arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 289 records · Page 16Linked to original sources

arXiv2025

Fast square-oscillations in semiconductor VCSELs with delayed orthogonal polarization feedback

We present an experimental investigation into the generation of self-sustained and fast square oscillations from the TE mode of semiconductor VCSELs with delayed orthogonal polarization feedback. We find that the low frequency switching originates from the rotation of the TE and TM modes facilitated by a long time delay, but the fast oscillations are anchored to the frequency beating between the TE and TM modes and are modified by a half-wavelength ($λ/2$) plate. A comprehensive analysis of the evolution of the nonlinear dynamics is conducted and the related mechanism is discussed. Our study not only deepens our comprehension of laser nonlinear dynamics but also offers an all-optical approach for producing specialized signals, which could be instrumental in applications such as optical communications and photonic computing leveraging the complexity of long-delay systems.

physics.optics↗

arXiv2025

Engineering anisotropic Dicke model with dipole-dipole interaction for Rydberg atom arrays in cavity

The anisotropic Dicke model reveals the important role that counter-rotating wave terms play in the coupling between light and two level atoms. It is intriguing to generate the model to the strongly correlated many body case, where the competition between atomic interaction and light-atom coupling will induce exotic phenomenon. In this paper, we provide a periodically driving method for engineering an anisotropic Dicke model with strong dipole-dipole interaction between atoms based on Rydberg atom arrays in cavity. By modulating the pumping laser, the ratio of counter-rotating wave terms to rotating ones could be tuned from zero to infinity. As an illustrative example, the superiority of this tunability in the adiabatic state preparation is revealed. Our engineered model provides an extensive foundation for subsequet research of quantum simulation, many-body physics and even information processing.

quant-ph↗

arXiv2025

Valley: Video Assistant with Large Language model Enhanced abilitY

Large Language Models (LLMs), with remarkable conversational capability, have emerged as AI assistants that can handle both visual and textual modalities. However, their effectiveness in joint video and language understanding has not been extensively explored. In the paper, we introduce Valley, a multi-modal foundation model that is designed to enable enhanced video comprehension and instruction-following capabilities. To this end, we construct two datasets, namely Valley-702k and Valley-instruct-73k, to cover a diverse range of video-text alignment and video-based instruction tasks, such as multi-shot captions, long video descriptions, action recognition, causal inference, etc. Then, we adopt ViT-L/14 as the vision encoder and explore three different temporal modeling modules to learn multifaceted features for enhanced video understanding. In addition, we implement a two-phase training approach for Valley: the first phase focuses solely on training the projection module to facilitate the LLM's capacity to understand visual input, and the second phase jointly trains the projection module and the LLM to improve their instruction following ability. Extensive experiments demonstrate that Valley has the potential to serve as an effective video assistant, simplifying complex video-understanding scenarios. Our code and data are published anonymously at https://github.com/valley-vl/Valley.

cs.CV↗

arXiv2025

Trend-Aware Supervision: On Learning Invariance for Semi-Supervised Facial Action Unit Intensity Estimation

With the increasing need for facial behavior analysis, semi-supervised AU intensity estimation using only keyframe annotations has emerged as a practical and effective solution to relieve the burden of annotation. However, the lack of annotations makes the spurious correlation problem caused by AU co-occurrences and subject variation much more prominent, leading to non-robust intensity estimation that is entangled among AUs and biased among subjects. We observe that trend information inherent in keyframe annotations could act as extra supervision and raising the awareness of AU-specific facial appearance changing trends during training is the key to learning invariant AU-specific features. To this end, we propose \textbf{T}rend-\textbf{A}ware \textbf{S}upervision (TAS), which pursues three kinds of trend awareness, including intra-trend ranking awareness, intra-trend speed awareness, and inter-trend subject awareness. TAS alleviates the spurious correlation problem by raising trend awareness during training to learn AU-specific features that represent the corresponding facial appearance changes, to achieve intensity estimation invariance. Experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of each kind of awareness. And under trend-aware supervision, the performance can be improved without extra computational or storage costs during inference.

cs.CV↗

arXiv2025

The critical role of dark matter halos in driving star formation

Understanding the physical mechanisms that drive star formation is crucial for advancing our knowledge of galaxy evolution. We explore the interrelationships between key galaxy properties associated with star formation, with a particular focus on the impact of dark matter halos. Given the sensitivity of atomic hydrogen (HI) to external processes, we concentrate exclusively on central spiral galaxies. We find that the molecular-to-atomic gas mass ratio ($M_{\rm H_2}/M_{\rm HI}$) strongly depends on stellar mass and specific star formation rate (sSFR). In the star formation efficiency (SFE)-sSFR plane, most galaxies fall below the H$_2$ fundamental formation relation (FFR), with SFE$_{\rm HI}$ being consistently lower than SFE$_{\rm H_2}$. Using the improved halo masses derived by Zhao et al. (2025), for star-forming galaxies, both SFE$_{\rm HI}$ and $M_{\rm H_2}/M_{\rm HI}$ increase rapidly and monotonically with halo mass, indicating a higher efficiency in converting HI to H$_2$ in more massive halos. This trend ultimately leads to the unsustainable state where SFE$_{\rm HI}$ exceeds SFE$_{\rm H_2}$ at halo mass around $10^{12} \hbox{$M_{\odot}$}$. For halos with masses exceeding $10^{12} \hbox{$M_{\odot}$}$, galaxies predominantly experience quenching. We propose a plausible evolutionary scenario in which the growth of halo mass regulates the conversion of HI to H$_2$, star formation, and the eventual quenching of galaxies. The disk size, primarily regulated by the mass, spin and concentration of the dark matter halo, also significantly influences HI to H$_2$ conversion and star formation. These findings underscore the critical role of dark matter halos as a global regulator of galaxy-wide star formation, a key factor that has been largely underappreciated in previous studies.

astro-ph.GA↗

arXiv2025

Quantum Geometric Engineering of Dual Hall Effects in 2D Antiferromagnetic Bilayers via Interlayer Magnetic Coupling

The interplay between quantum geometry and magnetic order offers a novel strategy for designing next-generation nanodevices. Here, we demonstrate that interlayer magnetic coupling in two-dimensional (2D) CoPSe3 bilayers enables precise control over quantum geometric mechanisms, unlocking dual intrinsic Hall effects. Our first-principles calculations reveal that the altermagnetic (AM) phase exhibits a giant anisotropic anomalous Hall effect (AHE) ($σ_{xy}$ is approximately 46 S/cm) driven by Berry curvature localized at generic k-points, while the PT-symmetric antiferromagnetic (AFM) phase hosts an intrinsic second-order nonlinear anomalous Hall effect (NAHE) ($χ_{xyy}$ is approximately 160 $μ$S/V) originating from quantum metric accumulation at high-symmetry k-points. By tuning interlayer magnetic couplings, we achieve reversible switching between these phases, leveraging their distinct band structures and symmetry constraints. The Neel-vector-dependent AHE in the AM phase and the symmetry-protected NAHE in the AFM phase highlight quantum geometry as a versatile tool for manipulating transport properties. Our work establishes 2D antiferromagnets as a promising platform for multifunctional device architectures, bridging linear and nonlinear magnetoelectric responses through tailored quantum geometric engineering.

cond-mat.mes-hall↗

arXiv2025

The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation

Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of neural machine translation. The test suite consists of three test sets, covering lexical and contextless/contextual syntactic ambiguity that requires commonsense knowledge to resolve. We manually create 1,200 triples, each of which contain a source sentence and two contrastive translations, involving 7 different common sense types. Language models pretrained on large-scale corpora, such as BERT, GPT-2, achieve a commonsense reasoning accuracy of lower than 72% on target translations of this test suite. We conduct extensive experiments on the test suite to evaluate commonsense reasoning in neural machine translation and investigate factors that have impact on this capability. Our experiments and analyses demonstrate that neural machine translation performs poorly on commonsense reasoning of the three ambiguity types in terms of both reasoning accuracy (60.1%) and reasoning consistency (31%). The built commonsense test suite is available at https://github.com/tjunlp-lab/CommonMT.

cs.CL↗

arXiv2025

Feature Point Extraction for Extra-Affine Image

The issue concerning the significant decline in the stability of feature extraction for images subjected to large-angle affine transformations, where the angle exceeds 50 degrees, still awaits a satisfactory solution. Even ASIFT, which is built upon SIFT and entails a considerable number of image comparisons simulated by affine transformations, inevitably exhibits the drawbacks of being time-consuming and imposing high demands on memory usage. And the stability of feature extraction drops rapidly under large-view affine transformations. Consequently, we propose a method that represents an improvement over ASIFT. On the premise of improving the precision and maintaining the affine invariance, it currently ranks as the fastest feature extraction method for extra-affine images that we know of at present. Simultaneously, the stability of feature extraction regarding affine transformation images has been approximated to the maximum limits. Both the angle between the shooting direction and the normal direction of the photographed object (absolute tilt angle), and the shooting transformation angle between two images (transition tilt angle) are close to 90 degrees. The central idea of the method lies in obtaining the optimal parameter set by simulating affine transformation with the reference image. And the simulated affine transformation is reproduced by combining it with the Lanczos interpolation based on the optimal parameter set. Subsequently, it is combined with ORB, which exhibits excellent real-time performance for rapid orientation binary description. Moreover, a scale parameter simulation is introduced to further augment the operational efficiency.

cs.CV↗

arXiv2025

StickMotion: Generating 3D Human Motions by Drawing a Stickman

Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and our proposed stickman conditions for global and local control of these motions, respectively. We address the challenges introduced by the user-friendly stickman from three perspectives: 1) Data generation. We develop an algorithm to generate hand-drawn stickmen automatically across different dataset formats. 2) Multi-condition fusion. We propose a multi-condition module that integrates into the diffusion process and obtains outputs of all possible condition combinations, reducing computational complexity and enhancing StickMotion's performance compared to conventional approaches with the self-attention module. 3) Dynamic supervision. We empower StickMotion to make minor adjustments to the stickman's position within the output sequences, generating more natural movements through our proposed dynamic supervision strategy. Through quantitative experiments and user studies, sketching stickmen saves users about 51.5% of their time generating motions consistent with their imagination. Our codes, demos, and relevant data will be released to facilitate further research and validation within the scientific community.

cs.CV↗

arXiv2025

Microscopic theory of a precessing ferromagnet for ultrasensitive magnetometry

Levitated systems have great potential in quantum sensing and exploring quantum effects at the macroscopic scale. Of particular interest are recent works suggesting that a levitated ferromagnet can beat the standard quantum limit of magnetometry. This work offers a theoretical model to analyze and understand critical features of the precessing dynamics of a levitated ferromagnetic needle, indeed much like a macrospin, in the presence of a weak magnetic field. The dynamics from the atomic scale reveals how the standard quantum limit is surpassed, thus verifying sensing advantages when compared with a collection of independent spins. Our theory further takes us to two additional experimental designs of immediate interest: measurement of the celebrated Berry phase with a precessing ferromagnetic needle and the use of its nutation motion to sense a low-frequency oscillating magnetic field. With a microscopic theory established for levitated ferromagnetic needles, future studies of macroscopic quantum effects and the associated quantum-classical transition also become possible.

quant-ph↗

arXiv2025

Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM

The advent of Large Language Models (LLMs) has revolutionized natural language processing. However, these models face challenges in retrieving precise information from vast datasets. Retrieval-Augmented Generation (RAG) was developed to combining LLMs with external information retrieval systems to enhance the accuracy and context of responses. Despite improvements, RAG still struggles with comprehensive retrieval in high-volume, low-information-density databases and lacks relational awareness, leading to fragmented answers. To address this, this paper introduces the Pseudo-Knowledge Graph (PKG) framework, designed to overcome these limitations by integrating Meta-path Retrieval, In-graph Text and Vector Retrieval into LLMs. By preserving natural language text and leveraging various retrieval techniques, the PKG offers a richer knowledge representation and improves accuracy in information retrieval. Extensive evaluations using Open Compass and MultiHop-RAG benchmarks demonstrate the framework's effectiveness in managing large volumes of data and complex relationships.

cs.IR↗

arXiv2025

Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning

Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods. Leveraging advancements in artificial intelligence, we introduce a deep learning-based method for efficiently characterizing heterostructures and 2D materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes with varying shapes and thicknesses. By utilizing YOLO models, we achieve an accuracy rate of over 94.67% in identifying these materials. Additionally, we explore the application of transfer learning across different materials, which further enhances model performance. This model exhibits robust generalization and anti-interference ability, ensuring reliable results in diverse scenarios. To facilitate practical use, we have developed an application that enables real-time analysis directly from optical microscope images, making the process significantly faster and more cost-effective than traditional methods. This deep learning-driven approach represents a promising tool for the rapid and accurate characterization of 2D materials, opening new avenues for research and development in material science.

cs.LG↗

arXiv2025

The boson number hypothesis and the boson number odd-even effect in $^{196-204}$Hg

In the SU3-IBM the oblate shape is described by the \textrm{SU(3)} third-order Casimir operator in the large-$N$ limit. However for finite $N$, this interaction can produce a boson number odd-even effect. In this Letter, we find that, the unique odd-even effect really exists in the nuclei $^{196-204}$Hg. This finding implies that realistic low-lying excitations are sensitive to certain boson number $N$. The boson number hypothesis is verified for the first time since the advent of the interacting boson model. This also proves the accuracy and validity of the SU3-IBM directly. The SU(3) symmetry and the higher-order interactions are both indispensable for understanding the nuclear quadrupole deformations.

nucl-th↗

arXiv2025

Peculiar velocities of galaxy clusters in a IllustrisTNG simulation based on mock observations of Sunyaev-Zel'dovich effects

Galaxy clusters are the largest self-gravitational systems in the Universe. They are valuable probes of the structure growth of the Universe when we estimate their peculiar velocities with the kinematic Sunyaev-Zel'dovich (kSZ) effects. We investigate whether there is a systematic offset between the peculiar velocities ($v_{\rm z,kSZ}$) estimated with the kSZ effect and the true velocities of halos. We first created mocks of the 2D maps of SZ effects in seven frequency bands for galaxy clusters spanning a broad range of masses in TNG 300-3. We then derived the line-of-sight (LOS) peculiar velocities of galaxy clusters by applying an analytical formula to fit the spectra of the SZ effect. We find that the analytical formula-fitting method tends to overestimate the peculiar velocities of galaxy clusters, regardless of whether they approach us or recede from us. However, when galaxy clusters larger than 500 km/s were excluded, the slopes of the relations between $v_{\rm z,kSZ}$ and the real LOS velocities of galaxy clusters ($v_{\rm z,halo}$) were consistent with unity within the errors. Additionally, we further accounted for observational noise for different-aperture telescopes under different precipitable water vapor. The slopes for most cases are consistent with unity within the errors, and the errors of the slopes slightly increase with higher observation noise. The $|v_{\rm z,kSZ}-v_{\rm z,halo}|$ exhibits no obvious trend with increasing concentration and $M_{\rm 200}$. It is noteworthy that the $|v_{\rm z,kSZ}-v_{\rm z,halo}|$ is relatively high for galaxy clusters with strong active galactic nucleus feedback and high star formation rates. This indicates that these physical processes affect the cluster dynamic state and may reflect on the peculiar velocity estimated from the kSZ effect.

astro-ph.CO↗

arXiv2025

DiffBrush:Just Painting the Art by Your Hands

The rapid development of image generation and editing algorithms in recent years has enabled ordinary user to produce realistic images. However, the current AI painting ecosystem predominantly relies on text-driven diffusion models (T2I), which pose challenges in accurately capturing user requirements. Furthermore, achieving compatibility with other modalities incurs substantial training costs. To this end, we introduce DiffBrush, which is compatible with T2I models and allows users to draw and edit images. By manipulating and adapting the internal representation of the diffusion model, DiffBrush guides the model-generated images to converge towards the user's hand-drawn sketches for user's specific needs without additional training. DiffBrush achieves control over the color, semantic, and instance of objects in images by continuously guiding the latent and instance-level attention map during the denoising process of the diffusion model. Besides, we propose a latent regeneration, which refines the randomly sampled noise in the diffusion model, obtaining a better image generation layout. Finally, users only need to roughly draw the mask of the instance (acceptable colors) on the canvas, DiffBrush can naturally generate the corresponding instance at the corresponding location.

cs.CV↗

arXiv2025

Variable degeneracy on toroidal graphs

DP-coloring was introduced by Dvořák and Postle as a generalization of list coloring and signed coloring. A new coloring, strictly $f$-degenerate transversal, is a further generalization of DP-coloring and $L$-forested-coloring. In this paper, we present some structural results on planar and toroidal graphs with forbidden configurations, and establish some sufficient conditions for the existence of strictly $f$-degenerate transversal based on these structural results. Consequently, (i) every toroidal graph without subgraphs isomorphic to the configurations in Fig.2 is DP-$4$-colorable, and has list vertex arboricity at most $2$, (ii) every toroidal graph without $4$-cycles is DP-$4$-colorable, and has list vertex arboricity at most $2$, (iii) every planar graph without subgraphs isomorphic to the configurations in Fig.3 is DP-$4$-colorable, and has list vertex arboricity at most $2$. These results improve upon previous results on DP-$4$-coloring [Discrete Math. 341~(7) (2018) 1983--1986; Bull. Malays. Math. Sci. Soc. 43~(3) (2020) 2271--2285] and (list) vertex arboricity [Discrete Math. 333 (2014) 101--105; Int. J. Math. Stat. 16~(1) (2015) 97--105; Iranian Math. Soc. 42~(5) (2016) 1293--1303].

math.CO↗

arXiv2025

Decomposition of toroidal graphs without some subgraphs

We consider a family of toroidal graphs, denoted by $\mathcal{T}_{i, j}$, which contain neither $i$-cycles nor $j$-cycles. A graph $G$ is $(d, h)$-decomposable if it contains a subgraph $H$ with $Δ(H) \leq h$ such that $G - E(H)$ is a $d$-degenerate graph. For each pair $(i, j) \in \{(3, 4), (3, 6), (4, 6), (4, 7)\}$, Lu and Li proved that every graph in $\mathcal{T}_{i, j}$ is $(2, 1)$-decomposable. In this short note, we present a unified approach to prove that a common superclass of $\mathcal{T}_{i, j}$ is also $(2, 1)$-decomposable.

math.CO↗

arXiv2025

Planar graphs without 4-, 7-, 9-cycles and 5-cycles normally adjacent to 3-cycles

A graph is \emph{$(\mathcal{I}, \mathcal{F})$-partitionable} if its vertex set can be partitioned into two parts such that one part $\mathcal{I}$ is an independent set, and the other $\mathcal{F}$ induces a forest. A graph is \emph{$k$-degenerate} if every subgraph $H$ contains a vertex of degree at most $k$ in $H$. Bernshteyn and Lee defined a generalization of $k$-degenerate graphs, which is called \emph{weakly $k$-degenerate}. In this paper, we show that planar graphs without $4$-, $7$-, $9$-cycles, and $5$-cycles normally adjacent to $3$-cycles are both $(\mathcal{I}, \mathcal{F})$-partitionable and weakly $2$-degenerate.

math.CO↗