arXiv subjects

Yan Cao

Publications and source records attributed to Yan Cao.

At least 19 recordsLinked to original sources

arXiv2026

From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning

Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally handle visuo-linguistic inputs, they are often computationally intensive and opaque. We demonstrate that a pure Large Language Model (LLM), when equipped with specialized modules, can rival state-of-the-art LMMs on complex geometry problems. Our framework integrates a Geometric Vision Parser, which translates diagrams into symbolic form, with a Symbolic Solver that performs formal deductions, thereby mitigating hallucinations and promoting interpretable reasoning. To enable rigorous evaluation, we curate a benchmark of challenging problems from the 2025 Chinese Zhongkao examinations, ensuring data novelty and testing deeper deductive skills. Experiments demonstrate that our approach achieves performance comparable to Gemini 2.5 Pro while delivering clearer, human-like solutions.

cs.AI↗

arXiv2026

Phonon heat transport with squeezing-based symmetry breaking

The controllability of phonon thermal transport is fundamental for numerous technologies, from cooling high-performance chips to managing heat in quantum computing. Despite extensive efforts, on-demand, real-time control of phonon heat flow remains elusive, being fundamentally constrained by the temperature gradient. Here we achieve this long-sought phonon heat transport by introducing a novel mechanism using quantum squeezing in a cavity optomechanical system. We find that phonon squeezing of near-ground-state mechanical resonators via an optomechanically induced parametric process breaks the continuous U(1) rotation symmetry in phase space, and consequently the symmetry in the heat current structure. We reveal that heat flow under a constant temperature gradient can be deterministically amplified, reduced, or even reversed, a capability previously unattainable. With phonon squeezing, we achieve over twentyfold amplification of heat flow and reversal within 30 milliseconds. Importantly, quantum discord analysis reveals a direct connection between heat flow reversal and quantum correlation. Our results establish quantum squeezing as a versatile platform for on-demand phononic thermal control, opening avenues for active heat management in quantum devices and thermal logic circuits.

physics.optics↗

arXiv2026

Constraining Ultralight Scalar Dark Matter in the Galactic Center with the S2 Orbit

The dense environment of our Galactic Center (GC) offers a unique laboratory for probing ultralight dark matter (ULDM). We explore the prospect of detecting a scalar ULDM field through its effects on the orbital dynamics of S-stars around the supermassive black hole in the GC, Sgr A$^*$. We consider both linear and quadratic couplings between the real scalar field $\phi$ and Standard Model particles, and analyze two representative ULDM structures: the scalar gravitational atom and the spherical soliton. We first find that quadratic coupling induces a non-oscillatory perturbation, leading to a long-term secular orbital evolution. We use the observed periastron precession rate of S2 star to put stringent constraints on the total ULDM mass in the GC and the quadratic coupling constant. For the gravitational atom $|211\rangle$ state, assuming an equatorial S2 orbit, we constrain the mass ratio of ULDM to Sgr A$^*$ to $\beta \lesssim 10^{-3}$ at $m \sim 10^{-18}$ eV, and for the spherical soliton which extends to $\sim 0.2\,$pc, the mass ratio is limited to $\beta \lesssim 1$ at $m \sim 3\times10^{-20}$ eV. Notably, over part of the mass range $10^{-20}\,\mathrm{eV}\lesssim m\lesssim10^{-18}\,\mathrm{eV}$, the S2 constraints are stronger than the corresponding bounds from Cassini and MICROSCOPE.

hep-ph↗

arXiv2026

Probing soft signals of gravitational-wave memory with space-based interferometers

Gravitational-wave displacement memory is a remarkable and ubiquitous phenomenon predicted by general relativity, which has not yet been detected. Unlike the oscillatory components of gravitational waveforms, displacement memory is associated with soft gravitons, making it the only observable signal of its parent event at sufficiently low frequencies. Similarly, soft waveforms may arise from velocity and integrated-displacement memory. The simple and universal spectral shapes of soft waveforms also provide effective templates for matched filtering and parameter estimation. In this paper, we investigate the detection prospects for such soft memory signals with future space-based laser interferometers. As realistic examples, we examine the infrared spectral features of gravitational waves from moderately relativistic compact binary scattering and nearly equal-mass quasi-circular, non-precessing black hole mergers. In both cases, the low frequency spectrum can be described by a corrected soft waveform of displacement memory. The results of simulated Bayesian parameter estimation demonstrate that independent measurement of a soft displacement-memory signal with a single LISA-like detector is achievable at signal-to-noise ratios $\gtrsim 10$. The measurement precision can be significantly improved by joint observations with a LISA-Taiji network. A single BBO detector could be capable of separately measuring the null memory from stellar-mass compact binary mergers. We also evaluate the detectability of an idealized stochastic background of soft displacement-memory signals. Our results indicate that gravitational-wave bursts with memory can be promising targets for space-based interferometers.

gr-qc↗

arXiv2026

Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.

cs.CV↗

arXiv2025

Short Brooms in Edge-chromatic Critical Graphs

This paper studies short brooms in edge-chromatic critical graphs. We prove that for any short broom in a $\Delta$-critical graph, at most one color is missing at more than one vertex. Moreover, this color (if exists) is missing at exactly two vertices. Applying this result, we verify the Vertex-splitting Conjecture for graphs with $\Delta \geq 2(n-1)/3$ and the Overfull Conjecture for $\Delta$-critical graphs satisfying $\Delta \geq (2n+5\delta-12)/3$.

math.CO↗

arXiv2025

Detecting ultralight dark matter in the Galactic Center with pulsars around Sgr A*

Ultralight dark matter (ULDM) model is a leading dark matter candidate that arises naturally in extensions of the Standard Model. In the Galactic Center, ULDM manifests as dense hydrogen-like boson clouds or self-gravitating soliton cores. We present the first study of the gravitational effects of these ULDM structures on pulsar orbits around Sgr A*, using pulsar timing as a precision dynamical probe, based on a comprehensive and practical framework that includes various kinds of black hole and orbital parameters. Our analysis shows that long-term pulsar monitoring -- one of the key objectives of future SKA science -- could detect a boson cloud with a total mass as low as $O(M_\odot)$ for boson mass $m \sim 10^{-18}\,\mathrm{eV}$, and probe a wide range of soliton core masses in the lower-mass regime, assuming a conservative timing precision of $\sigma_{\mathrm{TOA}}=1\,\mathrm{ms}$.

astro-ph.HE↗

arXiv2025

Enhancing WiFi CSI Fingerprinting: A Deep Auxiliary Learning Approach

Radio frequency (RF) fingerprinting techniques provide a promising supplement to cryptography-based approaches but rely on dedicated equipment to capture in-phase and quadrature (IQ) samples, hindering their wide adoption. Recent advances advocate easily obtainable channel state information (CSI) by commercial WiFi devices for lightweight RF fingerprinting, while falling short in addressing the challenges of coarse granularity of CSI measurements in an open-world setting. In this paper, we propose CSI2Q, a novel CSI fingerprinting system that achieves comparable performance to IQ-based approaches. Instead of extracting fingerprints directly from raw CSI measurements, CSI2Q first transforms frequency-domain CSI measurements into time-domain signals that share the same feature space with IQ samples. Then, we employ a deep auxiliary learning strategy to transfer useful knowledge from an IQ fingerprinting model to the CSI counterpart. Finally, the trained CSI model is combined with an OpenMax function to estimate the likelihood of unknown ones. We evaluate CSI2Q on one synthetic CSI dataset involving 85 devices and two real CSI datasets, including 10 and 25 WiFi routers, respectively. Our system achieves accuracy increases of at least 16% on the synthetic CSI dataset, 20% on the in-lab CSI dataset, and 17% on the in-the-wild CSI dataset.

eess.SP↗

arXiv2025

Effects of dark dipole radiation on eccentric supermassive black hole binary inspirals

The final-parsec problem has long posed a central challenge in understanding the merger of supermassive black hole binaries. In this paper, we investigate a scenario in which a dark scalar or vector field is sourced by eccentric binaries, leading to accelerated mergers through additional dipole radiation, and thereby extending the range of masses for which the binary can merge within a Hubble time. The Newtonian-order radiation fluxes from an eccentric charged Keplerian binary are derived using general results for localized periodic sources in flat spacetime. We find that dipole radiation, although insufficient to fully resolve the final-parsec problem, can alter the low-frequency spectrum of the stochastic gravitational-wave background from supermassive black hole binary inspirals. We construct a simplified model for the spectrum and perform a Bayesian analysis using the current pulsar timing array data.

gr-qc↗

arXiv2025

Nakayama automorphisms of graded double Ore extensions of Koszul Artin-Schelter regular algebras with nontrivial skew derivations

Let $A$ be a Koszul Artin-Schelter regular algebra and $B=A_P[y_1,y_2;\varsigma,\nu]$ be a graded double Ore extension of $A$ where $\varsigma:A\to M_{2\times 2}(A)$ is a graded algebra homomorphism and $\nu:A\to A^{\oplus 2}$ is a degree one $\varsigma$-derivation. We construct a minimal free resolution for the trivial module of $B$, and it implies that $B$ is still Koszul. We introduce a homological invariant called $\varsigma$-divergence of $\nu$, and with its aid, we obtain a precise description of the Nakayama automorphism of $B$. A twisted superpotential $\hat{\omega}$ for $B$ with respect to the Nakayama automorphism is constructed so that $B$ is isomorphic to the derivation quotient algebra of $\hat{\omega}$.

math.RA↗

arXiv2025

MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction

Three-dimensional (3D) ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multi-branch structure that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multi-level consistency constraint that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits both scan-level velocity consistency, path-level appearance consistency, and patch-level motion consistency to supervise inter-frame transformation estimation. Third, we distill an online multi-modal self-supervised strategy that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.

eess.IV↗

arXiv2025

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: (1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. (2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents' erasing process precisely, thereby avoiding both under- and over-segmentation. (3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms.

cs.CV↗

arXiv2025

RADx Data Hub: A Cloud Platform for FAIR, Harmonized COVID-19 Data

The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address these challenges, the U.S. National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative in 2020, with the RADx Data Hub, a centralized repository for de-identified and curated COVID-19 data, as its cornerstone. The RADx Data Hub hosts diverse study data, including clinical data, testing results, smart sensor outputs, self-reported symptoms, and information on social determinants of health. Built on cloud infrastructure, the RADx Data Hub integrates metadata standards, interoperable formats, and ontology-based tools to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data sharing. Initially developed for COVID-19 research, its architecture and processes are adaptable to other scientific disciplines. This paper provides an overview of the data hosted by the RADx Data Hub and describes the platform's capabilities and architecture.

cs.DB↗

arXiv2024

Optomechanical dark-mode-breaking cooling

Optomechanical cooling of multiple degenerate mechanical modes is prevented by the mechanical dark mode due to destructive interference. Here we report the first experimental demonstration of simultaneous cooling of two near-degenerate mechanical modes by breaking the mechanical dark mode in a two-membrane cavity optomechanical system. The dark mode is generated as the system passes the exceptional point of the antiparity-time symmetric scheme. By introducing a second cavity mode for the additional dissipative channel, the dark mode is broken and the total phonon number is reduced by more than an order of magnitude below the dark mode cooling limit. Owing to the flexible tunability of the optomechanical coupling rates of such a four-mode coupled system, the optimized cooling efficiency can be achieved by investigating different parameter ranges. Our results provide an important step towards the ground state cooling and entanglement among multiple degenerate mechanical resonators.

quant-ph↗

arXiv2024

Probing vector gravitational atoms with eccentric intermediate mass-ratio inspirals

Ultralight bosons, proposed as candidates for dark matter (DM), are predicted by various new physics models. In the presence of bosons with suitable masses, superradiant (SR) instability can naturally transform a spinning black hole (BH) into a gravitational atom (GA). Here we study the dynamics of intermediate mass-ratio inspirals (IMRIs) around a GA formed by ultralight vector field saturated in its SR ground state. We employ a perturbative model at the leading Newtonian order to consistently account for both the conservative effect of cloud gravity and the dissipative effect of cloud ionization. We find the cloud can make a sizable negative contribution to the secular periastron precession at binary separations comparable to the gravitational Bohr radius. Meanwhile, the backreaction of ionization could significantly accelerate the process of orbital decay and circularization. Considering reasonably small vector boson masses, we examine the adiabatic orbital evolution and gravitational waveforms of eccentric inspirals. The results indicate that vector GAs might be detectable through observations of low-frequency IMRIs by the future space-based gravitational-wave detectors, such as LISA and Taiji.

gr-qc↗

arXiv2024

Effects of black hole environments on extreme mass-ratio hyperbolic encounters

Extreme mass-ratio hyperbolic encounters (EMRHEs) around the supermassive black holes will be observable at the future gravitational-wave (GW) detectors in space, such as LISA and Taiji. Here we consider such EMRHEs in the presence of surrounding matter distribution including baryonic accretion disk and dark matter (DM) spike, and estimate their effects on the orbital evolution and GW waveforms. We find that large possible impacts come from the gravitational potential of accretion disk, while the influence of DM spike is small. We also illustrate that environments can leave distinctive imprints on the GW waveforms, but resolving such modifications is found to be challenging for LISA-like detectors in the near future.

gr-qc↗

arXiv2024

A note on the planar triangles in Minkowski spacetime

The geometry of 2D Minkowski spacetime $\mathbb{R}^{1,1}$ (or Minkowski plane) is similar but fundamentally different from the more familiar Euclidean plane geometry. This note gives an elementary discussion on some basic properties of a triangle on the Minkowski plane. In particular, we show that the theorem of Feuerbach also holds and a use of the incenter/excenters is pointed out.

physics.class-ph↗

arXiv2024

Robust Box Prompt based SAM for Medical Image Segmentation

The Segment Anything Model (SAM) can achieve satisfactory segmentation performance under high-quality box prompts. However, SAM's robustness is compromised by the decline in box quality, limiting its practicality in clinical reality. In this study, we propose a novel Robust Box prompt based SAM (\textbf{RoBox-SAM}) to ensure SAM's segmentation performance under prompts with different qualities. Our contribution is three-fold. First, we propose a prompt refinement module to implicitly perceive the potential targets, and output the offsets to directly transform the low-quality box prompt into a high-quality one. We then provide an online iterative strategy for further prompt refinement. Second, we introduce a prompt enhancement module to automatically generate point prompts to assist the box-promptable segmentation effectively. Last, we build a self-information extractor to encode the prior information from the input image. These features can optimize the image embeddings and attention calculation, thus, the robustness of SAM can be further enhanced. Extensive experiments on the large medical segmentation dataset including 99,299 images, 5 modalities, and 25 organs/targets validated the efficacy of our proposed RoBox-SAM.

cs.CV↗