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We systematically investigate the observable properties of dark matter-admixed quark stars (DQSs) using the confined-isospin-density-dependent-mass model in combination with the generic bosonic self-interacting dark matter model. Our results show that the dark matter (DM) can significantly influence the properties of quark stars including the mass, radius, and the central pressure at the maximum mass configurations. Moreover, we observe that the mass of DMparticles and the DMfraction significantly affect the types of stellar configurations, and we study these configurations in detail under various scenarios and predict the possibility that two recently observed peculiar objects HESS J1731-347 and PSR J014-4002E are DQSs., Comment: 13 pages, 10 figures

Using a three-flavor Nambu--Jona-Lasinio model to describe the charge-parity violating effects through axion field, we investigate the axion effects on quark matter and quark-matter cores in massive hybrid stars. The properties of quark matter vary with the scaled axion field $a/f_a$ in a periodic manner, with a period of $2\pi$. Within the range from 0 to $\pi $, axion field decrease the baryon chemical potential of the first-order phase transition, leading to an increase in normalized pressure and stiffening of the quark matter equation of state. The effect of axions on hybrid star matter that includes the hadron-quark phase transition is contrary to expectations. The axion field shifts the onset of the hadron-quark mixed phase to lower densities but slightly softens the equation of state of the mixed phase matter, which also results in a slight decrease in the maximum mass and corresponding radius of the hybrid stars. However, we also find that the lowering of the onset of the mixed phase significantly increases the radius and mass of the quark-matter core in the hybrid star. Therefore, our results indicate with axion effects, a sizable quark-matter core can appear in $2M_{\odot}$ massive neutron stars., Comment: 7 pages, 3 figures

Using an extended Nambu-Jona-Lasinio model to describe the baryon density fluctuations of quark matter along the isentropic trajectories corresponding to different $s/\rho_B$ values extracted from Au+Au collisions at energies $\sqrt{s_{NN}} = 7.7-200$ GeV, we investigate the effects of the two-phase coexistence region during a first-order phase transition on the light nucleus yield ratio $N_t \times N_p/N_d^2$. The results indicate that the second-order scalar density moment $y_2$, used to quantify density fluctuations, rapidly increases to form a peak when the isentropic trajectories pass through the coexistence region. We extract the yield ratios $N_t\times N_p/N_d^2$ at chemical freeze-out from the isentropic trajectories at different collision energies and found the significant enhancements at 19.6 GeV and 27 GeV. This is similar to the trends observed by the STAR experiment, suggesting that the enhancements in the yield ratios $N_t\times N_p/N_d^2$ observed in the STAR experiment should be attributed to the density fluctuations generated in the first-order phase transition region., Comment: 7 pages, 3 figures

This study extended noncanonical warm inflation to the nonminimal derivative coupling scenario. The fundamental equations, including the evolution equations and the slow roll equations of this new framework, were derived. The enlarged damping term, which encompasses both gravitationally enhanced friction and thermal damping, resulted in a well overdamped inflationary process, ensuring that the slow roll approximations can be satisfactorily satisfied. A linear stability analysis corroborated the viability of this approach, yielding significantly relaxed slow roll conditions within the context of noncanonical warm inflation with nonminimal derivative coupling. Subsequently, the density fluctuations in this new framework were analyzed, leading to approximately analytic results for the power spectrum, spectral index, and related quantities. Both the energy scale at horizon crossing and the tensor-to-scalar ratio decreased considerably because of the effects of thermal damping and nonminimal derivative coupling. The upper bound for field excursion remained safely sub-Planckian in this inflationary scenario. Thus we reached a successful and meaningful model to broad the scope of warm inflation., Comment: 15 pages, 0 figures

The development of Audio Description (AD) has been a pivotal step forward in making video content more accessible and inclusive. Traditionally, AD production has demanded a considerable amount of skilled labor, while existing automated approaches still necessitate extensive training to integrate multimodal inputs and tailor the output from a captioning style to an AD style. In this paper, we introduce an automated AD generation pipeline that harnesses the potent multimodal and instruction-following capacities of GPT-4V(ision). Notably, our methodology employs readily available components, eliminating the need for additional training. It produces ADs that not only comply with established natural language AD production standards but also maintain contextually consistent character information across frames, courtesy of a tracking-based character recognition module. A thorough analysis on the MAD dataset reveals that our approach achieves a performance on par with learning-based methods in automated AD production, as substantiated by a CIDEr score of 20.5.

In the present work, the cluster radioactivity preformation probability Pc in the scheme of NpNn for the effective number of the valence particles (holes) in trans-lead nuclei has been systematically investigated. This quantity has been explored in the simplified parametrization of NpNn as well as the multiplication NpNnI of this product with the isospin asymmetry I. The calculations for Pc are both performed in microscopic and model-dependent way. Within the microscopic approach, based on our previous work [Chin. Phys. C 47,014101 (2023)], Pc is calculated in cluster formation model (CFM) combined with the exponential relationship of Pc to the alpha decay preformation probability P alpha when the mass number of the emitted cluster Ac less than 28. While Ac greater than 28, Pc is obtained through the charge-number dependence of Pc on the decay products proposed by Ren et al. [Phys. Rev. C 70,034304 (2004)]. In the model-dependent approach, Pc is extracted through the ratios from calculated cluster radioactivity half-lives in the framework of unified fission model (UFM) proposed by Dong et al. [Eur. Phys. J. A 41,197 (2009)] to experimental ones. Both of the results show Pc in logarithmic form are linear to NpNn as well as NpNnI. For comparison, the parent-mass-number dependence analytical formula as well as the model proposed by K. Wei and H. F. Zhang [Phys. Rev. C 96,021601(R)(2017)] are also used. Furthermore, the preformation mechanic for cluster radioactivity has also been discussed.

In the context of observed massive neutron stars (NSs), we examine the internal structure, phase transitions, and the impacts of the equation of state (EOS) in maximum NSs. We investigate the stiffness changes in the EOS during the hadron-quark phase transition within the NSs. The relativistic mean-field (RMF) model and RMF model with a density-dependent isovector coupling, known as the RMFL model, are used to describe hadronic matter, while to the represent quark matter, the Nambu-Jona-Lasinio (NJL) model is applied. We explore the strength of vector coupling in quark matter, which delayed the onset density and reduced the maximum mass of NS, but does not exhibit a clear correlation with the NS central density. A considerable size of the mixed phase core could exist in the maximum mass NS but with corresponding small mixed phase mass., Comment: 7 fighres

We investigate the speed of sound and polytropic index of quantum chromodynamics (QCD) matter in the full phase diagram based on a 3-flavor Polyakov-looped Nambu-Jona-Lasinio (pNJL) model. The speed of sound and polytropic index in isothermal and adiabatic cases all have a dip structure at the low chemical potential side of the chiral phase transition boundary, and these quantities reach their global minimum values at the critical endpoint (CEP) but are not completely zero, where the values in adiabatic are lightly greater than those in isothermal. Different from the speed of sound, the polytropic index also exists a peak around the chiral phase transition boundary. Along the hypothetical chemical freeze-out lines, the speed of sound rapidly decreases near the CEP, followed by a small spinodal behavior, while the polytropic index, especially in isothermal, exhibits a more pronounced and nearly closed to zero dip structure as it approaches the CEP., Comment: 7 pages, 5 figures

We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using sequence context. RefineVIS learns two separate representations on top of an off-the-shelf frame-level image instance segmentation model: an association representation responsible for associating objects across frames and a segmentation representation that produces accurate segmentation masks. Contrastive learning is utilized to learn temporally stable association representations. A Temporal Attention Refinement (TAR) module learns discriminative segmentation representations by exploiting temporal relationships and a novel temporal contrastive denoising technique. Our method supports both online and offline inference. It achieves state-of-the-art video instance segmentation accuracy on YouTube-VIS 2019 (64.4 AP), Youtube-VIS 2021 (61.4 AP), and OVIS (46.1 AP) datasets. The visualization shows that the TAR module can generate more accurate instance segmentation masks, particularly for challenging cases such as highly occluded objects.

In this study, we examine the impact of the gluon condensate on holographic entanglement entropy within an Einstein-Dilaton model at both zero and finite temperatures. A critical length exists for the difference in entanglement entropy between connected and disconnected surfaces in this model, which is typically interpreted as an indicator of phase transition. As the gluon condensate increases, the critical length decreases, suggesting that confinement strengthens at zero temperature. Additionally, the entropic C-function abruptly drops to zero at the critical length, indicating the absence of entangled states. At finite temperatures, the results show that the effect of the gluon condensate on the critical length is qualitatively similar to that at zero temperature. We observe that the entropic C-function increases as a function of $L$ at finite temperature, though it exhibits competitive behaviors when the gluon condensate is large.

Using the constraints from astrophysical observations and heavy-ion experiments, we investigate the equation of state (EOS) of hybrid star matter and the properties of quark-matter cores in hybrid stars. The quark matter interactions in hybrid stars are described based on 3-flavor Nambu-Jona-Lasinio model with various vector and vector-isovector coupling constants. In this work, we find that the hybrid star matter EOS is more sensitive to the strength of the vector interaction, and the EOS becomes stiffer with increasing vector strength $R_V$. The vector-isovector interaction characterized by the coupling constant $R_{IV}$ make main contribution to the hadron-quark mixed phase. Meanwhile, we note that a step change of both the sound velocity and the polytropic index $\gamma$ occurs in the hadron-quark phase transition, and it is restored with the decrease of nucleon and lepton degrees of freedom in the high density quark phase. Although the coupling constants increase the hybrid star maximum mass up to $2.08M_{\odot}$, they also decrease the mass and radius of the quark core and the mixed core. With different quark coupling constants, we also find that the maximum mass and radius of the quark matter core in a stable hybrid star can reach $0.80M_{\odot}$ and 6.95 km, which are close to half of the maximum mass and radius of the complete star. However, properties of quark matter have no effect on the $M = 1.4M_{\odot}$ hybrid star as a result of no quark matter inner core, which can also be confirmed by the criterion of the polytropic index, and thus our results also indicate that the quark interactions have no effect on the tidal deformability $\Lambda_{1.4}$ of hybrid stars., Comment: 10 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2305.01246

We investigate the properties of hybrid stars with the hadron-quark phase transition by using a quasiparticle model. Results from our study indicate that the coupling constant $g$ can stiffen the EOS of hybrid star matter and thus increase the hybrid star maximum mass and its tidal deformability, whereas it also decreases the mass and radius of the pure quark core. In addition, we find that a step change of the sound velocity occurs in the hadron-quark mixed phase, and it is restored with the decrease of nucleon and lepton degrees of freedom in the high density quark phase. The approximate rule that the polytropic index $\gamma \leq 1.75$ can also be used as a criterion for separating hadronic from quark matter in our work. The hypothesis of absolutely stable SQM (or "Witten hypothesis") suggests that a hybrid star containing a sufficient amount of SQM in its core will rapidly convert into a strange quark star. The SQM in hybrid stars therefore should break the absolutely stable condition, and the energy per nucleon ($E/A$) of both $ud$QM and SQM must exceed the lowest energy per nucleon 930 MeV. As a result, we provide the maximum mass, minimum radius $R_{1.4}$ and minimum tidal deformation $\Lambda_{1.4}$ of the hybrid stars as well as the maximum mass and radius of the quark matter core with different $g$ values within the allowable regions ($E/A>930$ MeV) on the $g-B^{1/4}$ plane. Using the constraints from astrophysical observations and heavy-ion experiments for comparison, our results indicate that the recently discovered massive neutron stars be well described as hybrid stars in the quasiparticle model, and confirm that the sizable quark-matter cores ($R_{QC}>6.5$ km) containing the mixed phase can appear in $2M_{\odot}$ massive stars., Comment: 10 pages, 8 figures

This paper presents and investigates non-Gaussian perturbations for the warm k-inflation model that is driven by pure kinetic energy. The two complementary components of the overall non-Gaussianity are the three-point and four-point correlations. The intrinsic non-Gaussian component, denoted as the nonlinear parameter f_{NL}^{int}, is rooted in the three-point correlation for the inflaton field. Meanwhile, the \delta N part non-Gaussianity, denoted as f_{NL}^{\delta N}, is the contribution attributed to the four-point correlation function of the inflaton field. In this paper, the above two components in warm k-inflation are individually computed and analyzed. Then, comparisons and discussions between them are conducted, and the non-Gaussian theoretical results are compared with experimental observations to determine the range of model parameters within the allowable range of observation.

In the present work, we systematically study the proton radioactivity half-lives of 33 spherical nuclei based on the relationship between the Skyrme parameters and the macroscopic quantities of nuclear matter. Using the two-potential approach with the spherical Skyrme-Hartree-Fock model, the correlation between proton radioactivity half-life and macroscopic quantities was analyzed. Moreover, we obtained a new Skyrme parameter set by fitting the two most weighted macroscopic quantities. Compared with Skyrme parameters MSL0 and the theoretical model of proton radioactivity UDLP, the theoretical proton radioactivity half-life calculated by the new Skyrme parameter set can better reproduce the experimental data., Comment: 8 pages, 3 figures

Considering the preformation probability of the two emitted protons in the parent nucleus, we extend the Coulomb and proximity potential model (CPPM) to systematically study two-proton (2p) radioactivity half-lives of the nuclei close to proton drip line. The proximity potential chosen is Prox. 81 proposed by Blocki et al. in 1981. Furthermore, we apply this model to predict the half-lives of possible 2p radioactive candidates whose 2p radioactivity is energetically allowed or observed but not yet quantified in the evaluated nuclear properties table NUBASE2016. The predicted results are in good agreement with those from other theoretical models and empirical formulas, namely the effective liquid drop model (ELDM), generalized liquid drop model (GLDM), Gamow-like model, Sreeja formula and Liu formula.

Video instance segmentation aims at predicting object segmentation masks for each frame, as well as associating the instances across multiple frames. Recent end-to-end video instance segmentation methods are capable of performing object segmentation and instance association together in a direct parallel sequence decoding/prediction framework. Although these methods generally predict higher quality object segmentation masks, they can fail to associate instances in challenging cases because they do not explicitly model the temporal instance consistency for adjacent frames. We propose a consistent end-to-end video instance segmentation framework with Inter-Frame Recurrent Attention to model both the temporal instance consistency for adjacent frames and the global temporal context. Our extensive experiments demonstrate that the Inter-Frame Recurrent Attention significantly improves temporal instance consistency while maintaining the quality of the object segmentation masks. Our model achieves state-of-the-art accuracy on both YouTubeVIS-2019 (62.1\%) and YouTubeVIS-2021 (54.7\%) datasets. In addition, quantitative and qualitative results show that the proposed methods predict more temporally consistent instance segmentation masks., Comment: 11 pages, 5 figures, 4 tables

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In this work, we demonstrate that contrastive learning is a computationally efficient and flexible method to incorporate a physical constraint in ML-based density functional design. We propose a schematic approach to incorporate the uniform density scaling property of electron density for exchange energies by adopting contrastive representation learning during the pretraining task. The pretrained hidden representation is transferred to the downstream task to predict the exchange energies calculated by DFT. The electron density encoder transferred from the pretraining task based on contrastive learning predicts exchange energies that satisfy the scaling property, while the model trained without using contrastive learning gives poor predictions for the scaling-transformed electron density systems. Furthermore, the model with pretrained encoder gives a satisfactory performance with only small fractions of the whole augmented dataset labeled, comparable to the model trained from scratch using the whole dataset. The results demonstrate that incorporating exact constraints through contrastive learning can enhance the understanding of density-energy mapping using neural network (NN) models with less data labeling, which will be beneficial to generalizing the application of NN-based XC functionals in a wide range of scenarios that are not always available experimentally but theoretically justified. This work represents a viable pathway toward the machine learning design of a universal density functional via representation learning.

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval., Comment: Accepted by CVPR2023

Symmetry energy is an important part of the equation of state of isospin asymmetry matter. However, the huge uncertainties of symmetry energy remain at suprasaturation densities, where the phase transitions of strongly interacting matter and the quark matter symmetry energy are likely to be taken into account. In this work, we investigate the properties of symmetry energy by using a hybrid star with the hadron-quark phase transition. The interaction among strange quark matter (SQM) in hybrid stars is based on a 3-flavor NJL model with different vector and isovector channels, while the equation of state (EOS) of the nuclear matter is obtained by considering the ImMDI-ST interaction by varying the parameters x, y, and z. Our results indicate that the various parameters and coupling constants of the interactions from the ImMDI-ST and NJL model can lead to widely different trends for the symmetry energy in the hadron-quark mixed phase and the different onsets of the hadron-quark phase transition. In addition, it has been found that the radii and tidal deformabilities of hybrid stars constrain mostly the density dependence of symmetry energy while the observed maximum masses of hybrid stars constrain mostly the EOS of symmetric nuclear and quark matter., Comment: arXiv admin note: text overlap with arXiv:1905.13175 by other authors

Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain data can be accessible all at once. However, for the real-world streaming data, this hinders the timely adaptation to changing data statistics and sufficient exploitation of increasing samples. In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data in the target environments while alleviating catastrophic forgetting for such a fine-grained person retrieval task. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation. Specifically, a meta-based Coordinated Data Replay strategy is proposed to replay old data and update the network with a coordinated optimization direction for both adaptation and memorization. Moreover, we propose Relational Consistency Learning for old knowledge distillation/inheritance in line with the objective of retrieval-based tasks. We set up two evaluation settings to simulate the practical application scenarios. Extensive experiments demonstrate the effectiveness of our CLUDA-ReID for both scenarios with stationary target streams and scenarios with dynamic target streams., Comment: Accepted by CVPR2022

Using the gauge/gravity duality, we investigate the string breaking and dissolution of two heavy quarks coupled to a light quark at finite temperature. It is found that there exist three configurations of QQq with the increase of separate distance for heavy quarks in the confined phase. Besides, the string breaking occurs at the distance $L_{\rm{QQq}} = 1.27 \rm{fm}$($T = 0.1 \rm{GeV}$) for the decay mode $\rm{Q Q q \rightarrow Q q q+Q \bar{q}}$. In the deconfined phase, QQq will melt at a certain distance then becomes free quarks. At last, we compare the potential of QQq with that of $\rm{Q\bar{Q}}$ and find $\rm{Q\bar{Q}}$ is more stable than QQq at high temperature., Comment: 23 pages, 24 figures

Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout because monocular multi-object tracking (MOT) systems often fail in cluttered and crowded environments due to occlusion. Multiple highly overlapped cameras can significantly alleviate the problem by recovering partial 3D information. However, the cost of creating a high-quality multi-camera tracking dataset with diverse camera settings and backgrounds has limited the dataset scale in this domain. In this paper, we provide a large-scale densely-labeled multi-camera tracking dataset in five different environments with the help of an auto-annotation system. The system uses overlapped and calibrated depth and RGB cameras to build a high-performance 3D tracker that automatically generates the 3D tracking results. The 3D tracking results are projected to each RGB camera view using camera parameters to create 2D tracking results. Then, we manually check and correct the 3D tracking results to ensure the label quality, which is much cheaper than fully manual annotation. We have conducted extensive experiments using two real-time multi-camera trackers and a person re-identification (ReID) model with different settings. This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments. Also, our results demonstrate that adapting the trackers and ReID models on this dataset significantly improves their performance. Our dataset will be publicly released upon the acceptance of this work.

In the present work we systematically study the half--lives of proton radioactivity for spherical proton emitters with ${Z\ge 69}$ based on two--potential approach. While the nuclear potential of the emitted proton--daughter nucleus is adopted by a parameterized cosh type, the parameters of the depth and diffuseness for nuclear potential are determined by fitting experimental data of 32 spherical proton emitters. In order to reduce the deviations between experimental half-lives and calculated ones, we propose a simple analytic expression for formation probability of proton radioactivity with the same orbital angular momentum $l$. The results indicate that the formation probability can be simply described by a formula of $A_d^{1/3}$. Moreover, the linear relationship between the formation probability and the fragmentation potential also exists. The calculated half-lives can well reproduce the experimental data., Comment: 9 pages,6 figures

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerful graph transformers to efficiently model the spatial and temporal interactions among the objects. TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs. TransMOT is not only more computationally efficient than the traditional Transformer, but it also achieves better tracking accuracy. To further improve the tracking speed and accuracy, we propose a cascade association framework to handle low-score detections and long-term occlusions that require large computational resources to model in TransMOT. The proposed method is evaluated on multiple benchmark datasets including MOT15, MOT16, MOT17, and MOT20, and it achieves state-of-the-art performance on all the datasets.