arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 703 records · Page 39Linked to original sources

arXiv2022

Graph-based Extractive Explainer for Recommendations

Explanations in a recommender system assist users in making informed decisions among a set of recommended items. Great research attention has been devoted to generating natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes, and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage, and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of the proposed solution.

cs.IR↗

arXiv2022

Quenching of Massive Disk Galaxies in the IllustrisTNG Simulation

A rare population of massive disk galaxies have been found to invade the red sequence dominated by early-type galaxies. These red/quenched massive disk galaxies have recently gained great interest into their formation and origins. The usually proposed quenching mechanisms, such as bar quenching and environment quenching, seem not suitable for those bulge-less quenched disks in low-density environment. In this paper, we use the IllustrisTNG-300 simulation to investigate the formation of massive quenched central disk galaxies. It is found that these galaxies contain less gas and harbor giant supermassive black holes(SMBHs) (above $ 10^{8}M_{\odot}$) than their star forming counterparts. By tracing their formation history, we found that quenched disk galaxies formed early and preserved disk morphology for cosmological time scales. They have experienced less than one major merger on average and it is mainly mini-mergers (mass ratio $<$1/10) that contribute to the growth of their SMBHs. In the Illustris-TNG simulation the black hole feedback mode switches from thermal to kinetic feedback when the black hole mass is more massive than $\sim 10^{8}M_{\odot}$, which is more efficient to eject gas outside of the galaxy and to suppress further cooling of hot gaseous halo. We conclude that kinetic AGN feedback in massive red/quenched disk galaxy is the dominant quenching mechanism.

astro-ph.GA↗

arXiv2022

Diffraction properties of lights with transverse orbital angular momentum

Spatiotemporal optical vortex (STOV) is a unique optical vortex with phase singularity in the space-time domain and the photons in a STOV can carry transverse orbital angular momentum (OAM). The STOV shows many fantastic properties which are worth exploring. Here, we theoretically and experimentally study the diffraction property of STOV, which is a fundamental wave phenomenon. The diffraction behaviors of STOVs are obviously affected by the transverse OAM. The diffraction patterns of STOV pulses diffracted by a grating show multi-lobe structure with each gap corresponding to 1 topological charge. The diffraction properties of lights with transverse OAM are demonstrated clearly and help us understanding the physical properties of STOV, which will be of special applications, such as the realization of fast detection of STOVs with different topological charges, which may pay the way for STOV based optical communication.

physics.optics↗

arXiv2022

Thermodynamic Geometry of Black Holes Enclosed by a Cavity in Extended Phase Space

Recently, the phase space of black holes in a spherical cavity of radius $r_{B}$ has been extended by introducing a thermodynamic volume $V\equiv4πr_{B}^{3}/3$. In the extended phase space, we consider the thermodynamic geometry, which provides a powerful tool to understand the microscopic structure of black holes, of Reissner-Nordström (RN) black holes in a cavity, as well as that of Reissner-Nordström-AdS black holes. Although the phase structures of the cavity and AdS cases show striking resemblance, we find that there exist significant differences between the thermodynamic geometries of these two cases. In particular, a reentrant transition of the type of the microstructure interactions, i.e., repulsive $\rightarrow$ attractive $\rightarrow$ repulsive with increasing temperature in an isobaric process, is observed for RN black holes in a cavity.

gr-qc↗

arXiv2022

Negative-ResNet: Noisy Ambulatory Electrocardiogram Signal Classification Scheme

With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing. However, data labelling quality has become one of the most significant issues for AI applications, especially when it requires domain knowledge (e.g. medical image labelling). In addition, there might be noisy labels in practical datasets, which might impair the training process of neural networks. In this work, we propose a semi-supervised algorithm for training data samples with noisy labels by performing selected Positive Learning (PL) and Negative Learning (NL). To verify the effectiveness of the proposed scheme, we designed a portable ECG patch -- iRealCare -- and applied the algorithm on a real-life dataset. Our experimental results show that we can achieve an accuracy of 91.0 %, which is 6.2 % higher than a normal training process with ResNet. There are 65 patients in our dataset and we randomly picked 2 patients to perform validation.

eess.SP↗

arXiv2022

A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection

Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.

eess.SP↗

arXiv2022

Multistep pulse compressor based on single-pass single-grating-pair main compressor

A multistage smoothing multistep pulse compressor (MPC) based on a single-pass single-grating-pair (SSGP) main compressor is proposed to simplify the entire petawatt (PW) compressor. Only one grating pair with relatively long distance is used to generate the same amount of spectral dispersion in the main compressor compared with a four-grating main compressor. As the SSGP induces the largest spatial dispersion, it can introduce the best beam-smoothing effect to the laser beam on the last grating. When considering the diffraction loss of only two gratings, the total compression efficiency of the SSGP main compressor is even larger than that of a four-grating main compressor. Furthermore, the spatiotemporal aberration induced by single-grating-pair can be compensated effectively by using deformable mirrors, however it is difficult or complicated to be well compensated in a four-grating compressor. Approximately 50-100 PW laser pulses can be obtained using this SSGP-based multistage smoothing MPC with a single laser beam

physics.optics↗

arXiv2022

Leveraging Structural Information to Improve Point Line Visual-Inertial Odometry

Leveraging line features can help to improve the localization accuracy of point-based monocular Visual-Inertial Odometry (VIO) system, as lines provide additional constraints. Moreover, in an artificial environment, some straight lines are parallel to each other. In this paper, we designed a VIO system based on points and straight lines, which divides straight lines into structural straight lines (that is, straight lines parallel to each other) and non-structural straight lines. In addition, unlike the orthogonal representation using four parameters to represent the 3D straight line, we only used two parameters to minimize the representation of the structural straight line and the non-structural straight line. Furthermore, we designed a straight line matching strategy based on sampling points to improve the efficiency and success rate of straight line matching. The effectiveness of our method is verified on both public datasets of EuRoc and TUM VI benchmark and compared with other state-of-the-art algorithms.

cs.RO↗

arXiv2022

Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification

Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the image quality may affect the identification. Thus, samples could be observed at any level of the hierarchy, e.g., ["Albatross"] or ["Albatross", "Laysan Albatross"], and examples discerned at coarse categories are often neglected in the conventional setting of HMC. In this paper, we study the HMC problem in which objects are labeled at any level of the hierarchy. The essential designs of the proposed method are derived from two motivations: (1) learning with objects labeled at various levels should transfer hierarchical knowledge between levels; (2) lower-level classes should inherit attributes related to upper-level superclasses. The proposed combinatorial loss maximizes the marginal probability of the observed ground truth label by aggregating information from related labels defined in the tree hierarchy. If the observed label is at the leaf level, the combinatorial loss further imposes the multi-class cross-entropy loss to increase the weight of fine-grained classification loss. Considering the hierarchical feature interaction, we propose a hierarchical residual network (HRN), in which granularity-specific features from parent levels acting as residual connections are added to features of children levels. Experiments on three commonly used datasets demonstrate the effectiveness of our approach compared to the state-of-the-art HMC approaches and fine-grained visual classification (FGVC) methods exploiting the label hierarchy.

cs.CV↗

arXiv2022

Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator". This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free "AutoDL self-service".

cs.LG↗

arXiv2022

Multistage smoothing based multistep pulse compressor for ultrahigh peak power lasers

Ultrahigh peak power lasers are important scientific tools for frontier laser-physics researches, in which both the peak power improvement and operating safety are very important meanwhile limited by the damage threshold and size of compression gratings currently. Based on a recent reported method "multistep pulse compressor (MPC)", a multistage smoothing based MPC (MS-MPC) is proposed here to further improve the running safety, operating convenience, and simplify the whole setup of the MPC. In this optimized design, the beam smoothing is not simply executed in the pre-compressor or main-compressor, but separated into multistage. Then, it can protect important optics in every stage directly and reduce the executing difficult of typical MPC at the same time. The prism pair based pre-compressor will induce suitable spatial dispersion which is easier to be achieved and enough to protect the first grating directly. At the same time, the asymmetric four-grating compressor (AFGC) will also induce spatial dispersion to further smooth the laser beam which helps to protect the last grating directly. In this way, 10s-100s PW lasers can be compressed by using current available optics with improved operating safety owing to remove random spatial intensity modulations. Furthermore, an additional beam smoothing stage can be added before the main amplifier to protect the biggest amplification crystal away from damage. This MS-MPC optical design can be easily extended to be used in all exist PW laser facilities to improve their potential compressed pulse energy and running safety.

physics.optics↗

arXiv2022

Multi-Domain Joint Training for Person Re-Identification

Deep learning-based person Re-IDentification (ReID) often requires a large amount of training data to achieve good performance. Thus it appears that collecting more training data from diverse environments tends to improve the ReID performance. This paper re-examines this common belief and makes a somehow surprising observation: using more samples, i.e., training with samples from multiple datasets, does not necessarily lead to better performance by using the popular ReID models. In some cases, training with more samples may even hurt the performance of the evaluation is carried out in one of those datasets. We postulate that this phenomenon is due to the incapability of the standard network in adapting to diverse environments. To overcome this issue, we propose an approach called Domain-Camera-Sample Dynamic network (DCSD) whose parameters can be adaptive to various factors. Specifically, we consider the internal domain-related factor that can be identified from the input features, and external domain-related factors, such as domain information or camera information. Our discovery is that training with such an adaptive model can better benefit from more training samples. Experimental results show that our DCSD can greatly boost the performance (up to 12.3%) while joint training in multiple datasets.

cs.CV↗

arXiv2022

Quasinormal Modes of Black Holes with Multiple Photon Spheres

For a static and spherically symmetric black hole, a photon sphere is composed of circular null geodesics of fixed radius, and plays an important role in observing the black hole. Recently, in an Einstein-Maxwell-scalar model with a non-minimal coupling between the scalar and electromagnetic fields, a class of hairy black holes has been found to possess two unstable and one stable circular null geodesics on the equatorial plane, corresponding to three photon spheres outside the event horizon. In this paper, we study quasinormal modes of the scalar field, which are associated with these circular null geodesics, in the hairy black hole spacetime. In the eikonal regime with $l\gg1$, the real part of the quasinormal modes is determined by the angular velocity of the corresponding circular geodesics. The imaginary part of the quasinormal modes associated with the unstable circular null geodesics encodes the information about the Lyapunov exponent of the corresponding circular geodesics. Interestingly, we find long-lived and sub-long-lived modes, which are associated with the stable and one of the unstable circular null geodesics, respectively. Due to tunneling through potential barriers, the damping times of the long-lived and sub-long-lived modes can be exponentially and logarithmically large in terms of $l$, respectively.

gr-qc↗

arXiv2021

The Emission Order of Hydrogen Isotopes via Correlation Functions in 30 MeV/u Ar+Au Reactions

The intensity interferometry is applied as a chronometer of the particle emission of hydrogen isotopes from the intermediate velocity source formed in $^{40}$Ar+$^{197}$Au reactions at 30 MeV/u. The dynamic emission order of $τ_{\rm p}>τ_{\rm d}>τ_{\rm t}$ is evidenced via the correlation functions of nonidentical particle pairs. Assuming the similar source size, the same emission order is inferred from the correlation functions of identical particle pairs, where $τ_{\rm p} \approx 100 {\rm ~fm/c}$ is extracted by the fit of Koonin-Pratt equation to p-p correlation function. Transport model simulations demonstrate that the dynamic emission order of light charged particles depends on the stiffness of the nuclear symmetry energy.

nucl-ex↗

arXiv2021

Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation

The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance. Motivated by the fact that human usually attend to the difference when they compare two similar objects, we propose a dual-path cross-modality feature learning framework which preserves intrinsic spatial strictures and attends to the difference of input cross-modality image pairs. Our framework is composed by two main components: a Dual-path Spatial-structure-preserving Common Space Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds cross-modality images into a common 3D tensor space without losing spatial structures, while the latter extracts contrastive features by dynamically comparing input image pairs. Note that the representations generated for the input RGB and Infrared images are mutually dependant to each other. We conduct extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01 and RegDB, and our proposed method outperforms state-of-the-art algorithms by a large margin with both full and simplified evaluation modes.

cs.CV↗

arXiv2021

Structured Multimodal Attentions for TextVQA

In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above. SMA first uses a structural graph representation to encode the object-object, object-text and text-text relationships appearing in the image, and then designs a multimodal graph attention network to reason over it. Finally, the outputs from the above modules are processed by a global-local attentional answering module to produce an answer splicing together tokens from both OCR and general vocabulary iteratively by following M4C. Our proposed model outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQA dataset among all models except pre-training based TAP. Demonstrating strong reasoning ability, it also won first place in TextVQA Challenge 2020. We extensively test different OCR methods on several reasoning models and investigate the impact of gradually increased OCR performance on TextVQA benchmark. With better OCR results, different models share dramatic improvement over the VQA accuracy, but our model benefits most blessed by strong textual-visual reasoning ability. To grant our method an upper bound and make a fair testing base available for further works, we also provide human-annotated ground-truth OCR annotations for the TextVQA dataset, which were not given in the original release. The code and ground-truth OCR annotations for the TextVQA dataset are available at https://github.com/ChenyuGAO-CS/SMA

cs.CV↗

arXiv2021

Holographic Superconductors in a Non-minimally Coupled Einstein-Maxwell-scalar Model

In this paper, we investigate holographic superconductors dual to asymptotically anti-de Sitter black holes in an Einstein-Maxwell-scalar model with a non-minimal coupling between the scalar and Maxwell fields. In the probe limit, it shows that the scalar condensate occurs below the critical temperature $T_{c}$, and decreases with the increase of the coupling constant $α$. On the other hand, the the critical temperature $T_{c}$ increases as the coupling constant $α$ grows. We also calculate the optical conductivity of the holographic superconductor, and observe that a gap forms below $T_{c}$. Interestingly, the non-minimal coupling can lead to a spike occurring in the gap at a low temperature.

hep-th↗

arXiv2021

Spatial-Interference Aware Cooperative Resource Allocation for 5G NR Sidelink Communications

Distributed resource allocation (RA) schemes have been introduced in cellular vehicle-to-everything (C-V2X) standard for vehicle-to-vehicle (V2V) sidelink (SL) communications to share the limited spectrum (sub-6GHz) efficiently. However, the recent progress in connected and automated vehicles and mobility services requires a huge amount of available spectrum resources. Therefore, millimeter-wave and sub-THz frequencies are being considered as they offer a large free bandwidth. However, they require beamforming techniques to compensate for the higher path loss attenuation. The current fifth-generation (5G) RA standard for SL communication is inherited from the previous C-V2X standard, which is not suited for beam-based communication since it does not explore the spatial dimension. In this context, we propose a novel RA scheme that addresses the directional component by adding this third spatial dimension to the bandwidth part structure and promotes cooperation between vehicles in resource selection, namely cooperative three-dimensional RA. Numerical results show an average of 10% improvement in packet delivery ratio, an average 50% decrease in collision probability, and a 30% better channel busy ratio compared to the current standard, thus, confirming the validity of the proposed method.

eess.SP↗