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\textbf{Background:} The initial observation of a negative-parity state in proximity to the ground state in the 1950s marked the advent of extensive research into octupole deformed nuclei. Since then, the physics of octupole deformed nuclei has consistently held a special interest within the field of nuclear physics. In the present era, with the advent of sophisticated radioactive ion beam (RIB) facilities and advanced detectors, coupled with the remarkable capabilities of high-performance computing, extensive and intensive explorations are being conducted from both experimental and theoretical perspectives to elucidate the physics of octupole deformed nuclei. \textbf{Results:} This work establishes the OD-RHF model, which provides a reliable tool for studying octupole nuclei over a fairly wide range. The reliability of the newly developed OD-RHF model is illustrated by taking the octupole nucleus $^{144}$Ba as an example. Furthermore, the octupole deformation effects in $^{144}$Ba is verified by using the RHF Lagrangians PKO$i$ ($i=1,2,3$) and the RMF one DD-ME2. The intrusion of the neutron $1i_{13/2}$ and proton $1h_{11/2}$ components is demonstrated to play an essential role in determining the notable octupole deformation of $^{144}$Ba using PKO$i$ ($i=1,2,3$) and DD-ME2. It is indicated that the Fock terms play an important role in stabilizing the octupole deformation. More specifically, due to the repulsive tensor coupling between the intrude components and the core of $^{144}$Ba, the tensor force component carried by the $\pi$-PV coupling, that contributes only via the Fock terms, plays an opposing role in the formation of the octupole deformation of $^{144}$Ba., Comment: 20pages, 3 figures

This work establishes the time-dependent relativistic Hartree-Fock (TD-RHF) model with spherical symmetry for the first time. The time-dependent integro-differential Dirac equations are solved by expanding Dirac spinors on the spherical Dirac Woods-Saxon (DWS) basis. The numerical verification demonstrates the high conservation qualities for both the total binding energy and the particle number, as well as the time-reversal invariance of the system, which ensures the precision and reliability of the newly developed TD-RHF model. Subsequently, the isoscalar giant monopole resonance (ISGMR) mode of $^{208}$Pb is investigated using the RHF Lagrangian PKO1. The constrained energy of the ISGMR calculated by PKO1 is found to be in close agreement with the experimental data, and the strength function is similar to the results given by the relativistic Hartree-Fock plus random phase approximation. Based on the advantage of the TD-RHF model in avoiding complicated calculations of the residual interactions, the ISGMR mode of $^{208}$Pb is calculated by twelve relativistic effective Lagrangians. The results indicate that the value of the incompressibility of nuclear matter $K_\infty$ constrained by relativistic effective Lagrangians is in the range of $237\sim246$ MeV, which is lower than the previous investigations based on the relativistic models., Comment: 9 pages, 5 figures

Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task. In contrast, traditional video codecs employ a flexible encoder controller, enabling the adaptation of a single codec to different tasks through mechanisms like mode prediction. Drawing inspiration from this, we introduce an innovative encoder controller for deep video compression for machines. This controller features a mode prediction and a Group of Pictures (GoP) selection module. Our approach centralizes control at the encoding stage, allowing for adaptable encoder adjustments across different tasks, such as detection and tracking, while maintaining compatibility with a standard pre-trained DVC decoder. Empirical evidence demonstrates that our method is applicable across multiple tasks with various existing pre-trained DVCs. Moreover, extensive experiments demonstrate that our method outperforms previous DVC by about 25% bitrate for different tasks, with only one pre-trained decoder., Comment: Accepted by CVPR 2024

Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available. However, this requirement often hinders their use on low-end devices with limited memory. In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage. We first introduce 2D Gaussian to represent the image, where each Gaussian has 8 parameters including position, covariance and color. Subsequently, we unveil a novel rendering algorithm based on accumulated summation. Remarkably, our method with a minimum of 3$\times$ lower GPU memory usage and 5$\times$ faster fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation performance, but also delivers a faster rendering speed of 1500-2000 FPS regardless of parameter size. Furthermore, we integrate existing vector quantization technique to build an image codec. Experimental results demonstrate that our codec attains rate-distortion performance comparable to compression-based INRs such as COIN and COIN++, while facilitating decoding speeds of approximately 2000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses COIN and COIN++ in performance when using partial bits-back coding. Code is available at https://github.com/Xinjie-Q/GaussianImage., Comment: Accepted by ECCV 2024. Project Page:https://xingtongge.github.io/GaussianImage-page/ Code: https://github.com/Xinjie-Q/GaussianImage

Contrastive learning has achieved promising performance in the field of multi-view clustering recently. However, the positive and negative sample construction mechanisms ignoring semantic consistency lead to false negative pairs, limiting the performance of existing algorithms from further improvement. To solve this problem, we propose a multi-view clustering framework named Deep Contrastive Multi-view Clustering under Semantic feature guidance (DCMCS) to alleviate the influence of false negative pairs. Specifically, view-specific features are firstly extracted from raw features and fused to obtain fusion view features according to view importance. To mitigate the interference of view-private information, specific view and fusion view semantic features are learned by cluster-level contrastive learning and concatenated to measure the semantic similarity of instances. By minimizing instance-level contrastive loss weighted by semantic similarity, DCMCS adaptively weakens contrastive leaning between false negative pairs. Experimental results on several public datasets demonstrate the proposed framework outperforms the state-of-the-art methods.

Deformed ground state of $^{32}$Mg is investigated using the axially deformed relativistic Hartree-Fock-Bogoliubov (D-RHFB) model with the effective Lagrangian PKA1, which provides coincident description with the experimental measurements. It is illustrated that obvious breaking of the pseudo-spin symmetry (PSS) given by PKA1, being consistent with the experimental observation in nearby isotone $^{40}$Ca, is crucial for describing correctly the deformed ground state by producing unique shape evolution of neutron orbit $1/2_4^+$ in $^{32}$Mg. The PSS breaking is essentially determined by characteristic in-medium balance between nuclear attractions and repulsions that is manifested as unparalleled density dependent behaviors for coupling strengths $g_\sigma$ and $g_\omega$ in dominant $\sigma$-scalar and $\omega$-vector channels., Comment: 10 pages, 6 figures, 2 tables

Using the axially deformed relativistic Hartree-Fock-Bogoliubov (D-RHFB) model, we explore the mechanism behind the parity inversion and halo occurrence in $^{11}$Be, which are well reproduced by the RHF Lagrangian PKA1. It is illustrated that evidently enhanced deformation effects by the $\pi$-pseudo-vector and $\rho$-tensor couplings in PKA1 are crucial for correctly describing both even-parity ground state (GS) and neutron halo of $^{11}$Be. Coupling with the deformation, the intrude $1d_{5/2}$ component largely enhances the couplings between the even-parity orbit $1/2_2^+$ and the nuclear core to promise the even-parity GS, whereas the $2s_{1/2}$ component therein dominates the halo formation in $^{11}$Be. Moreover, the deformed halo in $^{11}$Be is found to be stabilized by the attractive inherent correlations between the $1d_{5/2}$ and $2s_{1/2}$ components of the halo orbit $1/2_2^+$, instead of pairing correlations, which paves a new way to understand the halo pictures in deformed unstable nuclei., Comment: 4 figures, 1 table, 9 pages

As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting. Recent work has shown that the extraction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed, which fully considers the tonal characteristics and the representative color of the original oil painting, in order to fit the tone of the original oil painting image into the stylized image and make it close to the artist's creative effect. The experiments have validated the efficacy of the proposed model. This method would be more suitable for the works of pointillism painters with a relatively uniform sense of direction, especially for natural scenes. When the original painting brush strokes have a clearer sense of direction, using this method to simulate brushwork texture features can be less satisfactory.

Recently, more and more images are compressed and sent to the back-end devices for the machine analysis tasks~(\textit{e.g.,} object detection) instead of being purely watched by humans. However, most traditional or learned image codecs are designed to minimize the distortion of the human visual system without considering the increased demand from machine vision systems. In this work, we propose a preprocessing enhanced image compression method for machine vision tasks to address this challenge. Instead of relying on the learned image codecs for end-to-end optimization, our framework is built upon the traditional non-differential codecs, which means it is standard compatible and can be easily deployed in practical applications. Specifically, we propose a neural preprocessing module before the encoder to maintain the useful semantic information for the downstream tasks and suppress the irrelevant information for bitrate saving. Furthermore, our neural preprocessing module is quantization adaptive and can be used in different compression ratios. More importantly, to jointly optimize the preprocessing module with the downstream machine vision tasks, we introduce the proxy network for the traditional non-differential codecs in the back-propagation stage. We provide extensive experiments by evaluating our compression method for two representative downstream tasks with different backbone networks. Experimental results show our method achieves a better trade-off between the coding bitrate and the performance of the downstream machine vision tasks by saving about 20% bitrate.

Staring from the Lagrangian density that foots on the meson-propagated picture of nuclear force, the full Hamiltonian, that contains both mean field and pairing contributions, is derived by quantizing the Dirac spinor field in the Bogoliubov quasi-particle space, and the expectation with respect to the Bogoliubov ground state gives the full energy functional. As an extension of the D-RHF model, the degree of freedom associated with the $\rho$-tensor ($\rho$-T) coupling is implemented, and incorporating with the Bogoliubov scheme the finite-range Gogny force D1S is utilized as the pairing force. Moreover, qualitative analysis on the nature of the $\pi$-PV and $\rho$-T couplings are presented for better understanding their enhancements on the deformation effects. Space convergence related to the spherical DWS base is confirmed for the D-RHFB model by taking light nucleus $^{24}$Mg and mid-heavy one $^{156}$Sm as candidates. Compared to light nuclei, extraordinary more negative energy states are necessitated to keep the expansion completeness on the spherical DWS base for mid-heavy and heavy nuclei, due to the enhanced correlations between the expansion components with large $\kappa$-quantity as indicated by the nature of the $\pi$-PV and $\rho$-T couplings. Furthermore, because of the enhanced deformation effects by the $\pi$-PV and $\rho$-T couplings, the RHF Lagrangian PKA1 presents deeper bound ground state for $^{24}$Mg than the other selected Lagrangians, in addition to predicting a fairly deep bound local minimum with large oblate deformation., Comment: 27 pages, 4 figures and 2 tables

The shaking force balancing is a well-known problem in the design of high-speed robotic systems because the variable dynamic loads cause noises, wear and fatigue of mechanical structures. Different solutions, for full or partial shaking force balancing, via internal mass redistribution or by adding auxiliary links were developed. The paper deals with the shaking force balancing of the Orthoglide. The suggested solution based on the optimal acceleration control of the manipulator's common center of mass allows a significant reduction of the shaking force. Compared with the balancing method via adding counterweights or auxiliary substructures, the proposed method can avoid some drawbacks: the increase of the total mass, the overall size and the complexity of the mechanism, which become especially challenging for special parallel manipulators. Using the proposed motion control method, the maximal value of the total mass center acceleration is reduced, as a consequence, the shaking force of the manipulator decreases. The efficiency of the suggested method via numerical simulations carried out with ADAMS is demonstrated.