arXiv subjects

Jin Wang

Publications and source records attributed to Jin Wang.

At least 127 records · Page 7Linked to original sources

arXiv2025

BREAKFAST: A Framework for general joint BA duty and follow-up guidance of multiple $γ$-ray monitors

With the growing number of gamma-ray monitors in operation, several research teams have adopted a strategy of joint operation and scientific duty to improve efficiency. A successful example is the GECAM-HXMT-SVOM (GHS) constellation collaboration, which sets a precedent for other gamma-ray monitor constellations. However, joint duty also presents challenges to Burst Advocates (BAs), including the increased number of triggers and, more importantly, the frequent switching between various systems due to incompatibilities among different missions, which complicates the situation. To address the current requirements of multi-wavelength and multi-messenger astronomy, we developed a customized framework for unified trigger processing within the GHS joint duty, named "BA's Rapid Evaluation and Analysis Kit for Formulating Alerts and Summary Tools" (BREAKFAST). This framework incorporates a series of automated, semi-automated, and manual pipelines designed to rapidly process triggers of prompt emissions in the gamma-ray band from different instruments, while maintaining flexible compatibility for future missions. The pursuit of BREAKFAST goes beyond merely providing trigger processing for BAs. BREAKFAST also aims to filtering high-value targets and guiding follow-up telescopes through rapid analysis and reporting, thus serving as an important bridge between prompt emission observations and afterglow observations. To this end, a suite of comprehensive analysis modules is included in BREAKFAST, particularly the specially designed module that predicts X-ray afterglow brightness based on prompt emission properties. The framework's effectiveness has already been demonstrated in recent observational campaigns, and it is expected to play a significant role in the discovery and observation of peculiar transients in the future.

astro-ph.IM↗

arXiv2025

FSRF: Factorization-guided Semantic Recovery for Incomplete Multimodal Sentiment Analysis

In recent years, Multimodal Sentiment Analysis (MSA) has become a research hotspot that aims to utilize multimodal data for human sentiment understanding. Previous MSA studies have mainly focused on performing interaction and fusion on complete multimodal data, ignoring the problem of missing modalities in real-world applications due to occlusion, personal privacy constraints, and device malfunctions, resulting in low generalizability. To this end, we propose a Factorization-guided Semantic Recovery Framework (FSRF) to mitigate the modality missing problem in the MSA task. Specifically, we propose a de-redundant homo-heterogeneous factorization module that factorizes modality into modality-homogeneous, modality-heterogeneous, and noisy representations and design elaborate constraint paradigms for representation learning. Furthermore, we design a distribution-aligned self-distillation module that fully recovers the missing semantics by utilizing bidirectional knowledge transfer. Comprehensive experiments on two datasets indicate that FSRF has a significant performance advantage over previous methods with uncertain missing modalities.

cs.LG↗

arXiv2025

Falcon: A Remote Sensing Vision-Language Foundation Model (Technical Report)

This paper introduces a holistic vision-language foundation model tailored for remote sensing, named Falcon. Falcon offers a unified, prompt-based paradigm that effectively executes comprehensive and complex remote sensing tasks. Falcon demonstrates powerful understanding and reasoning abilities at the image, region, and pixel levels. Specifically, given simple natural language instructions and remote sensing images, Falcon can produce impressive results in text form across 14 distinct tasks, i.e., image classification, object detection, segmentation, image captioning, and etc. To facilitate Falcon's training and empower its representation capacity to encode rich spatial and semantic information, we developed Falcon_SFT, a large-scale, multi-task, instruction-tuning dataset in the field of remote sensing. The Falcon_SFT dataset consists of approximately 78 million high-quality data samples, covering 5.6 million multi-spatial resolution and multi-view remote sensing images with diverse instructions. It features hierarchical annotations and undergoes manual sampling verification to ensure high data quality and reliability. Extensive comparative experiments are conducted, which verify that Falcon achieves remarkable performance over 67 datasets and 14 tasks, despite having only 0.7B parameters. We release the complete dataset, code, and model weights at https://github.com/TianHuiLab/Falcon, hoping to help further develop the open-source community.

cs.CV↗

arXiv2025

Quantum Teleportation in Non-equilibrium Environments and Fixed-point Fidelity

Quantum teleportation, a fundamental protocol in quantum information science, enables the transfer of quantum states through entangled particle pairs and classical communication channels. While ideal quantum teleportation requires maximally entangled states as resources, real-world implementations inevitably face environmental noise and decoherence effects. In this work, we investigate quantum teleportation in non-equilibrium environments with different temperatures or chemical potentials. We apply the Bloch-Redfield equation to characterize the non-equilibrium dynamics. In both bosonic and fermionic setups, the fidelity can be enhanced beyond the equilibrium values. Under specific non-equilibrium conditions, the fidelities of all input states are identical. We call it teleportation with a fixed-point fidelity. Notably, at the fixed-point, fidelity can also be enhanced by combining the two detuned qubits and non-equilibrium environments. These findings provide important guidance for implementing quantum communication protocols in realistic environments, while the fixed-point mechanism offers a promising pathway toward simplifying practical quantum teleportation schemes.

quant-ph↗

arXiv2025

A 3D Generation Framework from Cross Modality to Parameterized Primitive

Recent advancements in AI-driven 3D model generation have leveraged cross modality, yet generating models with smooth surfaces and minimizing storage overhead remain challenges. This paper introduces a novel multi-stage framework for generating 3D models composed of parameterized primitives, guided by textual and image inputs. In the framework, A model generation algorithm based on parameterized primitives, is proposed, which can identifies the shape features of the model constituent elements, and replace the elements with parameterized primitives with high quality surface. In addition, a corresponding model storage method is proposed, it can ensure the original surface quality of the model, while retaining only the parameters of parameterized primitives. Experiments on virtual scene dataset and real scene dataset demonstrate the effectiveness of our method, achieving a Chamfer Distance of 0.003092, a VIoU of 0.545, a F1-Score of 0.9139 and a NC of 0.8369, with primitive parameter files approximately 6KB in size. Our approach is particularly suitable for rapid prototyping of simple models.

cs.GR↗

arXiv2025

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots

Adaptive recovery from fall incidents are essential skills for the practical deployment of wheeled-legged robots, which uniquely combine the agility of legs with the speed of wheels for rapid recovery. However, traditional methods relying on preplanned recovery motions, simplified dynamics or sparse rewards often fail to produce robust recovery policies. This paper presents a learning-based framework integrating Episode-based Dynamic Reward Shaping and curriculum learning, which dynamically balances exploration of diverse recovery maneuvers with precise posture refinement. An asymmetric actor-critic architecture accelerates training by leveraging privileged information in simulation, while noise-injected observations enhance robustness against uncertainties. We further demonstrate that synergistic wheel-leg coordination reduces joint torque consumption by 15.8% and 26.2% and improves stabilization through energy transfer mechanisms. Extensive evaluations on two distinct quadruped platforms achieve recovery success rates up to 99.1% and 97.8% without platform-specific tuning. The supplementary material is available at https://boyuandeng.github.io/L2R-WheelLegCoordination/

cs.RO↗

arXiv2025

ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training

Large-scale LLM pretraining now runs across $10^5$--$10^6$ accelerators, making failures routine and elasticity mandatory. We posit that an elastic-native training system must jointly deliver (i) parameter consistency, (ii) low mean time to recovery (MTTR), (iii) high post-change throughput, and (iv) computation consistency. No prior system achieves all four simultaneously. To achieve these goals, we present ElasWave, which delivers per-step fault tolerance via multi-dimensional scheduling across graph, dataflow, DVFS, and RNG. ElasWave reshapes and reshards micro-batches while preserving the global batch size and gradient scale. It performs online pipeline resharding with asynchronous parameter migration and interleaves ZeRO partitions, reducing parameter recovery processes to disjoint rank-to-rank transfers. It further leverages DVFS to absorb pipeline bubbles and reshards RNG to keep computation consistency. Together, a dynamic communicator enables in-place communication group edits, while per-step in-memory snapshots support online verification and redistribution. We evaluate ElasWave on 96 NPUs and benchmark it against state-of-the-art baselines: throughput improves by $1.35\times$ over ReCycle and $1.60\times$ over TorchFT; communicator recovery completes within one second (up to $82\times/3.6\times$ faster than full/partial rebuilds); migration MTTR drops by as much as $51\%$; and convergence deviation is reduced by approximately $78\%$.

cs.DC↗

arXiv2025

Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird's eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Code will be released after publication.

cs.RO↗

arXiv2025

INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perception

Collaborative perception systems overcome single-vehicle limitations in long-range detection and occlusion scenarios by integrating multi-agent sensory data, improving accuracy and safety. However, frequent cooperative interactions and real-time requirements impose stringent bandwidth constraints. Previous works proves that query-based instance-level interaction reduces bandwidth demands and manual priors, however, LiDAR-focused implementations in collaborative perception remain underdeveloped, with performance still trailing state-of-the-art approaches. To bridge this gap, we propose INSTINCT (INSTance-level INteraCtion ArchiTecture), a novel collaborative perception framework featuring three core components: 1) a quality-aware filtering mechanism for high-quality instance feature selection; 2) a dual-branch detection routing scheme to decouple collaboration-irrelevant and collaboration-relevant instances; and 3) a Cross Agent Local Instance Fusion module to aggregate local hybrid instance features. Additionally, we enhance the ground truth (GT) sampling technique to facilitate training with diverse hybrid instance features. Extensive experiments across multiple datasets demonstrate that INSTINCT achieves superior performance. Specifically, our method achieves an improvement in accuracy 13.23%/33.08% in DAIR-V2X and V2V4Real while reducing the communication bandwidth to 1/281 and 1/264 compared to state-of-the-art methods. The code is available at https://github.com/CrazyShout/INSTINCT.

cs.CV↗

arXiv2025

PerchMobi^3: A Multi-Modal Robot with Power-Reuse Quad-Fan Mechanism for Air-Ground-Wall Locomotion

Achieving seamless integration of aerial flight, ground driving, and wall climbing within a single robotic platform remains a major challenge, as existing designs often rely on additional adhesion actuators that increase complexity, reduce efficiency, and compromise reliability. To address these limitations, we present PerchMobi^3, a quad-fan, negative-pressure, air-ground-wall robot that implements a propulsion-adhesion power-reuse mechanism. By repurposing four ducted fans to simultaneously provide aerial thrust and negative-pressure adhesion, and integrating them with four actively driven wheels, PerchMobi^3 eliminates dedicated pumps while maintaining a lightweight and compact design. To the best of our knowledge, this is the first quad-fan prototype to demonstrate functional power reuse for multi-modal locomotion. A modeling and control framework enables coordinated operation across ground, wall, and aerial domains with fan-assisted transitions. The feasibility of the design is validated through a comprehensive set of experiments covering ground driving, payload-assisted wall climbing, aerial flight, and cross-mode transitions, demonstrating robust adaptability across locomotion scenarios. These results highlight the potential of PerchMobi^3 as a novel design paradigm for multi-modal robotic mobility, paving the way for future extensions toward autonomous and application-oriented deployment.

cs.RO↗

arXiv2025

Local Differential Privacy for Tensors in Distributed Computing Systems

Tensor-valued data, increasingly common in distributed big data applications like autonomous driving and smart healthcare, poses unique challenges for privacy protection due to its multidimensional structure and the risk of losing critical structural information. Traditional local differential privacy methods, designed for scalars and matrices, are insufficient for tensors, as they fail to preserve essential relationships among tensor elements. We introduce TLDP, a novel LDP algorithm for Tensors, which employs a randomized response mechanism to perturb tensor components while maintaining structural integrity. To strike a better balance between utility and privacy, we incorporate a weight matrix that selectively protects sensitive regions. Both theoretical analysis and empirical findings from real-world datasets show that TLDP achieves superior utility while preserving privacy, making it a robust solution for high-dimensional tensor data.

cs.CR↗

arXiv2025

Probing new light scalars with the lepton anomalous magnetic moment and the weak equivalence principle violation

A new scalar particle with generic couplings to the standard-model particles is a possible source for the lepton anomalous magnetic moment and the violation of the weak equivalence principle. Here, one-loop contributions to the lepton anomalous magnetic moment, involving the scalar-photon and scalar-lepton couplings, are calculated. Then, employing the recent experimental results of the electron anomalous magnetic moment, the muon anomalous magnetic moment, and the MICROSCOPE mission, we find the improved constraints on scalar-lepton and scalar-photon couplings: $|λ_e|\leq 6.0 \times 10^{-6}$, $|λ_μ|\leq 3.5\times 10^{-4}$, and $|λ_γ|\leq 4.5 \times 10^{-13}$ ${\rm eV^{-1}}$ for scalar mass below $10^4$ eV. We find that the naive scaling relationship between the scalar-muon coupling and the scalar-electron coupling is favored by three experimental results. Furthermore, the minimal standard-model extension by one scalar is also favored by all three experiments, and the model parameter is constrained best to $|\mathcal{A}|\leq 1.7 \times 10^{-11}$ eV for $m_ϕ< 10^{-13}$ eV.

hep-ph↗

arXiv2025

Analysis of the Dick Effect for AI-based Dynamic Gravimeter

Atom interferometer (AI)-based dynamic gravimeter enable high-precision absolute gravity measurements, crucial for applications in geophysics, navigation, resource exploration, and metrology. Understanding their underlying mechanisms and minimizing measurement noise are essential for enhancing performance. This work investigates the gravity measurement noise in AI-based systems induced by the dead time of the classical accelerometer. Using actual dynamic gravity measurement data, we demonstrate that a dead time of 0.12 s introduces significant gravity measurement noise of 8 mGal. To elucidate the mechanism of this noise, we derive a formula for this noise in frequency domain, identifying high-frequency aliasing as its source. Analysis of the derived expressions indicates that reducing the dead time duration and suppressing the high-frequency noise of the acceleration are effective strategies for mitigating this noise. This work provides significant insights for noise analysis and future scheme design of AI-based dynamic gravimeters.

physics.atom-ph↗

arXiv2025

Self-organized learning emerges from coherent coupling of critical neurons

Deep artificial neural networks have surpassed human-level performance across a diverse array of complex learning tasks, establishing themselves as indispensable tools in both social applications and scientific research. Despite these advances, the underlying mechanisms of training in artificial neural networks remain elusive. Here, we propose that artificial neural networks function as adaptive, self-organizing information processing systems in which training is mediated by the coherent coupling of strongly activated, task-specific critical neurons. We demonstrate that such neuronal coupling gives rise to Hebbian-like neural correlation graphs, which undergo a dynamic, second-order connectivity phase transition during the initial stages of training. Concurrently, the connection weights among critical neurons are consistently reinforced while being simultaneously redistributed in a stochastic manner. As a result, a precise balance of neuronal contributions is established, inducing a local concentration within the random loss landscape which provides theoretical explanation for generalization capacity. We further identify a later on convergence phase transition characterized by a phase boundary in hyperparameter space, driven by the nonequilibrium probability flux through weight space. The critical computational graphs resulting from coherent coupling also decode the predictive rules learned by artificial neural networks, drawing analogies to avalanche-like dynamics observed in biological neural circuits. Our findings suggest that the coherent coupling of critical neurons and the ensuing local concentration within the loss landscapes may represent universal learning mechanisms shared by both artificial and biological neural computation.

cond-mat.dis-nn↗

arXiv2025

SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control

The rapid advancement of large vision language models (LVLMs) and agent systems has heightened interest in mobile GUI agents that can reliably translate natural language into interface operations. Existing single-agent approaches, however, remain limited by structural constraints. Although multi-agent systems naturally decouple different competencies, recent progress in multi-agent reinforcement learning (MARL) has often been hindered by inefficiency and remains incompatible with current LVLM architectures. To address these challenges, we introduce SWIRL, a staged workflow for interleaved reinforcement learning designed for multi-agent systems. SWIRL reformulates MARL into a sequence of single-agent reinforcement learning tasks, updating one agent at a time while keeping the others fixed. This formulation enables stable training and promotes efficient coordination across agents. Theoretically, we provide a stepwise safety bound, a cross-round monotonic improvement theorem, and convergence guarantees on return, ensuring robust and principled optimization. In application to mobile GUI control, SWIRL instantiates a Navigator that converts language and screen context into structured plans, and an Interactor that grounds these plans into executable atomic actions. Extensive experiments demonstrate superior performance on both high-level and low-level GUI benchmarks. Beyond GUI tasks, SWIRL also demonstrates strong capability in multi-agent mathematical reasoning, underscoring its potential as a general framework for developing efficient and robust multi-agent systems.

cs.AI↗

arXiv2025

AdS$_{3}$ spacetime wormhole fluctuations and their impact on false vacuum decay

In this work, we studied the boundary wormhole fluctuations of AdS$_{3}$ spacetime and their impact on false vacuum decay. The model consists of three dimensional general relativity coupled to a real scalar field with self-interaction potential. Our analysis focuses on handlebody spacetimes, which correspond to boundary wormhole fluctuations. These fluctuations result in variations in the spacetime genus. Using the dilute wormhole gas approximation, we computed the boundary wormhole creation rate in the semiclassical region. Our results indicate that boundary wormholes are more likely to be created in spacetimes with larger absolute values of the cosmological constant. Furthermore, we find that smaller wormholes are more easily created compared to larger ones. Additionally, we investigated the impact of boundary wormhole fluctuations on false vacuum decay. The vacuum decay rate increases when the effects of wormhole fluctuations are considered. We show that all results remain qualitatively unchanged when certain parameter values are varied.

hep-th↗

arXiv2025

INTENTION: Inferring Tendencies of Humanoid Robot Motion Through Interactive Intuition and Grounded VLM

Traditional control and planning for robotic manipulation heavily rely on precise physical models and predefined action sequences. While effective in structured environments, such approaches often fail in real-world scenarios due to modeling inaccuracies and struggle to generalize to novel tasks. In contrast, humans intuitively interact with their surroundings, demonstrating remarkable adaptability, making efficient decisions through implicit physical understanding. In this work, we propose INTENTION, a novel framework enabling robots with learned interactive intuition and autonomous manipulation in diverse scenarios, by integrating Vision-Language Models (VLMs) based scene reasoning with interaction-driven memory. We introduce Memory Graph to record scenes from previous task interactions which embodies human-like understanding and decision-making about different tasks in real world. Meanwhile, we design an Intuitive Perceptor that extracts physical relations and affordances from visual scenes. Together, these components empower robots to infer appropriate interaction behaviors in new scenes without relying on repetitive instructions. Videos: https://robo-intention.github.io

cs.RO↗

arXiv2025

Impacts of wormholes on false vacuum bubble tunneling

In this study, we generalize the work of Farhi, Guth and Guven [Nucl. Phys. B 339 (1990) 417] to include a wormhole effect. We study the influence of the wormhole on the tunneling of the false vacuum bubble. The spherically symmetric bubble has two domain walls. The classical dynamics of each domain wall are constrained by two classically forbidden regions. We find that the wormhole has increased the number of instantons and thus can enhance the tunneling rate. We have analytically derived the formula for the bubble tunneling rate, which is consistent with the result obtained by Farhi, Guth and Guven when the wormhole disappears. We show that the tunneling rate increases with the throat radius of the wormhole. Furthermore, we illustrate that the tunneling rate increases with an increases in the mass of the black hole or the surface tension. In the case where a wormhole exists within a bubble and is connected to another universe, we demonstrate that the bubble can enter into another universe through quantum tunneling.

gr-qc↗