arXiv subjects

Tong Luo

Publications and source records attributed to Tong Luo.

8 recordsLinked to original sources

arXiv2026

Heterogeneous-Gradient Phase--Polarization Alignment and Maximal-Ratio Weight Allocation for Multi-Aperture Coherent FSO Reception

Multi-aperture coherent reception can improve freespace optical (FSO) links by converting spatial diversity into coherent combining gain. In turbulent links, the aperture branches are simultaneously affected by relative phase errors, polarization mismatch, and unequal signal-to-noise ratios (SNRs). Existing methods treat phase/polarization alignment and branch-weight allocation as separate operations, or absorb all impairments into a high-dimensional MIMO equalizer that obscures the physical meaning of each aperture's contribution. This paper proposes a structured blind combining method based on heterogeneous gradient sources: phase and per-aperture polarization parameters are updated by closed-form analytical gradients that maximize the combined output power, while aperture weights and an optional global polarization angle are updated by gradients derived from the constellation-radius error. An exponential parameterization pn = eqn/N ensures positivity without clipping. The internal variable qn is adapted by radius-error gradients, thereby allocating maximal-ratio-combining-like weights according to the quality of the already aligned branches.

physics.optics↗

arXiv2026

Blind Gradient-Ascent Phase Alignment for Multi-Aperture Coherent Digital Combining Under Aperture-Dependent Phase Disturbance

Multi-aperture reception can provide spatial diversity in free-space optical (FSO) communication by collecting signal replicas at separate apertures. When the branches are accurately phase-aligned, their received optical fields can also be added constructively to obtain coherent-combining gain. In this paper, we propose blind gradient-ascent phase alignment (BGAPA), which iteratively adjusts one phase correction per aperture by directly maximizing the combined output power. Closed-form analytical gradients provide a deterministic update that requires no symbol decisions, unlike the stochastic perturbation-based estimate of SPGD or the decision-directed feedback of DD-LMS. To isolate phase-tracking capability, the numerical model includes independent aperture-dependent phase disturbance but excludes amplitude scintillation and polarization-dependent distortion. Under this controlled phase-only setting, BGAPA obtains an SNR improvement closer to the ideal 6.02~dB coherent-combining gain than block-wise cross-correlation, SPGD, DD-LMS, and CMA/RDE-based equalization when the aperture count is increased by a factor of four. In particular, increasing the aperture count from 64 to 256 yields an SNR improvement of about 5.7~dB. In a separate amplitude-tolerance test with $N=16$ and $f_{\max}=1$~MHz, the first observed BGAPA trial above the HD-FEC threshold of $3.8\times10^{-3}$ occurs at an actual phase RMS of approximately 278~rad, whereas DD-LMS becomes unreliable at substantially smaller phase excursions. The reported step size is optimized separately at each operating point. BGAPA is fully blind and updates its phase parameters directly from the received aperture fields without training symbols, pilots, or decision-directed feedback.

physics.optics↗

arXiv2026

Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes

Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time data streams.Existing sequential approaches capture dependencies among event tokens but ignore the continuous evolution between events, while Neural Ordinary Differential Equation (Neural ODE) methods model smooth dynamics yet fail to account for how event types influence future timing.To overcome these limitations, we propose NEXTPP, a dual-channel framework that unifies discrete and continuous representations via Event-granular Neural Evolution with Cross-Interaction for Marked Temporal Point Processes. Specifically, NEXTPP encodes discrete event marks via a self-attention mechanism, simultaneously evolving a latent continuous-time state using a Neural ODE. These parallel streams are then fused through a crossattention module to enable explicit bidirectional interaction between continuous and discrete representations. The fused representations drive the conditional intensity function of the neural Hawkes process, while an iterative thinning sampler is employed to generate future events. Extensive evaluations on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models. The source code can be found at https://github.com/AONE-NLP/NEXTPP.

cs.LG↗

arXiv2025

3rd Place Solution to Large-scale Fine-grained Food Recognition

Food analysis is becoming a hot topic in health area, in which fine-grained food recognition task plays an important role. In this paper, we describe the details of our solution to the LargeFineFoodAI-ICCV Workshop-Recognition challenge held on Kaggle. We find a proper combination of Arcface loss[1] and Circle loss[9] can bring improvement to the performance. With Arcface and the combined loss, model was trained with carefully tuned configurations and ensembled to get the final results. Our solution won the 3rd place in the competition.

cs.CV↗

arXiv2024

Analytical computation of bifurcation of orbits near collinear libration point in the restricted three-body problem

A unified analytical solution is presented for constructing the phase space near collinear libration points in the Circular Restricted Three-body Problem (CRTBP), encompassing Lissajous orbits and quasihalo orbits, their invariant manifolds, as well as transit and non-transit orbits. Traditional methods could only derive separate analytical solutions for the invariant manifolds of Lissajous orbits and halo orbits, falling short for the invariant manifolds of quasihalo orbits. By introducing a coupling coefficient {\eta} and a bifurcation equation, a unified series solution for these orbits is systematically developed using a coupling-induced bifurcation mechanism and Lindstedt-Poincar\'e method. Analyzing the third-order bifurcation equation reveals bifurcation conditions for halo orbits, quasihalo orbits, and their invariant manifolds. Furthermore, new families of periodic orbits similar to halo orbits are discovered, and non-periodic/quasi-periodic orbits, such as transit orbits and non-transit orbits, are found to undergo bifurcations. When {\eta} = 0, the series solution describes Lissajous orbits and their invariant manifolds, transit, and non-transit orbits. As {\eta} varies from zero to non-zero values, the solution seamlessly transitions to describe quasihalo orbits and their invariant manifolds, as well as newly bifurcated transit and non-transit orbits. This unified analytical framework provides a more comprehensive understanding of the complex phase space structures near collinear libration points in the CRTBP.

math-ph↗

arXiv2023

V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network

Object detection is the central issue of intelligent traffic systems, and recent advancements in single-vehicle lidar-based 3D detection indicate that it can provide accurate position information for intelligent agents to make decisions and plan. Compared with single-vehicle perception, multi-view vehicle-road cooperation perception has fundamental advantages, such as the elimination of blind spots and a broader range of perception, and has become a research hotspot. However, the current perception of cooperation focuses on improving the complexity of fusion while ignoring the fundamental problems caused by the absence of single-view outlines. We propose a multi-view vehicle-road cooperation perception system, vehicle-to-everything cooperative perception (V2X-AHD), in order to enhance the identification capability, particularly for predicting the vehicle's shape. At first, we propose an asymmetric heterogeneous distillation network fed with different training data to improve the accuracy of contour recognition, with multi-view teacher features transferring to single-view student features. While the point cloud data are sparse, we propose Spara Pillar, a spare convolutional-based plug-in feature extraction backbone, to reduce the number of parameters and improve and enhance feature extraction capabilities. Moreover, we leverage the multi-head self-attention (MSA) to fuse the single-view feature, and the lightweight design makes the fusion feature a smooth expression. The results of applying our algorithm to the massive open dataset V2Xset demonstrate that our method achieves the state-of-the-art result. The V2X-AHD can effectively improve the accuracy of 3D object detection and reduce the number of network parameters, according to this study, which serves as a benchmark for cooperative perception. The code for this article is available at https://github.com/feeling0414-lab/V2X-AHD.

cs.AI↗

arXiv2023

Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google tagging API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision.

cs.CV↗

arXiv2022

Quantum algorithm for evaluating operator size with Bell measurements

Operator size growth describes the scrambling of operators in quantum dynamics and stands out as an essential physical concept for characterizing quantum chaos. Important as it is, a scheme for direct measuring operator size on a quantum computer is still absent. Here, we propose a quantum algorithm for direct measuring the operator size and its distribution based on Bell measurement. The algorithm is verified with spin chains and meanwhile, the effects of Trotterization error and quantum noise are analyzed. It is revealed that saturation of operator size growth can be due to quantum chaos itself or be a consequence of quantum noises, which make a distinction between quantum integrable and chaotic systems difficulty on noisy quantum processors. Nevertheless, it is found that the error mitigation will effectively reduce the influence of noise, so as to restore the distinguishability of quantum chaotic systems. Our work provides a feasible protocol for investigating quantum chaos on noisy quantum computers by measuring operator size growth.

quant-ph↗