arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 721 records · Page 40Linked to original sources

arXiv2023

Spectropolarimetry of the type IIP supernova 2021yja: an unusually high continuum polarization during the photospheric phase

We present six epochs of optical spectropolarimetry of the Type IIP supernova (SN) 2021yja ranging from $\sim$ 25 to 95 days after the explosion. An unusually high continuum linear polarization of $p \sim 0.9\%$ is measured during the early photospheric phase, followed by a steady decrease well before the onset of the nebular phase. This behavior has not been observed before in Type IIP supernovae (SNe IIP). The observed continuum polarization angle does not change significantly during the photospheric phase. We find a pronounced axis of symmetry in the global ejecta that is shared in common with the H$α$ and Ca II near-infrared triplet lines. These observations are consistent with an ellipsoidal geometry. The temporal evolution of the continuum polarization is also compatible with the SN ejecta interacting with aspherical circumstellar matter, although no spectroscopic features that may be associated with strong interaction can be identified. Alternatively, we consider the source of the high polarization to be an extended hydrogen envelope that is indistinguishable from low-density circumstellar matter.

astro-ph.HE↗

arXiv2023

Multiple Rings and Asymmetric Structures in the Disk of SR 21

Crescent-like asymmetric dust structures discovered in protoplanetary disks indicate dust aggregations. Thus, the research on them helps us understand the planet formation process. Here we analyze the ALMA data of the protoplanetary disk around the T-Tauri star SR 21, which has asymmetric structures detected in previous sub-millimeter observations. Imaged at ALMA Band 6 (1.3 mm) with a spatial resolution of about 0.$\arcsec$04, the disk is found to consist of two rings and three asymmetric structures, with two of the asymmetric structures being in the same ring. Compared to the Band 6 image, the Band 3 (2.7 mm) image also shows the three asymmetric structures but with some clumps. The elongated asymmetric structures in the outer ring could be due to the interactions of a growing planet. By fitting the Band 3 and Band 6 dust continuum data, two branches of solutions of maximum dust size in the disk are suggested: one is larger than 1 mm, and the other is smaller than 300 $μm$. High-resolution continuum observations at longer wavelengths as well as polarization observations can help break the degeneracy. We also suggest that the prominent spiral previously identified in VLT/SPHERE observations to the south of the star at 0.$\arcsec$25 may be the scattered light counterpart of the Inner Arc, and the structure is a dust-trapping vortex in nature. The discovered features in SR 21 make it a good target for studying the evolution of asymmetric structures and planet formation.

astro-ph.EP↗

arXiv2023

Circumstellar Medium Interaction in SN 2018lab, A Low-Luminosity II-P Supernova observed with TESS

We present photometric and spectroscopic data of SN 2018lab, a low luminosity type IIP supernova (LLSN) with a V-band peak luminosity of $-15.1\pm0.1$ mag. SN 2018lab was discovered by the Distance Less Than 40 Mpc (DLT40) SNe survey only 0.73 days post-explosion, as determined by observations from the Transiting Exoplanet Survey Satellite (TESS). TESS observations of SN 2018lab yield a densely sampled, fast-rising, early time light curve likely powered by circumstellar medium (CSM) interaction. The blue-shifted, broadened flash feature in the earliest spectra ($<$2 days) of SN 2018lab provide further evidence for ejecta-CSM interaction. The early emission features in the spectra of SN 2018lab are well described by models of a red supergiant progenitor with an extended envelope and close-in CSM. As one of the few LLSNe with observed flash features, SN 2018lab highlights the need for more early spectra to explain the diversity of flash feature morphology in type II SNe.

astro-ph.HE↗

arXiv2023

DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zero-shot transfer capability in many discriminative tasks. Their adaptation to zero-shot image-conditioned text generation tasks has drawn increasing interest. Prior arts approach to zero-shot captioning by either utilizing the existing large language models (e.g., GPT-2) or pre-training the encoder-decoder network in an end-to-end manner. In this work, we propose a simple framework, named DeCap, for zero-shot captioning. We introduce a lightweight visual-aware language decoder. This decoder is both data-efficient and computation-efficient: 1) it only requires the text data for training, easing the burden on the collection of paired data. 2) it does not require end-to-end training. When trained with text-only data, the decoder takes the text embedding extracted from the off-the-shelf CLIP encoder as a prefix embedding. The challenge is that the decoder is trained on the text corpus but at the inference stage, it needs to generate captions based on visual inputs. The modality gap issue is widely observed in multi-modal contrastive models that prevents us from directly taking the visual embedding as the prefix embedding. We propose a training-free mechanism to reduce the modality gap. We project the visual embedding into the CLIP text embedding space, while the projected embedding retains the information of the visual input. Taking the projected embedding as the prefix embedding, the decoder generates high-quality descriptions that match the visual input. The experiments show that DeCap outperforms other zero-shot captioning methods and unpaired captioning methods on the typical image captioning benchmarks, i.e., MSCOCO and NoCaps.

cs.CV↗

arXiv2023

Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, which aims at detecting customer's opinion targets from reviews on product or service. The traditional supervised models can achieve promising results with annotated datasets, however, the performance dramatically decreases when they are applied to the task of cross-domain aspect term extraction. Existing cross-domain transfer learning methods either directly inject linguistic features into Language models, making it difficult to transfer linguistic knowledge to target domain, or rely on the fixed predefined prompts, which is time-consuming to construct the prompts over all potential aspect term spans. To resolve the limitations, we propose a soft prompt-based joint learning method for cross domain aspect term extraction in this paper. Specifically, by incorporating external linguistic features, the proposed method learn domain-invariant representations between source and target domains via multiple objectives, which bridges the gap between domains with varied distributions of aspect terms. Further, the proposed method interpolates a set of transferable soft prompts consisted of multiple learnable vectors that are beneficial to detect aspect terms in target domain. Extensive experiments are conducted on the benchmark datasets and the experimental results demonstrate the effectiveness of the proposed method for cross-domain aspect terms extraction.

cs.CL↗

arXiv2023

Free-electron Brewster radiation

Free-electron radiation offers an enticing route to create light emission at arbitrary spectral regime. However, this type of light emission is generally weak, which is intrinsically limited by the weak particle-matter interaction and unavoidably impedes the development of many promising applications, such as the miniaturization of free-electron radiation sources and high-energy particle detectors. Here we reveal a mechanism to enhance the particle-matter interaction by exploiting the pseudo-Brewster effect of gain materials - presenting an enhancement of at least four orders of magnitude for the light emission. This mechanism is enabled by the emergence of an unprecedented phase diagram that maps all phenomena of free-electron radiation into three distinct phases in a gain-thickness parameter space, namely the conventional, intermediate, and Brewster phases, when an electron penetrates a dielectric slab with a modest gain and a finite thickness. Essentially, our revealed mechanism corresponds to the free-electron radiation in the Brewster phase, which also uniquely features ultrahigh directionality, always at the Brewster angle, regardless of the electron velocity. Counterintuitively, we find that the intensity of this free-electron Brewster radiation is insensitive to the Fabry-Perot resonance condition and thus the variation of slab thickness, and moreover, a weaker gain could lead to a stronger enhancement for the light emission. The scheme of free-electron Brewster radiation, especially along with its compatibility with low-energy electrons, may enable the development of high-directionality high-intensity light sources at any frequency.

physics.optics↗

arXiv2023

Tele-Knowledge Pre-training for Fault Analysis

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents. To organize this knowledge from experts uniformly, we propose to create a Tele-KG (tele-knowledge graph). Using this valuable data, we further propose a tele-domain language pre-training model TeleBERT and its knowledge-enhanced version, a tele-knowledge re-training model KTeleBERT. which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms. Concretely, our proposal includes two stages: first, pre-training TeleBERT on 20 million tele-related corpora, and then re-training it on 1 million causal and machine-related corpora to obtain KTeleBERT. Our evaluation on multiple tasks related to fault analysis in tele-applications, including root-cause analysis, event association prediction, and fault chain tracing, shows that pre-training a language model with tele-domain data is beneficial for downstream tasks. Moreover, the KTeleBERT re-training further improves the performance of task models, highlighting the effectiveness of incorporating diverse tele-knowledge into the model.

cs.AI↗

arXiv2023

SN 2021zny: an early flux excess combined with late-time oxygen emission suggests a double white dwarf merger event

We present a photometric and spectroscopic analysis of the ultra-luminous and slowly evolving 03fg-like Type Ia SN 2021zny. Our observational campaign starts from $\sim5.3$ hours after explosion (making SN 2021zny one of the earliest observed members of its class), with dense multi-wavelength coverage from a variety of ground- and space-based telescopes, and is concluded with a nebular spectrum $\sim10$ months after peak brightness. SN 2021zny displayed several characteristics of its class, such as the peak brightness ($M_{B}=-19.95$ mag), the slow decline ($Δm_{15}(B) = 0.62$ mag), the blue early-time colours, the low ejecta velocities and the presence of significant unburned material above the photosphere. However, a flux excess for the first $\sim1.5$ days after explosion is observed in four photometric bands, making SN 2021zny the third 03fg-like event with this distinct behavior, while its $+313$ d spectrum shows prominent [O I] lines, a very unusual characteristic of thermonuclear SNe. The early flux excess can be explained as the outcome of the interaction of the ejecta with $\sim0.04\:\mathrm{M_{\odot}}$ of H/He-poor circumstellar material at a distance of $\sim10^{12}$ cm, while the low ionization state of the late-time spectrum reveals low abundances of stable iron-peak elements. All our observations are in accordance with a progenitor system of two carbon/oxygen white dwarfs that undergo a merger event, with the disrupted white dwarf ejecting carbon-rich circumstellar material prior to the primary white dwarf detonation.

astro-ph.HE↗

arXiv2023

ALMA Band 6 high-resolution observations of the transitional disk around SY Cha

In this study, we reported the results of high-resolution (0.14 arcsec) Atacama Large Millimeter/submillimeter Array (ALMA) observations of the 225 GHz dust continuum and CO molecular emission lines from the transitional disk around SY Cha. Our high-resolution observations clearly revealed the inner cavity and the central point source for the first time. The radial profile of the ring can be approximated by a bright narrow ring superimposed on a fainter wide ring. Furthermore, we found that there is a weak azimuthal asymmetry in dust continuum emission. For gas emissions, we detected $\rm{}^{12}CO$(2$-$1), $\rm{}^{13}CO$(2$-$1) and $\rm{}C^{18}O$(2$-$1), from which we estimated the total gas mass of the disk to be $2.2\times10^{-4}M_\odot$, assuming a CO/H$_2$ ratio of $10^{-4}$. The observations showed that the gas is present inside the dust cavity. The analysis of the velocity structure of the $\rm{}^{12}CO$(2$-$1) emission line revealed that the velocity is distorted at the location of the dust inner disk, which may be owing to warping of the disk or radial gas flow within the cavity of the dust disk. High-resolution observations of SY Cha showed that this system is composed of a ring and a distorted inner disk, which may be common, as indicated by the survey of transitional disk systems at a resolution of $\sim$0.1~arcsec.

astro-ph.EP↗

arXiv2023

Unsupervised Scene Adaptation with Memory Regularization in vivo

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.

cs.CV↗

arXiv2023

Analogical Inference Enhanced Knowledge Graph Embedding

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

cs.AI↗

arXiv2023

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, existing temporal grounding datasets are not carefully designed to evaluate the compositional generalizability. To systematically benchmark the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. When evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization. Based on this insight, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into hierarchical semantic graphs, respectively, and learns fine-grained semantic correspondence between the two graphs. Furthermore, we introduce a novel adaptive structured semantics learning approach to derive the structure-informed and domain-generalizable graph representations, which facilitate the fine-grained semantic correspondence reasoning between the two graphs. Extensive experiments validate the superior compositional generalizability of our approach.

cs.CV↗

arXiv2023

Temporal Perceiving Video-Language Pre-training

Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve global feature alignment across modalities. However, the local associations between videos and texts are not modeled, restricting the pre-training models' generality, especially for tasks requiring the temporal video boundary for certain query texts. This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment such that the trained model can accurately perceive temporal boundaries in videos given the text description. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features. To produce temporal boundaries, frame features in several videos are manually merged into a long video sequence that interacts with a text sequence. With the localization task, our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality. Notably, comprehensive experimental results show that our method significantly improves the state-of-the-art performance on various benchmarks, covering text-to-video retrieval, video question answering, video captioning, temporal action localization and temporal moment retrieval. The code will be released soon.

cs.CV↗

arXiv2023

DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking

Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimensional information to supervise lower-dimensional tasks is a feasible way to reduce datasets size. In this work, the DR-WLC, a dimensionality reduction cognitive model, which can perform both object detection and pose estimation tasks at the same time is proposed. The model only requires 3D model of objects and unlabeled environment images (with or without objects) to finish the training. In addition, a bounding boxes generation strategy is also proposed to build the relationship between 3D model and 2D object detection task. Experiments show that our method can qualify the work without any manual annotations and it is easy to deploy for practical applications. Source code is at https://github.com/IN2-ViAUn/DR-WLC.

cs.CV↗

arXiv2023

MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views

We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net. The non-rigid shape is first expressed as the sum of a coarse shape basis and a flexible shape deformation, then multiple hypotheses are generated with uncertainty modeling of the deformation part. MHR-Net is optimized with reprojection loss on the basis and the best hypothesis. Furthermore, we design a new Procrustean Residual Loss, which reduces the rigid rotations between similar shapes and further improves the performance. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.

cs.CV↗

arXiv2023

The Core Normal Type Ia Supernova 2019np: An Overall Spherical Explosion with an Aspherical Surface Layer and an Aspherical 56Ni Core

Optical spectropolarimetry of the normal thermonuclear supernova SN2019np from -14.5 to +14.5 days relative to B-band maximum detected an intrinsic continuum polarization, p(cont), of 0.21+-0.09% at the first epoch. Between days -11.5 to +05, p(cont) remained about 0 and by day +14.5 was again significant at 0.19+-0.10%. Not considering the first epoch, the dominant axis of SiII(6355A) was roughly constant, staying close to the continuum until both rotated in opposite directions on day +14.5. Detailed radiation-hydrodynamical simulations produce a very steep density slope in the outermost ejecta so that the low first-epoch p(const) of about 0.2% nevertheless suggests a separate structure with an axis ratio of about 2 in the outer carbon-rich 3...5E-3 Mo. Large-amplitude fluctuations in the polarization profiles and a flocculent appearance of the polar diagram for the CaII near-infrared triplet (NIR3) may be related by a common origin. The temporal evolution of the polarization spectra agrees with an off-center delayed detonation. The late-time increase in polarization and the possible change in position angle are also consistent with an aspherical 56Ni core. The p(cont) and the absorptions due to Si II(6355A) and the CaII NIR3 form in the same region of the extended photosphere, with an interplay between line occultation and thermalization producing p. Small-scale polarization features may be due to small-scale structures, but many could be related to atomic patterns of the quasi-continuum; the hardly have an equivalent in the total-flux spectrum. We compare SN2019np to other SNe and develop future objectives and strategies for SNIa spectropolarimetry.

astro-ph.SR↗

arXiv2023

A Systematic Study of Ia-CSM Supernovae from the ZTF Bright Transient Survey

Among the supernovae (SNe) that show strong interaction with the circumstellar medium, there is a rare subclass of Type Ia supernovae, SNe Ia-CSM, that show strong narrow hydrogen emission lines much like SNe IIn but on top of a diluted over-luminous Type Ia spectrum. In the only previous systematic study of this class (Silverman et al. 2013), 16 objects were identified, 8 historic and 8 from the Palomar Transient Factory (PTF). Now using the successor survey to PTF, the Zwicky Transient Facility (ZTF), we have classified 12 additional objects of this type through the systematic Bright Transient Survey (BTS). In this study, we present and analyze the optical and mid-IR light curves, optical spectra, and host galaxy properties of this sample. Consistent with previous studies, we find the objects to have slowly evolving light curves compared to normal SNe Ia with peak absolute magnitudes between -19.1 and -21, spectra having weak H$β$, large Balmer decrements of ~7 and strong Ca NIR emission. Out of 10 SNe from our sample observed by NEOWISE, 9 have $3σ$ detections, along with some showing a clear reduction in red-wing of H$α$, indicative of newly formed dust. We do not find our SN Ia-CSM sample to have a significantly different distribution of equivalent width of He I $\lambda5876$ than SNe IIn as observed in Silverman et al. 2013. The hosts tend to be late-type galaxies with recent star formation. We also derive a rate estimate of 29$^{+27}_{-21}$ Gpc$^{-3}$ yr$^{-1}$ for SNe Ia-CSM which is ~0.02--0.2 % of the SN Ia rate. This work nearly doubles the sample of well-studied Ia-CSM objects in Silverman et al. 2013, increasing the total number to 28.

astro-ph.HE↗

arXiv2023

One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation

Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.

cs.CV↗