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Visible Light Positioning (VLP) has emerged as a promising technology capable of delivering indoor localization with high accuracy. In VLP systems that use Photodiodes (PDs) as light receivers, the Received Signal Strength (RSS) is affected by the incidence angle of light, making the inclination of PDs a critical parameter in the positioning model. Currently, most studies assume the inclination to be constant, limiting the applications and positioning accuracy. Additionally, light blockages may severely interfere with the RSS measurements but the literature has not explored blockage detection in real-world experiments. To address these problems, we propose a tightly coupled VLP/INS (Inertial Navigation System) integrated navigation system that uses graph optimization to account for varying PD inclinations and VLP blockages. We also discussed the possibility of simultaneously estimating the robot's pose and the locations of some unknown LEDs. Simulations and two groups of real-world experiments demonstrate the efficiency of our approach, achieving an average positioning accuracy of 10 cm during movement and inclination accuracy within 1 degree despite inclination changes and blockages.

We explore the possibility of detecting the couplings of axion-like particle (ALP) with leptons from their loop-level impact on the ALP couplings to electroweak (EW) gauge bosons via the signal process $e^+ e^- \to \gamma \gamma /E$ at the Circular Electron Positron Collider (CEPC) and obtain prospective sensitivities to the ALP-lepton couplings. Our numerical results show that the CEPC with the center of mass energy $\sqrt{s}=240$ GeV and the integrated luminosity $\mathcal{L}=$ $5.6$ ab$^{-1}$ might be sensitive for probing the ALP-lepton couplings in the ALP mass range from $10$ GeV to $130$ GeV, where the prospective sensitivities to the ALP-neutrino coupling $Tr(g_{a\nu\nu})/f_a$ can be as low as $0.016$ GeV$^{-1}$ and $0.0031$ GeV$^{-1}$ for $c_{R}=0$ and $c_{R}=c_{L}$, and to the ALP-charged lepton coupling $Tr(g_{a\ell\ell})/f_a$ can be as low as $0.035$ GeV$^{-1}$ for $c_{L}=0$. We find that the prospective sensitivities given by the CEPC are not covered by the current exclusion regions in some ALP mass intervals. The CEPC has the potential for exploring the ALP-neutrino couplings via some promising processes induced by the ALP-EW gauge boson couplings in the future.

Human body restoration plays a vital role in various applications related to the human body. Despite recent advances in general image restoration using generative models, their performance in human body restoration remains mediocre, often resulting in foreground and background blending, over-smoothing surface textures, missing accessories, and distorted limbs. Addressing these challenges, we propose a novel approach by constructing a human body-aware diffusion model that leverages domain-specific knowledge to enhance performance. Specifically, we employ a pretrained body attention module to guide the diffusion model's focus on the foreground, addressing issues caused by blending between the subject and background. We also demonstrate the value of revisiting the language modality of the diffusion model in restoration tasks by seamlessly incorporating text prompt to improve the quality of surface texture and additional clothing and accessories details. Additionally, we introduce a diffusion sampler tailored for fine-grained human body parts, utilizing local semantic information to rectify limb distortions. Lastly, we collect a comprehensive dataset for benchmarking and advancing the field of human body restoration. Extensive experimental validation showcases the superiority of our approach, both quantitatively and qualitatively, over existing methods.

We present Dive Into the BoundarieS (DIBS), a novel pretraining framework for dense video captioning (DVC), that elaborates on improving the quality of the generated event captions and their associated pseudo event boundaries from unlabeled videos. By leveraging the capabilities of diverse large language models (LLMs), we generate rich DVC-oriented caption candidates and optimize the corresponding pseudo boundaries under several meticulously designed objectives, considering diversity, event-centricity, temporal ordering, and coherence. Moreover, we further introduce a novel online boundary refinement strategy that iteratively improves the quality of pseudo boundaries during training. Comprehensive experiments have been conducted to examine the effectiveness of the proposed technique components. By leveraging a substantial amount of unlabeled video data, such as HowTo100M, we achieve a remarkable advancement on standard DVC datasets like YouCook2 and ActivityNet. We outperform the previous state-of-the-art Vid2Seq across a majority of metrics, achieving this with just 0.4% of the unlabeled video data used for pre-training by Vid2Seq., Comment: Accepted by CVPR 2024

We report the detection of gamma-ray emission by the Fermi Large Area Telescope (Fermi-LAT) towards the young massive star cluster RCW 38 in the 1-500 GeV photon energy range. We found spatially extended GeV emission towards the direction of RCW 38, which is best modelled by a Gaussian disc of 0.23$\deg$ radius with a significance of the extension is $\sim 11.4 \sigma$. Furthermore, the spatial correlation with the ionized and molecular gas content favors the hadronic origin of the gamma-ray emission. The gamma-ray spectrum of RCW 38 has a relatively hard photon index of $2.44 \pm 0.03$, which is similar to other young massive star clusters. We argue that the diffuse GeV gamma-ray emission in this region likely originates from the interaction of accelerated protons in the stellar cluster with the ambient gas., Comment: 8 pages, 5 figures, accepted for publication in MNRAS. arXiv admin note: text overlap with arXiv:2210.01352

Neural rendering techniques have significantly advanced 3D human body modeling. However, previous approaches often overlook dynamics induced by factors such as motion inertia, leading to challenges in scenarios like abrupt stops after rotation, where the pose remains static while the appearance changes. This limitation arises from reliance on a single pose as conditional input, resulting in ambiguity in mapping one pose to multiple appearances. In this study, we elucidate that variations in human appearance depend not only on the current frame's pose condition but also on past pose states. Therefore, we introduce Dyco, a novel method utilizing the delta pose sequence representation for non-rigid deformations and canonical space to effectively model temporal appearance variations. To prevent a decrease in the model's generalization ability to novel poses, we further propose low-dimensional global context to reduce unnecessary inter-body part dependencies and a quantization operation to mitigate overfitting of the delta pose sequence by the model. To validate the effectiveness of our approach, we collected a novel dataset named I3D-Human, with a focus on capturing temporal changes in clothing appearance under approximate poses. Through extensive experiments on both I3D-Human and existing datasets, our approach demonstrates superior qualitative and quantitative performance. In addition, our inertia-aware 3D human method can unprecedentedly simulate appearance changes caused by inertia at different velocities.

Describing the dynamics of information dissemination within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information dissemination. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information dissemination process. Extensive experiments are conducted on six datasets to predict the information dissemination range and the dissemination trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information dissemination predictions., Comment: arXiv admin note: substantial text overlap with arXiv:2403.06385

Spin and orbital angular momenta are fundamental physical characteristics described by polarization and spatial degrees of freedom, respectively. Polarization is a feature of vector fields while spatial phase gradient determines the orbital angular momentum ubiquitous to any scalar field. Common wisdom treats these two degrees of freedom as distinct and independent principles to manipulate wave propagations. Here, we demonstrate their synergy. This is achieved by introducing two orthogonal $p$-orbitals as eigenbases, whose spatial modal features are exploited to generate orbital angular momenta and the associated orbital orientations provide means to simultaneously manipulate polarizations. Through periodic modulation and directional coupling, we realize a full cyclic evolution of the synchronized and synergized spin-orbital angular momenta. Remarkably, this evolution acquires a nontrivial geometric phase, leading to its representation on a M\"obius strip. Experimentally, an acoustic cavity array is designed, whose dipole resonances precisely mimic the $p$-orbitals. The acoustic waves, uniquely, see the pressure (scalar) field as a spatial feature and carry an intrinsic polarization defined by the velocity (vector) field, serving as an ideal platform to observe the synergy of spin and orbital angular momenta. Based on such a property, we further showcase a spin-orbital-Hall effect, highlighting the intricate locking of handedness, directionality, spin density and spatial mode profile. Our study unveils a fundamental connection between spin and orbital angular momenta, promising avenues for novel applications in information coding and high-capacity communications.

Purpose The aim of this study was to explore whether electroacupuncture regulates phenotypic transformation of smooth muscle cells by inhibiting ferroptosis and inhibiting fibrosis, thereby improving bladder urination function after suprasacral spinal cord injury (SSCI). Methods The experiment was divided into sham, model, and electroacupuncture group. After 10 days of electroacupuncture intervention, urodynamic examination was performed, and bladder neck was taken for HE staining, tandem mass tag (TMT)-based quantitative proteomics analysis, Western blot(WB) detection, ferrous ion concentration detection and Masson staining. Conclusion Electroacupuncture may prevent the phenotype of bladder neck smooth muscle cells from transforming from contraction type to synthesis type by inhibiting ferroptosis, inhibit bladder neck fibrosis, improve bladder neck compliance, and thus improve bladder urination function after SSCI.

Modeling the information dissemination process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information dissemination process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking the dynamic and evolving nature of user attitudes and the impact of the public opinion environment. In this paper, we propose a novel model, UAPE, which considers the influence of the aforementioned factors on the information dissemination process. Specifically, UAPE regards the user's attitude towards the topic as dynamically changing, with the change jointly affected by multiple users simultaneously. Furthermore, the joint influence of multiple users can be considered as the impact of the public opinion environment. Extensive experimental results demonstrate that the model achieves an accuracy range of 91.62% to 94.01%, surpassing the performance of existing research.

We present an independent catalog (FRIIRGcat) of 45,241 Fanaroff-Riley Type II (FR-II) radio galaxies compiled from the Very Large Array Faint Images of the Radio Sky at Twenty-centimeters (FIRST) survey and employed the deep learning method. Among them, optical and/or infrared counterparts are identified for 41,425 FR-IIs. This catalog spans luminosities $2.63\times10^{22}\leq L_{\rm rad}\leq6.76\times10^{29}\,{\rm W}\,{\rm Hz}^{-1}$ and redshifts up to $z=5.01$. The spectroscopic classification indicates that there are 1431 low-excitation radio galaxies and 260 high-excitation radio galaxies. Among the spectroscopically identified sources, black hole masses are estimated for 4837 FR-IIs, which are in $10^{7.5}\lesssim M_{\rm BH}\lesssim 10^{9.5}$ $M_{\odot}$. Interestingly, this catalog reveals a couple of giant radio galaxies (GRGs), which are already in the existing GRG catalog, confirming the efficiency of this FR-II catalog. Furthermore, 284 new GRGs are unveiled in this new FR-II sample; they have the largest projected sizes ranging from 701 to 1209 kpc and are located at redshifts $0.31

For different alternating-sign multi-pulse trains electric fields with oscillation, the effects of the electric field pulse number and the relative phase of the combined electric field on pair production are investigated by solving quantum Vlasov equation. It is found that the number density of created particles in the combined electric fields is increased by more than one order of magnitude compared to the results without oscillating structure for both zero transverse momentum and full momentum space. In the case of zero transverse momentum, the created particles longitudinal momentum spectrum are monochromatic for large pulse numbers and some suitable relative phases. The number density depends nonlinearly on the relative phase that enables the optimal relative phase parameters for the number density. Moreover, for the full momentum space, the created particles number density and momentum spectrum under different multi-pulse trains electric fields are given and discussed. We also find that the number density as a function of pulse number satisfies the power law with index 5.342 for the strong but slowly varying electric field with large pulse numbers., Comment: 24 pages, 14 figures

The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points. This simplified rendering approach presents challenges in accurately modeling images captured under adverse lighting conditions, such as low light or over-exposure. Motivated by the ancient Greek emission theory that posits visual perception as a result of rays emanating from the eyes, we slightly refine the conventional NeRF framework to train NeRF under challenging light conditions and generate normal-light condition novel views unsupervised. We introduce the concept of a "Concealing Field," which assigns transmittance values to the surrounding air to account for illumination effects. In dark scenarios, we assume that object emissions maintain a standard lighting level but are attenuated as they traverse the air during the rendering process. Concealing Field thus compel NeRF to learn reasonable density and colour estimations for objects even in dimly lit situations. Similarly, the Concealing Field can mitigate over-exposed emissions during the rendering stage. Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation. Our code and dataset available at https://github.com/cuiziteng/Aleth-NeRF, Comment: AAAI 2024, code available at https://cuiziteng.github.io/Aleth_NeRF_web/ Modified version of previous paper arXiv:2303.05807

Automatic estimation of 3D human pose from monocular RGB images is a challenging and unsolved problem in computer vision. In a supervised manner, approaches heavily rely on laborious annotations and present hampered generalization ability due to the limited diversity of 3D pose datasets. To address these challenges, we propose a unified framework that leverages mask as supervision for unsupervised 3D pose estimation. With general unsupervised segmentation algorithms, the proposed model employs skeleton and physique representations that exploit accurate pose information from coarse to fine. Compared with previous unsupervised approaches, we organize the human skeleton in a fully unsupervised way which enables the processing of annotation-free data and provides ready-to-use estimation results. Comprehensive experiments demonstrate our state-of-the-art pose estimation performance on Human3.6M and MPI-INF-3DHP datasets. Further experiments on in-the-wild datasets also illustrate the capability to access more data to boost our model. Code will be available at https://github.com/Charrrrrlie/Mask-as-Supervision.

Thermal transport has been used to probe the nature of $\alpha\mbox{-}\mathrm{RuCl}_3$, an important candidate of Kitaev material. Two remarkable observations were made under applied magnetic fields at low temperatures, and have stimulated extensive discussions. One is a sizable thermal Hall effect, and the other is an apparent "oscillation" of the longitudinal thermal conductivity with the magnetic field. It has been proposed that the former is due to a bosonic Chern band. Meanwhile, the origin of the latter has largely remained obscure. This work aims to resolve this "oscillation" puzzle. By examining the thermal transport data as well as other measured properties of $\alpha\mbox{-}\mathrm{RuCl}_3$, we argue that the most plausible scenario is that of phonons scattering with spin degrees of freedom across multiple phases. We substantiate this picture into a phenomenological theory, which reproduces the "oscillation" behavior in a simple manner and makes predictions that can be examined by future experiments. Moreover, our phenomenological theory and the aforementioned proposal for the thermal Hall effect support each other. We hope this work can thus help settle the physical mechanism behind the thermal transport puzzles in $\alpha\mbox{-}\mathrm{RuCl}_3$.

Existing video frame interpolation (VFI) methods blindly predict where each object is at a specific timestep t ("time indexing"), which struggles to predict precise object movements. Given two images of a baseball, there are infinitely many possible trajectories: accelerating or decelerating, straight or curved. This often results in blurry frames as the method averages out these possibilities. Instead of forcing the network to learn this complicated time-to-location mapping implicitly together with predicting the frames, we provide the network with an explicit hint on how far the object has traveled between start and end frames, a novel approach termed "distance indexing". This method offers a clearer learning goal for models, reducing the uncertainty tied to object speeds. We further observed that, even with this extra guidance, objects can still be blurry especially when they are equally far from both input frames (i.e., halfway in-between), due to the directional ambiguity in long-range motion. To solve this, we propose an iterative reference-based estimation strategy that breaks down a long-range prediction into several short-range steps. When integrating our plug-and-play strategies into state-of-the-art learning-based models, they exhibit markedly sharper outputs and superior perceptual quality in arbitrary time interpolations, using a uniform distance indexing map in the same format as time indexing. Additionally, distance indexing can be specified pixel-wise, which enables temporal manipulation of each object independently, offering a novel tool for video editing tasks like re-timing., Comment: Project page: https://zzh-tech.github.io/InterpAny-Clearer/ ; Code: https://github.com/zzh-tech/InterpAny-Clearer

Given the similarity between facial expression categories, the presence of compound facial expressions, and the subjectivity of annotators, facial expression recognition (FER) datasets often suffer from ambiguity and noisy labels. Ambiguous expressions are challenging to differentiate from expressions with noisy labels, which hurt the robustness of FER models. Furthermore, the difficulty of recognition varies across different expression categories, rendering a uniform approach unfair for all expressions. In this paper, we introduce a novel approach called Adaptive Sample Mining (ASM) to dynamically address ambiguity and noise within each expression category. First, the Adaptive Threshold Learning module generates two thresholds, namely the clean and noisy thresholds, for each category. These thresholds are based on the mean class probabilities at each training epoch. Next, the Sample Mining module partitions the dataset into three subsets: clean, ambiguity, and noise, by comparing the sample confidence with the clean and noisy thresholds. Finally, the Tri-Regularization module employs a mutual learning strategy for the ambiguity subset to enhance discrimination ability, and an unsupervised learning strategy for the noise subset to mitigate the impact of noisy labels. Extensive experiments prove that our method can effectively mine both ambiguity and noise, and outperform SOTA methods on both synthetic noisy and original datasets. The supplement material is available at https://github.com/zzzzzzyang/ASM.

In this paper, we present an analysis of the pulsating behavior of Kepler target KIC 9845907. Using the data from Kepler, we detected 85 significant frequencies, including the first overtone $f_{1}$ = 17.597 day$^{-1}$ as the dominant frequency, the non-radial independent frequency $f_{3}$ = 31.428 day$^{-1}$ ($\ell$=1), as well as two modulation terms $f_{m1}$ = 0.065 day$^{-1}$ and $f_{m2}$ = 1.693 day$^{-1}$. We found fourteen pairs of triplet structures with $f_{m1}$ or $f_{m2}$, four pairs of which can further form quintuplet structures. We note these are the most intriguing features discovered in this study and they were recognized for the first time in $\delta$ Scuti stars. We discussed several possible explanations, i.e., beating, the Blazhko effect, combination mode hypothesis, nonlinear mode coupling, large separation, and stellar rotational splitting for these equidistant structures. Our asteroseismic models indicate this modulation with $f_{m1}$ might be related to the rotational splitting. The study of more $\delta$ Scuti stars with triplet and/or quintuplet structures using high-precision space photometry would be helpful to further explore its origin., Comment: 28 pages, 10 figures, accepted for publication in ApJ

The goal of low-light image enhancement is to restore the color and details of the image and is of great significance for high-level visual tasks in autonomous driving. However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain. In this paper we introduce frequency as a new clue into the network and propose a novel DCT-driven enhancement transformer (DEFormer). First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE). CFE calculates the curvature of each channel to represent the detail richness of different frequency bands, then we divides the frequency features, which focuses on frequency bands with richer textures. In addition, we propose a cross domain fusion (CDF) for reducing the differences between the RGB domain and the frequency domain. We also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively improves the performance of the detector, bringing 2.1% and 3.4% improvement in ExDark and DARK FACE datasets on mAP respectively., Comment: submit to ICRA2024

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Pompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available., Comment: 8 pages, 4 figures

Shear particle acceleration is a promising candidate for the origin of extended high-energy emission in extra-galactic jets. In this paper, we explore the applicability of a shear model to 24 X-ray knots in the large-scale jets of FR II radio galaxies, and study the jet properties by modeling the multi-wavelength spectral energy distributions (SEDs) in a leptonic framework including synchrotron and inverse Compton - CMB processes. In order to improve spectral modelling, we analyze Fermi-LAT data for five sources and reanalyzed archival data of Chandra on 15 knots, exploring the radio to X-ray connection. We show that the X-ray SEDs of these knots can be satisfactorily modelled by synchrotron radiation from a second, shear-accelerated electron population reaching multi-TeV energies. The inferred flow speeds are compatible with large-scale jets being mildly relativistic. We explore two different shear flow profiles (i.e., linearly decreasing and power-law) and find that the required spine speeds differ only slightly, supporting the notion that for higher flow speeds the variations in particle spectral indices are less dependent on the presumed velocity profile. The derived magnetic field strengths are in the range of a few to ten microGauss, and the required power in non-thermal particles typically well below the Eddington constraint. Finally, the inferred parameters are used to constrain the potential of FR II jets as possible UHECR accelerators., Comment: 14 pages, 4 figures, 6 table, accepted for publication in MNRAS

Krylov subspace methods for solving linear systems of equations involving skew-symmetric matrices have gained recent attention. Numerical equivalences among Krylov subspace methods for nonsingular skew-symmetric linear systems have been given in Greif et al. [SIAM J. Matrix Anal. Appl., 37 (2016), pp. 1071--1087]. In this work, we extend the results of Greif et al. to singular skew-symmetric linear systems. In addition, we systematically study three Krylov subspace methods (called S$^3$CG, S$^3$MR, and S$^3$LQ) for solving shifted skew-symmetric linear systems. They all are based on Lanczos triangularization for skew-symmetric matrices, and correspond to CG, MINRES, and SYMMLQ for solving symmetric linear systems, respectively. To the best of our knowledge, this is the first work that studies S$^3$LQ. We give some new theoretical results on S$^3$CG, S$^3$MR, and S$^3$LQ. We also provide the relationship among the three methods and those based on Golub--Kahan bidiagonalization and Saunders--Simon--Yip tridiagonalization. Numerical examples are given to illustrate our theoretical findings., Comment: 23 pages, 3 figures

Reduced-dimensional CsPbBr$_3$ nanoplatelets (NPLs) are promising candidates for color-saturated blue emitters, yet their electroluminescence performance is hampered by non-radiative recombination, which is associated with bromine vacancies. Here, we show that a post-synthetic treatment of CsPbBr$_3$ NPLs with GdBr$_3$-dimethylformamide (DMF) can effectively eliminate defects while preserving the color. According to a combined experimental and theoretical study, Gd$^{3+}$ ions are less reactive with NPLs as a result of compact interaction between them and DMF, and this stable Gd$^{3+}$-DMF solvation structure makes Brions more available and allows them to move more freely. Consequently, defects are rapidly passivated and photoluminescence quantum yield increases dramatically (from 35 to ~100%), while the surface ligand density and emission color remain unchanged. The result is a remarkable electroluminescence efficiency of 2.4% (at 464 nm), one of the highest in pure blue perovskite NPL light-emitting diodes. It is noteworthy that the conductive NPL film shows a high photoluminescence quantum yield of 80%, demonstrating NPLs' significant electroluminescence potential with further device structure design.

Retrieval finds a small number of relevant candidates from a large corpus for information retrieval and recommendation applications. A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings. This formulation permits efficient inference, commonly known as Maximum Inner Product Search (MIPS). Despite its popularity, dot products cannot capture complex user-item interactions, which are multifaceted and likely high rank. We hence examine non-dot-product retrieval settings on accelerators, and propose \textit{mixture of logits} (MoL), which models (user, item) similarity as an adaptive composition of elementary similarity functions. This new formulation is expressive, capable of modeling high rank (user, item) interactions, and further generalizes to the long tail. When combined with a hierarchical retrieval strategy, \textit{h-indexer}, we are able to scale up MoL to 100M corpus on a single GPU with latency comparable to MIPS baselines. On public datasets, our approach leads to uplifts of up to 77.3\% in hit rate (HR). Experiments on a large recommendation surface at Meta showed strong metric gains and reduced popularity bias, validating the proposed approach's performance and improved generalization., Comment: To appear in the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)

We have obtained the polarization data cube of the VRO 42.05.01 supernova remnant at 1240 MHz using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Three-dimensional Faraday Synthesis is applied to the FAST data to derive the Faraday depth spectrum. The peak Faraday depth map shows a large area of enhanced foreground RM of ~60 rad m-2 extending along the remnant's "wing" section, which coincides with a large-scale HI shell at -20 km/s. The two depolarization patches within the "wing" region with RM of 97 rad m-2 and 55 rad m-2 coincide with two HI structures in the HI shell. Faraday screen model fitting on the Canadian Galactic Plane Survey (CGPS) 1420 MHz full-scale polarization data reveals a distance of 0.7-0.8d_{SNR} in front of the SNR with enhanced regular magnetic field there. The highly piled-up magnetic field indicates that the HI shell at -20 km/s could originate from an old evolved SNR., Comment: 9 pages, 8 figures, accepted by ApJ