arXiv subjects

Yu Gao

Publications and source records attributed to Yu Gao.

At least 91 records · Page 5Linked to original sources

arXiv2025

360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results

Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditional methods, such as compression and adaptive resolution, help but often compromise video quality and introduce artifacts that degrade the viewer experience. Additionally, the unique spherical geometry of 360-degree video presents challenges not encountered in traditional 2D video. To address these issues, we initiated the 360-degree Video Super Resolution and Quality Enhancement Challenge. This competition encourages participants to develop efficient machine learning solutions to enhance the quality of low-bitrate compressed 360-degree videos, with two tracks focusing on 2x and 4x super-resolution (SR). In this paper, we outline the challenge framework, detailing the two competition tracks and highlighting the SR solutions proposed by the top-performing models. We assess these models within a unified framework, considering quality enhancement, bitrate gain, and computational efficiency. This challenge aims to drive innovation in real-time 360-degree video streaming, improving the quality and accessibility of immersive visual experiences.

eess.IV↗

arXiv2025

Investigating Primordial Black Hole Accretion through Cosmic Optical Depth

Primordial black holes (PBH) accretion in the late Universe can lead to significant mass growth. A larger mass further accelerates the accretion radiation output for PBHs with initial masses greater than one solar mass, potentially leading to a stringent energy-dumping constraint derived from observations of the cosmic microwave background. The energy injected via PBH accretion is capable of ionizing and heating the intergalactic medium (IGM), ultimately affecting the optical depth of cosmic reionization and the 21-cm signal. This work investigates primordial black hole mass growth using the Bondi-Hoyle accretion model and accounts for additional ionization and heating induced by PBHs. We derive stringent PBH abundance limits using an upper limit on optical depth set by Planck 2018 CMB measurements. We find that accretion growth significantly strengthens late-time observational constraints for primordial black holes with initial masses ranging from several solar masses up to $10^4$ solar masses. The PBH fraction of the Universe's unobserved mass content can be constrained to $f_{\mathrm{PBH, ini}}\sim 10^{-2}$ to $10^{-7}$ in this mass range, and when accounting for mass evolution our constraints can be strengthened by up to one order of magnitude. In addition, we show that PBH mass growth will lead to an observable impact on the predicted hydrogen 21-cm brightness temperature.

astro-ph.CO↗

arXiv2025

Accuracy and Security-Guaranteed Participant Selection and Beamforming Design for RIS-Assisted Federated Learning

Federated learning (FL) has emerged as an effective approach for training neural network models without requiring the sharing of participants' raw data, thereby addressing data privacy concerns. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted FL framework in the presence of eavesdropping, where partial edge devices are selected to participate in the FL training process. In contrast, the remaining devices serve as cooperative jammers by transmitting jamming signals to disrupt eavesdropping. We aim to minimize the training latency in each FL round by jointly optimizing participant selection, bandwidth allocation, and RIS beamforming design, subject to the convergence accuracy of FL and the secure uploading requirements. To solve the resulting mixed-integer nonlinear programming problem, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm. Simulation results demonstrate that the proposed scheme reduces the FL training latency by approximately 27$\%$ compared to baselines.

cs.IT↗

arXiv2025

Seedream 3.0 Technical Report

We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in Seedream 2.0, including alignment with complicated prompts, fine-grained typography generation, suboptimal visual aesthetics and fidelity, and limited image resolutions. Specifically, the advancements of Seedream 3.0 stem from improvements across the entire pipeline, from data construction to model deployment. At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework. Furthermore, we adopt several effective techniques such as mixed-resolution training, cross-modality RoPE, representation alignment loss, and resolution-aware timestep sampling in the pre-training phase. During the post-training stage, we utilize diversified aesthetic captions in SFT, and a VLM-based reward model with scaling, thereby achieving outputs that well align with human preferences. Furthermore, Seedream 3.0 pioneers a novel acceleration paradigm. By employing consistent noise expectation and importance-aware timestep sampling, we achieve a 4 to 8 times speedup while maintaining image quality. Seedream 3.0 demonstrates significant improvements over Seedream 2.0: it enhances overall capabilities, in particular for text-rendering in complicated Chinese characters which is important to professional typography generation. In addition, it provides native high-resolution output (up to 2K), allowing it to generate images with high visual quality.

cs.CV↗

arXiv2025

Seedance 1.0: Exploring the Boundaries of Video Generation Models

Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation.

cs.CV↗

arXiv2025

The MALATANG survey: Dense gas distribution on sub-kiloparsec scales across the disk of M82

We present observations of HCN J=4-3 and HCO^+ J=4-3 lines obtained with the James Clerk Maxwell Telescope as part of the MALATANG survey, combined with archival HCN J=1-0 and HCO^+ J=1-0 data from the Green Bank Telescope, to study the spatial distribution and excitation conditions of dense molecular gas in the disk of M82. We detect HCN J=4-3 and HCO^+ J=4-3 emission within the central region (< 500 pc) of the galaxy, while the J=1-0 emission lines exhibit a more extended spatial distribution (> 700 pc). The dense gas shows a clear double-lobed structure in both spatial distribution and kinematics, with the HCN and HCO^+ J=4-3 lines in the southwest lobe blueshifted by ~ 40 km/s relative to the J=1-0 lines. The HCN J=4-3/1-0 and HCO^+ J=4-3/1-0 line-luminosity ratios range from 0.09 to 0.53 and from 0.14 to 0.87, respectively, with mean values of 0.18 +/- 0.04 and 0.36 +/- 0.06. The HCN ratio is lower than the typical average observed in nearby star-forming galaxies, whereas the HCO^+ ratio is comparatively higher, suggesting that the high-J HCN emission in M82 is significantly sub-thermally excited. Spatially, the peak values of the J=4-3/1-0 ratios are found in the northwest region of M82, coinciding with the galaxy-scale outflow. Elevated HCN/HCO^+ ratios are also detected in roughly the same area, potentially tracing local excitation enhancements driven by the outflow. The HCN/HCO^+ J=4-3 ratio across all detected regions ranges from 0.19 to 1.07 with a mean value of 0.41 +/- 0.11, which is significantly lower than the average J=1-0 ratio of 0.76 +/- 0.08. Both ratios are significantly lower than the average values observed in nearby star-forming galaxies, which could be related to the relatively low gas density and the presence of an extended photo-dissociation region in M82.

astro-ph.GA↗

arXiv2025

Experimental realization of the bucket-brigade quantum random access memory

Quantum random access memory (QRAM) enables efficient classical data access for quantum computers -- a prerequisite for many quantum algorithms to achieve quantum speedup. Despite various proposals, the experimental realization of QRAM remains largely unexplored. Here, we experimentally investigate the circuit-based bucket-brigade QRAM with a superconducting quantum processor. To facilitate the experimental implementation, we introduce a hardware-efficient gate decomposition scheme for quantum routers, which effectively reduces the depth of the QRAM circuit by more than 30% compared to the conventional controlled-SWAP-based implementation. We further propose an error mitigation method to boost the QRAM query fidelity. With these techniques, we are able to experimentally implement the QRAM architectures with two and three layers, achieving query fidelities up to 0.800 $\pm$ 0.026 and 0.604$\pm$0.005, respectively. Additionally, we study the error propagation mechanism and the scalability of our QRAM implementation, providing experimental evidence for the noise resilience nature of the bucket-brigade QRAM architecture. Our results highlight the potential of superconducting quantum processors for realizing a scalable QRAM architecture.

quant-ph↗

arXiv2025

DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving

Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we propose a novel hybrid sparse-dense diffusion policy, empowered by a Vision-Language Model (VLM), called Diff-VLA. We explore the sparse diffusion representation for efficient multi-modal driving behavior. Moreover, we rethink the effectiveness of VLM driving decision and improve the trajectory generation guidance through deep interaction across agent, map instances and VLM output. Our method shows superior performance in Autonomous Grand Challenge 2025 which contains challenging real and reactive synthetic scenarios. Our methods achieves 45.0 PDMS.

cs.AI↗

arXiv2025

Demonstration of low-overhead quantum error correction codes

Quantum computers hold the potential to surpass classical computers in solving complex computational problems. However, the fragility of quantum information and the error-prone nature of quantum operations make building large-scale, fault-tolerant quantum computers a prominent challenge. To combat errors, pioneering experiments have demonstrated a variety of quantum error correction codes. Yet, most of these codes suffer from low encoding efficiency, and their scalability is hindered by prohibitively high resource overheads. Here, we report the demonstration of two low-overhead quantum low-density parity-check (qLDPC) codes, a distance-4 bivariate bicycle code and a distance-3 qLDPC code, on our latest superconducting processor, Kunlun, featuring 32 long-range-coupled transmon qubits. Utilizing a two-dimensional architecture with overlapping long-range couplers, we demonstrate simultaneous measurements of all nonlocal weight-6 stabilizers via the periodic execution of an efficient syndrome extraction circuit. We achieve a logical error rate per logical qubit per cycle of $(8.91 \pm 0.17)\%$ for the distance-4 bivariate bicycle code with four logical qubits and $(7.77 \pm 0.12)\%$ for the distance-3 qLDPC code with six logical qubits. Our results establish the feasibility of implementing various qLDPC codes with long-range coupled superconducting processors, marking a crucial step towards large-scale low-overhead quantum error correction.

quant-ph↗

arXiv2025

SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction

Recent advancements in high-definition \emph{HD} map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird's-eye view \emph{BEV} features. While sparse representations offer a more efficient alternative by avoiding dense BEV processing, existing methods often lag behind due to the lack of tailored designs. These limitations have hindered the competitiveness of sparse representations in online HD map construction. In this work, we systematically revisit and enhance sparse representation techniques, identifying key architectural and algorithmic improvements that bridge the gap with--and ultimately surpass--dense approaches. We introduce a dedicated network architecture optimized for sparse map feature extraction, a sparse-dense segmentation auxiliary task to better leverage geometric and semantic cues, and a denoising module guided by physical priors to refine predictions. Through these enhancements, our method achieves state-of-the-art performance on the nuScenes dataset, significantly advancing HD map construction and centerline detection. Specifically, SparseMeXt-Tiny reaches a mean average precision \emph{mAP} of 55.5% at 32 frames per second \emph{fps}, while SparseMeXt-Base attains 65.2% mAP. Scaling the backbone and decoder further, SparseMeXt-Large achieves an mAP of 68.9% at over 20 fps, establishing a new benchmark for sparse representations in HD map construction. These results underscore the untapped potential of sparse methods, challenging the conventional reliance on dense representations and redefining efficiency-performance trade-offs in the field.

cs.CV↗

arXiv2025

Charting the Uncharted: The Landscape of Monero Peer-to-Peer Network

The Monero blockchain enables anonymous transactions through advanced cryptography in its peer-to-peer network, which underpins decentralization, security, and trustless interactions. However, privacy measures obscure peer connections, complicating network analysis. This study proposes a method to infer peer connections in Monero's latest protocol version, where timestamp data is unavailable. We collect peerlist data from TCP flows, validate our inference algorithm, and map the network structure. Our results show high accuracy, improving with longer observation periods. This work is the first to reveal connectivity patterns in Monero's updated protocol, providing visualizations and insights into its topology. Our findings enhance the understanding of Monero's P2P network, including the role of supernodes, and highlight potential protocol and security improvements.

cs.DC↗

arXiv2025

Monero Peer-to-peer Network Topology Analysis

Monero, a privacy-focused cryptocurrency, employs a decentralized peer-to-peer (P2P) network that plays a critical role in transaction propagation and consensus formation. While much research has explored Monero's privacy transaction mechanisms, its underlying P2P network architecture has remained relatively underexplored. In this study, building on our recent work on Monero network detection, we further investigate the network topology of Monero's P2P structure, which has evolved following recent protocol updates that enhanced security by obscuring peer information. Using k-core decomposition, we confirm that the Monero network exhibits a core-periphery structure, where a tightly interconnected core of supernodes is crucial for maintaining network cohesion, while peripheral nodes rely on these core nodes for connectivity. This structure explains why targeting central nodes does not easily lead to the rapid disintegration of the network's largest connected component while also providing a deeper understanding of the true architecture of Monero's peer protocol.

cs.NI↗

arXiv2025

Limiting on ALP-photon Coupling through GRB221009A

This study investigates the constraints on ALPs parameters through the photon-ALP oscillation model, based on the high-energy photon propagation characteristics of the gamma-ray burst GRB 221009A. We briefly describe the Primakoff process and use it to derive the oscillation probability. Numerical simulations incorporate the GMF, IGMF, and EBL, utilizing the open-source code gammaALPs to simulate photon propagation from the source to Earth. By employing the chi2 statistical method and segmented spectral fitting of low-energy and high-energy observational data from HXMT-GECAM, the dependence of photon survival probability on ALP mass (ma) and coupling strength (ga) is analyzed. Results show that for the ALP mass range 10^-7 < ma < 10^2 neV, the upper limit on the coupling strength is ga < 0.27 * 10^-11 GeV^-1, improving constraints by one order of magnitude compared to the CAST experiment (6.6 * 10^-11 GeV^-1). Notably, high-energy data exhibit significantly stronger constraining power. This work provides a novel theoretical framework and observational basis for indirectly probing ALPs through extreme astrophysical phenomena.

astro-ph.HE↗

arXiv2025

CIDR interpolation: an enhanced SSA-based temporal filling framework for restoring continuity in downscaled GRACE(-FO) TWSA products

Global total water storage anomaly (TWSA) products derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) are critical for hydrological research and water resource management. However, persistent data gaps hinder their application in long-term water cycle monitoring. We propose a Correlation-based Iterative and Decompose-Restore (CIDR) interpolation method to address the dual limitations of conventional Singular Spectrum Analysis (SSA) methods: instability caused by flawed iterative strategy and accuracy degradation due to inadequate denoising. CIDR solves the above issues with two key ways: (i) a correlation-driven iteration stopping rule ensuring the method's overall consistency, and (ii) component-specific interpolation with optimized denoising through a decompose-restore framework. The 0.5° downscaled GRACE(-FO) TWSA dataset was employed with artificial gaps replicating intra-mission and inter-mission scenarios to evaluate the method's performance. At the grid scale, the global average relative Root Mean Square (rRMS) and Nash-Sutcliffe Efficiency (NSE) improved by 17.5% and 42.6% compared to the SSA method for intra-mission gaps, and by 18.6% and 27.6% for inter-mission gap interpolation, respectively. In addition, the CIDR method shows a significant improvement over the SSA method within the global basins, with the global basin area-weighted average rRMS improved by 20.3% and 20% for both gaps, respectively. This research is expected to provide an advanced gap-filling method to meet the dual requirements of hydrological and climate research for high spatial resolution and long-term continuous TWSA products, which is universally applicable for other hydrological and geodetic time series.

physics.geo-ph↗

arXiv2025

Implications of Pulsar Timing Array Results for High Frequency Gravitational Waves

Several pulsar timing array (PTA) experiments such as NANOGrav and PPTA recently reported evidence of a gravitational wave (GW) background at nano-Hz frequency band. This signal can originate from scalar-induced gravitational waves (SIGW) generated by the enhanced curvature perturbation. Production of SIGW is expected to be accompanied by formation of primordial black holes (PBH), which can emit GW through binary mergers. Here we perform a joint likelihood inference on PTA datasets in combination with existing limits on PBH abundance and GW density, we derive full Bayesian posteriors for PBH distribution and relevant PBH merger signal. Our results show that analysis using PTA data alone implies significant overproduction of PBHs, and accounting for current PBH limits causes visible shifts in SIGW posterior. If PTA signals are indeed of SIGW origin, the required curvature perturbation amplitude produces PBHs in a narrow mass window of $[6 \times 10^{-2}, 2 \times 10^{-1}]\ m_\odot$. Mergers of these PBHs can leave a strong GW signature in $[10^{-3}, 10^5]$ Hz frequency range, to be detectable at upcoming interferometers such as LISA, aLIGO, Einstein Telescope, DECIGO and BBO, etc. This offers a multi-frequency opportunity to further scrutinize the source of the observed PTA signal and can potentially improve current PBH constraints by up to 5 orders of magnitudes.

astro-ph.CO↗

arXiv2025

Efficient second-harmonic emission via strong modal overlap in single-resonant lithium niobate nanocavity

High-efficiency second-harmonic generation (SHG) in compact integrated photonic systems is crucial for advancing nonlinear optical technologies. However, achieving exceptional conversion efficiencies while maintaining stable performance remains a significant challenge. Here, we report a high-Q single-resonant photonic crystal nanobeam cavity (PCNBC) on a polymer-loaded lithium niobate on insulator (LNOI) platform, which enables bright second-harmonic (SH) emission. Through synergistic optimization of modal confinement and spatial overlap in a y-cut LN architecture, our device achieves a normalized SHG conversion efficiency of 163%/W, outperforming previous LN-based photonic crystal cavities LN-based photonic crystal cavities by over three orders of magnitude. The visible SH emission at 768.77 nm exhibits a single-lobe radiation pattern with precise spectral alignment between fundamental (FH) and second-harmonic (SH) modes, a critical feature for integrated photonic circuits. Remarkably, the conversion efficiency remains stable under thermal variations up to 20°C, addressing a key limitation of multi-resonant systems. High-order cavity modes are directly visualized via CCD imaging, confirming strong spatial overlap. This work establishes a record SHG conversion efficiency for LN microcavities and provides a scalable, temperature-insensitive architecture for nonlinear light sources, with immediate applications in quantum optics and chip-scale interconnects.

physics.optics↗

arXiv2025

Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can address the straggler effect compared to synchronous FL. However, Non-IID data and stale models pose significant challenges to AFL, as they can diminish the practicality of the global model and even lead to training failures. In this work, we propose a novel AFL framework called Federated Historical Learning (FedHist), which effectively addresses the challenges posed by both Non-IID data and gradient staleness based on the concept of knowledge rumination. FedHist enhances the stability of local gradients by performing weighted fusion with historical global gradients cached on the server. Relying on hindsight, it assigns aggregation weights to each participant in a multi-dimensional manner during each communication round. To further enhance the efficiency and stability of the training process, we introduce an intelligent $\ell_2$-norm amplification scheme, which dynamically regulates the learning progress based on the $\ell_2$-norms of the submitted gradients. Extensive experiments indicate FedHist outperforms state-of-the-art methods in terms of convergence performance and test accuracy.

cs.LG↗

arXiv2025

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery

Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.

cs.CV↗