Search

We theoretically explore the precise control of a molecular polariton by strongly coupling the lowest three rotational states of a single molecule with a single-mode cavity. We examine two distinct cavity resonance configurations: a fundamental frequency cavity ($\omega_c = 2B$ with the rotational constant $B$) resonating with the lowest two rotational states, and a second harmonic cavity ($\omega_c = 4B$) coupling with the first and second excited rotational states. We propose two control schemes based on the two polariton configurations and derive the corresponding pulse-area theorems to achieve a theoretical maximum orientation of 0.7746, identical to the molecule in the absence of the cavity. The control schemes are analyzed in Carbonyl Sulfide (OCS) molecules in their ground rotational state. Our numerical simulation results demonstrate the theoretical control schemes and analyze the sensitivity of the molecular polariton orientation degree to the control field bandwidth and phases. This work provides a valuable reference for achieving maximum field-free orientation of ultracold three-state molecules in a cavity using analytically designed pulses., Comment: 22 pages, 7 figures

This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation., Comment: This paper has been accepted by IEEE Journal on Selected Areas in Communications

Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text prompts. In this paper, we empirically investigate what text prompt encoders (e.g., CLIP or LLM) are good for adapting SAM for referring expression segmentation and introduce the Early Vision-language Fusion-based SAM (EVF-SAM). EVF-SAM is a simple yet effective referring segmentation method which exploits multimodal prompts (i.e., image and text) and comprises a pre-trained vision-language model to generate referring prompts and a SAM model for segmentation. Surprisingly, we observe that: (1) multimodal prompts and (2) vision-language models with early fusion (e.g., BEIT-3) are beneficial for prompting SAM for accurate referring segmentation. Our experiments show that the proposed EVF-SAM based on BEIT-3 can obtain state-of-the-art performance on RefCOCO/+/g for referring expression segmentation and demonstrate the superiority of prompting SAM with early vision-language fusion. In addition, the proposed EVF-SAM with 1.32B parameters achieves remarkably higher performance while reducing nearly 82% of parameters compared to previous SAM methods based on large multimodal models., Comment: Preprint. Update: SAM-2 and Visualizations. Code and models are available at: https://github.com/hustvl/EVF-SAM

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing., Comment: 6 pages, 6 figures, published to Globecom2023

Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene. However, in more realistic scenarios, only minimal annotations are available for a new scene, which poses significant challenges to existing RVOS methods. With this in mind, we propose a simple yet effective model with a newly designed cross-modal affinity (CMA) module based on a Transformer architecture. The CMA module builds multimodal affinity with a few samples, thus quickly learning new semantic information, and enabling the model to adapt to different scenarios. Since the proposed method targets limited samples for new scenes, we generalize the problem as - few-shot referring video object segmentation (FS-RVOS). To foster research in this direction, we build up a new FS-RVOS benchmark based on currently available datasets. The benchmark covers a wide range and includes multiple situations, which can maximally simulate real-world scenarios. Extensive experiments show that our model adapts well to different scenarios with only a few samples, reaching state-of-the-art performance on the benchmark. On Mini-Ref-YouTube-VOS, our model achieves an average performance of 53.1 J and 54.8 F, which are 10% better than the baselines. Furthermore, we show impressive results of 77.7 J and 74.8 F on Mini-Ref-SAIL-VOS, which are significantly better than the baselines. Code is publicly available at https://github.com/hengliusky/Few_shot_RVOS., Comment: Accepted by ICCV2023

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.

Aiming at providing wireless communication systems with environment-perceptive capacity, emerging integrated sensing and communication (ISAC) technologies face multiple difficulties, especially in balancing the performance trade-off between the communication and radar functions. In this paper, we introduce a reconfigurable intelligent surface (RIS) to assist both data transmission and target detection in a dual-functional ISAC system. To formulate a general optimization framework, diverse communication performance metrics have been taken into account including famous capacity maximization and mean-squared error (MSE) minimization. Whereas the target detection process is modeled as a general likelihood ratio test (GLRT) due to the practical limitations, and the monotonicity of the corresponding detection probability is proved. For the single-user and single-target (SUST) scenario, the minimum transmit power of the ISAC transceiver has been revealed. By exploiting the optimal conditions of the BS design, we validate that the BS is able to realize the maximum power allocation scheme and derive the optimal BS precoder in a semi-closed form. Moreover, an alternating direction method of multipliers (ADMM) based RIS design is proposed to address the optimization of unit-modulus RIS phase shifts. For the sake of further enhancing computational efficiency, we also develop a low-complexity RIS design based on Riemannian gradient descent. Furthermore, the ISAC transceiver design for the multiple-users and multiple-targets (MUMT) scenario is also investigated, where a zero-forcing (ZF) radar receiver is adopted to cancel the interferences. Then optimal BS precoder is derived under the maximum power allocation scheme, and the RIS phase shifts can be optimized by extending the proposed ADMM-based RIS design. Numerical simulation results verify the performance of our proposed transceiver designs., Comment: 30 pages, 8 figures, manuscript submitted to IEEE TCOM

Within the framework of the quantum molecular dynamics transport model, the collective flows of clusters and pions in heavy-ion collisions have been systematically investigated. The clusters are recognized by the Wigner phase-space density approach at the stage of freeze out in nuclear collisions, i.e., deuteron, triton, $^{3}$He and $\alpha$. The directed and elliptic flows of protons and deuterons in the reaction of $^{197}$Au+$^{197}$Au at incident energy 1.23\emph{A} GeV are nicely consistent with the recent HADES data. The higher order collective flows, i.e., triangular and quadrangle flows, manifest the opposite trends with the less amplitude in comparison with the rapidity distributions of directed and elliptic flows. The flow structure of $^{3}$He and $\alpha$ is very similar to the proton spectra. The influence of the pion potential on the pion production is systematically investigated and compared with the FOPI data via the transverse momentum, longitudinal rapidity and collective flows in collisions of $^{197}$Au + $^{197}$Au. It is manifested that the pion yields are slightly suppressed in the domain of mid-rapidity and high momentum. The antiflow phenomena is reduced by implementing the pion potential and more consistent with the FOPI data in collisions of $^{197}$Au+$^{197}$Au at the incident energy 1.5\emph{A} GeV., Comment: 11 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:2302.02131

Within the framework of the quantum molecular dynamics transport model, the pion production and constraint of the high-density symmetry energy in heavy-ion collisions near threshold energy have been thoroughly investigated. The energy conservation in the decay of resonances and reabsorption of pions as well as in the inelastic nucleon-nucleon and nucleon-resonance collisions are taken into account. The isospin diffusion in the low-density region (0.2$\rho_{0}$ - 0.8$\rho_{0}$) and high-density region (1.2$\rho_{0}$ - 1.8$\rho_{0}$) is investigated by analyzing the spectra of neutron/proton and $\pi^{-}/\pi^{+}$ ratios in the isotopic reactions of $^{132}$Sn + $^{124}$Sn and $^{108}$Sn + $^{112}$Sn at the incident energy of 270 MeV/nucleon, in which the symmetry energy manifests the opposite effect in the different density domain. The controversial conclusion of the $\pi^{-}/\pi^{+}$ ratio for constraining the high-density symmetry energy by different transport models with the FOPI data has been clarified. A soft symmetry energy with the slope parameter of $L(\rho_{0}) = 42\pm 25$ MeV by using the standard error analysis within the range of $1\sigma$ is obtained by analyzing the experimental data from the S$\pi$RIT collaboration. The neutron stars with the maximal mass of 2 $M_{\odot}$ and radius of 11-13 km are obtained with the constrained symmetry energy., Comment: 10 pages, 8 figures