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Recent observations discovered that some repeating fast radio bursts (FRBs) show a large value and complex variations of Faraday rotation measures (RMs). The binary systems containing a supermassive black hole (SMBH) and a neutron star (NS) can be used to explain such RM variations. Meanwhile, such systems produce low-frequency gravitational wave (GW) signals, which are one of the primary interests of three proposed space-based GW detectors: the Laser Interferometer Space Antenna (LISA), Tianqin and Taiji. These signals are known as extreme mass ratio inspirals (EMRIs). Therefore, FRBs can serve as candidates of electromagnetic (EM) counterparts for EMRI signals. In this letter, we study the EMRI signals in this binary system, which can be detected up to $z\sim0.04$ by LISA and Tianqin for the most optimistic case. Assuming the cosmic comb model for FRB production, the total event rate can be as high as $\sim1$ Gpc$^{-3}$ yr$^{-1}$. EMRI signals associated with FRBs can be used to reveal the progenitor of FRBs. It is also a new type of standard siren, which can be used as an independent cosmological probe., Comment: 11 pages, 3 figures, accepted for publication in ApJ Letters

Fast radio bursts (FRBs) are energetic millisecond phenomena in radio band. Polarimetric studies of repeating FRBs indicate that many of these sources occupy extreme and complex magneto-ionized environments. Recently, a frequency-dependent depolarization has been discovered in several repeating FRBs. However, the temporal evolution of polarization properties is limited by the burst rate and observational cadence of telescopes. In this letter, the temporal evolution of depolarization in repeating FRB 20201124A is explored. Using the simultaneous variation of rotation measure and dispersion measure, we also measure the strength of a magnetic field parallel to the line-of-sight. The strength ranges from a few $\mu {\rm G}$ to $10^3\ \mu {\rm G}$. In addition, we find that the evolution of depolarization and magnetic field traces the evolution of rotation measure. Our result supports that the variation of depolarization, rotation measure and the magnetic field are determined by the same complex magneto-ionized screen surrounding the FRB source. The derived properties of the screen are consistent with the wind and the decretion disk of a massive star., Comment: 9 pages, 3 figures, 1 table, accept for publication in ApJL

In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong augmentation, are still correctly classified with high confidence, resulting in a loss close to zero. It indicates that these samples have been already learned well and do not provide any additional optimization benefits to the model. We refer to these samples as ``naive samples". Unfortunately, existing SSL models overlook the characteristics of naive samples, and they just apply the same learning strategy to all samples. To further optimize the SSL model, we emphasize the importance of giving attention to naive samples and augmenting them in a more diverse manner. Sample adaptive augmentation (SAA) is proposed for this stated purpose and consists of two modules: 1) sample selection module; 2) sample augmentation module. Specifically, the sample selection module picks out {naive samples} based on historical training information at each epoch, then the naive samples will be augmented in a more diverse manner in the sample augmentation module. Thanks to the extreme ease of implementation of the above modules, SAA is advantageous for being simple and lightweight. We add SAA on top of FixMatch and FlexMatch respectively, and experiments demonstrate SAA can significantly improve the models. For example, SAA helped improve the accuracy of FixMatch from 92.50% to 94.76% and that of FlexMatch from 95.01% to 95.31% on CIFAR-10 with 40 labels., Comment: Accepted as International Conference on Computer Vision (ICCV) 2023

Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently. Due to the limited labelled data, SSMIS methods mainly focus on effectively leveraging unlabeled data to enhance the segmentation performance. However, despite their promising performance, current state-of-the-art methods often prioritize integrating complex techniques and loss terms rather than addressing the core challenges of semi-supervised scenarios directly. We argue that the key to SSMIS lies in generating substantial and appropriate prediction disagreement on unlabeled data. To this end, we emphasize the crutiality of data perturbation and model stabilization in semi-supervised segmentation, and propose a simple yet effective approach to boost SSMIS performance significantly, dubbed DPMS. Specifically, we first revisit SSMIS from three distinct perspectives: the data, the model, and the loss, and conduct a comprehensive study of corresponding strategies to examine their effectiveness. Based on these examinations, we then propose DPMS, which adopts a plain teacher-student framework with a standard supervised loss and unsupervised consistency loss. To produce appropriate prediction disagreements, DPMS perturbs the unlabeled data via strong augmentations to enlarge prediction disagreements considerably. On the other hand, using EMA teacher when strong augmentation is applied does not necessarily improve performance. DPMS further utilizes a forwarding-twice and momentum updating strategies for normalization statistics to stabilize the training on unlabeled data effectively. Despite its simplicity, DPMS can obtain new state-of-the-art performance on the public 2D ACDC and 3D LA datasets across various semi-supervised settings, e.g. obtaining a remarkable 22.62% improvement against previous SOTA on ACDC with 5% labels., Comment: Code and logs are available at https://github.com/ZhenZHAO/DPMS

Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data. While existing SSL methods focus on the traditional setting, a practical and challenging scenario called label Missing Not At Random (MNAR) is usually ignored. In MNAR, the labeled and unlabeled data fall into different class distributions resulting in biased label imputation, which deteriorates the performance of SSL models. In this work, class transition tracking based Pseudo-Rectifying Guidance (PRG) is devised for MNAR. We explore the class-level guidance information obtained by the Markov random walk, which is modeled on a dynamically created graph built over the class tracking matrix. PRG unifies the historical information of class distribution and class transitions caused by the pseudo-rectifying procedure to maintain the model's unbiased enthusiasm towards assigning pseudo-labels to all classes, so as the quality of pseudo-labels on both popular classes and rare classes in MNAR could be improved. Finally, we show the superior performance of PRG across a variety of MNAR scenarios, outperforming the latest SSL approaches combining bias removal solutions by a large margin. Code and model weights are available at https://github.com/NJUyued/PRG4SSL-MNAR., Comment: Accepted by ICCV 2023

The formation of charge density waves (CDW) is a long-standing open problem particularly in dimensions higher than one. Various observations in the vanadium antimonides discovered recently, such as the missing Kohn anomaly in the acoustic phonons or the latent heat at the transition $T_{\rm CDW}$ = 95 K , further underpin this notion. Here, we study the Kagome metal CsV$_3$Sb$_5$ using polarized inelastic light scattering. The electronic energy gap $2\Delta$ as derived from the redistribution of the continuum is much larger than expected from mean-field theory and reaches values above 20 for $2\Delta/k_{\rm B}T_{\rm CDW}$. The A$_{1g}$ phonon has a discontinuity at $T_{\rm CDW}$ and a precursor starting 20 K above $T_{\rm CDW}$. Density functional theory qualitatively reproduces the redistribution of the electronic continuum at the CDW transition and the phonon energies of the pristine and distorted structures. The linewidths of all A$_{1g}$ and E$_{2g}$ phonon lines including those emerging below $T_{\rm CDW}$ were analyzed in terms of anharmonic symmetric decay revealing strong phonon-phonon coupling. In addition, we observe two CDW amplitude modes (AMs): one in A$_{1g}$ symmetry and one in E$_{2g}$ symmetry. The temperature dependence of both modes deviates from the prediction of mean-field theory. The A$_{1g}$ AM displays an asymmetric Fano-type lineshape, suggestive of strong electron-phonon coupling. The asymmetric A$_{1g}$ AM, along with the discontinuity of the A$_{1g}$ phonon, the large phonon-phonon coupling parameters and the large gap ratio, indicate the importance of anharmonic strong phonon-phonon and electron-phonon coupling for the CDW formation in CsV$_3$Sb$_5$., Comment: 19 pages, 5 figures

Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This practice ensures high-quality pseudo labels, but incurs a relatively low utilization of the whole unlabeled set. In this work, our key insight is that these uncertain samples can be turned into certain ones, as long as the confusion classes for the top-1 class are detected and removed. Invoked by this, we propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class, as well as remaining less likely classes. Since the confusion ones are removed in this space, the re-calculated top-1 confidence can satisfy the pre-defined threshold. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations. Furthermore, considering the varied reliability among uncertain samples and the gradually improved model during training, we correspondingly design two reweighting principles for our uncertain loss. Our method exhibits impressive performance on widely adopted benchmarks. Code is available at https://github.com/LiheYoung/ShrinkMatch., Comment: Accepted by ICCV 2023

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm., Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 2023

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies., Comment: 20 pages, 15 figures, Accepted by Astronomy and Computing

Recently, remarkable anti-glitch and glitch accompanied by bright radio bursts of the Galactic magnetar SGR J1935+2154 were discovered. These two infrequent temporal coincidences between the glitch/anti-glitch and the fast radio burst (FRB)-like bursts reveal their physical connection of them. Here we propose that the anti-glitch/glitch and FRB-like bursts can be well understood by an asteroid tidally captured by a magnetar. In this model, an asteroid is tidally captured and disrupted by a magnetar. Then, the disrupted asteroid will transfer the angular momentum to the magnetar producing a sudden change in the magnetar rotational frequency at the magnetosphere radius. If the orbital angular momentum of the asteroid is parallel (or anti-parallel) to that of the spinning magnetar, a glitch (or anti-glitch) will occur. Subsequently, the bound asteroid materials fall back to the pericenter and eventually are accreted to the surface of the magnetar. Massive fragments of the asteroid cross magnetic field lines and produce bright radio bursts through coherent curvature radiation. Our model can explain the sudden magnetar spin changes and FRB-like bursts in a unified way., Comment: 6 pages, 1 figure, published by MNRAS https://doi.org/10.1093/mnras/stad1585

The exchange-correlation potential formalism previously introduced and applied to the one-dimensional Hubbard model has been extended to spin systems and applied to the case of the one-dimensional antiferromagnetic spin$-\frac{1}{2}$ Heisenberg model. Within the spin exchange-correlation potential formulation, a new sum rule for spin-systems is derived. The exchange-correlation potential for the Heisenberg model is extrapolated from exact diagonalization results of small antiferromagnetic Heisenberg clusters. This procedure is also employed to revisit and computationally improve the previous investigation of the exchange-correlation potential of the half-filled Hubbard model, which was based on the exchange-correlation potential of the dimer. Numerical comparisons with exact benchmark calculations for both the Heisenberg and the Hubbard models indicate that, starting from the exchange-correlation potential of a finite cluster, the extrapolation procedure yields a one-particle spectral function with favorable accuracy at a relatively low computational cost. In addition, a comparison between the ground state energies for the one-dimensional Hubbard and Heisenberg models displays how the well known similarity in behavior of the two models at large interactions manifests within the exchange-correlation potential formalism., Comment: 17 pages, 9 figures

In this work, we perform a QCD sum rules analysis on the $\Xi_{Q}-\Xi_{Q}^{\prime}$ mixing. Contributions from up to dimension-6 four-quark operators are considered. However, it turns out that, only dimension-4 and dimension-5 operators contribute, which reveals the non-perturbative nature of mixing. Especially we notice that only the diagrams with the two light quarks participating in gluon exchange contribute to the mixing. Our results indicate that the mixing angle $\theta_{c}=(1.2\sim2.8)^{\circ}$ for the $Q=c$ case and $\theta_{b}=(0.28\sim0.34)^{\circ}$ for the $Q=b$ case. Our prediction of $\theta_{c}$ is consistent with the most recent Lattice QCD result within error. Such a small mixing angle seems unlikely to resolve the tension between the recent experimental measurement from Belle and Lattice QCD calculation for the semileptonic decay $\Xi_{c}^{0}\to\Xi^{-} e^{+}\nu_{e}$., Comment: 13 pages, 4 figures; version accepted by EPJC

In this work, a three-quark picture is constructed using a bottom-up approach for baryons in light-front quark model. The shape parameters, which characterize the momentum distribution inside a baryon, are determined with the help of the pole residue of the baryon. The relation between the three-quark picture and the diquark picture is clarified. When building the model, we find that Lorentz boost plays a crucial role, and the bottom-up modeling approach can be generalized to multiquark states. Based on this, a unified theoretical framework for describing multiquark states may be established. As a by-product of model construction, we can easily obtain a newly improved definition of baryon interpolating current. The hadron interpolating currents are the starting point of Lattice QCD and QCD sum rules, and therefore are of great importance., Comment: 13 pages; version accepted by PRD

Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoOp and ProDA, tend to adopt soft prompts to learn an appropriate prompt for each specific task. Recent CoCoOp further boosts the base-to-new generalization performance via an image-conditional prompt. However, it directly fuses identical image semantics to prompts of different labels and significantly weakens the discrimination among different classes as shown in our experiments. Motivated by this observation, we first propose a class-aware text prompt (CTP) to enrich generated prompts with label-related image information. Unlike CoCoOp, CTP can effectively involve image semantics and avoid introducing extra ambiguities into different prompts. On the other hand, instead of reserving the complete image representations, we propose text-guided feature tuning (TFT) to make the image branch attend to class-related representation. A contrastive loss is employed to align such augmented text and image representations on downstream tasks. In this way, the image-to-text CTP and text-to-image TFT can be mutually promoted to enhance the adaptation of VLMs for downstream tasks. Extensive experiments demonstrate that our method outperforms the existing methods by a significant margin. Especially, compared to CoCoOp, we achieve an average improvement of 4.03% on new classes and 3.19% on harmonic-mean over eleven classification benchmarks.

Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC., Comment: accepted by CVPR2023