Search

Rydberg electromagnetically-induced transparency has been widely studied as a medium supporting light propagation under the action of nonlocal nonlinearities. Recently, optical potentials based on moir\'e lattices (MLs) were introduced for exploring unconventional physical states. Here, we predict a possibility of creating fully three-dimensional (3D) light bullets (LBs) in cold Rydberg gases under the action of ML potentials. The nonlinearity includes local self-defocusing and long-range focusing terms, the latter one induced by the Rydberg-Rydberg interaction. We produce zero-vorticity LB families of the fundamental, dipole, and quadrupole types, as well as vortex LBs. They all are gap solitons populating finite bandgaps of the underlying ML spectrum. Stable subfamilies are identified utilizing the combination of the anti-Vakhitov-Kolokolov criterion, computation of eigenvalues for small perturbations, and direct simulations.

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code is available at https://github.com/bearyi26/DCPT., Comment: Accepted by ICRA2024

Scene Graph Generation is a critical enabler of environmental comprehension for autonomous robotic systems. Most of existing methods, however, are often thwarted by the intricate dynamics of background complexity, which limits their ability to fully decode the inherent topological information of the environment. Additionally, the wealth of contextual information encapsulated within depth cues is often left untapped, rendering existing approaches less effective. To address these shortcomings, we present STDG, an avant-garde Depth-Guided One-Stage Scene Graph Generation methodology. The innovative architecture of STDG is a triad of custom-built modules: The Depth Guided HHA Representation Generation Module, the Depth Guided Semi-Teaching Network Learning Module, and the Depth Guided Scene Graph Generation Module. This trifecta of modules synergistically harnesses depth information, covering all aspects from depth signal generation and depth feature utilization, to the final scene graph prediction. Importantly, this is achieved without imposing additional computational burden during the inference phase. Experimental results confirm that our method significantly enhances the performance of one-stage scene graph generation baselines.

Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture. With the success of transformers, we introduce the Refined Temporal Pyramidal Compression-and-Amplification (RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends intra-block temporal modeling via its Temporal Pyramidal Compression-and-Amplification (TPCA) structure and refines inter-block feature interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA block exploits a temporal pyramid paradigm, reinforcing key and value representation capabilities and seamlessly extracting spatial semantics from motion sequences. We stitch these TPCA blocks with XLR that promotes rich semantic representation through continuous interaction of queries, keys, and values. This strategy embodies early-stage information with current flows, addressing typical deficits in detail and stability seen in other transformer-based methods. We demonstrate the effectiveness of RTPCA by achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP benchmarks with minimal computational overhead. The source code is available at https://github.com/hbing-l/RTPCA., Comment: 11 pages, 5 figures

The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, {\color{red} we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multi-objective optimization problem subject to fitness levels.} The tightest metric bounds from fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes without and with shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function

In this work, a study of the $D_s\bar{D}_s$ interaction and its couplings to the channels $D\bar{D}$ and $J/\psi \phi$ is performed in a quasipotential Bethe-Salpeter equation approach. The $D_s^+{D}_s^-$ and $D^+{D}^-$ invariant mass spectra in three-body $B$ decays are investigated in order to understand the origin of $X(3960)$, $\chi_{c0}(3930)$, and $X_0(4140)$ structures reported at LHCb. With the help of effective Lagrangians, the potential kernel can be constructed with meson exchanges, from which the scattering amplitudes can be obtained. By inserting it into the three-body decay processes, the invariant mass spectra can be calculated with an additional Breit-Wigner resonance introduced. The $D_s^+{D}_s^-$ invariant mass spectrum in the decay process $B^+\to D^+_sD^-_sK^+$ is well reproduced, and the $X(3960)$ structure can be explained as a molecular state from the $D_s\bar{D}_s$ interaction. The state also exhibits as a peak in the $D^+{D}^-$ invariant mass spectrum in the decay $B^+\to D^+D^-K^+$, however, it is too narrow to reproduce the experimental structure around 3930~MeV. The dip around 4140~MeV in the $D_s^+{D}_s^-$ invariant mass spectrum can be reproduced by the additional resonance with interference effect. However, a small bump instead of a dip is produced from the $D_s\bar{D}_s-J/\psi \phi$ coupled-channel effect, which suggests that the $X_0(4140)$ cannot be interpreted as the $D_s\bar{D}_s-J/\psi \phi$ coupling in the current model., Comment: 4 figures, to be published in Euro. Phys. J. C

In this work, we perform an investigation of possible molecular states composed of two charmed strange baryons from the $\Xi_c^{(',*)}{\Xi}_c^{(',*)}$ interaction, and their hidden-charm hidden-strange partners from the $\Xi_c^{(',*)}\bar{\Xi}_c^{(',*)}$ interaction. With the help of the heavy quark chiral effective Lagrangians, the interactions of charmed strange baryons are described with light meson exchanges. The potential kernels are constructed, and inserted into the quasipotential Bethe-Salpeter equation. The bound states are produced from most interactions considered, which suggests that strong attractions exist widely between the charmed strange baryons. Experimental searching for such molecular states is suggested in future high-precision measurements., Comment: 2 tables, 6 figures. To be published in Chinese Physics C. arXiv admin note: text overlap with arXiv:2208.01879

Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis \textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge this data disparity gap in target domain. Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse pose hypotheses and aligns them with the target domain. To do this, it first utilizes target-specific source augmentation to obtain the target domain distribution data from the source domain by decoupling the scale and position parameters. The process is then further refined through the teacher-student paradigm and low-rank adaptation. With extensive comparison of benchmarks such as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance, even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This work paves the way for the practical application of 3D human pose estimation in unseen domains. The code is available at https://github.com/hbing-l/PoSynDA., Comment: Accepted to ACM Multimedia 2023; 10 pages, 4 figures, 8 tables; the code is at https://github.com/hbing-l/PoSynDA

Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pursue unified frameworks to tackle this challenge but mostly still treat it as two individual learning tasks, which limits their potential for exploring cross-domain information. We propose a deeply unified framework for depth-aware panoptic segmentation, which performs joint segmentation and depth estimation both in a per-segment manner with identical object queries. To narrow the gap between the two tasks, we further design a geometric query enhancement method, which is able to integrate scene geometry into object queries using latent representations. In addition, we propose a bi-directional guidance learning approach to facilitate cross-task feature learning by taking advantage of their mutual relations. Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets. Moreover, our guidance learning approach is shown to deliver performance improvement even under incomplete supervision labels., Comment: to be published in ICCV 2023

Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject and box/mask-based tracking. Among them, the Segment Anything Model (SAM) attracts much attention. In this report, we propose HQTrack, a framework for High Quality Tracking anything in videos. HQTrack mainly consists of a video multi-object segmenter (VMOS) and a mask refiner (MR). Given the object to be tracked in the initial frame of a video, VMOS propagates the object masks to the current frame. The mask results at this stage are not accurate enough since VMOS is trained on several closeset video object segmentation (VOS) datasets, which has limited ability to generalize to complex and corner scenes. To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results. As a compelling testament to the effectiveness of our paradigm, without employing any tricks such as test-time data augmentations and model ensemble, HQTrack ranks the 2nd place in the Visual Object Tracking and Segmentation (VOTS2023) challenge. Code and models are available at https://github.com/jiawen-zhu/HQTrack., Comment: Technical Report

Narrow-linewidth lasers have high spectral purity, long coherent length and low phase noise, so they have important applications in cold atom physics, quantum communication, quantum information processing and optical precision measurement. We inject transmitted laser from a narrow-linewidth (15 kHz) flat-concave Fabry-Perot (F-P) cavity made of ultra-low expansion (ULE) optical glass into 852-nm distributed-Bragg-reflector type laser diode (DBR-LD), of which the comprehensive linewidth of 1.67 MHz for the free running case. With the increase of self-injection power, the laser linewidth is gradually narrowed, and the inject-locking current range is gradually increased. The narrowest linewidth measured by the delayed frequency-shifted self-heterodyne (DFSSH) method is 263 Hz. Moreover, to characterize the laser phase noise, we use a detuned F-P cavity to measure the conversion signal from laser phase noise to intensity noise for both the free running case and self-injection lock case. Laser phase noise for the self-injection lock case is significantly suppressed in the analysis frequency range of 0.1-10 MHz compared to the free running case. Especially, the phase noise is suppressed by more than 30dB at the analysis frequency of 100 kHz., Comment: 12 pages, 5 figures

External-cavity diode lasers (ECDL) are widely used as light sources in laser spectroscopy, atomic physics and quantum optics. This paper presents a home-made 852-nm ECDL with variable feedback, using the combined dual narrow-band interference filters (IFs) as the laser longitudinal mode selection element. The combination of narrow-band IFs, mainly for the current commercially available narrow-band IFs with the full width at half maximum (FWHM) of about 0.5 nm for 780 to 895 nm. We design different narrow-band IFs combinations to achieve narrower FWHM, and apply them in ECDL. The combination of dual narrow-band IFs further reduces the laser linewidth. In our experiments, we measured laser linewidth using a high-finesse Fabry-Perot cavity with the cavity linewidth of about 10 kHz. The laser linewidth is about 176 kHz for the Single-IF-ECDL and about 96 kHz for Dual-IF-ECDL with the same feedback. In addition, we have experimentally verified the results of narrower laser linewidth and larger tuning range as feedback increases. The developed Dual-IF-ECDL has the capability of the narrow linewidth and wavelength tunability, and can be applied to precision spectroscopy and cooling and trapping of neutral atoms., Comment: 15 pages, 13 figures

This paper introduces a method for characterizing the current noise of commercial constant-current sources(CCSs) using a free-induction-decay(FID) type optically-pumped rubidium atomic magnetometer driven by a radio-frequency(RF) magnetic field. We convert the sensitivity of the atomic magnetometer into the current noise of CCS by calibrating the coil constant. At the same time, the current noise characteristics of six typical commercial low-noise CCSs are compared. The current noise level of the KeySight Model B2961A is the lowest among the six tested CCSs, which is 36.233 0.022 nA / Hz1/2 at 1-25 Hz and 133.905 0.080 nA / Hz1/2 at 1-100 Hz respectively. The sensitivity of atomic magnetometer is dependent on the current noise level of the CCS. The CCS with low noise is of great significance for high-sensitivity atomic magnetometer. The research provides an important reference for promoting the development of high precision CCS, metrology and basic physics research., Comment: 7pages,7figures

Two-dimensional organic porous networks (2DOPNs) have opened new vistas for tailoring the physicochemical characteristics of metallic surfaces. These typically chemically bound nanoporous structures act as periodical quantum wells leading to the 2D confinements of surface electron gases, adatoms and molecular guests. Here we propose a new type of porous network with weakly interacting 2,4,6-triphenyl-1,3,5-triazine (TPT) molecules on a Cu(111) surface, in which a temperature-driven (T-driven) phase transition can reversibly alter the supramolecular structures from a close-packed (CP-TPT) phase to a porous-network (PN-TPT) phase. Crucially, only the low-temperature PN-TPT exhibits subnano-scale cavities that can confine the surface state electrons and metal adatoms. The confined surface electrons undergo a significant electronic band renormalization. To activate the spin degree of freedom, the T-driven PN-TPT structure can additionally trap Co atoms within the cavities, forming highly ordered quantum dots. Our theoretical simulation reveals a complex spin carrier transfer from the confined Co cluster to the neighbouring TPT molecules via the underlying substrate. Our results demonstrate that weakly interacting 2DOPN offers a unique quantum switch capable of steering and controlling electrons and spin at surfaces via tailored quantum confinements.

Kagome lattices have an inherent two-dimensional nature. Despite previous realizations in the monolayer limit, their abilities to drive emergent electronic states such as correlated insulators have remained unobserved. Here, we report the experimental realization of a new structural phase of monolayer Mo33Te56, characterized by its virtually global uniformity as a mirror-twin boundary loop superlattice embedded in an H-MoTe2 monolayer. Through a combination of scanning tunnelling microscopy (STM) and theoretical calculations, we unveil a kagome geometry along with multiple associated sets of kagome flat bands. Crucially, the partial filling of these kagome bands induces ferromagnetism as revealed by spin-polarized STM, and leads to a correlated insulating state exhibiting a hard gap as large as 15 meV. Our findings represent a major advance in kagome materials, offering a framework with clearer band structures and more intrinsic two-dimensional properties for exploring flat-band physics., Comment: 22 pages, 4 figures

Speech-driven 3D face animation technique, extending its applications to various multimedia fields. Previous research has generated promising realistic lip movements and facial expressions from audio signals. However, traditional regression models solely driven by data face several essential problems, such as difficulties in accessing precise labels and domain gaps between different modalities, leading to unsatisfactory results lacking precision and coherence. To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces. The framework constructs a network system consisting of three modules: facial animator, speech recognizer, and lip-reading interpreter. The core of SelfTalk is a commutative training diagram that facilitates compatible features exchange among audio, text, and lip shape, enabling our models to learn the intricate connection between these factors. The proposed framework leverages the knowledge learned from the lip-reading interpreter to generate more plausible lip shapes. Extensive experiments and user studies demonstrate that our proposed approach achieves state-of-the-art performance both qualitatively and quantitatively. We recommend watching the supplementary video., Comment: Accepted by ACM MM 2023

We reported the quasiparticle relaxation dynamics of an optimally doped triclinic iron-based superconductor (Ca$_{0.85}$La$_{0.15}$)$_{10}$(Pt$_3$As$_8$)(Fe$_2$As$_2$)$_5$ with bulk $T_c$ = 30 K using polarized ultrafast optical pump-probe spectroscopy. Our results reveal anisotropic transient reflectivity induced by nematic fluctuations develops below $T_{nem}$ $\approx$ 120 K and persists in the superconducting states. Measurements under high pump fluence reveal three distinct, coherent phonon modes at frequencies of 1.6, 3.5, and 4.7 THz, corresponding to $A_{1g}(1)$, $E_g$, and $A_{1g}(2)$ modes, respectively. The high-frequency $A_{1g}(2)$ mode corresponds to the $c$-axis polarized vibrations of FeAs planes with a nominal electron-phonon coupling constant $\lambda _{A_{1g}(2)}$ $\approx$ 0.139 $\pm$ 0.02. Our findings suggest that the superconductivity and nematic state are compatible but competitive at low temperatures, and the $A_{1g}$ phonons play an important role in the formation of Cooper pairs in (Ca$_{0.85}$La$_{0.15}$)$_{10}$(Pt$_3$As$_8$)(Fe$_2$As$_2$)$_5$., Comment: 6 pages, 3 figures and Supplemental Materials

Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges, we pioneer the application of True-Range Multilateration, originally devised for GPS localization, to facial landmark detection. We propose KeyPoint Positioning System (KeyPosS) - the first framework to deduce exact landmark coordinates by triangulating distances between points of interest and anchor points predicted by a fully convolutional network. A key advantage of KeyPosS is its plug-and-play nature, enabling flexible integration into diverse decoding pipelines. Extensive experiments on four datasets demonstrate state-of-the-art performance, with KeyPosS outperforming existing methods in low-resolution settings despite minimal computational overhead. By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection. The code is available at https://github.com/zhiqic/KeyPosS., Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the code is at https://github.com/zhiqic/KeyPosS

Graph Convolutional Networks (GCNs) have long defined the state-of-the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However, an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process, while seemingly efficient, causes a gradual decay of bone connectivity data, culminating in a model indifferent to the very topology it sought to map. As a remedy, we propose a threefold strategy: (1) We forge an innovative pathway that encodes bone connectivity by harnessing the power of graph distances. This approach preserves the vital topological nuances often lost in conventional GCNs. (2) We highlight an oft-overlooked feature - the temporal mean of a skeletal sequence, which, despite its modest guise, carries highly action-specific information. (3) Our investigation revealed strong variations in joint-to-joint relationships across different actions. This finding exposes the limitations of a single adjacency matrix in capturing the variations of relational configurations emblematic of human movement, which we remedy by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC. This evolution slashes parameters by a substantial margin (above 40%), while elevating performance beyond original GCNs. Our full model, the BlockGCN, establishes new standards in skeleton-based action recognition for small model sizes. Its high accuracy, notably on the large-scale NTU RGB+D 120 dataset, stand as compelling proof of the efficacy of BlockGCN.

The three-dimensional electronic structure and the nature of Ce 4f electrons of the Kondo insulator CeRu4Sn6 are investigated by angle-resolved photoemission spectroscopy, utilizing tunable photon energies. Our results reveal (i) the three-dimensional k-space nature of the Fermi surface, (ii) the localized-to-itinerant transition of f electrons occurs at a much high temperature than the hybridization gap opening temperature, and (iii) the "relocalization" of itinerant f electrons below 25 K, which could be the precursor to the establishment of magnetic order., Comment: 6 pages, 4 figures

Inspired by the neuro-synaptic frameworks in the human brain, neuromorphic computing is expected to overcome the bottleneck of traditional von-Neumann architecture and be used in artificial intelligence. Here, we predict a class of potential candidate materials, MCu$_3$X$_4$ (M = V, Nb, Ta; X = S, Se, Te), for neuromorphic computing applications through first-principles calculations based on density functional theory. We find that when MCu$_3$X$_4$ are inserted with Li atom, the systems would transform from semiconductors to metals due to the considerable electron filling [~0.8 electrons per formula unit (f.u.)] and still maintain well structural stability. Meanwhile, the inserted Li atom also has a low diffusion barrier (~0.6 eV/f.u.), which ensures the feasibility to control the insertion/extraction of Li by gate voltage. These results establish that the system can achieve the reversible switching between two stable memory states, i.e., high/low resistance state, indicating that it could potentially be used to design synaptic transistor to enable neuromorphic computing. Our work provides inspiration for advancing the search of candidate materials related to neuromorphic computing from the perspective of theoretical calculations., Comment: 28+8 pages, 18 figures

In this work, we systematically investigate the charmed-strange dibaryon systems with $csssqq$ quarks and their baryon-antibaryon partners from the interactions $\Xi^{(',*)}_{c}\Xi^{(*)}$, $\Omega^{(*)}_c\Lambda$, $\Omega^{(*)}_c\Sigma^{(*)}$, $\Lambda_c\Omega$ and $\Sigma^{(*)}_c\Omega$ and their baryon-antibaryon partners from interactions $\Xi^{(',*)}_{c}\bar{\Xi}^{(*)}$, $\Omega^{(*)}_c\bar{\Lambda}$, $\Omega^{(*)}_c\bar{\Sigma}^{(*)}$, $\Lambda_c\bar{\Omega}$ and $\Sigma^{(*)}_c\bar{\Omega}$. The potential kernels are constructed with the help of effective Lagrangians under SU(3), heavy quark, and chiral symmetries to describe these interactions. To search for possible molecular states, the kernels are inserted into the quasipotential Bethe-Salpeter equation, which is solved to find poles from scattering amplitude. The results suggest that 36 and 24 bound states can be found in the baryon-baryon and baryon-antibaryon interactions, respectively. However, much large values of parameter $\alpha$ are required to produce the bound states from the baryon-antibaryon interactions, which questions the existence of these bound states. Possible coupled-channel effect are considered in the current work to estimate the couplings of the molecular states to the channels considered., Comment: 13 pages, 5 figures. arXiv admin note: text overlap with arXiv:2208.11962