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Bin Chen

Publications and source records attributed to Bin Chen.

At least 415 records · Page 23Linked to original sources
arXiv2023

The Focusing Optics X-ray Solar Imager (FOXSI)

FOXSI is a direct-imaging, hard X-ray (HXR) telescope optimized for solar flare observations. It detects hot plasma and energetic electrons in and near energy release sites in the solar corona via bremsstrahlung emission, measuring both spatial structure and particle energy distributions. It provides two orders of magnitude faster imaging spectroscopy than previously available, probing physically relevant timescales (<1s) never before accessible to address fundamental questions of energy release and efficient particle acceleration that have importance far beyond their solar application (e.g., planetary magnetospheres, flaring stars, accretion disks). FOXSI measures not only the bright chromospheric X-ray emission where electrons lose most of their energy, but also simultaneous emission from electrons as they are accelerated in the corona and propagate along magnetic field lines. FOXSI detects emission from high in the tenuous corona, where previous instruments have been blinded by nearby bright features and will fully characterizes the accelerated electrons and hottest plasmas as they evolve in energy, space, and time to solve the mystery of how impulsive energy release leads to solar eruptions, the primary drivers of space weather at Earth, and how those eruptions are energized and evolve.

astro-ph.IM↗
arXiv2023

Circular orbits and polarized images of charged particles orbiting Kerr black hole with a weak magnetic field

In this work, we study circular motions of charged particles and their polarized images around the Kerr black hole immersed in a weak magnetic field. We pay special attention to the case that both the magnetic field and the charge-to-mass ratio are not big, thus the effective potential along the radial motion reduce to a cubic form approximately so that we can express the radius of the innermost stable circular orbit analytically in terms of the energy and angular momentum of charged particles. Moreover, we computed the polarized synchrotron radiations of these particles and obtained the polarized images semi-analytically for various spins, observational angles and prograde and retrograde orbits. In particular, We find that these parameters have significant impacts on the polarization rotation and the magnitude of the polarization flux.

gr-qc↗
arXiv2023

Electromagnetic effects on charged particles in NHEK

We investigate the motions of charged particles in the near horizon region of an extreme Kerr black hole with weak electromagnetic fields. There is an enhanced symmetry in the NHEK geometry. We find that when the electromagnetic field respects this enhanced symmetry, which we refer to as the maximally symmetric electromagnetic (MSEM) field, the equations of motion of charged particles get simplified into a set of decoupled first-order differential equations. We discuss the motions of charged particles in two MSEM fields, one being the force-free field and the other being the vacuum fields. Even though the radial motions are similar to the geodesics in NHEK geometry, the angular motions could be affected by the electromagnetic field significantly. In particular, for the vacuum solution which is produced by a weakly charged black hole, there exist stable vortical motions if the electromagnetic parameter is above the critical value $\mB_c = \sqrt{3}$. These vortical motions do not cross the equatorial planes, and the charged particles in them radiate non-thermally. We discuss the corresponding astrophysical implications.

gr-qc↗
arXiv2023

Particle acceleration in solar flares with imaging-spectroscopy in soft X-rays

Particles are accelerated to very high, non-thermal energies during explosive energy-release phenomena in space, solar, and astrophysical plasma environments. In the case of solar flares, it has been established that magnetic reconnection plays an important role for releasing the magnetic energy, but it remains unclear if or how magnetic reconnection can further explain particle acceleration during flares. Here we argue that the key issue is the lack of understanding of the precise context of particle acceleration but it can be overcome, in the near future, by performing imaging-spectroscopy in soft X-rays (SXRs). Such observations should be complemented by observations in other wavelengths such as extreme-ultraviolets (EUVs), microwaves, hard X-rays (HXRs), and gamma-rays. Also, numerical simulations will be crucial for further narrowing down the particle acceleration mechanism in the context revealed by the observations. Of all these efforts, imaging-spectroscopy in SXRs, if successfully applied to large limb flares, will be a milestone in our challenge of understanding electron acceleration in solar flares and beyond, i.e. the Plasma Universe.

astro-ph.SR↗
arXiv2023

The Shadow Formalism of Galilean CFT$_2$

In this work, we develop the shadow formalism for two-dimensional Galilean conformal field theory (GCFT$_2$). We define the principal series representation of Galilean conformal symmetry group and find its relation with the Wigner classification, then we determine the shadow transform of local operators. Using this formalism we derive the OPE blocks, Clebsch-Gordan kernels, conformal blocks and conformal partial waves. A new feature is that the conformal block admits additional branch points, which would destroy the convergence of OPE for certain parameters. We establish another inversion formula different from the previous one, but get the same result when decomposing the four-point functions in the mean field theory (MFT). We also construct a continuous series of bilocal actions of MFT, and an exceptional series of local actions, one of which is the BMS free scalar model. We notice that there is an outer automorphism of the Galilean conformal symmetry, and the GCFT$_2$ can be regarded as null defect in higher dimensional CFTs.

hep-th↗
arXiv2023

Fine structures of radio bursts from flare star AD Leo with FAST observations

Radio bursts from nearby active M-dwarfs have been frequently reported and extensively studied in solar or planetary paradigms. Whereas, their sub-structures or fine structures remain rarely explored despite their potential significance in diagnosing the plasma and magnetic field properties of the star. Such studies in the past have been limited by the sensitivity of radio telescopes. Here we report the inspiring results from the high time-resolution observations of a known flare star AD Leo with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We detected many radio bursts in the two days of observations with fine structures in the form of numerous millisecond-scale sub-bursts. Sub-bursts on the first day display stripe-like shapes with nearly uniform frequency drift rates, which are possibly stellar analogs to Jovian S-bursts. Sub-bursts on the second day, however, reveal a different blob-like shape with random occurrence patterns and are akin to solar radio spikes. The new observational results suggest that the intense emission from AD Leo is driven by electron cyclotron maser instability which may be related to stellar flares or interactions with a planetary companion.

astro-ph.SR↗
arXiv2023

An inverse Gauss curvature flow and its application to p-capacitary Orlicz-Minkowski problem

In [Calc. Var., 57:5 (2018)], Hong-Ye-Zhang proposed the $p$-capacitary Orlicz-Minkowski problem and proved the existence of convex solutions to this problem by variational method for $p\in(1,n)$. However, the smoothness and uniqueness of solutions are still open. Notice that the $p$-capacitary Orlicz-Minkowski problem can be converted equivalently to a Monge-Ampère type equation in smooth case: \begin{align}\label{0.1} fϕ(h_K)|\nablaΨ|^p=τG \end{align} for $p\in(1,n)$ and some constant $τ>0$, where $f$ is a positive function defined on the unit sphere $\mathcal{S}^{n-1}$, $ϕ$ is a continuous positive function defined in $(0,+\infty)$, and $G$ is the Gauss curvature. In this paper, we confirm the existence of smooth solutions to $p$-capacitary Orlicz-Minkowski problem with $p\in(1,n)$ for the first time by a class of inverse Gauss curvature flows, which converges smoothly to the solution of Equation (\ref{0.1}). Furthermore, we prove the uniqueness result for Equation (\ref{0.1}) in a special case.

math.AP↗
arXiv2023

Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding

In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks. However, when extended to point cloud data, existing works mainly focus on building task-specific models, and fail to extract universal 3D vision-language embedding that generalize well. We carefully investigate three common tasks in semantic 3D scene understanding, and derive key insights into the development of a pre-training model. Motivated by these observations, we propose a vision-language pre-training framework 3DVLP (3D vision-language pre-training with object contrastive learning), which transfers flexibly on 3D vision-language downstream tasks. 3DVLP takes visual grounding as the proxy task and introduces Object-level IoU-guided Detection (OID) loss to obtain high-quality proposals in the scene. Moreover, we design Object-level Cross-Contrastive alignment (OCC) task and Object-level Self-Contrastive learning (OSC) task to align the objects with descriptions and distinguish different objects in the scene, respectively. Extensive experiments verify the excellent performance of 3DVLP on three 3D vision-language tasks, reflecting its superiority in semantic 3D scene understanding.

cs.CV↗
arXiv2023

On emergent conformal symmetry near the photon ring

In this note we revisit the emergent conformal symmetry in the near-ring region of warped spacetime. In particular, we propose a novel construction of the emergent near-ring $sl(2,R)_{\text{QNM}}$ symmetry. We show that each eikonal QNM family falls into one highest-weight representation of this algebra, and $sl(2,R)_{\text{QNM}}$ can be related to the near-ring isometry group $sl(2,R)_{\text{ISO}}$ in a simple way. Furthermore we find that the coherent state space of $sl(2,R)_{\text{QNM}}$ can be identified with the phase space of the photon ring.

hep-th↗
arXiv2023

Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers

Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in disease diagnosis. Recently, deep auto-encoder with its variants has demonstrated its advantages in many UAD scenarios. Training on the normal data, these models are expected to locate anomalies by producing higher reconstruction error for the abnormal areas than the normal ones. However, this assumption does not always hold because of the uncontrollable generalization capability. To solve this problem, we present LSGS, a method that builds on Vector Quantised-Variational Autoencoder (VQVAE) with a novel aggregated codebook and transformers with global attention. In this work, the VQVAE focus on feature extraction and reconstruction of images, and the transformers fit the manifold and locate anomalies in the latent space. Then, leveraging the generated encoding sequences that conform to a normal distribution, we can reconstruct a more accurate image for locating the anomalies. Experiments on various datasets demonstrate the effectiveness of the proposed method.

cs.CV↗
arXiv2023

On Higher-dimensional Carrollian and Galilean Conformal Field Theories

In this paper, we study the Carrollian and Galilean conformal field theories (CCFT and GCFT) in $d>2$ dimensions. We construct the highest weight representations (HWR) of Carrollian and Galilean conformal algebra (CCA and GCA). Even though the two algebras have different structures, their HWRs share similar structure, because their rotation subalgebras are isomorphic. In both cases, we find that the finite dimensional representations are generally reducible but indecomposable, and can be organized into the multiplets. Moreover, it turns out that the multiplet representations in $d>2$ CCA and GCA carry not only the simple chain structure appeared in logCFT or $2d$ GCFT, but also more generally the net structures. We manage to classify all the allowed chain representations. Furthermore we discuss the two-point and three-point correlators by using the Ward identities. We mainly focus on the two-point correlators of the operators in chain representations. Even in this relative simple case, we find some novel features: multiple-level structure, shortage of the selection rule on the representations, undetermined 2-pt coefficients, etc.. We find that the non-trivial correlators could only appear for the representations of certain structure, and the correlators are generally polynomials of time coordinates for CCFT (spacial coordinates for GCFT), whose orders depend on the levels of the correlators.

hep-th↗
arXiv2023

Multifrequency microwave imaging of weak transients from the quiet solar corona

Understanding the dynamics of the quiet solar corona is important for answering key questions including the coronal heating problem. Multiple studies have suggested small-scale magnetic reconnection events may play a crucial role. These reconnection events are expected to involve acceleration of electrons to suprathermal energies, which can then produce nonthermal observational signatures. However, due to the paucity of sensitive high-fidelity observations capable of probing these nonthermal signatures, most studies were unable to quantify their nonthermal nature. Here we use joint radio observations from the Very Large Array (VLA) and the Expanded Owens Valley Solar Array (EOVSA) to detect transient emissions from the quiet solar corona in the microwave (GHz) domain. While similar transients have been reported in the past, their nonthermal nature could not be adequately quantified due to the unavailability of broadband observations. Using a much larger bandwidth available now with the VLA and EOVSA, in this study, we are able to quantify the nonthermal energy associated with two of these transients. We find that the total nonthermal energy associated with some of these transients can be comparable to or even larger than the total thermal energy of a nanoflare, which underpins the importance of nonthermal energy in the total coronal energy budget.

astro-ph.SR↗
arXiv2023

Progressive Content-aware Coded Hyperspectral Compressive Imaging

Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors, the coded aperture snapshot spectral imaging (CASSI) has achieved great success due to its hardware-friendly implementation and fast imaging speed. However, for some less spectrally sparse scenes, single snapshot and unreasonable coded aperture design tend to make HSI recovery more ill-posed and yield poor spatial and spectral fidelity. In this paper, we propose a novel Progressive Content-Aware CASSI framework, dubbed PCA-CASSI, which captures HSIs with multiple optimized content-aware coded apertures and fuses all the snapshots for reconstruction progressively. Simultaneously, by mapping the Range-Null space Decomposition (RND) into a deep network with several phases, an RND-HRNet is proposed for HSI recovery. Each recovery phase can fully exploit the hidden physical information in the coded apertures via explicit $\mathcal{R}$$-$$\mathcal{N}$ decomposition and explore the spatial-spectral correlation by dual transformer blocks. Our method is validated to surpass other state-of-the-art methods on both multiple- and single-shot HSI imaging tasks by large margins.

eess.IV↗
arXiv2023

Learning Transferable Spatiotemporal Representations from Natural Script Knowledge

Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., +13.6% gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS.

cs.CV↗
arXiv2023

Singleton-Optimal LRCs and Perfect LRCs via Cyclic and Constacyclic Codes

Locally repairable codes (LRCs) have emerged as an important coding scheme in distributed storage systems (DSSs) with relatively low repair cost by accessing fewer non-failure nodes. Theoretical bounds and optimal constructions of LRCs have been widely investigated. Optimal LRCs via cyclic and constacyclic codes provide significant benefit of elegant algebraic structure and efficient encoding procedure. In this paper, we continue to consider the constructions of optimal LRCs via cyclic and constacyclic codes with long code length. Specifically, we first obtain two classes of $q$-ary cyclic Singleton-optimal $(n, k, d=6;r=2)$-LRCs with length $n=3(q+1)$ when $3 \mid (q-1)$ and $q$ is even, and length $n=\frac{3}{2}(q+1)$ when $3 \mid (q-1)$ and $q \equiv 1(\bmod~4)$, respectively. To the best of our knowledge, this is the first construction of $q$-ary cyclic Singleton-optimal LRCs with length $n>q+1$ and minimum distance $d \geq 5$. On the other hand, an LRC acheiving the Hamming-type bound is called a perfect LRC. By using cyclic and constacyclic codes, we construct two new families of $q$-ary perfect LRCs with length $n=\frac{q^m-1}{q-1}$, minimum distance $d=5$ and locality $r=2$.

cs.IT↗
arXiv2023

Defining the Middle Corona

The middle corona, the region roughly spanning heliocentric altitudes from $1.5$ to $6\,R_\odot$, encompasses almost all of the influential physical transitions and processes that govern the behavior of coronal outflow into the heliosphere. Eruptions that could disrupt the near-Earth environment propagate through it. Importantly, it modulates inflow from above that can drive dynamic changes at lower heights in the inner corona. Consequently, this region is essential for comprehensively connecting the corona to the heliosphere and for developing corresponding global models. Nonetheless, because it is challenging to observe, the middle corona has been poorly studied by major solar remote sensing missions and instruments, extending back to the Solar and Heliospheric Observatory (SoHO) era. Thanks to recent advances in instrumentation, observational processing techniques, and a realization of the importance of the region, interest in the middle corona has increased. Although the region cannot be intrinsically separated from other regions of the solar atmosphere, there has emerged a need to define the region in terms of its location and extension in the solar atmosphere, its composition, the physical transitions it covers, and the underlying physics believed to be encapsulated by the region. This paper aims to define the middle corona and give an overview of the processes that occur there.

astro-ph.SR↗
arXiv2023

Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection

With the pandemic of COVID-19, relevant fake news is spreading all over the sky throughout the social media. Believing in them without discrimination can cause great trouble to people's life. However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge. While the model trained on corresponding corpora is also mediocre for insufficient learning. In this paper, we propose a novel transformer-based language model fine-tuning approach for these fake news detection. First, the token vocabulary of individual model is expanded for the actual semantics of professional phrases. Second, we adapt the heated-up softmax loss to distinguish the hard-mining samples, which are common for fake news because of the disambiguation of short text. Then, we involve adversarial training to improve the model's robustness. Last, the predicted features extracted by universal language model RoBERTa and domain-specific model CT-BERT are fused by one multiple layer perception to integrate fine-grained and high-level specific representations. Quantitative experimental results evaluated on existing COVID-19 fake news dataset show its superior performances compared to the state-of-the-art methods among various evaluation metrics. Furthermore, the best weighted average F1 score achieves 99.02%.

cs.CL↗
arXiv2023

Revisiting Image Deblurring with an Efficient ConvNet

Image deblurring aims to recover the latent sharp image from its blurry counterpart and has a wide range of applications in computer vision. The Convolution Neural Networks (CNNs) have performed well in this domain for many years, and until recently an alternative network architecture, namely Transformer, has demonstrated even stronger performance. One can attribute its superiority to the multi-head self-attention (MHSA) mechanism, which offers a larger receptive field and better input content adaptability than CNNs. However, as MHSA demands high computational costs that grow quadratically with respect to the input resolution, it becomes impractical for high-resolution image deblurring tasks. In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational costs. Our key design is an efficient CNN block dubbed LaKD, equipped with a large kernel depth-wise convolution and spatial-channel mixing structure, attaining comparable or larger ERF than Transformers but with a smaller parameter scale. Specifically, we achieve +0.17dB / +0.43dB PSNR over the state-of-the-art Restormer on defocus / motion deblurring benchmark datasets with 32% fewer parameters and 39% fewer MACs. Extensive experiments demonstrate the superior performance of our network and the effectiveness of each module. Furthermore, we propose a compact and intuitive ERFMeter metric that quantitatively characterizes ERF, and shows a high correlation to the network performance. We hope this work can inspire the research community to further explore the pros and cons of CNN and Transformer architectures beyond image deblurring tasks.

cs.CV↗