arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 973 records · Page 54Linked to original sources

arXiv2019

Inclusive diffractive heavy quarkonium photoproduction in pp, pA and AA collisions

The inclusive $J/Ψ$ production by direct and resolved photoproduction in the $γ$p scattering is calculated based on the nonrelativistic quantum chromodynamics (NRQCD) factorization formalism, which is in good agreement with the experimental data of total cross section distribution of heavy quarkonium production at HERA. Then we extend the formalism including the direct and resolved photoproduction processes to resolved pomeron model to study the heavy quarkonium photoproduction at the LHC energies. We present the predictions of rapidity and transverse momentum distributions of the inclusive diffractive $J/Ψ$, $Ψ(2S)$ and $Υ$ photoproduction in pp, pPb and PbPb collisions at the LHC energies. Our numerical results indicate that the resolved photoproduction processes play an important role in the heavy quarkonium production. Especially for pp collisions, the contribution of resolved photoproduction processes is the largest, which can reach to $28\%$, $13\%$ and $44\%$ for the rapidity distributions of $J/Ψ$, $Ψ(2S)$ and $Υ$ inclusive diffractive photoproduction, respectively.

hep-ph↗

arXiv2019

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with ''relatively less'' importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/filter-pruning-geometric-median

cs.CV↗

arXiv2019

Baidu-UTS Submission to the EPIC-Kitchens Action Recognition Challenge 2019

In this report, we present the Baidu-UTS submission to the EPIC-Kitchens Action Recognition Challenge in CVPR 2019. This is the winning solution to this challenge. In this task, the goal is to predict verbs, nouns, and actions from the vocabulary for each video segment. The EPIC-Kitchens dataset contains various small objects, intense motion blur, and occlusions. It is challenging to locate and recognize the object that an actor interacts with. To address these problems, we utilize object detection features to guide the training of 3D Convolutional Neural Networks (CNN), which can significantly improve the accuracy of noun prediction. Specifically, we introduce a Gated Feature Aggregator module to learn from the clip feature and the object feature. This module can strengthen the interaction between the two kinds of activations and avoid gradient exploding. Experimental results demonstrate our approach outperforms other methods on both seen and unseen test set.

cs.CV↗

arXiv2019

Observations of Type Ia Supernova 2014J for Nearly 900 Days and Constraints on Its Progenitor System

We present extensive ground-based and $Hubble~Space~Telescope$ ($HST$) photometry of the highly reddened, very nearby type Ia supernova (SN Ia) 2014J in M82, covering the phases from 9 days before to about 900 days after the $B$-band maximum. SN 2014J is similar to other normal SNe Ia near the maximum light, but it shows flux excess in the $B$ band in the early nebular phase. This excess flux emission can be due to light scattering by some structures of circumstellar materials located at a few 10$^{17}$ cm, consistent with a single degenerate progenitor system or a double degenerate progenitor system with mass outflows in the final evolution or magnetically driven winds around the binary system. At t$\sim$+300 to $\sim$+500 days past the $B$-band maximum, the light curve of SN 2014J shows a faster decline relative to the $^{56}$Ni decay. Such a feature can be attributed to the significant weakening of the emission features around [Fe III] $λ$4700 and [Fe II] $λ$5200 rather than the positron escape as previously suggested. Analysis of the $HST$ images taken at t$>$600 days confirms that the luminosity of SN 2014J maintains a flat evolution at the very late phase. Fitting the late-time pseudo-bolometric light curve with radioactive decay of $^{56}$Ni, $^{57}$Ni and $^{55}$Fe isotopes, we obtain the mass ratio $^{57}$Ni/$^{56}$Ni as $0.035 \pm 0.011$, which is consistent with the corresponding value predicted from the 2D and 3D delayed-detonation models. Combined with early-time analysis, we propose that delayed-detonation through single degenerate scenario is most likely favored for SN 2014J.

astro-ph.SR↗

arXiv2019

The Cold and Dusty Circumstellar Matter around Fast-expanding Type Ia Supernovae

Type Ia supernovae (SNe Ia) play key roles in revealing the accelerating expansion of the universe, but our knowledge about their progenitors is still very limited. Here we report the discovery of a rigid dichotomy in circumstellar (CS) environments around two subclasses of type Ia supernovae (SNe Ia) as defined by their distinct photospheric velocities. For the SNe Ia with high photospheric velocities (HV), we found a significant excess flux in blue light during 60-100 days past maximum, while this phenomenon is absent for SNe with normal photospheric velocity (Normal). This blue excess can be attributed to light echoes by circumstellar dust located at a distance of about 1-3x10^{17} cm from the HV subclass. Moreover, we also found that the HV SNe Ia show systematically evolving Na I absorption line by performing a systematic search of variable Na I absorption lines in spectra of all SNe Ia, whereas this evolution is rarely seen in Normal ones. The evolving Na I absorption can be modeled in terms of photoionization model, with the location of the gas clouds at a distance of about 2x10^{17} cm, in striking agreement with the location of CS dust inferred from B-band light curve excess. These observations show clearly that the progenitors of HV and Normal subclasses are systematically different, suggesting that they are likely from single and double degenerate progenitor systems, respectively.

astro-ph.HE↗

arXiv2019

Improving Person Re-identification by Attribute and Identity Learning

Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.

cs.CV↗

arXiv2019

Synthesis and Observation of Non-Abelian Gauge Fields in Real Space

Gauge fields, real or synthetic, are crucial for understanding and manipulation of physical systems. The associated geometric phases can be measured, for example, from the Aharonov--Bohm interference. So far, real-space realizations of gauge fields have been limited to Abelian (commutative) ones. Here we report an experimental synthesis of non-Abelian gauge fields in real space and the observation of the non-Abelian Aharonov--Bohm effect with classical waves and classical fluxes. Based on optical mode degeneracy, we break time-reversal symmetry in different manners---via temporal modulation and the Faraday effect---to synthesize tunable non-Abelian gauge fields. The Sagnac interference of two final states, obtained by reversely-ordered path integrals, demonstrates the non-commutativity of the gauge fields. Our work introduces real-space building blocks for non-Abelian gauge fields, relevant for classical and quantum exotic topological phenomena.

physics.optics↗

arXiv2019

Syntax-Infused Variational Autoencoder for Text Generation

We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound objective is redesigned correspondingly, by optimizing a joint distribution that accommodates two encoders and two decoders. SIVAE works with long short-term memory architectures to simultaneously generate sentences and syntactic trees. Two versions of SIVAE are proposed: one captures the dependencies between the latent variables through a conditional prior network, and the other treats the latent variables independently such that syntactically-controlled sentence generation can be performed. Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. Finally, we show that the proposed models can be used for unsupervised paraphrasing given different syntactic tree templates.

stat.ML↗

arXiv2019

Spin polarization independence of hard polarized fermion string scattering amplitudes

We calculate a class of polarized fermion string scattering amplitudes (PFSSA) at arbitrary mass levels. We discover that, in the hard scattering limit, the functional forms of the non-vanishing PFSSA at each fixed mass level are independent of the choices of spin polarizations. This result justifies and extends Gross conjecture on high energy string scattering amplitudes to the fermionic sector. In addition, this peculiar property of hard PFSSA is to be compared with the usual spin polarization dependence of the hard polarized fermion field theory scatterings.

hep-th↗

arXiv2019

Cubic LSTMs for Video Prediction

Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.

cs.CV↗

arXiv2019

Contrastive Adaptation Network for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a new metric which explicitly models the intra-class domain discrepancy and the inter-class domain discrepancy. We design an alternating update strategy for training CAN in an end-to-end manner. Experiments on two real-world benchmarks Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the state-of-the-art methods and produces more discriminative features.

cs.CV↗

arXiv2019

Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation

There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by rendering. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. Therefore two types of information, i.e. visual representation and policy behavior, need to be adapted in the reinforcement model. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We propose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer and a policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.

cs.CV↗

arXiv2019

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intra-domain variations in the target domain, which contain critical factors influencing the testing performance on the target domain. In this work, we comprehensively investigate into the intra-domain variations of the target domain and propose to generalize the re-ID model w.r.t three types of the underlying invariance, i.e., exemplar-invariance, camera-invariance and neighborhood-invariance. To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties. The memory allows us to enforce the invariance constraints over global training batch without significantly increasing computation cost. Experiment demonstrates that the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system. Results on three re-ID domains show that our domain adaptation accuracy outperforms the state of the art by a large margin. Code is available at: https://github.com/zhunzhong07/ECN

cs.CV↗

arXiv2019

DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis

In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing text-to-image synthesis methods have two main problems. (1) These methods depend heavily on the quality of the initial images. If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality. (2) Each word contributes a different level of importance when depicting different image contents, however, unchanged text representation is used in existing image refinement processes. In this paper, we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to generate high-quality images. The proposed method introduces a dynamic memory module to refine fuzzy image contents, when the initial images are not well generated. A memory writing gate is designed to select the important text information based on the initial image content, which enables our method to accurately generate images from the text description. We also utilize a response gate to adaptively fuse the information read from the memories and the image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset and the Microsoft Common Objects in Context dataset. Experimental results demonstrate that our DM-GAN model performs favorably against the state-of-the-art approaches.

cs.CV↗

arXiv2019

Operation-aware Neural Networks for User Response Prediction

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising system and recommendation system are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for different operations may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment.

cs.IR↗

arXiv2019

SN 2016hil-- a Type II supernova in the remote outskirts of an elliptical host and its origin

Type II supernovae (SNe) stem from the core collapse of massive ($>8\ M_{\odot}$) stars. Owing to their short lifespan, we expect a very low rate of such events in elliptical host galaxies, where the star-formation rate is low, and which mostly consist of an old stellar population. SN 2016hil (iPTF16hil) is a Type II supernova located in the extreme outskirts of an elliptical galaxy at redshift $z=0.0608$ (projected distance $27.2$ kpc). It was detected near peak brightness ($M_{r} \approx -17$ mag) 9 days after the last nondetection. SN 2016hil has some potentially peculiar properties: while presenting a characteristic spectrum, the event was unusually short lived and declined by $\sim 1.5$ mag in $< 40$ days, following an apparently double-peaked light curve. Its spectra suggest a low metallicity ($Z<0.4\ Z_{\odot}$). We place a tentative upper limit on the mass of a potential faint host at $\log(M/M_{\odot}) =7.27^{+0.43}_{-0.24}$ using deep Keck optical imaging. In light of this, we discuss the possibility of the progenitor forming locally, and other more exotic formation scenarios such as a merger or common-envelope evolution causing a time-delayed explosion. Further observations of the explosion site in the ultraviolet are needed in order to distinguish between the cases. Regardless of the origin of the transient, observing a population of such seemingly hostless Type II SNe could have many uses, including an estimate the number of faint galaxies in a given volume, and tests of the prediction of a time-delayed population of core-collapse SNe in locations otherwise unfavorable for the detection of such events.

astro-ph.HE↗

arXiv2019

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. A popular strategy is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the local category-level feature distribution. A possible consequence of the global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped. To address this problem, this paper introduces a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level data distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.

cs.CV↗

arXiv2019

Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation

For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex. In this work, we equip the adversarial network with a "significance-aware information bottleneck (SIB)", to address the above problem. The new network structure, called SIBAN, enables a significance-aware feature purification before the adversarial adaptation, which eases the feature alignment and stabilizes the adversarial training course. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method can yield leading results compared with other feature-space alternatives. Moreover, SIBAN can even match the state-of-the-art output-space methods in segmentation accuracy, while the latter are often considered to be better choices for domain adaptive segmentation task.

cs.CV↗