arXiv subjects

Kai Wang

Publications and source records attributed to Kai Wang.

At least 613 records · Page 34Linked to original sources

arXiv2022

Detection of diffuse gamma-ray emission towards a massive star forming region hosting Wolf-Rayet stars

Isotopic and elemental abundances seen in Galactic cosmic rays imply that $\sim20\%$ of the cosmic-ray (CR) nuclei are probably synthesized by massive Wolf-Rayet (WR) stars. Massive star clusters hosting WR and OB-type stars have been proposed as potential Galactic cosmic-ray accelerators for decades, in particular via diffusive shock acceleration at wind termination shocks. Here we report the analysis of {\em Fermi} Large Area Telescope's data towards the direction of Masgomas-6a, a young massive star cluster candidate hosting two WR stars. We detect an extended $γ$-ray source with $\rm{TS}=183$ in the vicinity of Masgomas-6a, spatially coincident with two unassociated {\em Fermi} 4FGL sources. We also present the CO observational results of molecular clouds in this region, using the data from the Milky Way Imaging Scroll Painting project. The $γ$-ray emission intensity correlates well with the distribution of molecular gas at the distance of Masgomas-6a, indicating that these gamma rays may be produced by CRs accelerated by massive stars in Masgomas-6a. At the distance of $3.9{\rm \ kpc}$ of Masgomas-6a, the luminosity of the extended source is $(1.81\pm0.02)\times 10^{35}{\rm \ erg \ s^{-1}}$. With a kinetic luminosity of $\sim 10^{37}{\rm erg \ s^{-1}}$ in the stellar winds, the WR stars are capable of powering the $γ$-ray emission via neutral pion decay resulted from cosmic ray $pp$ interactions. The size of the GeV source and the energetic requirement suggests a CR diffusion coefficient smaller than that in the Galactic interstellar medium, indicating strong suppression of CR diffusion in the molecular cloud.

astro-ph.HE↗

arXiv2022

Monitoring AGNs with H$β$ Asymmetry. III. Long-term Reverberation Mapping Results of 15 Palomar-Green Quasars

In this third paper of the series reporting on the reverberation mapping (RM) campaign of active galactic nuclei with asymmetric H$β$ emission-line profiles, we present results for 15 Palomar-Green (PG) quasars using spectra obtained between the end of 2016 to May 2021. This campaign combines long time spans with relatively high cadence. For 8 objects, both the time lags obtained from the entire light curves and the measurements from individual observing seasons are provided. Reverberation mapping of 9 of our targets has been attempted for the first time, while the results for 6 others can be compared with previous campaigns. We measure the H$β$ time lags over periods of years and estimate their black hole masses. The long duration of the campaign enables us to investigate their broad line region (BLR) geometry and kinematics for different years by using velocity-resolved lags, which demonstrate signatures of diverse BLR geometry and kinematics. The BLR geometry and kinematics of individual objects are discussed. In this sample, the BLR kinematics of Keplerian/virialized motion and inflow is more common than outflow.

astro-ph.GA↗

arXiv2022

Background-penalty-free waveguide enhancement of CARS signal in air-filled anti-resonance hollow-core fiber

We study coherent anti-Stokes Raman spectroscopy in air-filled anti-resonance hollow-core photonic crystal fiber, otherwise known as 'revolver' fiber. We compare the vibrational coherent anti-Stokes Raman signal of N$_2$, at 2331 cm$^{-1}$, generated in ambient air (no fiber present), with the one generated in a 2.96 cm of a revolver fiber. We show a 170 times enhancement for the signal produced in the fiber, due to an increased interaction path. Remarkably, the N$_2$ signal obtained in the revolver fiber shows near-zero non-resonant background, due to near-zero overlap between the laser field and the fiber cladding. Through our study, we find that the revolver fiber properties make it an ideal candidate for the coherent Raman spectroscopy signal enhancement.

physics.optics↗

arXiv2022

Could TDE outflows produce the PeV neutrino events?

A tidal disruption event (TDE), AT2019dsg, was observed to be associated with a PeV neutrino event, IceCube-191001A, lagging the optical outburst by a half year. It is known that TDEs may generate ultrafast outflows. If the TDE occurs in a cloudy environment, the outflow-cloud interactions may form shock waves which generate accelerated protons and hence delayed neutrinos from hadronic interactions in clouds. Here we investigate the neutrino production in AT2019dsg by examining the TDE outflow-cloud interaction model. We find that, for an outflow with a velocity of 0.07c and a kinetic luminosity of $10^{45}\rm erg\ s^{-1}$, protons may be accelerated up to $\sim$ 60 PeV by the bow shocks, and generate PeV neutrinos by interactions with clouds. The predicted neutrino number in this model depends on the uncertainties of model parameters and in order to match the observations, some challenging values of parameters have been involved. The PeV neutrino event number can be $\sim 4\times10^{-3}$ for a hard proton index $Γ=1.5$.

astro-ph.HE↗

arXiv2022

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

cs.DL↗

arXiv2022

Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.

eess.IV↗

arXiv2022

Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard domain adaptation. Existing DABP approaches mostly rely on model distillation from the black-box predictor, \emph{i.e.}, training the model with its noisy target-domain predictions, which however inevitably introduces the confirmation bias accumulated from the prediction noises. To mitigate such bias, we propose a new method, named BETA, to incorporate knowledge distillation and noisy label learning into one coherent framework. This is enabled by a new divide-to-adapt strategy. BETA divides the target domain into an easy-to-adapt subdomain with less noise and a hard-to-adapt subdomain. Then it deploys mutually-teaching twin networks to filter the predictor errors for each other and improve them progressively, from the easy to hard subdomains. As such, BETA effectively purifies the noisy labels and reduces error accumulation. We theoretically show that the target error of BETA is minimized by decreasing the noise ratio of the subdomains. Extensive experiments demonstrate BETA outperforms existing methods on all DABP benchmarks, and is even comparable with the standard domain adaptation methods that use the source-domain data.

cs.LG↗

arXiv2022

Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). Instead of the traditional Negative Sampling, we design a new loss function based on query sampling that can balance two important training targets, Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of recent low-dimensional hyperbolic models and propose a lightweight hardness-aware activation mechanism. The experimental results show that in the limited training time, HaLE can effectively improve the performance and training speed of KGE models on five commonly-used datasets. After training just a few minutes, the HaLE-trained models are competitive compared to the state-of-the-art models in both low- and high-dimensional conditions.

cs.LG↗

arXiv2022

FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders

Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and data utility. In this paper, we propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE. We tailor the instance relation matching (IRM) module to minimize the distribution gap between real faces and FaceMAE reconstructed ones. During the deployment phase, we use trained FaceMAE to reconstruct images from masked faces of unseen identities without extra training. The risk of privacy leakage is measured based on face retrieval between reconstructed and original datasets. Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. CASIA-WebFace and WebFace260M). Compared to previous state of the arts, FaceMAE consistently \textbf{reduces at least 50\% error rate} on LFW, CFP-FP and AgeDB.

cs.CV↗

arXiv2022

Saliency-Driven Active Contour Model for Image Segmentation

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

cs.CV↗

arXiv2022

Fermi Observations of GRB 220426A: a burst similar to GRB 090902B

We report on a very bright, long-duration gamma-ray burst (GRB), GRB~220426A, observed by \emph{Fermi} satellite. GRB~220426A with total duration of $T_{90}=6$~s is composed with two main pulses and some sub-peaks. The spectral analysis of this burst with Band function reveals that both the time-integrated and the time-resolved spectra are very narrow with high $α\gtrsim 0.2$ and low $β\lesssim -3.1$. It is strong reminiscent of GRB~090902B, a special GRB with identification of the photospheric emission. Then, we perform the spectral analysis of this burst based on a non-dissipated photospheric emission, which can be well modelled as the multicolor-blackbody with a cutoff power-law distribution of the thermal temperature. The spectral fittings reveal that the photospheric emission can well describe the radiation spectrum of this burst. We conclude that this burst would be a second burst in the class of GRB~090902B observed by \emph{Fermi} satellite. We also discuss the physics of photosphere and the origin of the high-energy component in GRB~220426A .

astro-ph.HE↗

arXiv2022

Truncations of random unitary matrices drawn from Hua-Pickrell distribution

Let $U$ be a random unitary matrix drawn from the Hua-Pickrell distribution $μ_{\mathrm{U}(n+m)}^{(δ)}$ on the unitary group $\mathrm{U}(n+m)$. We show that the eigenvalues of the truncated unitary matrix $[U_{i,j}]_{1\leq i,j\leq n}$ form a determinantal point process $\mathscr{X}_n^{(m,δ)}$ on the unit disc $\mathbb{D}$ for any $δ\in\mathbb{C}$ satisfying $\mathrm{Re}\,δ>-1/2$. We also prove that the limiting point process taken by $n\to\infty$ of the determinantal point process $\mathscr{X}_n^{(m,δ)}$ is always $\mathscr{X}^{[m]}$, independent of $δ$. Here $\mathscr{X}^{[m]}$ is the determinantal point process on $\mathbb{D}$ with weighted Bergman kernel \begin{equation*} \begin{split} K^{[m]}(z,w)=\frac{1}{(1-z\overline w)^{m+1}} \end{split} \end{equation*} with respect to the reference measure $dμ^{[m]}(z)=\frac{m}π(1-|z|)^{m-1}dσ(z)$, where $dσ(z)$ is the Lebesgue measure on $\mathbb{D}$.

math.PR↗

arXiv2022

Takagi Topological Insulator on the Honeycomb Lattice

Recently, real topological phases protected by $PT$ symmetry have been actively investigated. In two dimensions, the corresponding topological invariant is the Stiefel-Whitney number. A recent theoretical advance is that in the presence of the sublattice symmetry, the Stiefel-Whitney number can be equivalently formulated in terms of Takagi's factorization. The topological invariant gives rise to a novel second-order topological insulator with odd $PT$-related pairs of corner zero modes. In this article, we review the elements of this novel second-order topological insulator, and demonstrate the essential physics by a simple model on the honeycomb lattice.

cond-mat.mes-hall↗

arXiv2022

Groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys

Using the extended halo-based group finder developed by Yang et al. (2021), which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range ($0 < z < 6$) from the joint CFHT Large Area $U$-band Deep Survey (CLAUDS) and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) deep data set. Based on a selection of 5,607,052 galaxies with $i$-band magnitude $m_{i} < 26$ and a sky coverage of $34.41\ {\rm deg}^2$, we identify a total of 2,232,134 groups, within which 402,947 groups have at least three member galaxies. We have visually checked and discussed the general properties of those richest groups at redshift $z>2.0$. By checking the galaxy number distributions within a $5-7\ h^{-1}\mathrm{Mpc}$ projected separation and a redshift difference $Δz \le 0.1$ around those richest groups at redshift $z>2$, we identified a list of 761, 343 and 43 protocluster candidates in the redshift bins $2\leq z<3$, $3\leq z<4$ and $z \geq 4$, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along the cosmic time.

astro-ph.CO↗

arXiv2022

Trust-SIoT: Towards Trustworthy Object Classification in the Social Internet of Things

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as "things") in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst the human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust-SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify the trustworthy objects. Moreover, Trust-SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability, and benevolence of an object, credible recommendations, and the degree of relationship by employing a knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust-SIoT vis-a-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust-SIoT achieves a higher F1 and lower MAE and MSE scores.

cs.SI↗

arXiv2022

A Novel Speech-Driven Lip-Sync Model with CNN and LSTM

Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters. In this paper, we present a combined deep neural network of one-dimensional convolutions and LSTM to generate vertex displacement of a 3D template face model from variable-length speech input. The motion of the lower part of the face, which is represented by the vertex movement of 3D lip shapes, is consistent with the input speech. In order to enhance the robustness of the network to different sound signals, we adapt a trained speech recognition model to extract speech feature, and a velocity loss term is adopted to reduce the jitter of generated facial animation. We recorded a series of videos of a Chinese adult speaking Mandarin and created a new speech-animation dataset to compensate the lack of such public data. Qualitative and quantitative evaluations indicate that our model is able to generate smooth and natural lip movements synchronized with speech.

cs.SD↗

arXiv2022

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

The disentanglement of StyleGAN latent space has paved the way for realistic and controllable image editing, but does StyleGAN know anything about temporal motion, as it was only trained on static images? To study the motion features in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate that a series of meaningful, natural, and versatile small, local movements (referred to as "micromotion", such as expression, head movement, and aging effect) can be represented in low-rank spaces extracted from the latent space of a conventionally pre-trained StyleGAN-v2 model for face generation, with the guidance of proper "anchors" in the form of either short text or video clips. Starting from one target face image, with the editing direction decoded from the low-rank space, its micromotion features can be represented as simple as an affine transformation over its latent feature. Perhaps more surprisingly, such micromotion subspace, even learned from just single target face, can be painlessly transferred to other unseen face images, even those from vastly different domains (such as oil painting, cartoon, and sculpture faces). It demonstrates that the local feature geometry corresponding to one type of micromotion is aligned across different face subjects, and hence that StyleGAN-v2 is indeed "secretly" aware of the subject-disentangled feature variations caused by that micromotion. We present various successful examples of applying our low-dimensional micromotion subspace technique to directly and effortlessly manipulate faces, showing high robustness, low computational overhead, and impressive domain transferability. Our codes are available at https://github.com/wuqiuche/micromotion-StyleGAN.

cs.CV↗

arXiv2022

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.

cs.IR↗