the penalized optimal transport between the target distribution and the generated distribution. By noticing that both the prior $P_Z$ and the aggregated posterior $Q_Z$ of the latent code Z can be well captured by Gaussians, the proposed WWAE utilizes the closed-form of the squared Wasserstein-2 distance for two Gaussians in the optimization process. As a result, WWAE does not suffer from the sampling burden and it is computationally efficient by leveraging the reparameterization trick. Numerical results evaluated on multiple benchmark datasets including MNIST, fashion- MNIST and CelebA show that WWAE learns better latent structures than VAEs and generates samples of better visual quality and higher FID scores than VAEs and GANs.

cs.LG↗

arXiv2019

Deep Discriminative Representation Learning with Attention Map for Scene Classification

Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.

cs.CV↗

arXiv2019

Anomalous Spin-Orbit Torques in Magnetic Single-Layer Films

Spin-orbit interaction (SOI) couples charge and spin transport, enabling electrical control of magnetization. A quintessential example of SOI-induced transport is the anomalous Hall effect (AHE), first observed in 1880, in which an electric current perpendicular to the magnetization in a magnetic film generates charge accumulation on the surfaces. Here we report the observation of a counterpart of the AHE that we term the anomalous spin-orbit torque (ASOT), wherein an electric current parallel to the magnetization generates opposite spin-orbit torques on the surfaces of the magnetic film. We interpret the ASOT as due to a spin-Hall-like current generated with an efficiency of 0.053+/-0.003 in Ni80Fe20, comparable to the spin Hall angle of Pt. Similar effects are also observed in other common ferromagnetic metals, including Co, Ni, and Fe. First principles calculations corroborate the order of magnitude of the measured values. This work suggests that a strong spin current with spin polarization transverse to magnetization can exist in a ferromagnet, despite spin dephasing. It challenges the current understanding of spin-orbit torque in magnetic/nonmagnetic bilayers, in which the charge-spin conversion in the magnetic layer has been largely neglected.

cond-mat.mes-hall↗

arXiv2019

Palladium Diselenide Long-Wavelength Infrared Photodetector with High Sensitivity and Stability

A long-wavelength infrared (IR) photodetector based on two-dimensional materials working at room temperature would have wide applications in many aspects in remote sensing, thermal imaging, biomedical optics, and medical imaging. However, sub-bandgap light detection in graphene and black phosphorus has been a long-standing scientific challenge because of low photoresponsivity, instability in the air and highdark current. In this study, we report a highly sensitive, air-stable and operable long-wavelength infrared photodetector at room temperature based on PdSe2 phototransistors and its heterostructure. A high photoresponsivity of ~42.1 AW-1 (at 10.6 μm) was demonstrated, which is an order of magnitude higher than the current record of platinum diselenide. Moreover, the dark current and noise power density were suppressed effectively by fabricating a van der Waals heterostructure. This work fundamentally contributes to establishing long-wavelength infrared detection by PdSe2 at the forefront of long-IR two-dimensional-materials-based photonics.

physics.app-ph↗

arXiv2019

3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification

In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of the existing methods rely on 2D convolutional networks (ConvNets) to extract frame-wise deep features which are pooled temporally to generate the video-level representations. However, 2D ConvNets lose temporal input information immediately after the convolution, and a separate temporal pooling is limited in capturing human motion in shorter sequences. To this end, we present a \textit{global} video representation (3D PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the appearance and motion dynamics in full-length videos. However, encoding each video frame in its entirety and computing an aggregate global representation across all frames is tremendously challenging due to occlusions and misalignments. To resolve this, our proposed network is further augmented with 3D part alignment module to learn local features through soft-attention module. These attended features are statistically aggregated to yield identity-discriminative representations. Our global 3D features are demonstrated to achieve state-of-the-art results on three benchmark datasets: MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011

cs.CV↗

arXiv2019

Quantum coherence across bosonic superconductor-anomalous metal-insulator transitions

After decades of explorations, suffering from low critical temperature and subtle nature, whether a metallic ground state exists in a two-dimensional system beyond Anderson localization is still a mystery. Supremely, phase coherence could be the key that unlocks its intriguing nature. This work reveals how quantum phase coherence evolves across bosonic superconductor-metal-insulator transitions via magneto-conductance quantum oscillations in high-Tc superconducting films. A robust intervening anomalous metallic state characterized by both resistance and oscillation amplitude saturations in the low temperature regime is detected. By contrast, with decreasing temperature the oscillation amplitude monotonically grows on the superconducting side, but decreases at low temperatures on the insulating side. It suggests that the saturation of phase coherence plays a prominent role in the formation of this anomalous metallic state.

cond-mat.supr-con↗

arXiv2019

A Remote Sensing Image Dataset for Cloud Removal

Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in remote sensing image analysis. Deep learning has achieved great success in the field of remote sensing in recent years, including scene classification and change detection. However, deep learning is rarely applied in remote sensing image removal clouds. The reason is the lack of data sets for training neural networks. In order to solve this problem, this paper first proposed the Remote sensing Image Cloud rEmoving dataset (RICE). The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512*512; RICE2 contains 450 sets of images, each set contains three 512*512 size images. , respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud. The dataset is freely available at \url{https://github.com/BUPTLdy/RICE_DATASET}.

cs.CV↗

arXiv2018

VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art benchmark mobility models, and the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

cs.NI↗

arXiv2018

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.

cs.IR↗

arXiv2018

The Three Hundred Project: The influence of environment on simulated galaxy properties

The relationship between galaxy properties and environment is a widely discussed topic within astrophysics. Here we use galaxy samples from hydrodynamical re-simulations to examine this relationship. We use the over-density ($δ_1$) within a $1 h^{-1}{\rm Mpc}$ sphere around a galaxy to evaluate its environment. Then the relations between galaxy properties, such as specific star formation rate(sSFR), fraction of star forming galaxies, $g-r$ colour and $δ_1$ are examined within three galactic samples formed from galaxies within large clusters, those in the vicinity of large clusters and those in the field. We find tight environmental correlations for these galaxy properties. In brief, galaxies in denser environments tend to be redder and are more likely to be quenched. This is consistent with observations. We find that although the sSFR decreases with $δ_1$, this is mainly because that galaxies with higher stellar mass reside in environment with higher overdensity. At fixed over-density a galaxy's color is also independent of whether it lives within a cluster or within the field, but the relative fractions of the two samples varies dramatically with over-density and this drives an apparent evolution.

astro-ph.GA↗

arXiv2018

Probing the dark matter-electron interactions via hydrogen-atmosphere pulsating white dwarfs

In this work, we propose a novel scenario to probe the interactions between dark matter (DM) particles and electrons, via hydrogen-atmosphere pulsating white dwarfs (DAVs) in globular clusters. In this special configuration, the DM particles, which are predominantly captured by multiple scattering with the electrons in a DAV, would annihilate by pairs and provide extra energy source to the DAV. This mechanism slows down the natural cooling evolution which can be presented by the period variation rates of pulsation modes. The differences between the secular rates predicted by the precise asteroseismology and the secular rates obtained from observation can reveal the DM-electron interactions. An important observable has been proposed and corresponding estimations have been made. According to the estimation, if this scenario could be implemented in the near future, the potential sensitivity on $m_χ$ (DM particle's mass) and $σ_{χ,e}$ (elastic scattering cross section between DM and electron) could be hopefully extended to a region $5 \mathrm{GeV} \lesssim m_χ \lesssim 10^{4} \mathrm{GeV}$ and $σ_{χ,e} \gtrsim 10^{-40} \mathrm{cm}^{2}$. Combining with indirect DM detection results, this could give us a cross check on the existence of such leptonphilic DM particles to some extent.

astro-ph.HE↗

arXiv2018

Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition

Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB view. In this paper, we introduce a RGBD video-emotion dataset and a RGBD face-emotion dataset for research. To our best knowledge, this may be the first RGBD video-emotion dataset. We propose a new supervised nonlinear multi-view laplacian eigenmaps (MvLE) approach and a multihidden-layer out-of-sample network (MHON) for RGB-D humanemotion recognition. To get better representations of RGB view and depth view, MvLE is used to map the training set of both views from original space into the common subspace. As RGB view and depth view lie in different spaces, a new distance metric bag of neighbors (BON) used in MvLE can get the similar distributions of the two views. Finally, MHON is used to get the low-dimensional representations of test data and predict their labels. MvLE can deal with the cases that RGB view and depth view have different size of features, even different number of samples and classes. And our methods can be easily extended to more than two views. The experiment results indicate the effectiveness of our methods over some state-of-art methods.

cs.CV↗

arXiv2018

Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data

We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors.

cs.CL↗