Search

The optical Ramsey technique is used to obtain precise measurements of the hyperfine splittings in the $2\,^3\!S_1$ and $2\,^3\!P_J$ states of $^7$Li$^+$. Together with bound-state quantum electrodynamic theory, the Zemach radius and quadrupole moment of the $^7$Li nucleus are determined to be $3.35(1)$~fm and $-3.86(5)$~fm$^2$ respectively, with the quadrupole moment deviating from the recommended value of $-4.00(3)$~fm$^2$ by $1.75\sigma$. Furthermore, we determine the quadrupole moment ratio of $^6$Li to $^7$Li as $0.101(13)$, exhibiting a $6\sigma$ deviation from the previous measured value of $0.020161(13)$ by LiF molecular spectroscopy. The results taken together provide a sensitive test of nuclear structure models.

The magnification effect of wormholes and black holes has been extensively researched. It is crucial to provide a finite distance analysis to understand this magnification phenomenon better. In this article, the rotational Simpson-Visser metric (RSV) is chosen as the focus of research. By calculating the deflection of light in RSV metric, we determine the resulting magnification effect, then applied the RSV metric to specific examples such as the Ellis-Bronnikov wormhole, Schwarzschild black hole, and Kerr black hole (or wormhole) to analyze the magnification. We find that Ellis-Bronnikov wormhole only has single magnification peaks, while Kerr black hole has one to three magnification peaks. In addition, the article's findings suggest that the lensing effect of the Central Black Hole of the Milky Way Galaxy exhibits magnification of multiple peaks. However, it should be noted that these effects are not observable from Earth., Comment: 7 pages, 9 figures, Modifing details and typos

With the high frequency of highway accidents,studying how to use connected automated vehicle (CAV) to improve traffic efficiency and safety will become an important issue. In order to investigate how CAV can use the connected information for decision making, this study proposed a natural lane changing decision model for CAV to adapt the mixed traffic flow based on extreme value theory. Firstly, on the bias of the mixed vehicle behavior analysis, the acceleration, deceleration, and randomization rules of the cellular automata model of mixed traffic flow in two lanes are developed. Secondly,the maximum value of CAV's lane change probability at each distance by extreme value distribution are modeled. Finally, a numerical simulation is conducted to analyze the trajectory-velocity diagram of mixed traffic flow, average travel time and average speed under different penetration rates of CAV. The result shows that our model can avoid the traffic risk well and significantly improve traffic efficiency and safety.

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting., Comment: Camera-ready for CVPR 2023. Project page: https://wsj-sjtu.github.io/GANHead/

The study focuses on the impact of microlensing in modern cosmology and introduces a new framework for the static spherically symmetrical wormhole in terms of the radial equation of state. Following a standard procedure, the study calculates the lensing equation, magnification, and event rate based on the the radial equation of state. The analysis highlights that the image problem of the light source is complex. Furthermore, the study suggests that larger values for the throat radius of the wormhole and the radial equation of state lead to higher event rates. Additionally, it is proposed that the event rate of a wormhole will be larger compared to that of a black hole, provided their masses and distances from the light source and observer are comparable. This study offers the potential to distinguish between a wormhole and a black hole under similar conditions., Comment: To be published in PLB

We investigate the $2\,^3\!S_1$--$2\,^3\!P_J$ ($J = 0, 1, 2$) transitions in $\rm^6Li^+$ using the optical Ramsey technique and achieve the most precise values of the hyperfine splittings of the $2\,^3\!S_1$ and $2\,^3\!P_J$ states, with smallest uncertainty of about 10~kHz. The present results reduce the uncertainties of previous experiments by a factor of 5 for the $2\,^3\!S_1$ state and a factor of 50 for the $2\,^3\!P_J$ states, and are in better agreement with theoretical values. Combining our measured hyperfine intervals of the $2\,^3\!S_1$ state with the latest quantum electrodynamic (QED) calculations, the improved Zemach radius of the $\rm^6Li$ nucleus is determined to be 2.44(2)~fm, with the uncertainty entirely due to the uncalculated QED effects of order $m\alpha^7$. The result is in sharp disagreement with the value 3.71(16) fm determined from simple models of the nuclear charge and magnetization distribution. We call for a more definitive nuclear physics value of the $\rm^6Li$ Zemach radius., Comment: 6 pages, 6 figures

In this paper, we investigate the microlensing effects of wormholes associated to black hole spacetimes. Specifically, we work on three typical wormholes (WH): Schwarzschild WH, Kerr WH, and RN WH, as well as their blackhole correspondences. We evaluate the deflection angle upon the second order under weak field approximation using Gauss-Bonnet theorem. Then, we study their magnification with numerics.We find that a Kerr WH could lead to multi peaks in the magnification with certain parameters in the prograde case, while a Kerr BH predicts one peak. Therefore, the multi-peak feature of can be used to distinguish the Kerr WH from other compact objects. We also find that the magnification of RN BH will be one peak compared to RN WH, in which the magnification of RN WH is negative in some situations. For other cases, the behavior of magnification from wormholes and their corresponding blackholes is similar. Our result may shed new light on exploring compact objects through the microlensing effect., Comment: Match the publication version

Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enjoy portions of overlaps in users and items, while the distribution of different scenarios is different. The key point of multi-scenario modeling is to efficiently maximize the use of whole-scenario information and granularly generate adaptive representations both for users and items among multiple scenarios. we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios. (2) Insufficient exploitation of entire space samples. (3) Item's multi-scenario representation disentanglement problem. In this paper, we propose a Scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. Specifically, we design a Multi-Layer Scenario Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select and fuse effective transfer information from whole scenario to individual scenario in a quite fine-grained and decoupled way. To sufficiently exploit the power of entire space samples, a two-stage training process including pre-training and fine-tune is introduced. The pre-training stage is based on a scenario-supervised contrastive learning task with the training samples drawn from labeled and unlabeled data spaces. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. Extensive experimental results on public and industrial datasets demonstrate the superiority of the SASS model over state-of-the-art methods. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests., Comment: Accepted by CIKM 2022