arXiv subjects

Zixuan Zeng

Publications and source records attributed to Zixuan Zeng.

13 recordsLinked to original sources

arXiv2026

Inter-Reflective Gaussian Splatting for Robust and Efficient Inverse Rendering

Faithful inverse rendering requires visibility and indirect radiance to explain secondary illumination and inter-reflection, yet rasterization-oriented Gaussian representations do not naturally support the secondary-ray queries needed to recover them. We present IRGS++ (Inter-Reflective Gaussian Splatting), a unified robust and efficient Gaussian inverse rendering framework. During transport-aware optimization, IRGS++ employs differentiable 2D Gaussian ray tracing on surface-oriented Gaussian primitives to query visibility and indirect radiance on the fly and evaluate the full rendering equation for inter-reflective transport. This physical core makes Gaussian inverse rendering physically grounded beyond rasterized appearance modeling. To make this backbone useful beyond low-gloss dielectric scenes, the framework incorporates metallic-aware material modeling and robust reflective initialization for glossy, specular, and metallic materials. To make it practical, multiple importance sampling and denoising stabilize finite-sample rendering, while mesh-based secondary-attribute queries reduce the cost of relighting under novel illumination. Quantitative evaluations on low-gloss and glossy benchmarks show improved decomposition and relighting quality together with favorable quality--speed trade-offs under the reported configurations, while real-world studies illustrate plausible relighting under novel illumination.

cs.GR↗

arXiv2026

Capture velocities for direct loading of heavy molecules into conveyor-belt magneto-optical traps

Conveyor-belt magneto-optical traps (CB-MOTs) use blue-detuned polarization-gradient forces to provide simultaneous cooling, confinement, and loading on type-II molecular transitions. Recent experiments with \baf{138} showed that this mechanism can directly load a slowed molecular beam with an efficiency exceeding that of a conventional red-detuned MOT. Here we use established optical-Bloch-equation force calculations and classical trajectory propagation to ask whether this direct-loading strategy should extend beyond the specific molecule used in the first demonstration. For \baf{138}, the calculation reproduces the experimentally observed trend that the CB-MOT capture velocity increases with laser intensity. We then apply the same framework to two closely related but experimentally distinct cases: \baf{137}, whose dense hyperfine structure complicates a conventional dual-frequency MOT, and \bah{138}, whose narrower linewidth and longer wavelength reduce the available radiative force. In both cases, the CB-MOT retains a broad region of nonzero capture velocity. These results identify the molecular conditions under which direct CB-MOT loading should remain effective and show that the dipole-force-dominated conveyor-belt mechanism provides a practical loading route for heavy laser-coolable molecules whose MOT performance is otherwise limited by photon recoil, scattering rate, or hyperfine complexity.

physics.atom-ph↗

arXiv2026

Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign

Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiogram (BCG), and Bodyseismography (BSG), is fundamental for cardiovascular monitoring but is often hindered by artifacts and signal variability. Conventional algorithms are typically engineered with expert knowledge for a single signal modality, limiting their generalizability. Conversely, deep learning-based methods often lack interpretability, limiting transparency for expert verification and hindering expert-computer interaction. To address these limitations, we introduce Peak-Detector, a novel framework that leverages instruction-tuned Large Language Models (LLMs) for robust, cross-modal, and explainable peak detection. A core innovation of our framework is a "peak-representation" technique that transforms time-series data into a condensed format, preserving critical event information while significantly reducing signal length. This representation provides a crucial inductive bias, guiding the LLM to reason over physiologically meaningful events rather than raw, noisy data. The model is optimized through a two-stage process: supervised fine-tuning (SFT) followed by reinforcement learning (RL) with a multi-objective reward function. The model's self-explanation capabilities are cultivated by fine-tuning on a custom-built Peak-Explanation dataset. Across four modalities-ECG, PPG, BCG, and BSG-spanning seven datasets (six public benchmarks plus one real-world cohort), Peak-Detector demonstrates strong cross-modal performance, achieving best or tied-best detection under clinically relevant temporal tolerance. Beyond accuracy, the generated rationales surface failure modes and support verification and error analysis.

cs.LG↗

arXiv2025

Optical pumping and laser slowing of a heavy molecule

Precision measurements of the electron's electric dipole moment (eEDM) are critical for testing fundamental symmetries in particle physics, and heavy polar molecules-such as barium monofluoride (BaF)-have emerged as promising candidates for advancing the sensitivity. However, the achievement of a 3D magneto-optical trap (MOT) required slowing BaF molecules to near-zero velocity by scattering over 10^4 photons per molecule, demanding a quasi-cycling transition with minimal leakage. We present a detailed study of the leakage channels, including higher vibrational and rotational states. By combining microwave remixing with optical pumping of rotational and vibrational dark states, we reduced the total leakage fraction to 10^-5. Using frequency-chirped laser slowing, we slowed a subset of buffer-gas-cooled BaF molecules from approximately 80 m/s to near-zero velocity, which is critical for efficient MOT loading. This work establishes the technical foundation for precision eEDM measurements using laser-cooled heavy molecules.

physics.atom-ph↗

arXiv2025

Blue-detuned Magneto-optical Trap of BaF molecules

We report the realization of a blue-detuned magneto-optical trap (BDM) of BaF molecules. The (1 + 1) type BDM and (1 + 2) type conveyor-belt MOT are explored. While the (1+1) BDM provides only weak trapping force, the conveyor-belt MOT significantly compresses the molecular cloud, achieving a radius of 320(20) μm, a temperature of 240(60) μK, and a peak density of 1.3{\times}10^7 cm^{-3}, representing a significant improvement over the red MOT. Interestingly, the conveyor-belt MOT of BaF exhibits a large capture velocity, and the loading efficiency from red MOT reaches near unity even without gray molasses. We confirm this by directly loading slowed molecules into the conveyor-belt MOT.

physics.atom-ph↗

arXiv2025

IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing

In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate incident light, resulting in inaccurate material and lighting estimations. To this end, we introduce inter-reflective Gaussian splatting (IRGS) for inverse rendering. To capture inter-reflection, we apply the full rendering equation without simplification and compute incident radiance on the fly using the proposed differentiable 2D Gaussian ray tracing. Additionally, we present an efficient optimization scheme to handle the computational demands of Monte Carlo sampling for rendering equation evaluation. Furthermore, we introduce a novel strategy for querying the indirect radiance of incident light when relighting the optimized scenes. Extensive experiments on multiple standard benchmarks validate the effectiveness of IRGS, demonstrating its capability to accurately model complex inter-reflection effects.

cs.CV↗

arXiv2025

Reflective Gaussian Splatting

Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting (Ref-Gaussian) framework characterized with two components: (I) Physically based deferred rendering that empowers the rendering equation with pixel-level material properties via formulating split-sum approximation; (II) Gaussian-grounded inter-reflection that realizes the desired inter-reflection function within a Gaussian splatting paradigm for the first time. To enhance geometry modeling, we further introduce material-aware normal propagation and an initial per-Gaussian shading stage, along with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics, visual quality, and compute efficiency. Further, we show that our method serves as a unified solution for both reflective and non-reflective scenes, going beyond the previous alternatives focusing on only reflective scenes. Also, we illustrate that Ref-Gaussian supports more applications such as relighting and editing.

cs.CV↗

arXiv2024

Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods

Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization and Mapping (SLAM) systems. Utilizing datasets such as Replica and ScanNet, we assess performance based on tracking accuracy, mapping fidelity, and view synthesis. Findings reveal that NeRF excels in view synthesis, offering unique capabilities in generating new perspectives from existing data, albeit at slower processing speeds. Conversely, Gaussian-based methods provide rapid processing and significant expressiveness but lack comprehensive scene completion. Enhanced by global optimization and loop closure techniques, newer methods like NICE-SLAM and SplaTAM not only surpass older frameworks such as ORB-SLAM2 in terms of robustness but also demonstrate superior performance in dynamic and complex environments. This comparative analysis bridges theoretical research with practical implications, shedding light on future developments in robust 3D scene reconstruction across various real-world applications.

cs.CV↗

arXiv2024

A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner

This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep learning methods, which show superior EMI suppression by leveraging neural networks trained on MRI data. While deep learning improves EMI elimination and diagnostic capabilities, it introduces security and safety concerns, particularly in commercial applications. A balanced approach, integrating conventional reliability with deep learning's advanced capabilities, is proposed for more effective EMI suppression in MRI systems.

physics.med-ph↗

arXiv2024

Three-dimensional Magneto-optical Trapping of Barium Monofluoride

As a heavy molecule, barium monofluoride (BaF) presents itself as a promising candidate for measuring permanent electric dipole moment. The precision of such measurements can be significantly enhanced by utilizing a cold molecular sample. Here we report the realization of three-dimensional magneto-optical trapping (MOT) of BaF molecules. Through the repumping of all the vibrational states up to $v=3$, and rotational states up to $N=3$, we effectively close the transition to a leakage level lower than $10^{-5}$. This approach enables molecules to scatter a sufficient number of photons required for laser cooling and trapping. By employing a technique that involves chirping the slowing laser frequency, BaF molecules are decelerated to near-zero velocity, resulting in the capture of approximately $3\times 10^3$ molecules in a dual-frequency MOT setup. Our findings represent a significant step towards the realization of ultracold BaF molecules and the conduct of precision measurements with cold molecules.

physics.atom-ph↗

arXiv2022

Doppler cooling of buffer-gas-cooled Barium monofluoride molecules

We demonstrate one-dimensional Doppler cooling of a beam of buffer-gas cooled Barium monofluoride (BaF) molecules. The dependences of the cooling efficiency with the laser detuning, the bias filed and the laser intensity are carefully measured. We numerical simulate our experiment with a Monte Carlo method, and find the theoretic predictions consists with our experimental data. This result represents a key step towards further cooling and trapping of BaF molecules.

physics.atom-ph↗

arXiv2021

Isotope Separation of Potassium with a magneto-optical combined method

Due to the similar physical and chemical properties, isotopes are usually hard to separate. On the other hand, the isotope shifts are very well separated in a high-resolution spectrum, making them possible to be addressed individually by lasers, thus separated. Here we report such an isotope separation experiment with Potassium atoms. The isotopes are independently optical pumped to the desired spin states, and then separated with a Stern-Gerlach scheme. A micro-capillary oven is used to collimate the atomic beam, and a Halbach-type magnet array is used to deflect the desired atoms. Finally, the $^{40}$K is enriched by two orders of magnitude. This magneto-optical combined method provides an effective way to separate isotopes and can be extended to other elements if the relevant optical pumping scheme is feasible.

physics.atom-ph↗

arXiv2021

Argus: A Fully Transparent Incentive System for Anti-Piracy Campaigns (Extended Version)

Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-piracy incentive campaigns, but their effectiveness is publicly questioned due to the lack of transparency. We believe that full transparency of a campaign is necessary to truly incentivize people. It means that every role, e.g., content owner, licensee of the content, or every person in the open population, can understand the mechanism and be assured about its execution without trusting any single role. We see this as a distributed system problem. In this paper, we present Argus, a fully transparent incentive system for anti-piracy campaigns. The groundwork of Argus is to formulate the objectives for fully transparent incentive mechanisms, which securely and comprehensively consolidate the different interests of all roles. These objectives form the core of the Argus design, highlighted by our innovations about a Sybil-proof incentive function, a commit-and-reveal scheme, and an oblivious transfer scheme. In the implementation, we overcome a set of unavoidable obstacles to ensure security despite full transparency. Moreover, we effectively optimize several cryptographic operations so that the cost for a piracy reporting is reduced to an equivalent cost of sending about 14 ETH-transfer transactions to run on the public Ethereum network, which would otherwise correspond to thousands of transactions. With the security and practicality of Argus, we hope real-world anti-piracy campaigns will be truly effective by shifting to a fully transparent incentive mechanism.

cs.CR↗