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In this work we systematically study both the mass spectra and strong decays of the nucleon and $\Delta$ resonances up to the $N=2$ shell within a unified quark model framework with chiral dynamics. In this framework we achieve a good description of the strong decay properties of the well-established nucleon and $\Delta$ resonances. Meanwhile, the mass reversal between $N(1440)1/2^{+}$ as the first radial excitation state and the $1P$-wave nucleon resonances can be explained. We show that the three-body spin-orbit potential arising from the one-gluon exchange can cause a large configuration mixing between $N(1520)3/2^-$ and $N(1700)3/2^-$, and is also responsible for the large splitting between $\Delta(1600)1/2^-$ and $\Delta(1700)3/2^-$. Some of these baryon resonances turn to weakly couple to the $N\pi$, $N\eta$, $K\Lambda$, and $K\Sigma$ channels, which may answer the question why they have not been established in these channels via the $\pi N$ and $\gamma N$ scatterings. It shows that these ``missing resonances" may have large potentials to be established in the $N\pi\pi$ final state due to their large decay rates into either the $\Delta(1232)$ or $1P$-wave nucleon resonances via the pionic decays. Further experimental search for their signals in charmonium decays at BESIII is thus strongly recommended., Comment: 26 pages, 7 figures, 12 tables

Discrete flavor symmetries have been an appealing approach for explaining the observed flavor structure, which is not justified in the Standard Model (SM). Typically, these models require a so-called flavon field in order to give rise to the flavor structure upon the breaking of the flavor symmetry by the vacuum expectation value (VEV) of the flavon. Generally, in order to obtain the desired vacuum alignment, a flavon potential that includes additional so-called driving fields is required. On the other hand, allowing the flavor symmetry to be modular leads to a structure where the couplings are all holomorphic functions that depend only on a complex modulus, thus greatly reducing the number of parameters in the model. We show that these elements can be combined to simultaneously explain the flavor structure and dark matter (DM) relic abundance. We present a modular model with flavon vacuum alignment that allows for realistic flavor predictions while providing a successful fermionic DM candidate., Comment: 24 pages, 7 figures

This paper focuses on Human-Object Interaction (HOI) detection, addressing the challenge of identifying and understanding the interactions between humans and objects within a given image or video frame. Spearheaded by Detection Transformer (DETR), recent developments lead to significant improvements by replacing traditional region proposals by a set of learnable queries. However, despite the powerful representation capabilities provided by Transformers, existing Human-Object Interaction (HOI) detection methods still yield low confidence levels when dealing with complex interactions and are prone to overlooking interactive actions. To address these issues, we propose a novel approach \textsc{UAHOI}, Uncertainty-aware Robust Human-Object Interaction Learning that explicitly estimates prediction uncertainty during the training process to refine both detection and interaction predictions. Our model not only predicts the HOI triplets but also quantifies the uncertainty of these predictions. Specifically, we model this uncertainty through the variance of predictions and incorporate it into the optimization objective, allowing the model to adaptively adjust its confidence threshold based on prediction variance. This integration helps in mitigating the adverse effects of incorrect or ambiguous predictions that are common in traditional methods without any hand-designed components, serving as an automatic confidence threshold. Our method is flexible to existing HOI detection methods and demonstrates improved accuracy. We evaluate \textsc{UAHOI} on two standard benchmarks in the field: V-COCO and HICO-DET, which represent challenging scenarios for HOI detection. Through extensive experiments, we demonstrate that \textsc{UAHOI} achieves significant improvements over existing state-of-the-art methods, enhancing both the accuracy and robustness of HOI detection., Comment: Accepted by CVIU

Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains. This approach is crucial when labeled target domain data is scarce or unavailable. It seeks to align the feature representations of the source domain (where labeled data is available) and the target domain (where only unlabeled data is present), thus enabling the model to generalize well to the target domain. Current image- and video-level domain adaptation have been addressed using different and specialized frameworks, training strategies and optimizations despite their underlying connections. In this paper, we propose a unified framework PiPa++, which leverages the core idea of ``comparing'' to (1) explicitly encourage learning of discriminative pixel-wise features with intraclass compactness and inter-class separability, (2) promote the robust feature learning of the identical patch against different contexts or fluctuations, and (3) enable the learning of temporal continuity under dynamic environments. With the designed task-smart contrastive sampling strategy, PiPa++ enables the mining of more informative training samples according to the task demand. Extensive experiments demonstrate the effectiveness of our method on both image-level and video-level domain adaption benchmarks. Moreover, the proposed method is compatible with other UDA approaches to further improve the performance without introducing extra parameters., Comment: This study is under IEEE TMM review. arXiv admin note: substantial text overlap with arXiv:2211.07609

We present GvSeg, a general video segmentation framework for addressing four different video segmentation tasks (i.e., instance, semantic, panoptic, and exemplar-guided) while maintaining an identical architectural design. Currently, there is a trend towards developing general video segmentation solutions that can be applied across multiple tasks. This streamlines research endeavors and simplifies deployment. However, such a highly homogenized framework in current design, where each element maintains uniformity, could overlook the inherent diversity among different tasks and lead to suboptimal performance. To tackle this, GvSeg: i) provides a holistic disentanglement and modeling for segment targets, thoroughly examining them from the perspective of appearance, position, and shape, and on this basis, ii) reformulates the query initialization, matching and sampling strategies in alignment with the task-specific requirement. These architecture-agnostic innovations empower GvSeg to effectively address each unique task by accommodating the specific properties that characterize them. Extensive experiments on seven gold-standard benchmark datasets demonstrate that GvSeg surpasses all existing specialized/general solutions by a significant margin on four different video segmentation tasks., Comment: ECCV 2024; Project page: https://github.com/kagawa588/GvSeg

We investigate the prospect of probing massive fields and testing gravitational theories with multiband observations of gravitational waves emitted from coalescing compact binaries. Focusing on the dipole radiation induced by a massive field, we show that multiband observations can probe the field with mass ranging from $10^{-16} $ to $10^{-15} {\rm eV}$, a parameter space that cannot be probed by the millihertz band observations alone. Multiband observations can also improve the constraints obtained with the LIGO-Virgo-KAGRA binaries by at most 3 orders of magnitude in the mass range. Moreover, we show that multiband observations can discriminate the spin of the field, which cannot be identified with single-band observations., Comment: 9 pages,3 figures. Some comparative notes about $\Delta q$ are added

In the ultra-thin regime, Bi2Te3 films feature two surfaces (with each surface being a two-dimensional Dirac-fermion system) with complicated spin textures and a tunneling term between them. We find in this regime that the quasiparticle scattering is completely different compared with the thick-film case and even behaves differently at each thickness. The thickness-dependent warping effect and tunneling term are found to be the two main factors that govern the scattering behaviors. The inter-band back-scattering that signals the existence of a tunneling term is found to disappear at 4 quintuple layers by the step-edge reflection approach. A four-band model is presented that captures the main features of the thickness-dependent scattering behaviors. Our work clarifies that the prohibition of back-scattering guaranteed by symmetry in topological insulators breaks down in the ultra-thin regime., Comment: 31 pages, 13 figures including the supplementary materials

Optical metasurfaces have been widely used for manipulating electromagnetic waves due to their low intrinsic loss and easy fabrication. The metasurfaces employing the Pancharatnam-Berry (PB) geometric phase, called PB metasurfaces, have been extensively applied to realize spin-dependent functionalities, such as beam steering, focusing, holography, etc. The demand for PB metasurfaces in complex environments has brought about one challenging problem, i.e., the interference of multiple wave channels that limits the performance of PB metasurfaces. A promising solution is developing nonreciprocal PB metasurfaces that can isolate undesired wave channels and exhibit unidirectional functionalities. Here, we propose a mechanism to realize nonreciprocal PB metasurfaces of subwavelength thickness by using the magneto-optical effect of YIG material in synergy with the PB geometric phase of spatially rotating meta-atoms. Using full-wave numerical simulations, we show that the metasurface composed of dielectric cylinders and a thin YIG layer can achieve nearly 92% and 81% isolation of circularly polarized lights at 5.5 GHz and 6.5 GHz, respectively, attributed to the enhancement of the magneto-optical effect by the resonant Mie modes and Fabry-P\'erot cavity mode. In addition, the metasurface can enable efficient unidirectional wavefront manipulations of circularly polarized lights, including nonreciprocal beam steering and nonreciprocal beam focusing. The proposed metasurface can find highly useful applications in optical communications, optical sensing, and quantum information processing., Comment: 23 pages, 7 figures

In modular invariant models of flavor, observables must be modular invariant. The observables discussed so far in the literature are functions of the modulus $\tau$ and its conjugate, $\bar\tau$. We point out that certain combinations of observables depend only on $\tau$, i.e. are meromorphic, and in some cases even holomorphic functions of $\tau$. These functions, which we dub ``invariants'' in this Letter, are highly constrained, renormalization group invariant, and allow us to derive many of the models' features without the need for extensive parameter scans. We illustrate the robustness of these invariants in two existing models in the literature based on modular symmetries, $\Gamma_{3}$ and $\Gamma_{5}$. We find that, in some cases, the invariants give rise to robust relations among physical observables that are independent of $\tau$. Furthermore, there are instances where additional symmetries exist among the invariants. These symmetries are relevant phenomenologically and may provide a dynamical way to realize symmetries of mass matrices., Comment: 23 pages, 1 figure

The so-called Golden Mass Relation provides a testable correlation between charged-lepton and down-type quark masses, that arises in certain flavor models that do not rely on Grand Unification. Such models typically involve broken family symmetries. In this work, we demonstrate that realistic fermion mass relations can emerge naturally in modular invariant models, without relying on ad hoc flavon alignments. We provide a model-independent derivation of a class of mass relations that are experimentally testable. These relations are determined by both the Clebsch-Gordan coefficients of the specific finite modular group and the expansion coefficients of its modular forms, thus offering potential probes of modular invariant models. As a detailed example, we present a set of viable mass relations based on the $\Gamma_4\cong S_4$ symmetry, which have calculable deviations from the usual Golden Mass Relation., Comment: 23 pages, 5 figures. Comments welcome! v2: Minor comments added, references added, matches published version. v3: Plots updated and improved, Acknowledgments expanded

Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces the need for manual pixel-level annotations in the target domain. To facilitate domain-invariant feature learning, existing methods typically mix data from both the source domain and target domain by simply copying and pasting the pixels. Such vanilla methods are usually sub-optimal since they do not take into account how well the mixed layouts correspond to real-world scenarios. Real-world scenarios are with an inherent layout. We observe that semantic categories, such as sidewalks, buildings, and sky, display relatively consistent depth distributions, and could be clearly distinguished in a depth map. Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i.e., segmentation and depth learning in an end-to-end manner. In particular, the framework contains a Depth-guided Contextual Filter (DCF) forndata augmentation and a cross-task encoder for contextual learning. DCF simulates the real-world layouts, while the cross-task encoder further adaptively fuses the complementing features between two tasks. Besides, it is worth noting that several public datasets do not provide depth annotation. Therefore, we leverage the off-the-shelf depth estimation network to generate the pseudo depth. Extensive experiments show that our proposed methods, even with pseudo depth, achieve competitive performance on two widely-used bench-marks, i.e. 77.7 mIoU on GTA to Cityscapes and 69.3 mIoU on Synthia to Cityscapes., Comment: ACM MM 2024 (Oral)